Intelligent scenic spot multi-mode people flow data analysis algorithm

Abstract

The invention discloses an intelligent scenic spot multi-mode people flow data analysis algorithm, which comprises the following steps: step 1, arranging people flow acquisition equipment at an entrance of a scenic spot, and acquiring people flow information at the entrance; step 2, arranging people flow acquisition equipment at each scenic spot in the scenic area, and acquiring people flow information of each scenic spot; 3, image acquisition equipment is arranged at each scenic spot at the same time, and image information of each scenic spot is acquired; 4, acquiring access communication equipment information of the scenic spot at the moment through information of the communication base station; 5, arranging a visitor flow collection device at the exit of the scenic spot, and collecting the visitor flow information of the exit; and step 6, setting real-time mobile acquisition personnel to perform mobile information acquisition. According to the invention, more comprehensive and intelligent calculation and analysis of the total score of the visitor flow of the scenic area can be carried out.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to a multi-mode pedestrian flow data analysis algorithm for a smart scenic spot. Background Art

[0002] Scenic spot flow analysis is the process of collecting, organizing and analyzing relevant data on tourist flow in the scenic area to reveal the patterns and characteristics of tourist flow. By understanding the flow of tourists in the scenic area, it provides a scientific basis for the planning, operation and management of the scenic area, optimizes the tourist experience, and improves the management efficiency and service quality of the scenic area.

[0003] In the process of analyzing the flow of people in scenic spots, the flow of people data analysis algorithm will be applied to calculate and analyze the collected information related to the flow of people in scenic spots.

[0004] The existing crowd flow data analysis algorithm has a single type of analysis and calculation data and cannot comprehensively monitor the actual crowd flow conditions in the area, which has a certain impact on the use of the crowd flow data analysis algorithm. Therefore, a multi-mode crowd flow data analysis algorithm for a smart scenic spot is proposed. Summary of the invention

[0005] The technical problem to be solved by the present invention is: how to solve the problem that the existing crowd flow data analysis algorithm has a single type of analysis and calculation data and cannot comprehensively monitor the actual crowd flow conditions in the area, which has brought certain impacts on the use of the crowd flow data analysis algorithm. A multi-mode crowd flow data analysis algorithm for a smart scenic spot is provided.

[0006] The present invention solves the above technical problems through the following technical solutions, and the present invention comprises the following steps:

[0007] Step 1: Set up a crowd flow collection device at the entrance of the scenic spot to collect entrance crowd flow information;

[0008] Step 2: Set up crowd flow collection equipment at each scenic spot in the scenic area to collect crowd flow information of each scenic spot;

[0009] Step 3: Setting up image acquisition equipment at each scenic spot to collect image information of each scenic spot;

[0010] Step 4: Collect the access communication equipment information of the scenic spot at this time through the communication base station information;

[0011] Step 5: Set up a crowd flow collection device at the exit of the scenic area to collect exit crowd flow information;

[0012] Step 6: Set up real-time mobile collection personnel to collect mobile information;

[0013] Step 7: Process the entrance crowd flow information, the crowd flow information of each scenic spot, the image information of each scenic spot, the access communication equipment information, the exit crowd flow information and the flow information, and obtain the entrance crowd flow anomaly, scenic spot warning information, overall crowd flow assessment anomaly, communication analysis warning information and personnel flow warning information.

[0014] Furthermore, a flow collection device is set at the entrance of the scenic spot. The specific process of collecting the flow information of the entrance is as follows:

[0015] The crowd flow equipment installed at the entrance of the scenic spot collects the real-time number of people entering, the number of people entering per unit time, and the number of people waiting to enter. That is, the entrance crowd flow information includes the real-time number of people entering, the number of people entering per unit time, and the number of people waiting to enter.

[0016] Furthermore, the process of obtaining the abnormal entrance flow of people is as follows:

[0017] Extract the real-time number of people entering, the number of people entering per unit time, and the number of people waiting to enter from the entrance traffic information:

[0018] Then extract the current date information and analyze whether the current date is in the abnormal traffic time period;

[0019] If the current date is not in the abnormal traffic time period, and the number of people entering in real time is greater than the preset value and the number of people waiting to enter is greater than the preset value, an entrance traffic abnormality is generated;

[0020] When the number of people waiting to enter is greater than the preset value and the number of people entering per unit time is less than the preset value, an entrance flow abnormality is generated;

[0021] The current date is in an abnormal traffic time period, the real-time number of people entering is processed to obtain the first parameter, the number of people entering per unit time is processed to obtain the second parameter, and the number of people waiting to enter is processed to obtain the third parameter;

