Street lamp management method considering pedestrian and wild animal tracks in local community

Abstract

The invention belongs to the technical field of automatic control, and particularly relates to a street lamp management method, system and device considering pedestrian and wild animal tracks in a local community. According to the scheme, through the proximity radar, the echelette grating and the target tracking radar which are installed on the street lamp, animals or pedestrians in a community can be identified, and the motion trail of the pedestrians can be tracked. By using the data detected in real time, on one hand, the moving direction of the pedestrian is predicted, and the street lamps on the pedestrian forward road are lightened in advance; on the other hand, the future movement track of the wild animal is predicted according to the historical data of the appearing and disappearance track of the wild animal and the current appearing position, then whether the pedestrian and the wild animal meet or not is judged, the street lamp at the position where the pedestrian and the wild animal possibly meet is lightened in advance, and safety early warning is achieved. According to the scheme, the on-off state of the street lamp can be dynamically adjusted according to pedestrians and animals in a community, and then risks and hazards encountered by the pedestrians and the animals are effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the field of automatic control technology, and in particular relates to a street lamp management method, system and device taking into account the trajectories of pedestrians and wild animals in a local community. Background Art

[0002] As the environment improves, wild animals or pets raised or abandoned by residents often appear in some large residential areas, parks or industrial parks. When pedestrians encounter certain wild animals (such as wild boars), the animals may pose a direct threat to the life and safety of pedestrians, especially when the animals feel threatened or frightened. If there are a large number of animals in a certain area, it may even affect the normal activities of residents. Especially in the evening or at night, when wild animals (such as deer, wild boars, etc.) suddenly appear in human communities, they may collide with pedestrians or vehicles, posing a threat to the safety of residents' lives and property. Some animals (such as stray dogs or mother leopards, etc.) may attack pedestrians and cause harm when protecting their own territory or cubs. When pedestrians pass through areas where wild animals are active, they may come into contact with animal feces or be bitten, which may increase the risk of contracting diseases (such as rabies, etc.). Anxiety or disgust about the presence of wild animals may cause pedestrians to feel nervous when they are outdoors, reducing their quality of life.

[0003] Regarding the problem of encounters between wild animals and humans, if the activity trajectories, time and frequency of aggressive animals can be predicted in a timely manner and the user is informed of the risks, the user will have enough reaction time to avoid the threat. However, the existing technology lacks technical solutions that can effectively track or predict the movement trajectories of wild animals. In addition, at night, the street lights on the roadside are usually dim, and the visual range of pedestrians is limited. This may cause pedestrians to be unable to detect wild animals in the distance in time, shortening the reaction time when pedestrians encounter wild animals. Summary of the invention

[0004] In order to solve the problem that the prior art lacks effective means to reduce the risks and hazards encountered by pedestrians and animals, the present invention provides a street light management method, system and device that take into account the trajectories of pedestrians and wild animals in a local community.

[0005] The technical solution provided by the present invention is:

[0006] A streetlight management method considering the trajectories of pedestrians and wild animals in a local community comprises the following steps:

[0007] S1: Establish a geographic image based on the spatial distribution of roads in the community; and use each street lamp as a node and the lines between street lamps as edges to create a connected graph G that represents the movement paths of animals in the community.

[0008] S2: Use proximity radar installed on street lamps to detect living targets in the community in real time.

[0009] S3: When the distance of any approach radar to a living target is less than a preset threshold, the type of living organism is identified through the infrared grating on the corresponding street lamp, and the following decision is made:

[0010] (1) If the living creature is a person, the target tracking radar on the corresponding street lamp is turned on. The target tracking radar detects the spatial position of the target person on the geographic image, and determines the direction and speed of the pedestrian's movement based on the change in the pedestrian's spatial position.

[0011] The pedestrian's future movement trajectory is predicted based on the pedestrian's movement direction and speed, and the street lights in the direction of the pedestrian's movement are lit in advance. After the pedestrian passes, the street lights are restored to a low-power state according to a preset delay.

[0012] (2) If the living organism is a wild animal, the time and order of the appearance of the wild animal at the nodes corresponding to each street light are recorded, and each node is connected in order to obtain a track of the wild animal's appearance.

[0013] The connected graph G is transformed into a directed graph G′ based on the historical data of the wildlife trajectories in the community. The edge weights of the directed graph G′ are updated according to the newly added wildlife trajectories, and the average speed of the wildlife on each edge in the directed graph is recorded.

[0014] Among them, the edge weight is used to characterize the probability of occurrence of wild animals.

[0015] S4: When there are pedestrians in the community, if any node detects the presence of wild animals, the movement trajectory of the wild animals is predicted based on the directed graph G′; then it is determined whether the animals and pedestrians will meet, and street lights at the locations where wild animals and pedestrians may meet are lit to provide early warning.

