A resource collaboration system and method for a smart scenic spot

Abstract

The present invention relates to the technical field of smart scenic spots, and in particular to a resource coordination system and method for smart scenic spots. The method comprises: obtaining real-time images of multiple scenic spots in the scenic spot, extracting identification marks of multiple types of scenic spot resources from the real-time images of the scenic spots, and counting the number of each type of scenic spot resources; obtaining historical tour data of multiple scenic spots in the scenic spot, presetting multiple popular scenic spots, and presetting scenic spot resource pre-allocation plans for each popular scenic spot; obtaining real-time images of popular scenic spots, and obtaining tourist distribution characteristics from the real-time images of popular scenic spots; importing scenic spot map data, presetting a reference area in a basic layer based on the location of the popular scenic spots and ticket verification data information, and deriving geometric characteristics of the reference area in the basic layer; constructing a tourist distribution area and deriving the geometric characteristics of the tourist distribution area; adjusting the scenic spot resource pre-allocation plan according to the geometric characteristics of the tourist distribution area and the geometric characteristics of the reference area, so as to realize the coordination of scenic spot resources among various scenic spots.

Description

Technical Field

[0001] The present invention relates to the technical field of smart scenic spots, and in particular to a resource collaboration system and method for smart scenic spots. Background Art

[0002] Smart scenic spots refer to tourist attractions that use modern information technology to achieve efficient management and utilization of scenic resources. At present, smart scenic spots mainly focus on the effective development and management of internal natural landscape resources and cultural heritage.

[0003] However, there is relatively little discussion on how to better integrate and dispatch service personnel resources (such as tour guides, ushers, ticket inspectors, etc.), optimize the configuration and distribution of commercial facility resources (such as souvenir shops, restaurants, etc.), and coordinate fire safety and power supply resources.

[0004] The present invention proposes a method for intelligently analyzing the collected scenic spot photos and dynamically adjusting various resource configurations according to the geometric characteristics of the spatial layout presented therein, thereby improving the overall service quality and customer satisfaction. Summary of the invention

[0005] The present invention collects data of various scenic spots in the scenic area, constructs scenic spot images, obtains the geometric characteristics of the tourist distribution area and the geometric characteristics of the reference area, and adjusts the scenic area resource pre-allocation plan based on the analysis of the geometric characteristics to achieve the coordination of scenic area resources among various scenic spots.

[0006] The technical solution proposed by the present invention is: a resource coordination method for a smart scenic spot, the method comprising:

[0007] Acquire real-time images of multiple scenic spots in the scenic area, extract identification marks of multiple types of scenic area resources from the real-time images of the scenic spots, and count the number of each type of scenic area resources;

[0008] Obtain historical tour data of multiple scenic spots in the scenic area, preset multiple popular scenic spots, and preset scenic area resource pre-allocation plans for each popular scenic spot;

[0009] Obtain scenic spot ticketing information and ticket verification data information from the scenic spot ticketing system and the ticket checking system of each scenic spot respectively;

[0010] Obtain real-time images of popular attractions and obtain tourist distribution characteristics from the real-time images of popular attractions;

[0011] Import scenic area map data, extract scenic spot distribution information and video surveillance point distribution information to form a basic layer, preset a reference area in the basic layer based on the location of popular scenic spots and ticket verification data information, and export the geometric characteristics of the reference area in the basic layer;

[0012] Associate scenic area map data with tourist distribution characteristics, construct tourist distribution areas and derive geometric characteristics of tourist distribution areas;

[0013] The scenic area resource pre-allocation plan is adjusted according to the geometric characteristics of the tourist distribution area and the geometric characteristics of the benchmark area to achieve the coordination of scenic area resources among various scenic spots.

[0014] Preferably, the scenic area resources include one or more of scenic area humanized service resources, maintenance resources, fire protection resources, emergency support resources and power resources;

[0015] The method of obtaining real-time images of multiple scenic spots in the scenic spot, extracting identification marks of multiple types of scenic spot resources from the real-time images of the scenic spots, and counting the number of each type of scenic spot resources includes the following steps:

[0016] Convert the acquired real-time images of multiple scenic spots into grayscale images;

[0017] The grayscale image is converted into a black and white image through threshold processing to facilitate contour detection and recognition;

[0018] Filter the black and white image to remove image noise;

[0019] Use the Canny edge detection algorithm to find the edges of each black and white image;

[0020] Find the edge-connected point set to detect the identification mark area appearing in the image, where the identification mark includes a QR code, a bar code and an identification character;