[0022] When any one of the first parameter, the second parameter and the third parameter is abnormal, an entrance flow abnormality is generated;

[0023] The acquisition of the first parameter, the second parameter and the third parameter and the abnormality determination process are as follows:

[0024] Extract the real-time number of people entering during the previous m abnormal traffic time periods, the number of people entering per unit time, and the number of people waiting to enter;

[0025] Calculate the mean number of real-time entry personnel in the m-time period of abnormal traffic flow, and obtain the parameter of the number of entry personnel; calculate the mean number of entry personnel per unit time in the m-time period of abnormal traffic flow, and obtain the parameter of entry personnel per unit time; calculate the mean number of waiting entry personnel in the m-time period of abnormal traffic flow, and obtain the parameter of waiting entry personnel, m≥5;

[0026] Then, the ratio of the real-time number of people entering the system to the number of people entering the system is calculated to obtain a first parameter. When the first parameter is greater than a preset value, it indicates that an abnormality exists.

[0027] Calculate the ratio of the number of people entering per unit time to the parameter of people entering per unit time, and obtain the second parameter. When the second parameter is greater than the preset value, it indicates that there is an abnormality;

[0028] The ratio of the number of people waiting to enter to the parameter of people waiting to enter is calculated to obtain a third parameter. When the third parameter is greater than a preset value, it indicates that an abnormality exists.

[0029] The specific process of obtaining the scenic spot warning information is as follows:

[0030] Extract the collected traffic information of each scenic spot and the image information of each scenic spot;

[0031] First, analyze the flow of people at each scenic spot. When the flow of people at a scenic spot is greater than a preset value, extract the image information of the scenic spot, analyze the image information of the scenic spot to obtain the flow of people speed information and personnel evaluation parameters;

[0032] When any one of the crowd movement speed information and the personnel evaluation parameters is greater than the preset value, the scenic spot warning information is generated.

[0033] Furthermore, the process of acquiring the crowd flow movement speed information is as follows: extracting the acquired image information of the scenic spot, selecting x calibrated face information from the image information of the scenic spot, recording the movement routes of the x calibrated face information within a preset time, and measuring the lengths of the x movement routes, marking them as P1...Px, and marking the preset time as T;

[0034] Then, the information on the moving speed of the flow of people is obtained through the formula [(P1 / T)+(P2 / T)…(Px / T)] / x=Pp;

[0035] Then process the image information of the scenic spot, collect the collection area of ​​the image information of the scenic spot, mark it as G, and then perform face recognition from the image information of the scenic spot. After the face is recognized, record the number of all recognized faces and mark it as U;

[0036] Then the ratio of U to G is calculated to obtain the personnel evaluation parameters.

[0037] Furthermore, the specific process of obtaining the communication analysis warning information is as follows:

[0038] Extracting the collected access communication device information, the access communication device information including the number of access communication devices and the communication volume occupied by each access communication device;

[0039] Then, the standard communication capacity of the base station is collected, the number of access communication devices is marked as H1, the communication volume occupied by each access communication device is marked as H2, and the standard communication capacity of the base station is marked as Y;

[0040] The base station evaluation parameter is obtained through the formula H1*H2-Y=Hy. When the base station evaluation parameter is greater than the preset value, communication analysis warning information is generated.

[0041] Furthermore, the process of obtaining the abnormal overall evaluation of the crowd flow is as follows: extracting the entrance crowd flow information, the exit crowd flow information and the crowd flow information of each scenic spot;

[0042] Calculate and process the flow of people at each scenic spot, and calculate the sum of the flow of people at each scenic spot to obtain the total flow of people at the scenic spot;

[0043] The number of people entering the scenic area per unit time is extracted from the entrance flow information, and the exit flow information is the number of people leaving the scenic area per unit time;

[0044] When the number of people entering the scenic area per unit time is greater than the preset value, the exit flow information is that the number of people leaving the scenic area per unit time is less than the preset value and the total flow information of the scenic spot is greater than the preset value, the overall flow assessment abnormal information is generated.

[0045] Furthermore, the specific process of setting up real-time mobile collection personnel to collect mobile information is as follows:

[0046] The number of collectors is set according to the number of scenic spots in the scenic area, that is, at least three groups of people are set up for each scenic spot, with two people in each group;

[0047] The personnel information of each group of people from the entrance to the scenic area to the scheduled scenic area is recorded, that is, the flow information is obtained.

