Crowd flow characteristics determination method, device, electronic device and storage medium

Abstract

The present disclosure provides a method for determining crowd flow characteristics, and the present disclosure relates to the field of data processing, and in particular to the field of big data, data analysis and mining. The specific implementation scheme includes: obtaining first usage data of multiple first users located in a target area using health codes within a first time range; inputting the first usage count information in each time interval into a floating interval model corresponding to the time interval, and outputting the floating interval of the number of times the health code is used within the time interval; obtaining second usage data of multiple second users located in the target area using health codes within a second time range; determining crowd flow characteristics of the target area within the second time range based on the floating interval and second usage data of the number of times the health code is used within each time interval. The present disclosure also provides a method, device, electronic device and storage medium for determining crowd flow characteristics.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, in particular to the field of big data, data analysis and mining, and more specifically to a method, device, electronic device, storage medium and program product for determining crowd flow characteristics. Background Art

[0002] In the process of ensuring public safety, changes in the population distribution and behavior patterns of the crowd often mean the occurrence of emergencies. How to promptly identify the emergencies that cause changes and accurately determine the characteristics of crowd flow has become an urgent need for people in related fields.

[0003] In the traditional observation mode, it is necessary to manually investigate the changes in the population in a specific area by watching surveillance videos at specific locations or conducting on-site field surveys. This traditional method has great limitations and cannot guarantee the accuracy of observations and the timeliness of abnormal status detection. Summary of the invention

[0004] In view of this, the present disclosure provides a method, device, electronic device, storage medium and program product for determining crowd flow characteristics to solve the above problems.

[0005] The first aspect of the disclosed embodiments provides a method for determining crowd flow characteristics, including: obtaining first usage data of a plurality of first users located in a target area using a health code within a first time range, wherein the first usage data includes first usage time information and first usage frequency information; classifying the first usage frequency information of the plurality of first users according to different time intervals according to the first usage time information of each first user; inputting the first usage frequency information within each time interval into a floating interval model corresponding to the time interval, respectively, and outputting a floating interval of the health code usage frequency within the time interval; obtaining second usage data of a plurality of second users located in the target area using a health code within a second time range, wherein the second usage data includes second usage time information and second usage frequency information, and the second user is the same as or different from the first user; and determining the crowd flow characteristics of the target area within the second time range according to the floating interval of the health code usage frequency within each time interval and the second usage data.

[0006] According to an embodiment of the present disclosure, determining the crowd flow characteristics of the target area within the second time range based on the floating interval of the number of times the health code is used in each time interval and the second usage data includes: classifying the second usage number information of multiple second users according to different time intervals based on the second usage time information of each second user; for the same time interval, determining the crowd flow sub-characteristics of the target area within the time interval based on the floating interval of the number of times the health code is used in the time interval and the second usage number information in the time interval; and determining the crowd flow characteristics of the target area within the second time range based on the crowd flow sub-characteristics of the target area in each time interval.

[0007] According to an embodiment of the present disclosure, a floating interval model is constructed according to an expected value formula and a standard deviation formula; the first usage count information in each time interval is respectively input into the floating interval model corresponding to the time interval, and the floating interval of the health code usage count in the time interval is output, including: calculating the expected value and standard deviation of the first usage count information in each time interval according to the expected value formula and the standard deviation formula; increasing each expected value by X standard deviations to obtain a first floating value of the health code usage count in the time interval; reducing each expected value by X standard deviations to obtain a second floating value of the health code usage count in the time interval; wherein X is a numerical parameter; and outputting the floating interval of the health code usage count in the time interval according to the first floating value and the second floating value of the health code usage count in the time interval.

[0008] According to an embodiment of the present disclosure, determining the crowd flow characteristics of a target area within a second time range based on the floating interval of the number of times the health code is used in each time interval and the second usage data includes: fitting a first floating interval range curve based on a first floating value of the number of times the health code is used in each time interval; fitting a second floating interval curve based on a second floating value of the number of times the health code is used in each time interval; determining the floating interval of the number of times the health code is used in the target area based on the first floating interval range curve and the second floating interval curve; and determining the crowd flow characteristics of the target area within the second time range based on the floating interval of the number of times the health code is used in the target area and the second usage data.

