Website state statistical method, device, equipment, medium and program product

Abstract

The invention provides a website state statistical method which can be applied to the technical field of financial science and technology. The website state statistical method comprises the following steps: collecting video data of websites in a preset period; based on the video data, counting a first visitor flow rate, a second visitor flow rate and a third visitor flow rate, the first visitor flow rate being the total visitor flow rate of the website, the second visitor flow rate being the visitor flow rate of a first target area, and the second visitor flow rate being the visitor flow rate of a second target area; based on the first visitor flow rate, the second visitor flow rate and the third visitor flow rate, calculating a region transfer coefficient, the region transfer coefficient being used for representing a transfer coefficient of the visitor flow rates among different regions, and N being a positive integer; and sending out corresponding network state information based on the area transfer coefficient. The invention further provides a network state statistics device and equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to a method, device, equipment, medium and program product for counting network node status. Background Art

[0002] With the development and popularization of the financial industry, the management of the flow of people in bank branches has become increasingly important. Effectively monitoring and managing the flow of people in bank branches can help banks improve service efficiency, enhance customer experience, and play an important role in security.

[0003] Traditional bank branch traffic statistics often use sensor systems or video surveillance to count the total number of branch traffic. For example, sensors or cameras are placed at the entrance of bank branches to count the total number of people visiting bank branches. Although the traditional method of counting traffic is simple and efficient, there are still problems such as lack of precision in traffic statistics. The traditional method of counting traffic at branches can only reflect the total passenger flow status of the branch due to the lack of precision in data, and it is difficult to truly understand the actual status of each area of ​​the branch. As a result, it cannot meet the increasing needs of branch operations. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a method, apparatus, device, medium and program product for collecting statistics on the status of a network point, which improve the level of detail of the status of the network point and meet the operational requirements.

[0005] According to a first aspect of the present disclosure, a method for counting network point status is provided, including: collecting video data of the network point within a preset period; counting a first human flow, a second human flow and a third human flow based on the video data, wherein the first human flow is the total human flow of the network point, the second human flow is the human flow of a first target area, and the second human flow is the human flow of a second target area; based on the first human flow, the second human flow and the third human flow, calculating a regional transfer coefficient, wherein the regional transfer coefficient is used to characterize the transfer coefficient of human flow between different areas, and N is a positive integer; and based on the regional transfer coefficient, issuing corresponding network point status information.

[0006] According to an embodiment of the present disclosure, the first flow of people, the second flow of people and the third flow of people based on the video data include: for the first flow of people, counting the flow of people passing through the entrance of the outlet based on the video data; and for the second flow of people or the third flow of people, counting the flow of people whose stay time in the first target area exceeds a first stay time threshold based on the video data, or counting the flow of people whose stay time in the second target area exceeds a second stay time threshold based on the video data.

[0007] According to an embodiment of the present disclosure, the flow of people whose stay time in the first target area exceeds a first dwell time threshold based on the video data statistics, or the flow of people whose stay time in the second target area exceeds a second dwell time threshold based on the video data statistics, includes: for each customer, detecting the area where the customer is located based on a target tracking algorithm; and when the customer stays in the first target area for more than the first dwell time threshold, or when the customer stays in the second target area for more than the second dwell time threshold, recording the flow of people in the corresponding area.

[0008] According to an embodiment of the present disclosure, wherein the first target area includes a waiting area, and the second target area includes a service area, the calculating of the area transfer coefficient based on the first human flow, the second human flow, and the third human flow includes: calculating the first area transfer coefficient based on the first human flow and the second human flow; the issuing of corresponding network point status information based on the area transfer coefficient includes: issuing first network point status information when the first area transfer coefficient is greater than a first preset threshold, and the first network point status information is used to characterize that the service area is busy.

[0009] According to an embodiment of the present disclosure, the calculating of the regional transfer coefficient based on the first human flow, the second human flow and the third human flow also includes: calculating the second regional transfer coefficient based on the first human flow and the third human flow; the issuing of corresponding network point status information based on the regional transfer coefficient also includes: issuing second network point status information when the second regional transfer coefficient is greater than a second preset threshold, the second network point status information being used to characterize that the network point is idle.