[0048] Furthermore, the process of obtaining the personnel flow warning information is as follows: extracting the collected flow information, the flow information including the moving speed information of each group of personnel and the distance between two personnel;

[0049] Calculate and process the movement speed information of a single group of personnel, calculate the average movement speed of two personnel, and obtain the movement parameters of a single group;

[0050] The three groups of movement parameters are calculated and processed, and their averages are calculated to obtain the average movement parameters of a single scenic spot;

[0051] The distance between two persons in the three single groups is calculated, and the average distance between two persons in the three single groups is calculated to obtain the average distance information;

[0052] When the average movement parameter of a single scenic spot is less than the preset value f1, a personnel flow warning message is directly generated;

[0053] When the average movement parameter of a single scenic spot is less than the preset value f2, but the average spacing information is greater than the preset value, personnel flow warning information is generated.

[0054] Furthermore, the moving speed information of the three groups of people at a single scenic spot is processed, and when the moving speeds of the three groups of people are abnormal, the flow information is collected again;

[0055] Extract the six moving speed information obtained by the three groups of people at a single scenic spot, calculate the difference between the six moving speed information, and obtain multiple speed differences. When two or more of the multiple speed differences are greater than the preset value, it means that the moving speeds of the three groups of people are abnormal;

[0056] Extract the distance information between two people in each group, and obtain the distance information of the three groups of people. When the distance information of the three groups of people is abnormal, re-collect the flow information;

[0057] The distance information of the three groups of personnel is extracted and marked as group g1, group g2 and group g3. The absolute value of the difference between group g1 and group g2, the absolute value of the difference between group g1 and group g3, and the absolute value of the difference between group g2 and group g3 are calculated to obtain the distance difference between the three groups of personnel. When the distance difference between the three groups of personnel is greater than the preset value, it means that there is an abnormality in the distance information of the three groups of personnel.

[0058] Compared with the prior art, the present invention has the following advantages: the multi-mode human flow data analysis algorithm of the smart scenic spot can realize comprehensive monitoring and management of the scenic spot. By setting human flow collection equipment at the entrance, scenic spots and exit of the scenic spot, comprehensive monitoring of the human flow in the scenic spot can be realized, which helps managers to understand the distribution of tourists in time and prevent crowding or safety accidents. The image collection equipment can capture the real-time picture of the scenic spot and provide managers with intuitive on-site management information, which helps to timely discover and deal with emergencies. The collected data such as human flow, images, communication equipment information, etc. can generate a variety of analysis reports after processing, such as abnormal entrance human flow, scenic spot warning information, abnormal overall human flow assessment, etc., providing a scientific basis for scenic spot management. Through real-time monitoring and analysis, the system can timely discover abnormal human flow, abnormal communication equipment access, etc., and issue early warnings, so that managers can quickly take countermeasures to ensure the safety of tourists. The setting of real-time mobile data collection personnel provides a flexible and dynamic monitoring method for scenic spots, which helps to discover and deal with on-site problems in a timely manner; by optimizing crowd management, it can reduce tourists' waiting time in queues and improve the sightseeing experience. The real-time information release and early warning system can help tourists understand the dynamics of scenic spots in a timely manner, avoid blind movement, improve sightseeing efficiency, and make the algorithm more worthy of promotion and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0060] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0061] like Figure 1 As shown, this embodiment provides a technical solution: a multi-mode human flow data analysis algorithm for a smart scenic spot, comprising the following steps:

[0062] Step 1: Set up a crowd flow collection device at the entrance of the scenic spot to collect entrance crowd flow information;

[0063] Step 2: Set up crowd flow collection equipment at each scenic spot in the scenic area to collect crowd flow information of each scenic spot;

[0064] Step 3: Setting up image acquisition equipment at each scenic spot to collect image information of each scenic spot;

[0065] Step 4: Collect the access communication equipment information of the scenic spot at this time through the communication base station information;

[0066] Step 5: Set up a crowd flow collection device at the exit of the scenic area to collect exit crowd flow information;

[0067] Step 6: Set up real-time mobile collection personnel to collect mobile information;

[0068] Step 7: Process the entrance crowd flow information, the crowd flow information of each scenic spot, the image information of each scenic spot, the access communication equipment information, the exit crowd flow information and the flow information, and obtain the entrance crowd flow anomaly, scenic spot warning information, overall crowd flow assessment anomaly, communication analysis warning information and personnel flow warning information.

[0069] A crowd flow collection device is set up at the entrance of the scenic spot. The specific process of collecting entrance crowd flow information is as follows:

[0070] The crowd flow equipment installed at the entrance of the scenic spot collects the real-time number of people entering, the number of people entering per unit time, and the number of people waiting to enter. That is, the entrance crowd flow information includes the real-time number of people entering, the number of people entering per unit time, and the number of people waiting to enter.