[0021] Determine the position of the identification mark area according to the shape characteristics of the contour;

[0022] Extracting a region of interest (ROI) from the detected identification area;

[0023] Use optical character recognition (OCR) technology to identify text information in ROI;

[0024] Decode or character-recognize the identification mark to obtain the information of the scenic spot resources corresponding to the identification mark;

[0025] Count the number of identification marks, obtain the number of each type of scenic spot resources, and form a resource classification set with each type of scenic spot resources and corresponding quantity data ,in, Indicates A set of scenic spot resource types. Indicates The number set of scenic spot-like resources;

[0026] in, , , Indicates The first scenic spot Scenic spot resources; Indicates The scenic spot resources are in the The number of scenic spots.

[0027] Preferably, the acquisition of historical tour data of multiple scenic spots in the scenic area and the presetting of multiple popular scenic spots include the following steps:

[0028] Obtain ticket sales data within a preset time period from the scenic spot ticketing system and add a ticket sales timestamp;

[0029] Obtain the ticket verification data of each attraction from the ticket checking system of each attraction and add the ticket checking timestamp;

[0030] The ticket sales data and the ticket verification data of the scenic spot constitute the scenic spot evaluation data set ,in, express The scenic spot A tourist attraction evaluation dataset of tourist attractions;

[0031] ,in, Respectively within the preset time period The number of tickets sold by the scenic spot The scenic spot The set of ticket inspection quantities for each attraction; , ;in, Indicates Scenic spots The number of tickets sold at a given time. express Moment Number of ticket inspections for each attraction; Indicates the starting point of the preset time period. Indicates the end point of a preset time period;

[0032] Assume the heat coefficient is ,if Then judge The attractions are popular attractions, including is the heat threshold;

[0033] Calculate and obtain the heat coefficients of multiple scenic spots, and arrange them in descending order to form a heat sequence; obtain the name of each scenic spot from the database, match the name of the scenic spot with the heat coefficient, and form a scenic spot heat sequence in the order of the heat coefficient arrangement;

[0034] Select the first one in the scenic spot popularity sequence attractions as popular sights make up popular attractions vector set ,in express The scenic spot Popular attractions vectors, Indicates the number of popular attractions;

[0035] set up ,in Indicates The names of popular attractions, Indicates The longitude and latitude of popular attractions.

[0036] Preferably, the method of presetting a scenic spot resource pre-allocation plan for each popular scenic spot includes the following steps:

[0037] Get popular attractions vector set and resource classification set;

[0038] For the Preset number of popular attractions reserved The scenic resources ;

[0039] in, and They represent basic proportion one and basic proportion two respectively; For the Experience matching value of scenic spot resources;

[0040] The step of importing scenic area map data and extracting scenic spot distribution information and video surveillance point distribution information to form a basic layer includes the following steps:

[0041] Obtain map data of scenic spots from geographic information systems;

[0042] Extract the latitude and longitude information of scenic spots and the latitude and longitude information of video surveillance points from the map data of the scenic area;

[0043] Use a geographic information system library to construct a basic layer, wherein the basic layer includes a scenic spot distribution location sublayer and a video monitoring point distribution sublayer;

[0044] Visualize the base layer.

[0045] Preferably, the method of presetting a reference area in a base layer based on the location of popular scenic spots and ticket verification data information, and deriving geometric characteristics of the reference area in the base layer, comprises the following steps:

[0046] Extract the latitude and longitude data of the central location of popular attractions;

[0047] Get the latitude and longitude data of the video surveillance points within the area with a radius of r from the center of the scenic spot;

[0048] Based on the latitude and longitude data of multiple selected video surveillances, a convex hull algorithm is used to construct a polygon on the base layer to represent the reference area;

[0049] By accessing the properties of the convex hull object, the geometric properties of the reference area are obtained, wherein the geometric properties include the area and length of the reference area;

[0050] The base ratio 1 is used to encode the area of ​​the reference area, and the base ratio 2 is used to encode the length of the reference area.