[0016] As a further improvement of the present invention, in step S1, the data format of the connectivity graph is: G = (V, E);

[0017] Among them, V is the node v corresponding to each street lamp i is a set of nodes, i = 1…n, n represents the number of street lights in the community. E is the set of edges between nodes. Any node v i and v j The edge between them is denoted as e ij , j=1…n.

[0018] As a further improvement of the present invention, the street lamps used in the community have three working modes: off, low power consumption and on. In the preset daytime period, the street lamps are in the off mode. In the preset nighttime busy period, the street lamps are in the on mode. In the preset nighttime idle period, the street lamps are in the low power consumption mode in the initial state, and are switched to the on mode according to the instruction.

[0019] and / or

[0020] There are also multiple cameras in the community. When there are pedestrians in the community, the camera's pan / tilt is adjusted according to the detected spatial position, movement direction and movement speed of the pedestrians to achieve real-time tracking of the target person.

[0021] As a further improvement of the present invention, in step S3, the infrared grating includes a plurality of sensing points evenly arranged at different heights on the lamp pole along the vertical direction; the infrared grating determines the type of the approaching living target according to the spatial distribution of the sensing signal along the vertical direction.

[0022] As a further improvement of the present invention, the process of converting the connected graph G into a directed graph G′ according to the historical data of the wild animal's appearance trajectories in the community is as follows:

[0023] (i) The direction of the line from the node at the previous moment to the node at the next moment in the wild animal's trajectory is taken as the direction of movement of the animal between the two nodes.

[0024] (ii) Based on the historical data of wildlife appearance trajectories, count the number of appearances of wildlife in a specified direction between any two nodes in the connected graph G.

[0025] (iii) Calculate the probability P of wild animals appearing in a specified direction between any two nodes based on the number of appearances ij , and the probability P will appear ij as the edge weight of the corresponding edge in the directed graph G′;

[0026]

[0027] In the above formula, N i Indicates the wild animals appearing in the historical data from node v i The number of times to reach any neighbor node; N ij Indicates the wild animals appearing in the historical data from node v i and depart to neighbor node v j The number of times.

[0028] (iv) The direction of the maximum edge weight starting from the specified node is taken as the high-frequency direction of the current node, and the high-frequency direction of each node is recorded.

[0029] (v) Calculate the average time it takes for wild animals to pass through any two nodes based on the historical data of wild animal trajectories, and then calculate the average speed of wild animals passing through any edge based on the distance between nodes.

[0030] As a further improvement of the present invention, in step S4, the method for predicting the movement trajectory of wild animals according to the directed graph G′ is as follows:

[0031] S041: The position where the wild animal is first detected is used as the initial position of the movement trajectory.

[0032] S042, taking the initial position as the starting point, taking the high-frequency direction of the current node in the directed graph as the movement direction, and the average speed of the edge in the high-frequency direction as the movement speed of the wild animal, predicting the position and time of the wild animal arriving at the next node.

[0033] S043, taking the next node predicted each time as the starting point, repeat step S042 to determine the movement direction and movement speed of the wild animal at the current node, and obtain the end point of the current path.

[0034] S044: When the predicted next node is a node on the edge of the directed graph, the lines from the initial position to all the predicted nodes in sequence are used as the predicted movement trajectory of the wild animal.

[0035] As a further improvement of the present invention, the historical data set of the wild animal's appearance trajectory collected is divided into multiple data sets of different time periods according to the appearance time, and corresponding directed graphs G' are established according to the data sets of different time periods. Then, the directed graphs G' of the corresponding time periods are combined to predict whether pedestrians and wild animals will meet.

[0036] As a further improvement of the present invention, in step S4, the method for determining whether pedestrians and wild animals will meet is as follows:

[0037] First, the spatial characteristics of the predicted movement trajectories of pedestrians and wild animals are analyzed to calculate whether the closest position between the two is less than the preset safe space distance: if so, it is considered that the two may meet, otherwise it is considered that the two will not meet.

[0038] Secondly, when pedestrians and wild animals are likely to meet, the time characteristics of the predicted movement trajectories of pedestrians and wild animals are further analyzed to determine whether the corresponding distance intervals between the two that are less than the safe distance overlap in the time domain: if so, it is considered that the two will meet, otherwise it is considered that the two will not meet.

[0039] The present invention also includes a streetlight management system, which is used to adopt the aforementioned streetlight management method that takes into account the trajectories of pedestrians and wild animals in a local community to achieve adaptive adjustment of the working mode of each streetlight in the community. The streetlight management system includes: multiple proximity radars, multiple infrared gratings, multiple target tracking radars, at least one image acquisition device, and a background server.

[0040] Among them, the proximity radar is installed on each street lamp in the community and is used to detect living targets around the street lamp. The infrared grating is installed on each street lamp in the community and is used to distinguish the type of living targets detected by the proximity radar. The target tracking radar is installed on the street lamp in the community and is used to track the position of pedestrians identified by the infrared grating. The image acquisition equipment includes a camera and a pan / tilt head, and the image acquisition equipment is used to obtain real-time image data of pedestrians appearing in the community.