[0071] The process of obtaining the abnormal entrance flow is as follows:

[0072] Extract the real-time number of people entering, the number of people entering per unit time, and the number of people waiting to enter from the entrance traffic information:

[0073] Then extract the current date information and analyze whether the current date is in an abnormal traffic time period, which includes holidays, weekends, or winter and summer vacations;

[0074] If the current date is not in the abnormal traffic time period, and the number of people entering in real time is greater than the preset value and the number of people waiting to enter is greater than the preset value, an entrance traffic abnormality is generated;

[0075] When the number of people waiting to enter is greater than the preset value and the number of people entering per unit time is less than the preset value, an entrance flow abnormality is generated;

[0076] The current date is in an abnormal traffic time period, the real-time number of people entering is processed to obtain the first parameter, the number of people entering per unit time is processed to obtain the second parameter, and the number of people waiting to enter is processed to obtain the third parameter;

[0077] When any one of the first parameter, the second parameter and the third parameter is abnormal, an entrance flow abnormality is generated;

[0078] The acquisition of the first parameter, the second parameter and the third parameter and the abnormality determination process are as follows:

[0079] Extract the real-time number of people entering during the previous m abnormal traffic time periods, the number of people entering per unit time, and the number of people waiting to enter;

[0080] Calculate the average number of real-time entry personnel in the m-time period of abnormal traffic flow, and obtain the number of entry personnel parameters; calculate the average number of entry personnel per unit time in the m-time period of abnormal traffic flow, and obtain the parameter of entry personnel per unit time; calculate the average number of waiting entry personnel in the m-time period of abnormal traffic flow, and obtain the parameter of waiting entry personnel;

[0081] Then, the ratio of the real-time number of people entering the system to the number of people entering the system is calculated to obtain a first parameter. When the first parameter is greater than a preset value, it indicates that an abnormality exists.

[0082] Calculate the ratio of the number of people entering per unit time to the parameter of people entering per unit time, and obtain the second parameter. When the second parameter is greater than the preset value, it indicates that there is an abnormality;

[0083] Calculate the ratio of the number of people waiting to enter to the parameter of people waiting to enter, and obtain a third parameter. When the third parameter is greater than a preset value, it indicates that an abnormality exists.

[0084] The above process achieves accurate identification of anomalies. By comprehensively considering multiple factors such as the number of people entering in real time, the number of people entering per unit time, the number of people waiting to enter, and whether the date is in an abnormal traffic time period, it can more accurately identify abnormal situations of entrance traffic flow.

[0085] By calculating the ratio of real-time data to data in previous abnormal traffic time periods (first parameter, second parameter, third parameter), and setting preset values ​​to judge abnormalities, the accuracy and reliability of abnormality identification are improved.

[0086] The characteristics of passenger flow in different time periods (abnormal flow time periods and non-abnormal flow time periods) are taken into consideration, and corresponding judgment criteria are formulated according to different situations, so that the system can flexibly respond to various changes in passenger flow.

[0087] For non-abnormal traffic time periods, the abnormality is judged directly based on the comparison between the real-time number of people entering and the number of people waiting to enter and the preset value; for abnormal traffic time periods, the abnormality is judged by calculating the ratio and comparing it with the preset value, which reflects the flexibility and adaptability of the method.

[0088] By real-time monitoring and analysis of entrance traffic data, the system can promptly detect abnormal trends in traffic flow and generate early warning information, providing managers with sufficient time to take countermeasures and prevent possible congestion or safety accidents.

[0089] Through accurate monitoring and analysis of entrance traffic, scenic area managers can more reasonably allocate human, material and other resources, optimize tourist service processes and enhance tourist experience.

[0090] Improved management efficiency. The automated and intelligent anomaly identification process reduces the need for manual intervention and improves management efficiency.

[0091] It has promoted the intelligent development of scenic spots. This method relies on advanced information technology and data analysis algorithms, which helps promote the intelligent and digital transformation of scenic spots.

[0092] By continuously accumulating and analyzing crowd flow data, we can continuously optimize the anomaly recognition algorithm and improve the accuracy and efficiency of recognition.

[0093] The specific process of obtaining the scenic spot warning information is as follows:

[0094] Extract the collected traffic information of each scenic spot and the image information of each scenic spot;

[0095] First, analyze the flow of people at each scenic spot. When the flow of people at a scenic spot is greater than a preset value, extract the image information of the scenic spot, analyze the image information of the scenic spot to obtain the flow of people speed information and personnel evaluation parameters;

[0096] When any one of the crowd movement speed information and the personnel evaluation parameter is greater than the preset value, a scenic spot warning message is generated;

[0097] Through the above process, timely response and efficient management are achieved. By real-time monitoring of the flow of people at each scenic spot, the system can quickly detect dense or abnormal situations, and immediately extract image information of relevant scenic spots for in-depth analysis.