[0009] According to an embodiment of the present disclosure, the numerical parameters X corresponding to different time intervals are the same or different.

[0010] According to an embodiment of the present disclosure, the floating interval model includes a first floating interval model for holidays and a second floating interval model for weekdays; the crowd flow feature determination method also includes: before inputting the first usage count information in each time interval into the floating interval model corresponding to the time interval, determining whether the first usage time information belongs to a weekday; if the first usage time information belongs to a weekday, inputting the first usage count information in each time interval into the second floating interval model corresponding to the time interval; if the first usage time information does not belong to a weekday, inputting the first usage count information in each time interval into the first floating interval model corresponding to the time interval.

[0011] The second aspect of the embodiments of the present disclosure provides a device for determining crowd flow characteristics, including: a first acquisition module, used to obtain first usage data of a plurality of first users located in a target area using a health code within a first time range, wherein the first usage data includes first usage time information and first usage frequency information; a classification module, used to classify the first usage frequency information of the plurality of first users according to different time intervals based on the first usage time information of each first user; an input-output module, used to input the first usage frequency information in each time interval into a floating interval model corresponding to the time interval, and output the floating interval of the health code usage frequency in the time interval; a second acquisition module, used to obtain second usage data of a plurality of second users located in the target area using a health code within a second time range, wherein the second usage data includes second usage time information and second usage frequency information, and the second user is the same as or different from the first user; and a determination module, used to determine the crowd flow characteristics of the target area within the second time range based on the floating interval of the health code usage frequency in each time interval and the second usage data.

[0012] According to an embodiment of the present disclosure, the determination module is also used to classify the second usage frequency information of multiple second users according to different time intervals based on the second usage time information of each second user; for the same time interval, the crowd flow sub-characteristics of the target area within the time interval are determined according to the floating interval of the health code usage frequency within the time interval and the second usage frequency information within the time interval; and the crowd flow characteristics of the target area within the second time range are determined according to the crowd flow sub-characteristics of the target area in each time interval.

[0013] According to an embodiment of the present disclosure, the input-output module is used to calculate the expected value and standard deviation of the first usage count information in each time interval according to the expected value formula and the standard deviation formula; increase each expected value by X standard deviations to obtain a first floating value of the health code usage count in the time interval; reduce each expected value by X standard deviations to obtain a second floating value of the health code usage count in the time interval; wherein X is a numerical parameter; and output a floating interval of the health code usage count in the time interval according to the first floating value and the second floating value of the health code usage count in the time interval.

[0014] According to an embodiment of the present disclosure, the determination module is also used to fit a first floating interval range curve based on a first floating value of the number of times the health code is used in each time interval; to fit a second floating interval curve based on a second floating value of the number of times the health code is used in each time interval; to determine the floating interval of the number of times the health code is used in the target area based on the first floating interval range curve and the second floating interval curve; and to determine the crowd flow characteristics of the target area within the second time range based on the floating interval of the number of times the health code is used in the target area and the second usage data.

[0015] According to an embodiment of the present disclosure, the numerical parameters X corresponding to different time intervals are the same or different.

[0016] According to an embodiment of the present disclosure, the floating interval model includes a first floating interval model for holidays and a second floating interval model for weekdays; the crowd flow feature determination device is also used to determine whether the first usage time information belongs to a weekday before inputting the first usage count information in each time interval into the floating interval model corresponding to the time interval; when the first usage time information belongs to a weekday, the first usage count information in each time interval is input into the second floating interval model corresponding to the time interval; when the first usage time information does not belong to a weekday, the first usage count information in each time interval is input into the first floating interval model corresponding to the time interval.

[0017] A third aspect of an embodiment of the present disclosure provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the crowd flow characteristic determination method as described above.

[0018] A fourth aspect of an embodiment of the present disclosure provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method for determining crowd flow characteristics as described above.

[0019] The fifth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above-mentioned crowd flow characteristic determination method when executed by a processor.