[0041] The backend server is electrically connected to the controllers of each proximity radar, infrared grating, target tracking radar, image acquisition equipment, and each street lamp in the community. The backend server is used to: (1) obtain the real-time detection signal of the proximity radar to determine whether there is a living target in the community. (2) turn on the infrared grating and distinguish the type of the living target according to the detection signal of the infrared grating. (3) turn on the target tracking radar and image acquisition equipment to track pedestrians in the community and predict their movement trajectory and movement speed; and switch the street lamps on the pedestrian path to the lighting mode. (4) record the time and order of wild animals appearing at the nodes corresponding to each street lamp, and connect each node in order to obtain a wild animal appearance trajectory. (5) dynamically update the directed graph G′ in the community according to the newly added wild animal appearance trajectory. (6) predict the movement trajectory and movement speed of wild animals appearing in the community in combination with the directed graph G′. (7) judge whether the movement trajectory of pedestrians and wild animals will meet based on the movement trajectory of the two, and if so, light up the street lamps at the corresponding positions.

[0042] The present invention also includes a streetlight management device, which includes a memory, a processor, and a computer program stored in the memory and running in the processor. When the processor executes the computer program, the streetlight management method considering the trajectories of pedestrians and wild animals in the local community as described above is adopted to realize adaptive adjustment of the working mode of each streetlight in the community according to the trajectories of pedestrians and wild animals in the community.

[0043] The technical solution provided by the present invention has the following beneficial effects:

[0044] The present invention designs a street lamp management method which takes into account the trajectories of pedestrians and wild animals in a local community. The method utilizes a living presence sensing radar and an infrared grating installed on each street lamp to form a scanning device for detecting humans or animals, and records the appearance frequency, activity trajectory, movement speed and regularity of the appearance of large, medium and small animals in the community at different times, thereby generating a directed graph which can predict the movement trajectory of animals in the community.

[0045] After the solution of the present invention identifies the user through the infrared grating, it also monitors the user's movement speed and spatial position in real time through the target tracking radar, and then predicts the user's movement trajectory in combination with the geographic image inside the community, and switches the working state of the street lamp to provide instant lighting for the user. The present invention also combines the predicted movement trajectory of animals and pedestrians to determine whether the two will meet, and then lights up the street lamp at the corresponding position when there is a risk of meeting between the two, providing an early warning for the user.

[0046] The street lamp management method provided by the present invention can not only reduce the operating power consumption of street lamps, but also effectively reduce the risks and hazards encountered by pedestrians and animals. Compared with the traditional sensing device that completely traverses each space, the solution of the present invention has a higher response efficiency, and can turn on the street lamp at the current location within the effective time and start the sensing device within the range of N street lamps with the user as the axis to provide the user with a forecast and warning. The solution of the present invention is particularly suitable for use in communities where wild animals and pets appear frequently, and is highly practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flowchart of the steps of the street light management method taking into account the trajectories of pedestrians and wild animals in a local community provided in Example 1 of the present invention.

[0048] Figure 2 It is a connectivity graph established according to the node corresponding to each street lamp in the community in Example 1 of the present invention.

[0049] Figure 3 This is a schematic diagram of the principle of using an infrared grating to identify the type of a target object in Example 1 of the present invention.

[0050] Figure 4 This is a flow chart of converting a connected graph into a directed graph based on historical data of wild animal appearance trajectories in a community in Example 1 of the present invention.

[0051] Figure 5 This is a flow chart of the steps of predicting the movement trajectory of wild animals based on a directed graph in Example 1 of the present invention.

[0052] Figure 6 This is a flow chart of the steps for determining whether pedestrians and wild animals will meet in Example 1 of the present invention.

[0053] Figure 7This is an architecture diagram of the street light management system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] Example 1

[0056] In view of the fact that wild animals may enter human communities at night and thus endanger the safety of life and property of community residents, this embodiment provides a street light management method that takes into account the trajectories of pedestrians and wild animals in local communities. The idea of ​​the technical solution provided in this embodiment is: through the proximity radar (living presence sensing radar), infrared grating and target tracking radar installed on the street lights, animals or pedestrians in the community can be identified and the movement trajectory of pedestrians can be tracked. Using the data detected in real time, on the one hand, the direction of movement of pedestrians can be predicted, and the street lights on the pedestrians' path can be "lit up" in advance. This not only ensures the lighting function of the street lights, but also reduces the lighting power of the street lights when necessary, achieving energy conservation and emission reduction. On the other hand, the future movement trajectory of wild animals can be predicted based on the historical data of the trajectories of wild animals and the current location of their appearance, and then when pedestrians and wild animals may meet, the street lights ahead can be lit in advance, thereby achieving safety warnings.