[0098] When the flow of people exceeds the preset value, the system can automatically trigger further image analysis, thereby achieving rapid acquisition and response to scenic spot management information, helping managers to take necessary measures in a timely manner, such as adding guides, adjusting tour routes, etc., to maintain scenic spot order and tourist safety.

[0099] Accurate assessment and decision support, by analyzing the image information of the scenic spot, can obtain the information on the movement speed of people and personnel assessment parameters, which can more intuitively reflect the actual situation of the scenic spot.

[0100] By comparing the crowd movement speed information with the preset value, it can be determined whether there is crowd congestion or poor flow at the scenic spot.

[0101] The process of obtaining the crowd flow moving speed information is as follows: extracting the acquired image information of the scenic spot, selecting x calibrated face information from the image information of the scenic spot, recording the moving routes of the x calibrated face information within a preset time, and measuring the lengths of the x moving routes, marking them as P1...Px, and marking the preset time as T, x≥10;

[0102] Then, the information on the speed of the flow of people is obtained through the formula [(P1 / T)+(P2 / T)…(Px / T)] / x=Pp;

[0103] Then process the image information of the scenic spot, collect the collection area of ​​the image information of the scenic spot, mark it as G, and then perform face recognition from the image information of the scenic spot. After the face is recognized, record the number of all recognized faces and mark it as U;

[0104] Then the ratio of U to G is calculated to obtain the personnel evaluation parameters;

[0105] Through the above process, the speed of human flow movement is accurately quantified. By selecting and calibrating facial information and recording the length of its moving route within a preset time, and then calculating the average value, the speed of human flow movement can be quantified more accurately. This method avoids the errors that may be caused by subjective judgment or improper sample selection in traditional methods, and improves the accuracy and reliability of data. Since the process is based on the real-time acquired scenic spot image information, it can reflect the flow of people in the scenic spot in real time. This is of great significance to managers, who can timely discover areas with dense or congested crowds and take corresponding measures to guide and manage them. By calculating the ratio of the number of recognized faces to the area of ​​scenic spot image information collection, the personnel evaluation parameter can be obtained. This parameter can intuitively reflect the density of people in the scenic spot, which helps managers to have a more comprehensive understanding and evaluation of the operating status of the scenic spot.

[0106] The specific process of obtaining the communication analysis warning information is as follows:

[0107] Extracting the collected access communication device information, the access communication device information including the number of access communication devices and the communication volume occupied by each access communication device;

[0108] Then, the standard communication capacity of the base station is collected, the number of access communication devices is marked as H1, the communication volume occupied by each access communication device is marked as H2, and the standard communication capacity of the base station is marked as Y;

[0109] The base station evaluation parameter is obtained through the formula H1*H2-Y=Hy. When the base station evaluation parameter is greater than the preset value, a communication analysis warning message is generated;

[0110] The above process monitors the communication status in real time. This process can monitor the number of communication devices connected to the scenic area and the communication volume occupied by each device in real time, so as to timely reflect the communication status of the scenic area. This is very important for managers, as they can detect communication congestion or abnormalities in time to avoid inconvenience or anxiety of tourists due to poor communication.

[0111] Communication failures are prevented by calculating the base station evaluation parameters and comparing them with the preset values. When the parameters exceed the preset values, the system will generate communication analysis warning information. This helps managers to discover potential communication failure risks in advance and take corresponding preventive measures, such as increasing base station capacity, optimizing communication networks, etc., to ensure smooth communication in the scenic area.

[0112] Real-time monitoring of communication status: This process can monitor the number of communication devices connected to the scenic area and the communication volume occupied by each device in real time, so as to timely reflect the communication status of the scenic area. This is very important for managers, as they can detect communication congestion or abnormalities in time to avoid inconvenience or anxiety for tourists due to poor communication.