[0010] According to an embodiment of the present disclosure, the calculating of the regional transfer coefficient based on the first human flow, the second human flow and the third human flow also includes: calculating a third regional transfer coefficient based on the second human flow and the third human flow; the issuing of corresponding network point status information based on the regional transfer coefficient also includes: issuing third network point status information when the third regional transfer coefficient is less than a third preset threshold, and the third network point status information is used to characterize that the service area is busy.

[0011] According to an embodiment of the present disclosure, after the first human flow, the second human flow and the third human flow are counted based on the video data, the method further includes: when any one of the first human flow, the second human flow or the third human flow exceeds the corresponding preset human flow threshold, issuing fourth network point status information, and the fourth network point status information is used to characterize that the network point is busy.

[0012] According to an embodiment of the present disclosure, the network point status statistics method is applied to a network point local server.

[0013] The second aspect of the present disclosure provides a network point status statistics device, including: a video acquisition module, used to collect video data of the network point within a preset period; a human flow statistics module, used to count a first human flow, a second human flow and a third human flow based on the video data, wherein the first human flow is the total human flow of the network point, the second human flow is the human flow of a first target area, and the second human flow is the human flow of a second target area; a coefficient statistics module, used to calculate a regional transfer coefficient based on the first human flow, the second human flow and the third human flow, wherein the regional transfer coefficient is used to characterize the transfer coefficient of human flow between different areas, and N is a positive integer; and a network point status prompt module, used to issue corresponding network point status information based on the regional transfer coefficient.

[0014] According to an embodiment of the present disclosure, the crowd flow statistics module is specifically used to, for the first crowd flow, count the crowd flow passing through the outlet entrance based on the video data; and for the second crowd flow or the third crowd flow, count the crowd flow whose stay time in the first target area exceeds a first stay time threshold based on the video data, or count the crowd flow whose stay time in the second target area exceeds a second stay time threshold based on the video data.

[0015] According to an embodiment of the present disclosure, the traffic statistics module is specifically used to detect the area where the customer is located for each customer based on a target tracking algorithm; and when the customer stays in the first target area for longer than the first residence time threshold, or when the customer stays in the second target area for longer than the second residence time threshold, record the traffic in the corresponding area.

[0016] According to an embodiment of the present disclosure, the coefficient statistics module is specifically used to calculate a first area transfer coefficient based on the first human flow and the second human flow; the network point status prompt module is specifically used to issue first network point status information when the first area transfer coefficient is greater than a first preset threshold, and the first network point status information is used to characterize that the service area is busy.

[0017] According to an embodiment of the present disclosure, the coefficient statistics module is specifically used to calculate the second area transfer coefficient based on the first human flow and the third human flow; the network point status prompt module is specifically used to issue second network point status information when the second area transfer coefficient is greater than a second preset threshold, and the second network point status information is used to characterize that the network point is idle.

[0018] According to an embodiment of the present disclosure, the coefficient statistics module is specifically used to calculate the third area transfer coefficient based on the second human flow and the third human flow; the network point status prompt module is specifically used to issue third network point status information when the third area transfer coefficient is less than a third preset threshold, and the third network point status information is used to characterize that the service area is busy.

[0019] According to an embodiment of the present disclosure, the network point status prompt module is also used to issue fourth network point status information when any one of the first human flow, the second human flow or the third human flow exceeds the corresponding preset human flow threshold, and the fourth network point status information is used to characterize that the network point is busy.

[0020] According to an embodiment of the present disclosure, the network point status statistics device is configured in a network point local server.

[0021] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0022] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

[0023] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor.