[0057] In the scheme of this embodiment, the street lights used in the community have three working modes: off, low power consumption and lighting. Among them, during the preset daytime period, the street lights are in the off mode. During the preset busy period at night, the street lights are in the lighting mode. During the preset idle period at night, the street lights are in the low power consumption mode in the initial state, and are switched to the lighting mode according to the instructions. Among them, both the low power consumption mode and the lighting mode have lighting effects, the difference is that the brightness of the street lights in the low power consumption mode is relatively dim, while the power of the street lights in the lighting mode is higher and brighter. This embodiment switches the street lights to the low power consumption mode when there are fewer pedestrians at night, and then switches the street lights at the corresponding positions to the lighting mode when pedestrians appear, or there is a safety risk of encounter between pedestrians and animals, so as to achieve the effect of saving energy.

[0058] Specifically, Figure 1 As shown, the street light management method provided in this embodiment taking into account the trajectories of pedestrians and wild animals in a local community includes the following steps:

[0059] S1: Establish a geographic image based on the spatial distribution of roads in the community; and use each street lamp as a node and the lines between street lamps as edges to create a connected graph G that represents the movement paths of animals in the community.

[0060] In the solution of this embodiment, the established geographic image is mainly used to predict the user's movement trajectory. Generally speaking, users only move along roads and open squares or lawns within the community. Therefore, it is only necessary to combine the real-time movement information of pedestrians and the geographic image information containing elements such as roads, buildings and terrain to predict the movement trajectory of pedestrians.

[0061] Unlike pedestrians, wild animals do not move along existing roads when they move. They often cross hedges or fences, etc. Therefore, the pedestrian movement trajectory prediction method is not applicable to wild animals. In the scheme of this embodiment, it is considered that although wild animals do not move along roads, their trajectories in the community still have statistical characteristics. For example, when most animals appear in an area, they mostly appear along one or several fixed routes. Therefore, this embodiment establishes a connected graph with street lights as nodes, and weights the connected graph in combination with the historical data of the trajectories of wild animals, converts it into a directed graph, and then uses the directed graph to characterize the statistical characteristics of wild animals appearing in the community; finally, the directed graph is used to assist in the prediction of the movement trajectory of wild animals.

[0062] Figure 2 is a connectivity graph within a typical community. The dots in the graph are the nodes in the connectivity graph, and the lines between the nodes are the edges of the connectivity graph. In this embodiment, the data format of the connectivity graph established based on the spatial positions of the street lamps within the community is: G = (V, E). Among them, V is the node v corresponding to each street lamp. i is a set of nodes, i = 1…n, n represents the number of street lights in the community. E is the set of edges between nodes. Any node v i and v j The edge between them is denoted as e ij , j=1…n.

[0063] S2: Use proximity radar installed on street lamps to detect living targets in the community in real time.

[0064] In this embodiment, the proximity radar can be used to detect whether there is a living target near the installation location. In this embodiment, a proximity radar is installed on each street lamp in the community. The detection range of each proximity radar basically covers all locations in the community. In other embodiments, in order to further expand the detection range of the proximity radar and eliminate blind spots, proximity radars can also be installed in other areas where wild animals appear frequently and street lamps are not installed.

[0065] S3: When the distance of any approach radar detecting a living target is less than a preset threshold, the type of the living organism is distinguished by the infrared grating on the corresponding street lamp.

[0066] like Figure 3As shown, the infrared grating in this embodiment includes a plurality of sensing points evenly arranged at different heights on the street lamp pole in the vertical direction; the infrared grating determines the type of the approaching living target according to the spatial distribution of the sensing signal in the vertical direction. For example, in a typical solution, more than 5 sensors can be set within the height range corresponding to the height of a human body. For most pedestrians, they can usually trigger all or part of the infrared sensing points. For most wild animals, they can only trigger one or several sensing points located at a lower position. The server can distinguish the type of living organisms based on the number and / or height information of the sensing signals in the infrared grating triggered by each approaching target.

[0067] Based on the identified target type, this embodiment makes the following decisions:

[0068] (1) If the living creature is a person, the target tracking radar on the corresponding street lamp is turned on. The target tracking radar detects the spatial position of the target person on the geographic image, and determines the direction and speed of the pedestrian's movement based on the change in the pedestrian's spatial position.

[0069] In the actual application process of the solution of this embodiment, the least square method can be used to linearly fit the movement path of the pedestrian and calculate the movement speed according to each spatial position point collected during the movement of the pedestrian; the process is as follows:

[0070] The server constructs a grid map with due north as the Y axis, due east as the X axis, and (0, 0) as the origin (Note: the maximum monitoring radius of the radar is d, and the coordinate points within the set time t are marked, and the obtained coordinate points and direction frequencies are brought into the following matrix model:

[0071] y=θ0+θ1x1+θ2x2

[0072] Among them, y represents the ordinate of the tracking target, ω0 is the function intercept, x1 is the current abscissa, and x2 is the direction frequency. If the current direction coincides with the high-frequency direction of the current position, then x2 is given so that the difference between y2 and the nearest y1 and y3 is the same. θ1 and θ2 are the regression coefficients to be tested. After obtaining multiple groups of y0, they are expressed as the following matrix:

[0073]

[0074] in, represents the predicted value of y; θ is the coefficient to be estimated, and X represents the coordinate matrix.