[0020] Through the methods, devices, electronic devices, storage media, and computer program products provided by the embodiments of the present disclosure, a reasonable floating interval of the number of times a health code is used can be obtained using a floating interval model. According to the floating interval of the number of times a health code is used and the second usage data, the crowd flow characteristics of the target area within the second time range can be determined, which can improve the timeliness and accuracy of determining the crowd flow characteristics and reduce the cost of manual observation. In addition, the obtained crowd flow characteristics can also be analyzed to further determine the specific conditions of the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0022] Figure 1 A diagram schematically shows an application scenario of a method, device, electronic device, storage medium, and program product for determining crowd flow characteristics according to an embodiment of the present disclosure;

[0023] Figure 2 A flow chart schematically shows a method for determining crowd flow characteristics according to an embodiment of the present disclosure;

[0024] Figure 3 A schematic diagram showing a floating interval of the number of times a health code is used in a target area according to an embodiment of the present disclosure;

[0025] Figure 4A A flowchart for determining a first floating interval for holidays and a second floating interval for working days according to an embodiment of the present disclosure is schematically shown;

[0026] Figure 4B Schematically shows a flow chart of determining crowd flow characteristics through a first floating interval on holidays and a second floating interval on weekdays according to an embodiment of the present disclosure;

[0027] Figure 5 A block diagram schematically shows a device for determining crowd flow characteristics according to an embodiment of the present disclosure; and

[0028] Figure 6 A block diagram of an electronic device applicable to a method for determining crowd flow characteristics according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0029] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0030] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0031] In the case of using expressions such as "at least one of A, B, and C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.). In the case of using expressions such as "at least one of A, B, or C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, or C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0032] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good morals.

[0033] In the process of ensuring public safety, the distribution and flow of people represent the development trend of certain emergencies, and how to timely identify the flow characteristics of the crowd has become an urgent need for the prevention and control department. In the traditional public safety prevention and control model, it is necessary to manually investigate the changes in the population in the jurisdiction, such as by watching surveillance videos of specific locations, or conducting field research on the spot, and using thresholds to determine whether there is an emergency.

[0034] In the process of realizing the concept disclosed herein, the inventors discovered that the method of manually investigating changes in the population in a region has obvious lags and limitations, and requires a large amount of manpower and material resources to collect data. For example, the accuracy of naked-eye observation, whether 24-hour continuous monitoring is possible, whether a large number of people during certain non-peak hours is considered an abnormal crowd gathering, etc. These are problems that are difficult to solve using traditional methods, and cannot meet the real-time and accuracy of judging crowd flow characteristics in the process of ensuring public safety.

[0035] Figure 1 The application scenario diagram of the crowd flow feature determination method, device, electronic device, storage medium and program product according to the embodiments of the present disclosure is schematically shown.

[0036] It should be noted that Figure 1 What is shown are merely examples of application scenarios to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0037] like Figure 1 As shown, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0038] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients and / or social platform software, etc. (only as examples).

[0039] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting the use of health codes, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0040] The server 105 may be a server providing health code services, such as a background management server (only as an example) that provides support for health codes used by users using terminal devices 101, 102, and 103. The background management server may analyze and process the received user health code usage data.

[0041] It should be noted that the crowd flow characteristics determination method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the crowd flow characteristics determination device provided in the embodiment of the present disclosure can generally be set in the server 105. The crowd flow characteristics determination method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the crowd flow characteristics determination device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.

[0042] For example, when the first user in the target area uses the health code using the terminal devices S101, S102, and S103, the server 105 obtains the first usage data of the first user using the health code within the first time range, wherein the first usage data includes the first usage time information and the first usage frequency information. Then the server 105 classifies the first usage frequency information of multiple first users according to different time intervals based on the first usage time information of each first user. Then the first usage frequency information in each time interval is respectively input into the floating interval model corresponding to the time interval, and the floating interval of the health code usage frequency in the time interval is output. Then the second usage data of the health code used by multiple second users in the target area within the second time range is obtained, wherein the second usage data includes the second usage time information and the second usage frequency information, and the second user is the same as or different from the first user. Afterwards, according to the floating interval and the second usage data of the health code usage frequency in each time interval, the crowd flow characteristics of the target area within the second time range are determined.

[0043] Alternatively, a server or server cluster that can communicate with the terminal devices 101, 102, 103 and / or the server 105 analyzes the first usage data and the second usage data, and uses a floating interval model to determine the crowd flow characteristics of the target area within the second time range.

[0044] It should be understood that Figure 1 The number of terminal devices and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0045] The following will be based on Figure 1 The scene described by Figure 2-3 , Figure 4A and Figure 4B The crowd flow characteristics determination method of the disclosed embodiment is described in detail.