[0024] In the embodiments of the present disclosure, in order to solve the technical problem that the statistics of the status of the outlets in the prior art are not accurate enough and cannot meet the needs of refined operations. The embodiments of the present disclosure use the videos collected from the outlets within a certain period to count the total flow of people in the outlets, the flow of people in the first target area, and the flow of people in the second target area, respectively, and calculate the coefficients of these data area transfers to reflect the transfer of the flow of people between different areas, deeply dig out the status of the outlets, and finally, by judging these coefficients and issuing corresponding outlet status information, to assist personnel in carrying out outlet operations. The embodiments of the present disclosure can at least achieve the following beneficial effects: The embodiments of the present disclosure have the advantages of high accuracy, strong real-time performance, high degree of automation, low cost, etc., and are suitable for bank outlets and other places of various sizes. At the same time, the embodiments of the present disclosure also have good scalability and intelligent analysis capabilities, and are expected to provide banks and other enterprises with more intelligent and efficient management solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0026] Figure 1 A diagram schematically shows an application scenario of a method for counting network node status according to an embodiment of the present disclosure;

[0027] Figure 2 A flowchart of a method for counting network point status according to an embodiment of the present disclosure is schematically shown;

[0028] Figure 3 A schematic diagram showing a structural block diagram of a network point status statistics device according to an embodiment of the present disclosure; and

[0029] Figure 4 A block diagram of an electronic device suitable for implementing a method for counting network node status according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0030] 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.

[0031] 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.

[0032] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0033] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly 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.).

[0034] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0035] In the prior art, statistics on the status of outlets are basically based on the total number of people flowing into the outlets, for example, the total number of people flowing into the outlets is counted daily, and the total number of people flowing into the outlets is counted every few hours. When the total number of people flowing into the outlets is too small, it means that the outlets are idle, and when the total number of people flowing into the outlets is too large, it means that the outlets are busy. The backend can guide operations based on the statistical status of the data of the people flowing into the outlets. For example, when it is found that the outlets are busy for a long time (such as being busy for several months), it can be considered to add new outlets in the area where the outlets are located. For another example, when it is found that the outlets have too much people flowing into the outlets within a certain period of time (for example, within one hour), it can guide the evacuation of the crowd and strengthen monitoring to ensure safety. However, with the improvement of the requirements for outlet operation, it becomes difficult to meet the operation requirements by only collecting the total number of people flowing into the outlets. Therefore, there is an urgent need for a means to enhance the accuracy and detail of outlet operations.

[0036] In order to solve the technical problems existing in the prior art, an embodiment of the present disclosure provides a method for counting the status of an outlet, including: collecting video data of the outlet within a preset period; counting a first flow of people, a second flow of people and a third flow of people based on the video data, wherein the first flow of people is the total flow of people in the outlet, the second flow of people is the flow of people in a first target area, and the second flow of people is the flow of people in a second target area; based on the first flow of people, the second flow of people and the third flow of people, calculating a regional transfer coefficient, wherein the regional transfer coefficient is used to characterize the transfer coefficient of the flow of people between different areas, and N is a positive integer; and based on the regional transfer coefficient, issuing corresponding outlet status information.

[0037] In the embodiments of the present disclosure, in order to solve the technical problem that the statistics of the status of the outlets in the prior art are not accurate enough and cannot meet the needs of refined operations. The embodiments of the present disclosure use the videos collected from the outlets within a certain period to count the total flow of people in the outlets, the flow of people in the first target area, and the flow of people in the second target area, respectively, and calculate the coefficients of these data area transfers to reflect the transfer of the flow of people between different areas, deeply dig out the status of the outlets, and finally, by judging these coefficients and issuing corresponding outlet status information, to assist personnel in carrying out outlet operations. The embodiments of the present disclosure can at least achieve the following beneficial effects: The embodiments of the present disclosure have the advantages of high accuracy, strong real-time performance, high degree of automation, low cost, etc., and are suitable for bank outlets and other places of various sizes. At the same time, the embodiments of the present disclosure also have good scalability and intelligent analysis capabilities, and are expected to provide banks and other enterprises with more intelligent and efficient management solutions.

[0038] Figure 1 The application scenario diagram of the network point status statistics method according to the embodiment of the present disclosure is schematically shown.