[0075] After adding a column matrix of 1 to θ0, it is expressed in the following matrix form:

[0076]

[0077] The least squares estimate θ is calculated by the residual sum of squares SSE (Sum of Squares for Error), and the calculation formula is as follows:

[0078]

[0079] Here, ε represents the residual.

[0080] The residual sum of squares formula is further expressed as the following matrix form:

[0081] SSE=(y-Xθ) T (y-Xθ)

[0082] Find the minimum value of SSE, that is, the first-order derivative after partial derivative of θ is 0, and find the minimum estimate:

[0083] θ=(X T X) -1 X T Y

[0084] Among them, Y represents the coordinate matrix, and the matrix derivation rule can be obtained:

[0085]

[0086]

[0087] Take the value of θ that gives a derivative of 0:

[0088]

[0089] The minimum estimated value θ can be obtained:

[0090] θ=(X T X) -1 X T Y

[0091] Post-analysis directly into Obtain the linear correlation function D(x), and the slope of the D(x) function is the target travel direction A. Compare it with the slope of the line connecting the initial value and the final value of the recorded position coordinate point. If the difference is large, use the slope of D(x).

[0092] At the same time, we can use functions such as polyfit(x,y,n) in matlab to perform linear fitting and get F(x). Then we can get the speed of the living body based on time and perform linear integration on L:

[0093] L=μ L F(x)ds

[0094] The distance L is obtained, and L / t can be used to get a speed value V. This speed is compared with the speed value detected by the radar itself. If the error is large, the server is warned that troubleshooting is required, otherwise the calculated value is returned. The final fitted function is the movement path of the living thing, based on which the speed V and path L can be obtained.

[0095] The pedestrian's future movement trajectory is predicted based on the pedestrian's movement direction and speed, and the street lights in the direction of the pedestrian's movement are lit in advance. After the pedestrian passes, the street lights are restored to a low-power state according to a preset delay.

[0096] (2) If the living organism is a wild animal, the time and order of the appearance of the wild animal at the nodes corresponding to each street light are recorded, and each node is connected in order to obtain a track of the wild animal's appearance.

[0097] The connected graph G is converted into a directed graph G′ based on the historical data of the wildlife trajectories in the community. The edge weights of the directed graph G′ are updated according to the newly added wildlife trajectories, and the average speed of the wildlife on each edge in the directed graph is recorded. The edge weights are used to characterize the probability of wildlife appearance.

[0098] like Figure 4 As shown, the process of converting the connected graph G into the directed graph G′ according to the historical data of the wild animal's trajectories in the community is as follows:

[0099] (i) The direction of the line from the node at the previous moment to the node at the next moment in the wild animal's trajectory is taken as the direction of movement of the animal between the two nodes.

[0100] (ii) Based on the historical data of wildlife appearance trajectories, count the number of appearances of wildlife in a specified direction between any two nodes in the connected graph G.

[0101] (iii) Calculate the probability P of wild animals appearing in a specified direction between any two nodes based on the number of appearances ij , and the probability P will appear ij as the edge weight of the corresponding edge in the directed graph G′;

[0102]

[0103] In the above formula, N i Indicates the wild animals appearing in the historical data from node v i The number of times to reach any neighbor node; N ij Indicates the wild animals appearing in the historical data from node v i and depart to neighbor node v j The number of times.

[0104] (iv) The direction of the maximum edge weight starting from the specified node is taken as the high-frequency direction of the current node, and the high-frequency direction of each node is recorded.

[0105] (v) Calculate the average time it takes for wild animals to pass through any two nodes based on the historical data of wild animal trajectories, and then calculate the average speed of wild animals passing through any edge based on the distance between nodes.

[0106] S4: When there are pedestrians in the community, if any node detects the presence of wild animals, the movement trajectory of the wild animals is predicted based on the directed graph G′; then it is determined whether the animals and pedestrians will meet, and street lights at the locations where wild animals and pedestrians may meet are lit to provide early warning.

[0107] In this embodiment, if Figure 5 As shown in FIG. 1 , the method for predicting the movement trajectory of wild animals based on the directed graph G′ is as follows:

[0108] S041: The position where the wild animal is first detected is used as the initial position of the movement trajectory.

[0109] S042, taking the initial position as the starting point, taking the high-frequency direction of the current node in the directed graph as the movement direction, and the average speed of the edge in the high-frequency direction as the movement speed of the wild animal, predicting the position and time of the wild animal arriving at the next node.

[0110] S043, taking the next node predicted each time as the starting point, repeat step S042 to determine the movement direction and movement speed of the wild animal at the current node, and obtain the end point of the current path.

[0111] S044: When the predicted next node is a node on the edge of the directed graph, the lines from the initial position to all the predicted nodes in sequence are used as the predicted movement trajectory of the wild animal.