[0051] Preferably, associating scenic area map data with tourist distribution characteristics, constructing tourist distribution areas, and deriving geometric characteristics of tourist distribution areas comprises the following steps:

[0052] Obtaining tourist video images through video surveillance points in the selected benchmark area, and analyzing the tourist video images to obtain tourist distribution characteristics;

[0053] Create a new layer on top of the base layer, construct the tourist distribution area in the new layer, and export the geometric characteristics of the tourist distribution area in the new layer;

[0054] The method of obtaining tourist video images through video surveillance points in the selected reference area and analyzing the tourist video images to obtain tourist distribution characteristics comprises the following steps:

[0055] Obtaining video images of tourists collected by video surveillance points within the selected reference area;

[0056] Decompose the video into multiple consecutive frames; use filters to remove noise from the frame images;

[0057] Convert the frame image into a grayscale image; use the averaging method or Gaussian mixture model to establish a background model; identify the foreground area through the current frame image and the background model;

[0058] Calculate the centroid coordinates of each foreground area; calculate the area or bounding box of each foreground area; count the locations and distribution areas of tourists in the used frames;

[0059] Count the areas where tourists gather and the areas where tourists are sparse; generate a tourist scatter plot to show the distribution of tourists.

[0060] Preferably, creating a new layer on top of the base layer, constructing a tourist distribution area in the new layer, and deriving geometric characteristics of the tourist distribution area in the new layer include the following steps:

[0061] Add a new layer on the base layer; extract the location and number of tourists from the tourist scatter plot;

[0062] Based on the location and number of tourists, the clustering algorithm K-means is used to construct multiple tourist distribution areas;

[0063] Taking the center point of each tourist distribution area as a representative point, a tourist distribution area coordinate set including the representative point is constructed;

[0064] Construct a polygonal area including all tourist distribution areas, specifically:

[0065] Extract the representative point with the smallest longitude value from the coordinate set of the tourist distribution area ;

[0066] by As a benchmark, all representative points are relative to The polar angles are arranged to form a representative point coordinate sequence;

[0067] Take out the corresponding representative points from the representative point coordinate sequence in turn, and check whether the corresponding representative points and the formed convex hull edge form a right turn; if they are a left turn or collinear, remove the last point on the convex hull until a right turn is formed;

[0068] Add the current representative point to the convex hull;

[0069] Repeat the above steps, and the final point set is the desired convex hull, and the desired convex hull is used as the tourist distribution area including all tourist distribution areas;

[0070] Get the area and perimeter of the convex hull, which are the area and perimeter of the tourist distribution area.

[0071] Preferably, the method of adjusting the scenic area resource pre-allocation plan according to the geometric characteristics of the tourist distribution area and the geometric characteristics of the reference area to achieve the coordination of scenic area resources among various scenic spots includes the following steps:

[0072] Get the area of ​​the tourist distribution area and the area of ​​the base area; calculate the ratio between the area of ​​the tourist distribution area and the area of ​​the base area ;

[0073] Get the perimeter of the tourist distribution area and the perimeter of the reference area; calculate the ratio between the perimeter of the tourist distribution area and the perimeter of the reference area ;

[0074] If the area of ​​the tourist distribution area is larger than the area of ​​the reference area, use Encode the outlines of tourist distribution areas;

[0075] If the perimeter of the tourist distribution area is larger than the perimeter of the reference area, use Encode the outlines of tourist distribution areas;

[0076] The distribution area outline code is displayed in numerical form;

[0077] Set up Number of scenic spot resources optimized for popular attractions ,in, Indicates Popular scenic spots The adjustment ratio of scenic spot resources; among them, ;

[0078] Get the contour code of the tourist distribution area and assign the value of the contour code to , to achieve the adjustment of scenic area resources;

[0079] Get resource classification set and scenic spot heat sequence, extract from scenic spot heat sequence The scenic spot data that is not selected as the preset popular scenic spots, and the corresponding number of scenic spot resources are obtained;

[0080] from Extracted from the scenic resources of scenic spots The number of This type of scenic spot resources is used to meet the needs of scenic spot resource adjustment.

[0081] The present invention also provides a resource coordination system for a smart scenic spot, and the system is used to execute the resource coordination method for a smart scenic spot.

[0082] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the resource collaboration method for a smart scenic spot.

[0083] Beneficial effects of the present invention:

[0084] 1. The present invention coordinates and allocates various scenic area resources (including personalized service resources, maintenance resources, fire protection resources, emergency support resources and power resources) in the scenic area, pre-allocates scenic area resources for each popular scenic spot, and adjusts the corresponding scenic area resources with reference to the actual distribution of tourists in the popular scenic spots, and provides corresponding resources for the popular scenic spots by drawing or increasing the number of resources.