[0039] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0040] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0041] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0042] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0043] It should be noted that the network point status statistics method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the network point status statistics device provided in the embodiment of the present disclosure can generally be set in the server 105. The network point status statistics 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 first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the network point status statistics 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 first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0044] It should be understood that Figure 1 The number of terminal devices, networks 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 The network point status statistics method of the disclosed embodiment is described in detail.

[0046] Figure 2 The flowchart of the network point status statistics method according to the embodiment of the present disclosure is schematically shown.

[0047] like Figure 2 As shown, the network point status statistics method of this embodiment includes operations S210 to S240, and the network point status statistics method can be executed by the server 105.

[0048] In operation S210, video data of dots within a preset period are collected.

[0049] Among them, video data of network points within a preset period is collected. The preset period is defined by R&D personnel based on actual demand orientation, and the actual demand orientation includes: real-time statistics, daily statistics, weekly statistics, etc. For example, the preset period can be 1 minute, 1 hour, 1 working day, or 1 week.

[0050] In a typical scenario, bank branches are equipped with cameras or surveillance cameras to collect video data inside the bank branches. These cameras should be able to cover the entire bank branch area to ensure comprehensive monitoring of the flow of people. The monitoring cycle can be real-time, used to adjust operations in real time to deal with emergencies; the monitoring cycle can also be daily, used to trace the flow of people in different areas of the branch every day, to adjust service windows, personnel positions, etc.; the monitoring cycle can also be multi-day, used to trace the flow of people in the branch over a long period of time, which can serve as data support for newly opened / closed branches.

[0051] In operation S220, a first flow of people, a second flow of people and a third flow of people are counted based on the video data, wherein the first flow of people is the total flow of people in the outlet, the second flow of people is the flow of people in the first target area, and the second flow of people is the flow of people in the second target area.

[0052] Specifically, the collected video data needs to be preprocessed for subsequent human flow detection. The preprocessing steps include video decoding, image frame extraction, etc., to ensure that clear and useful image frames are extracted from the video; then, the image frames in the video data captured by the network point camera are used for human body detection through the pre-trained recognition model, and the human body is recognized and located in the image to achieve human flow detection and statistics, so as to meet the statistics of human flow in different areas within the network point.

[0053] In a typical scenario, such as in a bank branch, it generally includes a waiting area (the first target area mentioned above) and a service area (the second target area mentioned above). The waiting area is an area for customers to rest while waiting for business to be processed, and is usually equipped with seats, a calling system, etc. The service area is an area for customers to receive services, including: cash service area, non-cash service area, self-service area, and financial consulting area, etc. Due to the business characteristics of different service areas, the flow curves of different areas have their own characteristics.

[0054] According to an embodiment of the present disclosure, the first flow of people, the second flow of people and the third flow of people based on the video data include: for the first flow of people, counting the flow of people passing through the entrance of the outlet based on the video data; and for the second flow of people or the third flow of people, counting the flow of people whose stay time in the first target area exceeds a first stay time threshold based on the video data, or counting the flow of people whose stay time in the second target area exceeds a second stay time threshold based on the video data.

[0055] Specifically, the statistical methods for the first flow of people, the second flow of people, and the third flow of people are different. The first flow of people does not need to be confirmed by the residence time, and can be directly counted. For example, the flow of people at the outlet is counted, and only the flow of people is identified without marking each person. The second flow of people and the third flow of people need to be determined by the residence time threshold corresponding to their respective areas. For example, whether the time a customer spends in the waiting area exceeds the time threshold corresponding to the waiting area is counted. For another example, whether the time a customer spends in the self-service area exceeds the time threshold corresponding to the self-service area is counted. Whether the time a customer spends in the non-cash service area exceeds the time threshold corresponding to the non-cash service area is counted. The advantage of counting whether the time a person spends exceeds the corresponding time threshold is that it can effectively screen out some passers-by or ineffective people, and ensure the accuracy of effective flow statistics in a specific area.