[0113] The process of obtaining the abnormal overall evaluation of the crowd flow is as follows: extracting the entrance crowd flow information, the exit crowd flow information and the crowd flow information of each scenic spot;

[0114] Calculate and process the flow of people at each scenic spot, and calculate the sum of the flow of people at each scenic spot to obtain the total flow of people at the scenic spot;

[0115] The number of people entering the scenic area per unit time is extracted from the entrance flow information, and the exit flow information is the number of people leaving the scenic area per unit time;

[0116] When the number of people entering the scenic area per unit time is greater than the preset value, the exit flow information is the number of people leaving the scenic area per unit time is less than the preset value, and the total flow information of the scenic spot is greater than the preset value, the overall flow evaluation abnormal information is generated;

[0117] The above process enables comprehensive monitoring of the dynamics of the flow of people. This process extracts the flow of people information at the entrance, exit and each scenic spot, thus achieving comprehensive monitoring of the dynamics of the flow of people in the scenic spot. This helps managers understand the distribution and flow of people in the scenic spot in real time, and provides data support for formulating reasonable management strategies.

[0118] Timely detection of abnormal conditions. By setting preset values, when too many people enter per unit time, too few people leave through the exit, and the total flow of people at the scenic spot is too large, the system will generate abnormal information for the overall assessment of the flow of people. This mechanism can timely detect abnormal conditions of the flow of people in the scenic area, such as overcrowding, detention, etc., thereby avoiding potential safety hazards.

[0119] Preventing safety accidents: The process of obtaining abnormal overall assessment of crowd flow helps managers to discover potential safety hazards in scenic spots in advance, such as stampedes caused by overcrowding, tourists getting lost or stranded, etc. By taking timely response measures, such as adding guides, adjusting tour routes, and strengthening safety monitoring, safety accidents can be effectively prevented.

[0120] The specific process of setting up real-time mobile collection personnel to collect mobile information is as follows:

[0121] The number of collectors is set according to the number of scenic spots in the scenic area, that is, at least three groups of people are set up for each scenic spot, with two people in each group;

[0122] The personnel information of each group of people from the entrance to the scenic area to the scheduled scenic area is recorded, that is, the flow information is obtained.

[0123] The process of obtaining the personnel flow warning information is as follows: extracting the collected flow information, the flow information including the moving speed information of each group of personnel and the distance between two personnel;

[0124] Calculate and process the movement speed information of a single group of personnel, calculate the average movement speed of two personnel, and obtain the movement parameters of a single group;

[0125] The three groups of movement parameters are calculated and processed, and their averages are calculated to obtain the average movement parameters of a single scenic spot;

[0126] The distance between two persons in the three single groups is calculated, and the average distance between two persons in the three single groups is calculated to obtain the average distance information;

[0127] When the average movement parameter of a single scenic spot is less than the preset value f1, a personnel flow warning message is directly generated;

[0128] When the average movement parameter of a single scenic spot is less than the preset value f2, but the average spacing information is greater than the preset value, a personnel flow warning message is generated;

[0129] f2>f1;

[0130] The above process settings achieve refined monitoring of the dynamics of the flow of people. By setting up at least three groups of data collectors at each scenic spot, with two people in each group, the dynamic changes of the flow of people in the scenic area can be monitored more carefully. This refined monitoring method helps managers understand the flow of tourists in the scenic area more accurately and provides strong data support for the formulation and adjustment of management strategies.

[0131] Timely detection of abnormal crowd flow. By calculating the average movement speed of a single group of people and the average movement parameters of three single groups, as well as the average distance information between two people, the system can timely detect abnormal crowd flow. For example, when the average movement parameter of a single scenic spot is lower than the preset value, it may mean that tourists have encountered congestion or poor flow at the scenic spot; and when the average distance information is too large, it may indicate that the distribution of tourists at the scenic spot is too dispersed, posing a safety hazard.

[0132] Preventing safety accidents, timely warning information about the flow of people helps managers to discover potential safety hazards in advance, such as crowd congestion, tourists getting lost or stranded, etc. By taking necessary countermeasures, such as adding guides, adjusting tour routes, and strengthening safety monitoring, safety accidents can be effectively prevented and the personal safety of tourists can be guaranteed.

[0133] Process the movement speed information of three groups of people at a single scenic spot. When the movement speed of the three groups of people is abnormal, re-collect the flow information;

[0134] Extract the six moving speed information obtained by the three groups of people at a single scenic spot, calculate the difference between the six moving speed information, and obtain multiple speed differences. When two or more of the multiple speed differences are greater than the preset value, it means that the moving speeds of the three groups of people are abnormal;

[0135] Extract the distance information between two people in each group, and obtain the distance information of the three groups of people. When the distance information of the three groups of people is abnormal, re-collect the flow information;

[0136] Extract the distance information of the three groups of personnel, mark them as group g1, group g2 and group g3, calculate the absolute value of the difference between group g1 and group g2, the absolute value of the difference between group g1 and group g3, and the absolute value of the difference between group g2 and group g3, and obtain the distance difference between the three groups of personnel. When the distance difference between the three groups of personnel is greater than the preset value, it means that the distance information of the three groups of personnel is abnormal;

[0137] Through the above process, it is possible to timely discover whether there are any anomalies in the acquired relevant data, and then determine whether it is necessary to collect and analyze again to ensure the accuracy of the calculation and analysis results.