[0112] In the solution of this embodiment, Figure 6 As shown, the method for judging whether pedestrians and wild animals will meet is as follows:

[0113] First, the spatial characteristics of the predicted movement trajectories of pedestrians and wild animals are analyzed to calculate whether the closest position between the two is less than the preset safe space distance: if so, it is considered that the two may meet, otherwise it is considered that the two will not meet.

[0114] Secondly, when pedestrians and wild animals are likely to meet, the time characteristics of the predicted movement trajectories of pedestrians and wild animals are further analyzed to determine whether the corresponding distance intervals between the two that are less than the safe distance overlap in the time domain: if so, it is considered that the two will meet, otherwise it is considered that the two will not meet.

[0115] In a further optimized solution of this embodiment, multiple cameras are also set up in the community. When there are pedestrians in the community, the server will adjust the camera's pan / tilt according to the detected spatial position, movement direction and movement speed of the pedestrians to achieve real-time tracking of the target person. The camera in this embodiment is used to record the video images of pedestrians when they appear in the community, so as to leave image evidence for possible losses.

[0116] In other embodiments, the technicians can also divide the collected historical data sets of wild animal appearance trajectories into multiple data sets of different time periods according to the appearance time, and establish corresponding directed graphs G′ according to the data sets of different time periods. Then, the directed graphs G′ of the corresponding time periods are combined to predict whether pedestrians and wild animals will meet.

[0117] For example, technicians divide the trajectories of wild animals into three categories according to 06:00-20:00, 20:00-24:00, and 24:00-06:00, and then establish directed graphs corresponding to the three time periods. Since the types and movement routes of wild animals that appear in different time periods may be different, the high-frequency directions and movement speeds of wild animals corresponding to the same node in the directed graphs of the three time periods may be different. On this basis, when wild animals are detected again within the specified time period, the directed graph of the corresponding time period can be queried, and the movement trajectory of wild animals in the future can be predicted based on the directed graph. Compared with the previous solution, the optimized directed graph is more accurate in predicting the movement trajectory of wild animals.

[0118] In addition, the solution of this embodiment can also record the body shape data of each wild animal's movement trajectory, and then establish corresponding directed graphs for wild animals of different body shapes. In the subsequent application process, when new wild animals appear in the community, the corresponding directed graph is queried according to the body shape of the wild animal, and the movement trajectory of the wild animal at the future moment is predicted.

[0119] Example 2

[0120] Based on the solution of Example 1, this embodiment provides a streetlight management system, which is used to adopt the streetlight management method in Example 1 that takes into account the trajectories of pedestrians and wild animals in a local community to achieve adaptive adjustment of the working mode of each streetlight in the community. Figure 7 As shown, the street lamp management system includes: multiple proximity radars, multiple infrared gratings, multiple target tracking radars, at least one image acquisition device and a background server.

[0121] Among them, the proximity radar is installed on each street lamp in the community and is used to detect living targets around the street lamp. The infrared grating is installed on each street lamp in the community and is used to distinguish the type of living targets detected by the proximity radar. The target tracking radar is installed on the street lamp in the community and is used to track the position of pedestrians identified by the infrared grating. The image acquisition equipment includes a camera and a pan / tilt head, and the image acquisition equipment is used to obtain real-time image data of pedestrians appearing in the community.

[0122] The backend server is electrically connected to the controllers of each proximity radar, infrared grating, target tracking radar, image acquisition equipment, and each street lamp in the community. The backend server receives signals from various detection devices and dynamically adjusts the working state of the street lamp according to the received signals, including: (1) obtaining the real-time detection signal of the proximity radar to determine whether a living target appears in the community. (2) turning on the infrared grating when a living target appears, and distinguishing the type of the living target according to the detection signal of the infrared grating. (3) when the living target is identified as a pedestrian, turning on the target tracking radar and image acquisition equipment to track the pedestrian in the community and predict its movement trajectory and movement speed; and switching the street lamp on the pedestrian path to the lighting mode. (4) recording the time and order of the appearance of wild animals at the nodes corresponding to each street lamp, and connecting each node in order to obtain a wild animal appearance trajectory. (5) dynamically updating the directed graph G′ in the community according to the newly added wild animal appearance trajectory. (6) combining the directed graph G′ to predict the movement trajectory and movement speed of wild animals appearing in the community. (7) Based on the movement trajectories of pedestrians and wild animals, determine whether the two will meet. If so, light up the street lights at the corresponding positions.

[0123] In practical applications, the street lamp management system provided by this embodiment can not only autonomously regulate the operating status of street lamps, but also actively send warning information of wild animals to users. For example, users can log in to the corresponding app to obtain their real-time location pushed by the background server to achieve navigation, and can also obtain warning information of wild animals in a timely manner.

[0124] Example 3

[0125] On the basis of Embodiments 1 and 2, this embodiment further provides a streetlight management device, which includes a memory, a processor, and a computer program stored in the memory and running in the processor. When the processor executes the computer program, the streetlight management method considering the trajectories of pedestrians and wild animals in the local community as in Embodiment 1 is adopted to realize adaptive adjustment of the working mode of each streetlight in the community according to the trajectories of pedestrians and wild animals in the community. Among them, the streetlight management device in this embodiment is the background server in the streetlight management system provided in Embodiment 2.