[0046] Figure 2The flowchart of the method for determining crowd flow characteristics according to an embodiment of the present disclosure is schematically shown.

[0047] like Figure 2 As shown, the method includes operations S210 to S250.

[0048] In operation S210, first usage data of a plurality of first users located in a target area using a health code within a first time range is obtained, wherein the first usage data includes first usage time information and first usage count information.

[0049] In operation S220, first usage frequency information of a plurality of first users is classified into different time intervals according to the first usage time information of each first user.

[0050] In operation S230, the first usage count information in each time interval is respectively input into a floating interval model corresponding to the time interval, and a floating interval of the health code usage count in the time interval is output.

[0051] In operation S240, second usage data of a plurality of second users located in the target area using the health code within a second time range is obtained, wherein the second usage data includes second usage time information and second usage count information, and the second user is the same as or different from the first user.

[0052] In operation S250, crowd flow characteristics of the target area within the second time range are determined based on the floating interval of the number of times the health code is used within each time interval and the second usage data.

[0053] According to the embodiments of the present disclosure, the target area can be manually selected, and the selection method can be the name of the administrative area, such as a district in a city, a key place, such as a railway station, a subway station, an airport, a specific longitude and latitude coordinates, or a Y-meter range near the specific longitude and latitude coordinates. The first time range can, for example, select a value within the range of greater than or equal to 15 days and less than or equal to 30 days. The time interval division method can be one of the following: every day, every half day, every hour, every half hour, every ten minutes, every minute. For example, in some embodiments, in order to obtain the crowd flow characteristics of the target area within each hour, an hourly division method can be adopted to divide a day into 24 time intervals.

[0054] According to an embodiment of the present disclosure, the second time range is usually different from the first time range, and the start time point of the second time range is usually later than the end time point of the first time range.

[0055] According to an embodiment of the present disclosure, the floating interval can be displayed in a graphical manner, for example, different time intervals divided by the first time information can be used as the horizontal coordinate, and the number of times the health code is used can be used as the vertical coordinate. By determining the first floating value and the second floating value of the number of times the health code is used in the coordinate system, the floating interval of the number of times the health code is used in different time intervals can be obtained. The crowd flow characteristics can be normal or abnormal.

[0056] Through the above-mentioned embodiments of the present disclosure, a floating interval model can be used to obtain a reasonable floating interval for the number of times the health code is used, thereby improving the accuracy of determining the crowd flow characteristics. According to the floating interval of the number of times the health code is used and the second usage data, the crowd flow characteristics of the target area within the second time range can be determined, thereby improving the timeliness of determining the crowd flow characteristics and reducing the cost of manual observation. In addition, the obtained crowd flow characteristics can also be analyzed to further determine the specific conditions of the target area.

[0057] In conjunction with specific embodiments, Figure 2 The method shown is further explained.

[0058] According to an embodiment of the present disclosure, a floating interval model can be constructed according to an expected value formula and a standard deviation formula. The first usage count information in each time interval is respectively input into the floating interval model corresponding to the time interval, and the floating interval of the health code usage count in the time interval is output, including: according to the expected value formula and the standard deviation formula, the expected value and standard deviation of the first usage count information in each time interval are calculated. Each expected value is increased by X standard deviations to obtain the first floating value of the health code usage count in the time interval. Each expected value is reduced by X standard deviations to obtain the second floating value of the health code usage count in the time interval. Wherein, X is a numerical parameter. And the floating interval of the health code usage count in the time interval is output according to the first floating value and the second floating value of the health code usage count in the time interval.

[0059] According to an embodiment of the present disclosure, the maximum value of the floating interval of the number of times the health code is used within each time interval may be a first floating value, and the minimum value may be a second floating value.

[0060] Through the above-mentioned embodiments of the present disclosure, a floating interval model is constructed through the expected value formula and the standard deviation formula, and then a reasonable floating interval of the number of times the health code is used is obtained, which provides a basis for determining the crowd flow characteristics according to the second usage data.