[0138] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0139] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0140] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A multi-mode crowd flow data analysis algorithm for a smart scenic spot, characterized in that: The following steps are involved: Step 1: Set up a crowd flow collection device at the entrance of the scenic spot to collect entrance crowd flow information; Step 2: Set up crowd flow collection equipment at each scenic spot in the scenic area to collect crowd flow information of each scenic spot; Step 3: Setting up image acquisition equipment at each scenic spot to collect image information of each scenic spot; Step 4: Collect the access communication equipment information of the scenic spot at this time through the communication base station information; Step 5: Set up a crowd flow collection device at the exit of the scenic area to collect exit crowd flow information; Step 6: Set up real-time mobile collection personnel to collect mobile information; Step 7: Process the entrance crowd flow information, the crowd flow information of each scenic spot, the image information of each scenic spot, the access communication equipment information, the exit crowd flow information and the flow information, and obtain the entrance crowd flow anomaly, scenic spot warning information, overall crowd flow assessment anomaly, communication analysis warning information and personnel flow warning information.

2. According to claim 1, a smart scenic spot multi-mode human flow data analysis algorithm is characterized by: A crowd flow collection device is set up at the entrance of the scenic spot. The specific process of collecting entrance crowd flow information is as follows: The crowd flow equipment installed at the entrance of the scenic spot collects the real-time number of people entering, the number of people entering per unit time, and the number of people waiting to enter. That is, the entrance crowd flow information includes the real-time number of people entering, the number of people entering per unit time, and the number of people waiting to enter.

3. According to claim 2, a smart scenic spot multi-mode human flow data analysis algorithm is characterized by: The process of obtaining the abnormal entrance flow is as follows: Extract the real-time number of people entering, the number of people entering per unit time, and the number of people waiting to enter from the entrance traffic information: Then extract the current date information and analyze whether the current date is in the abnormal traffic time period; If the current date is not in the abnormal traffic time period, and the number of people entering in real time is greater than the preset value and the number of people waiting to enter is greater than the preset value, an entrance traffic abnormality is generated; When the number of people waiting to enter is greater than the preset value and the number of people entering per unit time is less than the preset value, an entrance flow abnormality is generated; The current date is in an abnormal traffic time period, the real-time number of people entering is processed to obtain the first parameter, the number of people entering per unit time is processed to obtain the second parameter, and the number of people waiting to enter is processed to obtain the third parameter; When any one of the first parameter, the second parameter and the third parameter is abnormal, an entrance flow abnormality is generated; The acquisition of the first parameter, the second parameter and the third parameter and the abnormality determination process are as follows: Extract the real-time number of people entering during the previous m abnormal traffic time periods, the number of people entering per unit time, and the number of people waiting to enter; Calculate the average number of real-time inbound personnel in the m-time period of abnormal traffic flow, and obtain the number parameter of inbound personnel; calculate the average number of inbound personnel per unit time in the m-time period of abnormal traffic flow, and obtain the parameter of inbound personnel per unit time; Calculate the average number of people waiting to enter during the m abnormal traffic time periods, and obtain the parameters of people waiting to enter; Then, the ratio of the real-time number of people entering the system to the number of people entering the system is calculated to obtain a first parameter. When the first parameter is greater than a preset value, it indicates that an abnormality exists. Calculate the ratio of the number of people entering per unit time to the parameter of people entering per unit time, and obtain the second parameter. When the second parameter is greater than the preset value, it indicates that there is an abnormality; The ratio of the number of people waiting to enter to the parameter of people waiting to enter is calculated to obtain a third parameter. When the third parameter is greater than a preset value, it indicates that an abnormality exists.

4. According to claim 1, a smart scenic spot multi-mode human flow data analysis algorithm is characterized by: The specific process of obtaining the scenic spot warning information is as follows: Extract the collected traffic information of each scenic spot and the image information of each scenic spot; First, analyze the flow of people at each scenic spot. When the flow of people at a scenic spot is greater than a preset value, extract the image information of the scenic spot, analyze the image information of the scenic spot to obtain the flow of people speed information and personnel evaluation parameters; When any one of the crowd movement speed information and the personnel evaluation parameters is greater than the preset value, a scenic spot warning information is generated.