[0056] According to an embodiment of the present disclosure, the flow of people whose stay time in the first target area exceeds a first dwell time threshold based on the video data statistics, or the flow of people whose stay time in the second target area exceeds a second dwell time threshold based on the video data statistics, includes: for each customer, detecting the area where the customer is located based on a target tracking algorithm; and when the customer stays in the first target area for more than the first dwell time threshold, or when the customer stays in the second target area for more than the second dwell time threshold, recording the flow of people in the corresponding area.

[0057] Specifically, after the user enters the network point, the detection frame can be identified by capturing the valid frames in the video. The target tracking algorithm (or target detection algorithm) follows the detection frame and determines whether the detection frame stays in a certain area for a long time, which is then determined as the effective traffic flow in the area.

[0058] Of course, the target tracking algorithm can also analyze the detected human body position and dynamics, and identify abnormal behaviors, such as staying for a long time, running, etc., so as to timely discover and deal with potential safety risks.

[0059] S230: Calculate a regional transfer coefficient based on the first human flow, the second human flow, and the third human flow, wherein the regional transfer coefficient is used to characterize a transfer coefficient of human flow between different regions, and N is a positive integer.

[0060] Among them, the regional transfer coefficient is a coefficient that is calculated between areas with a logical relationship before and after business processing within a period of time and is used to reflect the change in the flow of people. For example, in a statistical period, only half of the people who finally complete the business processing are those who enter the bank. Then the regional transfer coefficient is the transfer from entering the bank to business processing, which is one-half. Furthermore, considering the exclusion of a small number of cases where there are accompanying people, it can still be inferred that there are still many people who have not completed the business processing.

[0061] In a typical scenario, a complete business process in a bank branch includes: the user enters the bank from the bank entrance, asks the information desk and then takes the business processing number plate on his own, goes to the waiting area to wait for the number to be called for business processing, and then goes to the service area to complete the business processing after waiting for a while. In this process, the flow of people in different areas with business sequences can be used to calculate the personnel's promotion to the next operation.

[0062] In operation S240, corresponding dot status information is issued based on the area transfer coefficient.

[0063] Specifically, for example, the regional transfer coefficient can be used to infer the changes in personnel, the amount of personnel flow, and whether an emergency occurs, and the corresponding network status information can be issued to prompt personnel to implement network operations.

[0064] For example, multiple high-definition cameras are installed inside a bank branch, covering the entire bank lobby area. The video data collected by these cameras is pre-processed and sent to the model for human body detection. The detected human flow data is analyzed and counted in real time, and the results are displayed in the bank's management and monitoring system in the form of intuitive charts and reports. Based on this data, bank managers can understand the flow of people in the branch in real time and make corresponding service adjustments and arrangements.

[0065] For example, by counting the flow of people in bank branches and the regional transfer coefficient over a period of time, the distribution of personnel in each area can be quickly calculated. Based on these data, the number of counter service staff can be flexibly adjusted to ensure that there are enough people to serve customers during peak hours, and the number of service staff can be appropriately reduced during off-peak hours to improve resource utilization efficiency.

[0066] According to an embodiment of the present disclosure, wherein the first target area includes a waiting area, and the second target area includes a service area, the calculating of the area transfer coefficient based on the first human flow, the second human flow, and the third human flow includes: calculating the first area transfer coefficient based on the first human flow and the second human flow; the issuing of corresponding network point status information based on the area transfer coefficient includes: issuing first network point status information when the first area transfer coefficient is greater than a first preset threshold, and the first network point status information is used to characterize that the service area is busy.

[0067] Specifically, by calculating the ratio of the number of people in the waiting area to the total number of people within a certain period of time, when the ratio of the number of people in the waiting area to the total number of people is too high, it can be determined that the service area is busy, resulting in a backlog of people in the waiting area, and further optimization of the service process and service efficiency of the outlets can be considered.

[0068] According to an embodiment of the present disclosure, the calculating of the regional transfer coefficient based on the first human flow, the second human flow and the third human flow also includes: calculating the second regional transfer coefficient based on the first human flow and the third human flow; the issuing of corresponding network point status information based on the regional transfer coefficient also includes: issuing second network point status information when the second regional transfer coefficient is greater than a second preset threshold, the second network point status information being used to characterize that the network point is idle.