[0061] According to an embodiment of the present disclosure, determining the crowd flow characteristics of the target area within the second time range includes: fitting a first floating interval range curve according to a first floating value of the number of times the health code is used in each time interval. Fitting a second floating interval curve according to a second floating value of the number of times the health code is used in each time interval. Determining the floating interval of the number of times the health code is used in the target area according to the first floating interval range curve and the second floating interval curve. And determining the crowd flow characteristics of the target area within the second time range according to the floating interval of the number of times the health code is used in the target area and the second usage data.

[0062] According to an embodiment of the present disclosure, the crowd flow characteristics of the target area within the second time range are determined based on the second usage number information and the floating interval of the health code in different time intervals. For example: in the time interval 11:00-12:00, the second usage number of the health code is 10000, and the first floating value of the health code usage number in this time interval is 13000, and the second floating value is 8000. It can be seen that in the time interval 11:00-12:00, the second usage number of the health code is less than the first floating value of the time interval, greater than the second floating value of the time interval, and falls within the floating interval. Therefore, it is determined that the crowd flow characteristics of the target area within the time interval 11:00-12:00 are normal.

[0063] According to an embodiment of the present disclosure, for example, in the time interval 12:00-13:00, the second usage count of the health code is 5000, the first floating value of the health code usage count in the time interval is 15000, and the second floating value is 9000. It can be seen that in the time interval 12:00-13:00, the second usage count of the health code is less than the second floating value of the time interval and does not fall into the floating interval, so it is determined that the crowd flow characteristics of the target area within the time interval 12:00-13:00 are abnormal.

[0064] Through the above embodiments of the present disclosure, the crowd flow characteristics are determined according to the second usage data and the floating interval, which can effectively improve the accuracy of determining the crowd flow characteristics. At the same time, the second usage data reflects the density of the crowd in the target area. Most emergencies will cause changes in the density of the crowd. Therefore, even if it is a new type of emergency, the above embodiments of the present disclosure can still accurately determine the crowd flow characteristics of the target area.

[0065] According to an embodiment of the present disclosure, the numerical parameters X corresponding to different time intervals may be the same or different.

[0066] According to an embodiment of the present disclosure, the floating interval model includes a first floating interval model for holidays and a second floating interval model for weekdays; the method further includes: before inputting the first usage count information in each time interval into the floating interval model corresponding to the time interval, determining whether the first usage time information belongs to a weekday. In the case where the first usage time information belongs to a weekday, the first usage count information in each time interval is input into the second floating interval model corresponding to the time interval. In the case where the first usage time information does not belong to a weekday, the first usage count information in each time interval is input into the first floating interval model corresponding to the time interval.

[0067] According to an embodiment of the present disclosure, it is determined whether the first usage time information belongs to a working day. For example, if the first usage time information is from October 1st to October 7th, it can be known that the first usage time information belongs to a holiday. At this time, the first usage count information is input into the second floating interval model to obtain the holiday floating interval.

[0068] Through the above-mentioned embodiments of the present disclosure, a first floating interval that conforms to the crowd flow characteristics on weekdays and a second floating interval that conforms to the crowd flow characteristics on holidays are obtained, thereby improving the accuracy of determining the crowd flow characteristics in different situations.

[0069] According to an embodiment of the present disclosure, the above-mentioned determination of the crowd flow characteristics of the target area within the second time range may include: classifying the second usage frequency information of multiple second users according to different time intervals according to the second usage time information of each second user. For the same time interval, the crowd flow sub-characteristics of the target area within the time interval are determined according to the floating interval of the health code usage frequency within the time interval and the second usage frequency information within the time interval. And according to the crowd flow sub-characteristics of the target area in each time interval, the crowd flow characteristics of the target area within the second time range are determined.

[0070] According to an embodiment of the present disclosure, the crowd flow sub-characteristics may be normal or abnormal. When the crowd flow sub-characteristics within each time interval are normal, the crowd flow characteristics within the second time range are normal; when the crowd flow sub-characteristics within one of the time intervals are abnormal, the crowd flow characteristics within the second time range are abnormal.

[0071] Through the above-mentioned embodiments of the present disclosure, a method for determining the crowd flow characteristics within a second time range is provided. Through this method, the crowd flow sub-characteristics of each time interval can be determined according to the floating interval of the number of times the health code is used in each time interval and the second number of times information, thereby obtaining the crowd flow characteristics of the target area within the second time range. If the crowd flow characteristics of the target area are abnormal within the time interval, an alarm message is issued to help relevant personnel take measures. In addition, by using health code data, the crowd flow information of the target area can be obtained faster and more accurately, avoiding the limitations of manual methods.