[0085] 2. The present invention obtains tourist video images through the video surveillance points in the selected reference area, analyzes the tourist video images to obtain tourist distribution characteristics, creates a new layer on the top of the base layer, constructs the tourist distribution area in the new layer, and derives the geometric characteristics of the tourist distribution area in the new layer. If the area of ​​the tourist distribution area is larger than the area of ​​the reference area, use Code the outline of the tourist distribution area; if the perimeter of the tourist distribution area is larger than the perimeter of the reference area, use Encode the outline of the tourist distribution area; and visualize the distribution area outline code in numerical form. By encoding the outline, managers can intuitively understand the relationship between the geometric shape of the tourist distribution area and the resource adjustment ratio, and guide the tourists in the scenic area to distribute according to the preset geometric shape by optimizing the pre-allocation of scenic area resources. In addition, it is also convenient to analyze the relationship between the geometric characteristics of tourist distribution and the resource adjustment ratio through machine learning algorithms in the later stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 The present invention is a flow chart of a resource collaboration method for a smart scenic spot. DETAILED DESCRIPTION

[0087] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0088] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the element may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0089] Embodiment 1:

[0090] refer to Figure 1 The technical solution provided by the present invention is: a resource coordination method for a smart scenic spot, comprising the following steps:

[0091] 1. Acquire real-time images of multiple scenic spots in a scenic spot, extract identification marks of multiple types of scenic spot resources from the real-time images of the scenic spots, and count the number of each type of scenic spot resources; scenic spot resources include one or more of scenic spot humanized service resources, maintenance resources, fire protection resources, emergency support resources and power resources. The specific steps are: convert the acquired real-time images of multiple scenic spots into grayscale images;

[0092] The grayscale image is converted into a black and white image through threshold processing to facilitate contour detection and recognition;

[0093] Filter the black and white image to remove image noise;

[0094] Use the Canny edge detection algorithm to find the edges of each black and white image;

[0095] Find the identification mark area appearing in the edge-connected point set detection image, where the identification mark includes a QR code, a barcode and an identification character; determine the position of the identification mark area according to the shape characteristics of the contour; extract the region of interest ROI from the detected identification mark area; use optical character recognition (OCR) technology to recognize the text information in the ROI; decode or perform text recognition on the identification mark to obtain the information of the scenic spot resources corresponding to the identification mark.

[0096] Count the number of identification marks, obtain the number of each type of scenic spot resources, and form a resource classification set with each type of scenic spot resources and corresponding quantity data ,in, Indicates A set of scenic spot resource types. Indicates The number set of scenic spot resources. , .

[0097] 2. Obtain historical tour data of multiple scenic spots in the scenic area, preset multiple popular scenic spots, and preset scenic area resource pre-allocation plans for each popular scenic spot, specifically including the following steps:

[0098] Obtain ticket sales data within a preset time period from the scenic spot ticketing system and add a ticket sales timestamp;

[0099] Obtain the ticket verification data of each attraction from the ticket checking system of each attraction and add the ticket checking timestamp;

[0100] The ticket sales data and the ticket verification data of the scenic spot constitute the scenic spot evaluation data set ,in, express The scenic spot A tourist attraction evaluation dataset of tourist attractions;

[0101] ,in, Respectively within the preset time period The number of tickets sold at the scenic spot The scenic spot The number of ticket inspections for each attraction; , ;in, Indicates Scenic spots The number of tickets sold at a given time. express Moment Number of ticket inspections for each attraction; Indicates the starting point of the preset time period. Indicates the end point of a preset time period;

[0102] Assume the heat coefficient is ,if Then judge The attractions are popular attractions, including is the heat threshold;

[0103] Calculate and obtain the heat coefficients of multiple scenic spots, and arrange them in descending order to form a heat sequence; obtain the name of each scenic spot from the database, match the name of the scenic spot with the heat coefficient, and form a scenic spot heat sequence in the order of the heat coefficient arrangement;

[0104] Select the first one in the scenic spot popularity sequence attractions as popular sights make up popular attractions vector set ,in express The scenic spot Popular attractions vectors, Indicates the number of popular attractions;

[0105] set up ,in Indicates The names of popular attractions, Indicates The longitude and latitude of popular attractions.

[0106] Among them, the method of presetting the scenic spot resource pre-allocation plan is:

[0107] Get the popular attractions vector set and resource classification set. Preset number of popular attractions reserved scenic area resources; ;in, and They represent basic proportion one and basic proportion two respectively; For the The experience matching value of scenic spot resources.