5. According to claim 4, a smart scenic spot multi-mode human flow data analysis algorithm is characterized by: The process of obtaining the crowd flow moving speed information is as follows: extracting the acquired image information of the scenic spot, selecting x calibrated face information from the image information of the scenic spot, recording the moving routes of the x calibrated face information within a preset time, and measuring the lengths of the x moving routes, marking them as P1...Px, and marking the preset time as T; Then, the information on the speed of the flow of people is obtained through the formula [(P1 / T)+(P2 / T)…(Px / T)] / x=Pp; Then process the image information of the scenic spot, collect the collection area of ​​the image information of the scenic spot, mark it as G, and then perform face recognition from the image information of the scenic spot. After the face is recognized, record the number of all recognized faces and mark it as U; Then the ratio of U to G is calculated to obtain the personnel evaluation parameters.

6. The multi-mode human flow data analysis algorithm for a smart scenic spot according to claim 1 is characterized by: The specific process of obtaining the communication analysis warning information is as follows: Extracting the collected access communication device information, the access communication device information including the number of access communication devices and the communication volume occupied by each access communication device; Then, the standard communication capacity of the base station is collected, the number of access communication devices is marked as H1, the communication volume occupied by each access communication device is marked as H2, and the standard communication capacity of the base station is marked as Y; The base station evaluation parameter is obtained through the formula H1*H2-Y=Hy. When the base station evaluation parameter is greater than the preset value, communication analysis warning information is generated.

7. The multi-mode human flow data analysis algorithm for a smart scenic spot according to claim 2 is characterized by: The process of obtaining the abnormal overall evaluation of the crowd flow is as follows: extracting the entrance crowd flow information, the exit crowd flow information and the crowd flow information of each scenic spot; Calculate and process the flow of people at each scenic spot, and calculate the sum of the flow of people at each scenic spot to obtain the total flow of people at the scenic spot; The number of people entering the scenic area per unit time is extracted from the entrance flow information, and the exit flow information is the number of people leaving the scenic area per unit time; When the number of people entering the scenic area per unit time is greater than the preset value, the exit flow information is that the number of people leaving the scenic area per unit time is less than the preset value and the total flow information of the scenic spot is greater than the preset value, the overall flow assessment abnormal information is generated.

8. The multi-mode human flow data analysis algorithm for a smart scenic spot according to claim 1 is characterized by: The specific process of setting up real-time mobile collection personnel to collect mobile information is as follows: The number of collectors is set according to the number of scenic spots in the scenic area, that is, at least three groups of people are set up for each scenic spot, with two people in each group; The personnel information of each group of people from the entrance to the scenic area to the scheduled scenic area is recorded, that is, the flow information is obtained.

9. The multi-mode human flow data analysis algorithm for a smart scenic spot according to claim 8 is characterized by: The process of obtaining the personnel flow warning information is as follows: extracting the collected flow information, the flow information including the moving speed information of each group of personnel and the distance between two personnel; Calculate and process the movement speed information of a single group of personnel, calculate the average movement speed of two personnel, and obtain the movement parameters of a single group; The three groups of movement parameters are calculated and processed, and their averages are calculated to obtain the average movement parameters of a single scenic spot; The distance between two persons in the three single groups is calculated, and the average distance between two persons in the three single groups is calculated to obtain the average distance information; When the average movement parameter of a single scenic spot is less than the preset value f1, a personnel flow warning message is directly generated; When the average movement parameter of a single scenic spot is less than the preset value f2, but the average spacing information is greater than the preset value, personnel flow warning information is generated.

10. The multi-mode human flow data analysis algorithm for a smart scenic spot according to claim 9 is characterized by: Process the movement speed information of three groups of people at a single scenic spot. When the movement speed of the three groups of people is abnormal, re-collect the flow information; Extract the six moving speed information obtained by the three groups of people at a single scenic spot, calculate the difference between the six moving speed information, and obtain multiple speed differences. When two or more of the multiple speed differences are greater than the preset value, it means that the moving speeds of the three groups of people are abnormal; Extract the distance information between two people in each group, and obtain the distance information of the three groups of people. When the distance information of the three groups of people is abnormal, re-collect the flow information; The distance information of the three groups of personnel is extracted and marked as group g1, group g2 and group g3. The absolute value of the difference between group g1 and group g2, the absolute value of the difference between group g1 and group g3, and the absolute value of the difference between group g2 and group g3 are calculated to obtain the distance difference between the three groups of personnel. When the distance difference between the three groups of personnel is greater than the preset value, it means that there is an abnormality in the distance information of the three groups of personnel.

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