[0072] Figure 3 A schematic diagram of the floating interval of the number of times the health code is used in the target area according to an embodiment of the present disclosure is schematically shown.

[0073] like Figure 3 As shown, the horizontal axis represents different time intervals, the vertical axis represents the number of times the health code is used, UP1 represents the first floating interval range curve, and DN1 represents the second floating interval range curve.

[0074] According to an embodiment of the present disclosure, a day is divided into 24 time intervals in an hourly manner. The first usage count information in each time interval is respectively input into the floating interval model corresponding to the time interval to obtain the expected value μ and standard deviation σ of the first usage count information in each time interval. The expected value μ in each time interval is increased by 3 standard deviations σ to obtain the first floating value of the health code usage count in the time interval; each expected value μ is reduced by 3 standard deviations σ to obtain the second floating value of the health code usage count in the time interval. Fit the first floating values ​​of different time intervals to obtain the first floating interval range curve UP1. Fit the second floating values ​​of different time intervals to obtain the second floating interval range curve DN1. Determine the floating interval of the health code usage count in the target area according to the first floating interval range curve and the second floating interval curve.

[0075] Figure 4A A flowchart for determining a first floating interval for holidays and a second floating interval for weekdays according to an embodiment of the present disclosure is schematically shown.

[0076] like Figure 4A As shown, the process includes operations S401 to S406.

[0077] In operation S401, first usage data of a plurality of first users in a target area using a health code within a first time range is obtained. The first usage data may include first usage time information and first usage number information.

[0078] In operation S402, it is determined whether the first usage time information belongs to a working day. If yes, operation S405 is executed; if no, operation S403 is executed.

[0079] In operation S403, the first usage count information in each time interval is respectively input into a first floating interval model corresponding to the time interval.

[0080] In operation S404, a first floating interval is generated.

[0081] In operation S405, the first usage count information in each time interval is respectively input into a second floating interval model corresponding to the time interval.

[0082] In operation S406 , a second floating interval is generated.

[0083] Through the above-mentioned embodiments of the present disclosure, in view of the differences in crowd flow characteristics on weekdays and holidays, two sets of models are trained according to holiday data and weekday data respectively, and a floating range of the number of uses of the health code that conforms to the crowd flow characteristics on weekdays and holidays is obtained, which provides a basis for subsequently determining the crowd flow characteristics using the second usage data.

[0084] Figure 4B A flowchart for determining crowd flow characteristics through a first floating interval on holidays and a second floating interval on weekdays according to an embodiment of the present disclosure is schematically shown.

[0085] like Figure 4B As shown, the process includes operations S407 to S414.

[0086] In operation S407, second usage data of the health code used by multiple second users in the target area within a second time range is obtained. The second usage data may include second usage time information and second usage number information.

[0087] In operation S408, it is determined whether the second usage time information belongs to a working day. If yes, operation S411 is executed; if no, operation S409 is executed.

[0088] In operation S409, the second usage count information is compared with the first floating interval.

[0089] In operation S410, it is determined whether the second usage count information conforms to the first floating interval. If so, operation S413 is executed to issue an alarm; if not, operation S414 is executed without processing.

[0090] In operation S411, the second usage count information is compared with the second floating interval.

[0091] In operation S412, it is determined whether the second usage count information conforms to the second floating interval. If so, operation S413 is executed to issue an alarm; if not, operation S414 is executed without processing.

[0092] Through the above-mentioned embodiments of the present disclosure, the second usage data on weekdays and holidays are compared with the second floating interval and the first floating interval respectively, and the crowd flow characteristics that conform to both weekdays and holidays are obtained, which effectively improves the accuracy of determining the crowd flow characteristics.

[0093] Figure 5 A block diagram of a device test task generating apparatus according to an embodiment of the present disclosure is schematically shown.

[0094] like Figure 5 As shown, the crowd flow characteristic determination device 500 includes a first acquisition module 510, a classification module 520, an input-output module 530, a second acquisition module 540, and a determination module 550.