[0108] 3. Obtain scenic spot ticket sales information and ticket verification data information from the scenic spot ticket sales system and the ticket inspection system of each scenic spot;

[0109] 4. Obtain real-time images of popular attractions and obtain tourist distribution characteristics from the real-time images of popular attractions;

[0110] 5. First, import the scenic area map data, extract the scenic spot distribution information and video surveillance point distribution information from it to form the basic layer, including the following steps:

[0111] Acquire the map data of the scenic area from the geographic information system; extract the latitude and longitude information of the scenic spot and the latitude and longitude information of the video monitoring points from the map data of the scenic area; use the geographic information system library to build a basic layer, the basic layer includes a scenic spot distribution location sublayer and a video monitoring point distribution sublayer; and visualize the basic layer.

[0112] Specifically, in this embodiment, the gpd.read_file function can be used to read the scenic spot and video surveillance point data in GeoJSON format. Create a Folium map object, set the center position to the average latitude and longitude of the scenic spot, and the zoom level to 12. Traverse the scenic spot data, add a blue mark to each scenic spot, and set a pop-up window to display the scenic spot name. Traverse the video surveillance point data, add a red mark to each video surveillance point, and set a pop-up window to display the surveillance point ID.

[0113] Secondly, based on the location of popular attractions and ticket verification data information, a reference area is preset in the base layer, and the geometric characteristics of the reference area in the base layer are exported; the following steps are included:

[0114] Extract the longitude and latitude data of the center of the popular scenic spot, and obtain the longitude and latitude data of the video surveillance points within the area with a radius of r from the center of the scenic spot;

[0115] Based on the latitude and longitude data of multiple selected video surveillances, a convex hull algorithm is used to construct a polygon on the base layer to represent the reference area; by accessing the properties of the convex hull object, the geometric characteristics of the reference area are obtained, and the geometric characteristics include the area and length of the reference area; the area of ​​the reference area is encoded using basic ratio one, and the length of the reference area is encoded using basic ratio two.

[0116] 6. Associating scenic area map data with tourist distribution characteristics, constructing tourist distribution areas and deriving geometric characteristics of tourist distribution areas; including the following steps:

[0117] Obtaining tourist video images through video surveillance points in the selected benchmark area, and analyzing the tourist video images to obtain tourist distribution characteristics;

[0118] Create a new layer on top of the base layer, construct the tourist distribution area in the new layer, and export the geometric characteristics of the tourist distribution area in the new layer;

[0119] The method of analyzing tourist video images to obtain tourist distribution characteristics includes the following steps:

[0120] Obtain the tourist video images collected by the video surveillance points in the selected reference area; decompose the video into multiple continuous frames; use a filter to remove noise in the frame image; convert the frame image into a grayscale image;

[0121] Use the averaging method or Gaussian mixture model to build a background model; identify the foreground area through the current frame image and the background model; calculate the centroid coordinates of each foreground area; calculate the area or bounding box of each foreground area; count the locations and distribution areas of tourists in the used frames; count the areas where tourists gather and the areas where tourists are sparse; generate a tourist scatter plot to show the distribution of tourists.

[0122] The method of constructing a tourist distribution area in a new layer includes the following steps:

[0123] Add a new layer on the base layer; extract the location and number of tourists from the tourist scatter plot;

[0124] According to the location and number of tourists, the clustering algorithm K-means is used to construct multiple tourist distribution areas; the center point of each tourist distribution area is taken as the representative point to construct a tourist distribution area coordinate set including the representative point; a polygonal area is constructed including all tourist distribution areas, specifically:

[0125] Extract the representative point with the smallest longitude value from the coordinate set of the tourist distribution area ;

[0126] by As a benchmark, all representative points are relative to The polar angles are arranged to form a representative point coordinate sequence;

[0127] The corresponding representative points are sequentially taken out from the representative point coordinate sequence, and it is detected whether the corresponding representative points and the formed convex hull edge constitute a right turn;

[0128] If it turns left or is collinear, remove the last point on the convex hull until a right turn is formed; add the current representative point to the convex hull;

[0129] Repeat the above steps, and the final point set is the desired convex hull, and the desired convex hull is used as the tourist distribution area including all tourist distribution areas; the area and perimeter of the convex hull are obtained, which are the area and perimeter of the tourist distribution area.