[0069] Specifically, by calculating the ratio of the number of people in the service area to the total number of people within a certain period of time, when the ratio of the number of people in the service area to the total number of people is too high, it can be determined that users do not need to wait in the waiting area and can go directly to the service area, indicating that the current outlet is not busy, which can further guide the optimization of personnel deployment.

[0070] According to an embodiment of the present disclosure, the calculating of the regional transfer coefficient based on the first human flow, the second human flow and the third human flow also includes: calculating a third regional transfer coefficient based on the second human flow and the third human flow; the issuing of corresponding network point status information based on the regional transfer coefficient also includes: issuing third network point status information when the third regional transfer coefficient is less than a third preset threshold, and the third network point status information is used to characterize that the service area is busy.

[0071] Specifically, taking into account that the same person may enter and exit the outlet multiple times during busy hours, by counting the ratio of the number of people in the service area to the number of people in the waiting area within a certain period of time, when the transfer coefficient of the third area is too low, it means that the service area is too busy, resulting in backlogs in the waiting area. Further optimization of the outlet service process and service efficiency can be considered.

[0072] According to an embodiment of the present disclosure, after the first human flow, the second human flow and the third human flow are counted based on the video data, the method further includes: when any one of the first human flow, the second human flow or the third human flow exceeds the corresponding preset human flow threshold, issuing fourth network point status information, and the fourth network point status information is used to characterize that the network point is busy.

[0073] Specifically, a threshold is set for the total number of people in the network, a threshold is set for the first target area, and a threshold is set for the second target area. When the number of people exceeds the corresponding threshold, the fourth network state information is issued, and the fourth network state information can be regarded as an early warning of excessive human flow.

[0074] For example, in a bank branch, excessive traffic may lead to safety hazards. By monitoring traffic in real time, the algorithm can detect abnormal crowding in time and send an early warning to the bank's security department so that appropriate safety measures can be taken to ensure the safety of customers and employees, or contact nearby branches with low traffic to make reasonable diversion suggestions.

[0075] According to an embodiment of the present disclosure, the network point status statistics method is applied to a network point local server.

[0076] The embodiments of the present disclosure are applied to the local server of the branch, and there is no need to constantly identify and follow individuals in the flow of people in the branch. It is suitable for situations where local computing power is limited, while also ensuring data security.

[0077] In the embodiments of the present disclosure, in order to solve the technical problem that the statistics of the status of the outlets in the prior art are not accurate enough and cannot meet the needs of refined operations. The embodiments of the present disclosure use the videos collected from the outlets within a certain period to count the total flow of people in the outlets, the flow of people in the first target area, and the flow of people in the second target area, respectively, and calculate the coefficients of these data area transfers to reflect the transfer of the flow of people between different areas, deeply dig out the status of the outlets, and finally, by judging these coefficients and issuing corresponding outlet status information, to assist personnel in carrying out outlet operations. The embodiments of the present disclosure can at least achieve the following beneficial effects: The embodiments of the present disclosure have the advantages of high accuracy, strong real-time performance, high degree of automation, low cost, etc., and are suitable for bank outlets and other places of various sizes. At the same time, the embodiments of the present disclosure also have good scalability and intelligent analysis capabilities, and are expected to provide banks and other enterprises with more intelligent and efficient management solutions.

[0078] Based on the above-mentioned network point status statistics method, the present disclosure also provides a network point status statistics device. Figure 3 The device is described in detail.

[0079] Figure 3 The structure block diagram of the network point status statistics device according to the embodiment of the present disclosure is schematically shown.

[0080] like Figure 3 As shown, the network point status statistics device 300 of this embodiment includes a video acquisition module 310 , a human flow statistics module 320 , a coefficient statistics module 330 and a network point status prompt module 340 .