[0126] The street light management device provided in this embodiment is essentially a computer device for implementing the solution in Embodiment 1. The computer device can be a smart terminal capable of executing a program, a tablet computer, a laptop computer, a desktop computer, a rack server, a blade server, a tower server or a cabinet server (including an independent server or a server cluster composed of multiple servers), etc.

[0127] The computer device in this embodiment at least includes but is not limited to: a memory and a processor that can be connected to each other through a system bus. Among them, the memory (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory can be an internal storage unit of a computer device, such as a hard disk or a memory of the computer device. In other embodiments, the memory can also be an external storage device of a computer device, such as a plug-in hard disk equipped on the computer device, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. Of course, the memory can also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory is generally used to store an operating system and various application software installed on the computer device. In addition, the memory can also be used to temporarily store various types of data that have been output or are to be output.

[0128] The processor may be a central processing unit (CPU), a graphics processing unit (GPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor is generally used to control the overall operation of a computer device. In this embodiment, the processor is used to run program codes stored in a memory or process data.

[0129] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A streetlight management method considering the trajectories of pedestrians and wild animals in a local community, characterized in that: It includes the following steps: S1: Build a geographic image based on the spatial distribution of roads in the community; and create a connected graph G representing the movement paths of animals in the community, using each street lamp as a node and the lines between street lamps as edges; S2: Real-time detection of live targets in the community through proximity radar installed on street lamps; S3: When the distance of any approach radar to a living target is less than a preset threshold, the type of living organism is identified through the infrared grating on the corresponding street lamp and the following decision is made: (1) If the living organism is a person, the target tracking radar on the corresponding street lamp is turned on; the spatial position of the target person on the geographic image is detected by the target tracking radar, and the movement direction and speed of the pedestrian are determined in combination with the change in the spatial position of the pedestrian; The pedestrian's future movement trajectory is predicted based on the pedestrian's movement direction and speed, and the street lights in the direction of the pedestrian's movement are lit in advance, and the street lights are restored to a low-power state according to a preset delay after the pedestrian passes. (2) If the living organism is a wild animal, the time and order of the appearance of the wild animal at the nodes corresponding to each street light are recorded, and each node is connected in order to obtain a track of the wild animal's appearance; The connected graph G is converted into a directed graph G′ according to the historical data of the wild animal's appearance trajectories in the community; the edge weights of the directed graph G′ are updated according to the newly added wild animal's appearance trajectories, and the average speed of the wild animal on each edge in the directed graph is recorded; Wherein, the edge weight is used to characterize the probability of occurrence of wild animals; S4: When there are pedestrians in the community, if any node detects the presence of wild animals, the movement trajectory of the wild animals is predicted according to the directed graph G′; then it is determined whether the animals and pedestrians will meet, and street lights at the locations where the wild animals and pedestrians may meet are lit to achieve early warning.

2. The method for streetlight management taking into account the trajectories of pedestrians and wild animals in a local community as claimed in claim 1, characterized in that: In step S1, the data format of the connectivity graph is: G = (V, E); Among them, V is the node v corresponding to each street lamp i is a set of nodes, i = 1…n, n represents the number of street lights in the community; E is the set of edges between nodes, any node v i and v j The edge between them is denoted as e ij , j=1…n.

3. The method for streetlight management taking into account the trajectories of pedestrians and wild animals in a local community as claimed in claim 1, characterized in that: The street lights used in the community have three working modes: off, low power consumption and on. In the preset daytime period, the street lights are in the off mode; in the preset nighttime busy period, the street lights are in the on mode; in the preset nighttime idle period, the street lights are in the low power consumption mode in the initial state, and switch to the on mode according to the instructions; and / or There are also multiple cameras in the community. When there are pedestrians in the community, the camera's pan / tilt is adjusted according to the detected spatial position, movement direction and movement speed of the pedestrians to achieve real-time tracking of the target person.

4. The method for streetlight management taking into account the trajectories of pedestrians and wild animals in a local community as claimed in claim 1, characterized in that: In step S3, the infrared grating includes a plurality of sensing points evenly arranged at different heights on the street lamp pole along the vertical direction; the infrared grating determines the type of the approaching living target according to the spatial distribution of the sensing signal along the vertical direction.