[0130] 7. Adjust the pre-allocation plan of scenic area resources according to the geometric characteristics of the tourist distribution area and the geometric characteristics of the reference area to achieve the coordination of scenic area resources among various scenic spots, including the following steps:

[0131] Get the area of ​​the tourist distribution area and the area of ​​the base area; calculate the ratio between the area of ​​the tourist distribution area and the area of ​​the base area ;

[0132] Get the perimeter of the tourist distribution area and the perimeter of the reference area; calculate the ratio between the perimeter of the tourist distribution area and the perimeter of the reference area ;

[0133] If the area of ​​the tourist distribution area is larger than the area of ​​the reference area, use Encode the outlines of tourist distribution areas;

[0134] If the perimeter of the tourist distribution area is larger than the perimeter of the reference area, use The tourist distribution area outline code is displayed in numerical form.

[0135] In this embodiment, it is assumed that Number of scenic spot resources optimized for popular attractions ,in, Indicates Popular scenic spots The adjustment ratio of scenic spot resources; among them, ;

[0136] Get the contour code of the tourist distribution area and assign the value of the contour code to , to achieve the adjustment of scenic area resources;

[0137] Get resource classification set and scenic spot heat sequence, extract from scenic spot heat sequence The scenic spot data that is not selected as the preset popular scenic spots, and the corresponding number of scenic spot resources are obtained;

[0138] from Select from the number of resources that are not selected as preset popular attractions The number of This type of scenic spot resources is used to meet the needs of scenic spot resource adjustment.

[0139] Using the ratio of the area of ​​the tourist distribution area to the area of ​​the reference area as the outline code for the tourist distribution area can show how much the area of ​​the tourist distribution area has expanded relative to the area of ​​the reference area. The expansion of the area of ​​the tourist distribution area can reflect the increase in the flow of people, and can also reflect that the speed of on-site ticket checking is slow, or that some service resources are insufficient, and there is a serious phenomenon of tourist queues. For this reason, it is necessary to adjust the resources of the scenic area, such as dispatching personnel (maintenance resources) from other attractions (non-popular attractions) to the ticket checking area of ​​the scenic spot to improve the efficiency of ticket checking; or increase the number of mobile vending machines (humanized service resources) to reduce queues. In addition, the increase in the distribution area requires the coordination of more fire-fighting equipment and communication base station equipment, etc. At the same time, increase the power supply of the scenic spot to ensure the power supply of the increased commercial, ticket checking machines and communication base station equipment.

[0140] Using the ratio of the perimeter of the tourist distribution area to the perimeter of the reference area to encode the contour of the tourist distribution area can show the degree of increased tourist mobility. To this end, it is necessary to increase mobile guides (maintenance resources) to provide route guidance along the way to avoid crowding and trampling.

[0141] Embodiment 2:

[0142] refer to Figure 1 ,The difference between this embodiment and the first embodiment is that a linear regression model is constructed as a prediction model for predicting the number of tourists to a scenic spot at a future time point;

[0143] The specific steps are as follows:

[0144] Get Ticket quantity set for scenic spots and The scenic spot Ticket checking quantity set for each attraction ;

[0145] Building a linear regression model ;in, is the intercept, is the regression coefficient, is the error term; and , calculate and obtain the total number of tickets sold and the total number of tickets checked within a preset time period;

[0146] The total number of tickets sold and the total number of tickets checked are normalized and input into the linear regression model to output the predicted value of the number of visitors to the scenic spot at a future time point.

[0147] The present invention also provides a resource coordination system for a smart scenic spot, and the system is used to execute the resource coordination method for a smart scenic spot.

[0148] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the resource collaboration method for a smart scenic spot.

[0149] The embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. The embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium. When the computer program is executed by the central processing unit (CPU), the above-mentioned functions defined in the method of the present application are executed. It should be noted that the above-mentioned computer-readable medium of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of an electrical, magnetic, optical, electromagnetic, infrared segment, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wire segments, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, electrical wire, optical cable, RF, etc., or any suitable combination of the foregoing.

[0150] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0151] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the principles, the implementation methods of the present invention may be deformed or modified in any way.