[0081] The video acquisition module 310 is used to acquire video data of a network point within a preset period. In one embodiment, the video acquisition module 310 can be used to perform the operation S210 described above, which will not be described in detail here.

[0082] The traffic statistics module 320 is used to count the first traffic, the second traffic and the third traffic based on the video data, wherein the first traffic is the total traffic of the outlet, the second traffic is the traffic of the first target area, and the second traffic is the traffic of the second target area. In one embodiment, the traffic statistics module 320 can be used to perform the operation S220 described above, which will not be repeated here.

[0083] The coefficient statistics module 330 is used to calculate the regional transfer coefficient based on the first human flow, the second human flow, and the third human flow, wherein the regional transfer coefficient is used to characterize the transfer coefficient of human flow between different regions, and N is a positive integer. In one embodiment, the coefficient statistics module 330 can be used to perform the operation S230 described above, which will not be repeated here.

[0084] The dot status prompt module 340 is used to issue corresponding dot status information based on the area transfer coefficient. In one embodiment, the dot status prompt module 340 can be used to perform the operation S240 described above, which will not be described in detail here.

[0085] In the embodiments of the present disclosure, in order to solve the technical problem that the statistics of the status of the outlets in the prior art are not accurate enough and cannot meet the needs of refined operations. The embodiments of the present disclosure use the videos collected from the outlets within a certain period to count the total flow of people in the outlets, the flow of people in the first target area, and the flow of people in the second target area, respectively, and calculate the coefficients of these data area transfers to reflect the transfer of the flow of people between different areas, deeply dig out the status of the outlets, and finally, by judging these coefficients and issuing corresponding outlet status information, to assist personnel in carrying out outlet operations. The embodiments of the present disclosure can at least achieve the following beneficial effects: The embodiments of the present disclosure have the advantages of high accuracy, strong real-time performance, high degree of automation, low cost, etc., and are suitable for bank outlets and other places of various sizes. At the same time, the embodiments of the present disclosure also have good scalability and intelligent analysis capabilities, and are expected to provide banks and other enterprises with more intelligent and efficient management solutions.

[0086] According to an embodiment of the present disclosure, the crowd flow statistics module is specifically used to, for the first crowd flow, count the crowd flow passing through the outlet entrance based on the video data; and for the second crowd flow or the third crowd flow, count the crowd flow whose stay time in the first target area exceeds a first stay time threshold based on the video data, or count the crowd flow whose stay time in the second target area exceeds a second stay time threshold based on the video data.

[0087] According to an embodiment of the present disclosure, the traffic statistics module is specifically used to detect the area where the customer is located for each customer based on a target tracking algorithm; and when the customer stays in the first target area for longer than the first residence time threshold, or when the customer stays in the second target area for longer than the second residence time threshold, record the traffic in the corresponding area.

[0088] According to an embodiment of the present disclosure, the coefficient statistics module is specifically used to calculate a first area transfer coefficient based on the first human flow and the second human flow; the network point status prompt module is specifically used to issue first network point status information when the first area transfer coefficient is greater than a first preset threshold, and the first network point status information is used to characterize that the service area is busy.

[0089] According to an embodiment of the present disclosure, the coefficient statistics module is specifically used to calculate the second area transfer coefficient based on the first human flow and the third human flow; the network point status prompt module is specifically used to issue second network point status information when the second area transfer coefficient is greater than a second preset threshold, and the second network point status information is used to characterize that the network point is idle.

[0090] According to an embodiment of the present disclosure, the coefficient statistics module is specifically used to calculate the third area transfer coefficient based on the second human flow and the third human flow; the network point status prompt module is specifically used to issue third network point status information when the third area transfer coefficient is less than a third preset threshold, and the third network point status information is used to characterize that the service area is busy.

[0091] According to an embodiment of the present disclosure, the network point status prompt module is also used to issue fourth network point status information when any one of the first human flow, the second human flow or the third human flow exceeds the corresponding preset human flow threshold, and the fourth network point status information is used to characterize that the network point is busy.

[0092] According to an embodiment of the present disclosure, the network point status statistics device is configured in a network point local server.