5. The method for streetlight management taking into account the trajectories of pedestrians and wild animals in a local community as claimed in claim 2, characterized in that: The process of converting the connected graph G into a directed graph G′ according to the historical data of the wild animal's trajectories in the community is as follows: (i) The direction of the line from the node at the previous moment to the node at the next moment in the wild animal's trajectory is taken as the direction of movement of the animal between the two nodes; (ii) based on the historical data of wildlife trajectories, count the number of times wildlife appears in a specified direction between any two nodes in the connected graph G; (iii) Calculate the probability P of wild animals appearing in a specified direction between any two nodes based on the number of appearances ij , and the probability P will appear ij as the edge weight of the corresponding edge in the directed graph G′; In the above formula, N i Indicates the wild animals appearing in the historical data from node v i The number of times to reach any neighbor node; N ij Indicates the wild animals appearing in the historical data from node v i and depart to neighbor node v j The number of times; (iv) The direction of the maximum edge weight starting from the specified node is taken as the high-frequency direction of the current node, and the high-frequency direction of each node is recorded; (v) Calculate the average time it takes for wild animals to pass through any two nodes based on the historical data of wild animal trajectories, and then calculate the average speed of wild animals passing through any edge based on the distance between nodes.

6. The method for streetlight management taking into account the trajectories of pedestrians and wild animals in a local community as claimed in claim 1, characterized in that: In step S4, the method for predicting the movement trajectory of wild animals according to the directed graph G′ is as follows: S041: The location where the wild animal is first detected is taken as the initial location of the movement trajectory; S042, taking the initial position as the starting point, taking the high frequency direction of the current node in the directed graph as the movement direction, and taking the average speed of the edge in the high frequency direction as the movement speed of the wild animal, and predicting the position and time of the wild animal arriving at the next node; S043, taking the next node predicted each time as the starting point, repeating step S042 to determine the movement direction and movement speed of the wild animal at the current node, and obtaining the end point of the current path; S044: When the predicted next node is a node on the edge of the directed graph, the lines from the initial position to all the predicted nodes in sequence are used as the predicted movement trajectory of the wild animal.

7. The method for streetlight management taking into account the trajectories of pedestrians and wild animals in a local community as claimed in claim 1, characterized in that: The collected historical data set of wild animal appearance trajectories is divided into data sets of multiple different time periods according to the appearance time, and corresponding directed graphs G′ are established according to the data sets of different time periods; then the directed graphs G′ of the corresponding time periods are combined to predict whether pedestrians and wild animals will encounter each other.

8. The method for streetlight management considering the trajectories of pedestrians and wild animals in a local community as claimed in claim 1, characterized in that: In step S4, the method for determining whether pedestrians and wild animals will meet is as follows: First, the spatial characteristics of the predicted movement trajectories of pedestrians and wild animals are analyzed to calculate whether the closest position between the two is less than the preset safe space distance: if so, it is considered that the two may meet, otherwise it is considered that the two will not meet; Secondly, when pedestrians and wild animals are likely to meet, we continue to judge whether the corresponding distance intervals between the two that are less than the safe distance overlap in the time domain based on the time characteristics of the predicted movement trajectories of pedestrians and wild animals: if so, it is considered that the two will meet, otherwise it is considered that the two will not meet.

9. A street light management system, characterized in that: It is used to adopt the street light management method considering the trajectories of pedestrians and wild animals in a local community as described in any one of claims 1 to 8 to achieve adaptive adjustment of the working mode of each street light within the community; The street light management system comprises: Multiple proximity radars are installed on various street lamps in the community and are used to detect living targets around the street lamps; A plurality of infrared gratings, which are respectively installed on various street lamps in the community and are used to distinguish the type of living targets detected by the proximity radar; A plurality of target tracking radars, which are installed on street lamps in the community and are used to track the positions of pedestrians identified by the infrared grating; At least one image acquisition device, including a camera and a pan / tilt head, the image acquisition device is used to acquire real-time image data of pedestrians appearing in the community; A backend server is electrically connected to the controllers of each of the proximity radars, infrared gratings, target tracking radars, image acquisition equipment, and each street lamp in the community; the backend server is used to: (1) obtain real-time detection signals from the proximity radar to determine whether a living target appears in the community; (2) turn on the infrared grating and distinguish the type of the living target according to the detection signal of the infrared grating; (3) turn on the target tracking radar and image acquisition equipment to track pedestrians in the community and predict their movement trajectory and movement speed, and switch the street lamps on the pedestrian path to a lighting mode; (4) record the time and order of appearance of wild animals at the nodes corresponding to each street lamp, and connect each node in order to obtain a wild animal appearance trajectory; (5) dynamically update the directed graph G′ in the community according to the newly added wild animal appearance trajectory; (6) predict the movement trajectory and movement speed of wild animals appearing in the community in combination with the directed graph G′; (7) determine whether the pedestrians’ movement trajectory and the wild animals’ movement trajectory will meet based on the pedestrians’ movement trajectory and the wild animals’ movement trajectory, and if so, light up the street lamps at the corresponding positions.

10. A street lamp management device, comprising a memory, a processor, and a computer program stored in the memory and running in the processor, characterized in that: When the processor executes the computer program, it adopts the street light management method that takes into account the trajectories of pedestrians and wild animals in the local community as described in any one of claims 1-8, so as to realize adaptive adjustment of the working mode of each street light within the community according to the trajectories of pedestrians and wild animals in the community.

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