Claims

1. A resource collaboration method for a smart scenic spot, characterized in that: The method comprises: Acquire real-time images of multiple scenic spots in the scenic area, extract identification marks of multiple types of scenic area resources from the real-time images of the scenic spots, and count the number of each type of scenic area resources; Obtain historical tour data of multiple scenic spots in the scenic area, preset multiple popular scenic spots, and preset scenic area resource pre-allocation plans for each popular scenic spot; Obtain scenic spot ticketing information and ticket verification data information from the scenic spot ticketing system and the ticket checking system of each scenic spot respectively; Obtain real-time images of popular attractions and obtain tourist distribution characteristics from the real-time images of popular attractions; Import scenic area map data, extract scenic spot distribution information and video surveillance point distribution information to form a basic layer, preset a reference area in the basic layer based on the location of popular scenic spots and ticket verification data information, and export the geometric characteristics of the reference area in the basic layer; Associating scenic area map data with tourist distribution characteristics, constructing tourist distribution areas and deriving geometric characteristics of tourist distribution areas; specifically including the following steps: Obtaining tourist video images through video surveillance points in the selected benchmark area, and analyzing the tourist video images to obtain tourist distribution characteristics; Create a new layer on top of the base layer, construct the tourist distribution area in the new layer, and export the geometric characteristics of the tourist distribution area in the new layer; The method of obtaining tourist video images through video surveillance points in the selected reference area and analyzing the tourist video images to obtain tourist distribution characteristics comprises the following steps: Obtaining video images of tourists collected by video surveillance points within the selected reference area; Decompose the video into multiple consecutive frames; Use a filter to remove noise from the frame image; Convert the frame image into a grayscale image; Use the averaging method or Gaussian mixture model to build the background model; Identify the foreground area through the current frame image and the background model; Calculate the centroid coordinates of each foreground region; Calculate the area or bounding box of each foreground region; The location and distribution area of ​​tourists in the frame used for statistics; Count the areas where tourists gather and the areas where they are sparse; Generate a tourist scatter plot to show the distribution of tourists; According to the geometric characteristics of the tourist distribution area and the geometric characteristics of the reference area, the scenic area resource pre-allocation plan is adjusted to achieve the coordination of scenic area resources among various scenic spots, including the following steps: Get the area of ​​the tourist distribution area and the area of ​​the base area; calculate the ratio between the area of ​​the tourist distribution area and the area of ​​the base area ; Get the perimeter of the tourist distribution area and the perimeter of the reference area; calculate the ratio between the perimeter of the tourist distribution area and the perimeter of the reference area ; If the area of ​​the tourist distribution area is larger than the area of ​​the reference area, use Encode the contours of tourist distribution areas; If the perimeter of the tourist distribution area is larger than the perimeter of the reference area, use Encode the contours of tourist distribution areas; The distribution area outline code is displayed in numerical form; Set up The number of scenic spot resources optimized for popular attractions ,in, Indicates Popular scenic spots The adjustment ratio of scenic spot resources; among them, ; Indicates the base ratio of one; For the Experience matching value of scenic spot resources; Get the contour code of the tourist distribution area and assign the value of the contour code to , realize the adjustment of scenic spot resources; Get resource classification set and scenic spot heat sequence, extract from scenic spot heat sequence The scenic spot data that is not selected as the preset popular scenic spots, and the corresponding number of scenic spot resources are obtained; from Extracted from the scenic resources of scenic spots The number of This type of scenic spot resources is used to meet the needs of scenic spot resource adjustment.

2. A resource collaboration method for a smart scenic spot according to claim 1, characterized in that: The scenic area resources include one or more of scenic area humanized service resources, maintenance resources, fire protection resources, emergency support resources and power resources; The method of obtaining real-time images of multiple scenic spots in a scenic spot, extracting identification marks of multiple types of scenic spot resources from the real-time images of the scenic spots, and counting the number of each type of scenic spot resources includes the following steps: Convert the acquired real-time images of multiple scenic spots into grayscale images; The grayscale image is converted into a black and white image through threshold processing to facilitate contour detection and recognition; Filter the black and white image to remove image noise; Use the Canny edge detection algorithm to find the edges of each black and white image; Find the edge-connected point set to detect the identification mark area appearing in the image, where the identification mark includes a QR code, a bar code and an identification character; Determine the position of the identification mark area according to the shape characteristics of the contour; Extracting a region of interest (ROI) from the detected identification area; Use optical character recognition (OCR) technology to identify text information in ROI; Decode or character-recognize the identification mark to obtain the information of the scenic spot resources corresponding to the identification mark; Count the number of identification marks, obtain the number of each type of scenic spot resources, and form a resource classification set with each type of scenic spot resources and corresponding quantity data ,in, Indicates A set of scenic spot resource types. Indicates The number set of scenic spot-like resources; in, , , Indicates The first scenic spot Scenic spot resources; Indicates The scenic spot resources are in the The number of scenic spots.