[0095] The first acquisition module 510 is used to obtain first usage data of the health code used by multiple first users in the target area within a first time range, wherein the first usage data includes first usage time information and first usage count information. In one embodiment, the first acquisition module 510 can be used to perform the operation S210 described above, which is not repeated here.

[0096] The classification module 520 is used to classify the first usage times information of multiple first users according to different time intervals according to the first usage time information of each first user. In one embodiment, the first acquisition module 520 can be used to perform the operation S220 described above, which will not be repeated here.

[0097] The input-output module 530 is used to input the first usage count information in each time interval into the floating interval model corresponding to the time interval, and output the floating interval of the health code usage count in the time interval. In one embodiment, the input-output module 530 can be used to perform the operation S230 described above, which will not be repeated here.

[0098] The second acquisition module 540 is used to obtain second usage data of the health code used by multiple second users in the target area within a second time range, wherein the second usage data includes second usage time information and second usage count information, and the second user is the same as or different from the first user. In one embodiment, the second acquisition module 540 can be used to perform the operation S240 described above, which is not repeated here.

[0099] The determination module 550 is used to determine the crowd flow characteristics of the target area within the second time range according to the floating interval of the number of times the health code is used in each time interval and the second usage data. In one embodiment, the determination module 550 can be used to perform the operation S250 described above, which will not be repeated here.

[0100] According to an embodiment of the present disclosure, any multiple modules among the first acquisition module 510, the classification module 520, the input-output module 530, the second acquisition module 540, and the determination module 550 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first acquisition module 510, the classification module 520, the input-output module 530, the second acquisition module 540, and the determination module 550 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware, and firmware, or in a suitable combination of any of them. Alternatively, at least one of the first acquisition module 510, the classification module 520, the input-output module 530, the second acquisition module 540, and the determination module 550 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0101] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0102] According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as described above.

[0103] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the method as described above.

[0104] According to an embodiment of the present disclosure, a computer program product includes a computer program, and when the computer program is executed by a processor, the computer program implements the method as described above.

[0105] Figure 6A block diagram of an electronic device suitable for a method for determining crowd flow characteristics according to an embodiment of the present disclosure is schematically shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0106] like Figure 6 As shown, the electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0107] In RAM 603, various programs and data required for the operation of electronic device 600 are stored. Processor 601, ROM 602 and RAM 603 are connected to each other via bus 604. Processor 601 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 602 and / or RAM 603. It should be noted that the program can also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0108] According to an embodiment of the present disclosure, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the I / O interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 608 as needed.

[0109] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0110] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: 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), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0111] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the item recommendation method provided by the embodiment of the present disclosure.

[0112] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 601. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0113] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0114] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, apparatus, module, unit, etc. described above can be implemented by a computer program module.

[0115] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0116] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a 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 box can also occur in a different order from the order marked in the accompanying drawings. For example, two 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 box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0117] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0118] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for determining crowd flow characteristics, comprising: Acquire first usage data of a plurality of first users located in a target area using a health code within a first time range, wherein the first usage data includes first usage time information and first usage count information; Classifying the first usage times information of a plurality of the first users according to different time intervals according to the first usage time information of each of the first users; Inputting the first usage count information in each of the time intervals into the floating interval model corresponding to the time interval, and outputting the floating interval of the health code usage count in the time interval; Acquire second usage data of a plurality of second users located in the target area using the health code within a second time range, wherein the second usage data includes second usage time information and second usage count information, and the second user is the same as or different from the first user; and Determine the crowd flow characteristics of the target area within the second time range according to the floating interval of the number of times the health code is used within each of the time intervals and the second usage data; The floating interval model is constructed according to the expected value formula and the standard deviation formula; the first usage count information in each time interval is respectively input into the floating interval model corresponding to the time interval, and the floating interval of the health code usage count in the time interval is outputted, including: Calculate the expected value and standard deviation of the first usage count information in each of the time intervals according to the expected value formula and the standard deviation formula; Each of the expected values ​​is increased by X standard deviations to obtain a first floating value of the number of times the health code is used within the time interval; Subtract X standard deviations from each expected value to obtain a second floating value of the number of times the health code is used within the time interval; wherein X is a numerical parameter; and Outputting a floating interval of the number of times the health code is used within the time interval according to the first floating value and the second floating value of the number of times the health code is used within the time interval; Wherein, determining the crowd flow characteristics of the target area within the second time range according to the floating interval of the number of times the health code is used within each of the time intervals and the second usage data includes: According to the first floating value of the number of times the health code is used in each of the time intervals, a first floating interval range curve is obtained by fitting; According to the second floating value of the number of times the health code is used in each of the time intervals, a second floating interval curve is obtained by fitting; Determining a floating interval of the number of times the health code is used in the target area according to the first floating interval range curve and the second floating interval curve; and According to the floating range of the number of times the health code is used in the target area and the second usage data, the crowd flow characteristics in the target area within the second time range are determined.

2. The method according to claim 1, wherein: Determining the crowd flow characteristics of the target area within the second time range according to the floating interval of the number of times the health code is used within each of the time intervals and the second usage data includes: According to the second usage time information of each second user, classify the second usage frequency information of a plurality of second users according to different time intervals; For the same time interval, according to the floating interval of the number of times the health code is used in the time interval and the second number of times information in the time interval, determine the crowd flow sub-characteristics of the target area in the time interval; and The crowd flow characteristics of the target area within the second time range are determined according to the crowd flow sub-characteristics of the target area within each of the time intervals.

3. The method according to claim 1, wherein: The numerical parameters X corresponding to different time intervals are the same or different.

4. The method according to claim 1, wherein: The floating interval model includes a first floating interval model for holidays and a second floating interval model for working days; the method further includes: Before inputting the first usage count information in each of the time intervals into the floating interval model corresponding to the time interval, determining whether the first usage time information belongs to a working day; In the case where the first usage time information belongs to a working day, the first usage count information in each of the time intervals is respectively input into a second floating interval model corresponding to the time interval; When the first usage time information does not belong to a working day, the first usage frequency information in each of the time intervals is respectively input into a first floating interval model corresponding to the time interval.

5. A device for determining crowd flow characteristics, comprising: A first acquisition module is used to acquire first usage data of a plurality of first users located in a target area using a health code within a first time range, wherein the first usage data includes first usage time information and first usage count information; a classification module, configured to classify the first usage frequency information of a plurality of the first users according to different time intervals according to the first usage time information of each of the first users; An input-output module, used to input the first usage count information in each of the time intervals into a floating interval model corresponding to the time interval, and output a floating interval of the health code usage count in the time interval; A second acquisition module is used to acquire second usage data of a plurality of second users located in the target area using the health code within a second time range, wherein the second usage data includes second usage time information and second usage count information, and the second user is the same as or different from the first user; and A determination module, configured to determine the crowd flow characteristics of the target area within the second time range according to the floating interval of the number of times the health code is used within each of the time intervals and the second usage data; The floating interval model is constructed according to the expected value formula and the standard deviation formula; the first usage count information in each time interval is respectively input into the floating interval model corresponding to the time interval, and the floating interval of the health code usage count in the time interval is outputted, including: Calculate the expected value and standard deviation of the first usage count information in each of the time intervals according to the expected value formula and the standard deviation formula; Each of the expected values ​​is increased by X standard deviations to obtain a first floating value of the number of times the health code is used within the time interval; Subtract X standard deviations from each expected value to obtain a second floating value of the number of times the health code is used within the time interval; wherein X is a numerical parameter; and Outputting a floating interval of the number of times the health code is used within the time interval according to the first floating value and the second floating value of the number of times the health code is used within the time interval; Wherein, determining the crowd flow characteristics of the target area within the second time range according to the floating interval of the number of times the health code is used within each of the time intervals and the second usage data includes: According to the first floating value of the number of times the health code is used in each of the time intervals, a first floating interval range curve is obtained by fitting; According to the second floating value of the number of times the health code is used in each of the time intervals, a second floating interval curve is obtained by fitting; Determining a floating interval of the number of times the health code is used in the target area according to the first floating interval range curve and the second floating interval curve; and According to the floating range of the number of times the health code is used in the target area and the second usage data, the crowd flow characteristics in the target area within the second time range are determined.

6. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having executable instructions stored thereon, wherein when the executable instructions are executed by a processor, the processor is enabled to implement the method according to any one of claims 1 to 4.

8. A computer program product, comprising computer executable instructions, wherein the computer executable instructions are used to implement the method according to any one of claims 1 to 4 when executed.

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