3. A resource collaboration method for a smart scenic spot according to claim 2, characterized in that: The method of obtaining historical tour data of multiple scenic spots in the scenic area and presetting multiple popular scenic spots includes the following steps: Obtain ticket sales data within a preset time period from the scenic spot ticketing system and add a ticket sales timestamp; Obtain the ticket verification data of each attraction from the ticket checking system of each attraction and add the ticket checking timestamp; The ticket sales data and the ticket verification data of the scenic spot constitute the scenic spot evaluation data set ,in, express The scenic spot A tourist attraction evaluation dataset of tourist attractions; ,in, Respectively within the preset time period The number of tickets sold at the scenic spot The scenic spot The number of ticket inspections for each attraction; , ;in, Indicates Scenic spots The number of tickets sold at a given time. express Moment Number of ticket inspections for each attraction; Indicates the starting point of the preset time period. Indicates the end point of a preset time period; Assume the heat coefficient is ,if Then judge The attractions are popular attractions, including is the heat threshold; Calculate and obtain the heat coefficients of multiple scenic spots, and arrange them in descending order to form a heat sequence; obtain the name of each scenic spot from the database, match the name of the scenic spot with the heat coefficient, and form a heat sequence of the scenic spot according to the order of the heat coefficient; Select the first one in the scenic spot popularity sequence attractions as popular sights make up popular attractions vector set ,in express The scenic spot Popular attractions vectors, Indicates the number of popular attractions; set up ,in Indicates The names of popular attractions, Indicates The longitude and latitude of popular attractions.

4. The resource coordination method of a smart scenic spot according to claim 3, characterized in that: The method of presetting a scenic spot resource pre-allocation plan for each popular scenic spot includes the following steps: Get popular attractions vector set and resource classification set; For the A preset number of scenic spot resources are reserved for each popular scenic spot. ; in, Indicates basic ratio two; The step of importing scenic area map data and extracting scenic spot distribution information and video surveillance point distribution information to form a basic layer includes the following steps: Obtain map data of scenic spots from geographic information systems; Extract the latitude and longitude information of scenic spots and the latitude and longitude information of video surveillance points from the map data of the scenic area; Use a geographic information system library to construct a basic layer, wherein the basic layer includes a scenic spot distribution location sublayer and a video monitoring point distribution sublayer; Visualize the base layer.

5. A resource collaboration method for a smart scenic spot according to claim 4, characterized in that: The method of presetting a reference area in a base layer based on the location of popular scenic spots and ticket verification data information, and deriving geometric characteristics of the reference area in the base layer, comprises the following steps: Extract the latitude and longitude data of the central location of popular attractions; Get the latitude and longitude data of the video surveillance points within the area with a radius of r from the center of the scenic spot; Based on the latitude and longitude data of multiple selected video surveillances, a convex hull algorithm is used to construct a polygon on the base layer to represent the reference area; By accessing the properties of the convex hull object, the geometric properties of the reference area are obtained, wherein the geometric properties include the area and length of the reference area; The base ratio 1 is used to encode the area of ​​the reference area, and the base ratio 2 is used to encode the length of the reference area.

6. A resource coordination method for a smart scenic spot according to claim 5, characterized in that: The method of creating a new layer on the top of the base layer, constructing a tourist distribution area in the new layer, and exporting geometric characteristics of the tourist distribution area in the new layer includes the following steps: Add a new layer on top of the base layer; Extract the location and number of tourists from the tourist scatter plot; Based on the location and number of tourists, the clustering algorithm K-means is used to construct multiple tourist distribution areas; Taking the center point of each tourist distribution area as a representative point, a tourist distribution area coordinate set including the representative point is constructed; Construct a polygonal area including all tourist distribution areas, specifically: Extract the representative point with the smallest longitude value from the coordinate set of the tourist distribution area ; by As a benchmark, all representative points are relative to The polar angles are arranged to form a representative point coordinate sequence; Take out the corresponding representative points from the representative point coordinate sequence in turn, and check whether the corresponding representative points and the formed convex hull edge form a right turn; if they are a left turn or collinear, remove the last point on the convex hull until a right turn is formed; Add the current representative point to the convex hull; Repeat the above steps, and the final point set is the desired convex hull, and the desired convex hull is used as the tourist distribution area including all tourist distribution areas; Get the area and perimeter of the convex hull, which are the area and perimeter of the tourist distribution area.

7. A resource collaboration system for a smart scenic spot, characterized in that: The system is used to execute a resource collaboration method for a smart scenic spot as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement a resource collaboration method for a smart scenic spot as described in any one of claims 1 to 6.

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