[0093] According to an embodiment of the present disclosure, any multiple modules of the video acquisition module 310, the human flow statistics module 320, the coefficient statistics module 330, and the network point status prompt module 340 can be combined into 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 video acquisition module 310, the human flow statistics module 320, the coefficient statistics module 330, and the network point status prompt module 340 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 methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the video acquisition module 310, the human flow statistics module 320, the coefficient statistics module 330 and the network point status prompt module 340 can be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function can be executed.

[0094] Figure 4 A block diagram of an electronic device suitable for implementing a method for counting network node status according to an embodiment of the present disclosure is schematically shown.

[0095] like Figure 4 As shown, the electronic device 400 according to an embodiment of the present disclosure includes a processor 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage part 408 to a random access memory (RAM) 403. The processor 401 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 401 may also include an onboard memory for caching purposes. The processor 401 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.

[0096] In RAM 403, various programs and data required for the operation of electronic device 400 are stored. Processor 401, ROM 402 and RAM 403 are connected to each other via bus 404. Processor 401 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 402 and / or RAM 403. It should be noted that the program can also be stored in one or more memories other than ROM 402 and RAM 403. Processor 401 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.

[0097] According to an embodiment of the present disclosure, the electronic device 400 may further include an input / output (I / O) interface 405, which is also connected to the bus 404. The electronic device 400 may further include one or more of the following components connected to the input / output (I / O) interface 405: an input portion 406 including a keyboard, a mouse, etc.; an output portion 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 408 including a hard disk, etc.; and a communication portion 409 including a network interface card such as a LAN card, a modem, etc. The communication portion 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output (I / O) interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage portion 408 as needed.

[0098] 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.

[0099] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it 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, the computer-readable storage medium may include the ROM 402 and / or RAM 403 described above and / or one or more memories other than ROM 402 and RAM 403.

[0100] 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 method provided by the embodiment of the present disclosure.

[0101] 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 401. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0102] In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of information on a network medium, and downloaded and installed through the communication part 409, and / or installed from a removable medium 411. 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.

[0103] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 409, and / or installed from the removable medium 411. When the computer program is executed by the processor 401, 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.

[0104] 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).

[0105] 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.

[0106] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0107] 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, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. 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 counting network point status, characterized in that: The method comprises: Collect video data of network points within a preset period; Counting a first flow of people, a second flow of people and a third flow of people based on the video data, wherein the first flow of people is the total flow of people at the outlet, the second flow of people is the flow of people in the first target area, and the second flow of people is the flow of people in the second target area; Calculating a regional transfer coefficient based on the first human flow, the second human flow, and the third human flow, wherein the regional transfer coefficient is used to characterize a transfer coefficient of human flow between different regions, and N is a positive integer; and Based on the area transfer coefficient, corresponding network point status information is issued.

2. The method according to claim 1, characterized in that The counting of the first person flow, the second person flow and the third person flow based on the video data includes: For the first human flow, counting the human flow through the entrance of the network point based on the video data; and For the second human flow or the third human flow, the human flow whose staying time in the first target area exceeds the first residence time threshold is counted based on the video data, or the human flow whose staying time in the second target area exceeds the second residence time threshold is counted based on the video data.

3. The method according to claim 2, characterized in that The counting of the flow of people whose stay time in the first target area exceeds a first stay time threshold based on the video data, or the counting of the flow of people whose stay time in the second target area exceeds a second stay time threshold based on the video data, includes: For each customer, detecting the area where the customer is located based on a target following algorithm; and When the customer stays in the first target area for a time longer than the first stay time threshold, or when the customer stays in the second target area for a time longer than the second stay time threshold, the flow of people in the corresponding area is recorded.

4. The method according to any one of claims 1 to 3, characterized in that in, The first target area includes a waiting area, the second target area includes a service area, The calculating the area transfer coefficient based on the first human flow, the second human flow, and the third human flow includes: Calculating a first area transfer coefficient based on the first human flow and the second human flow; The issuing of corresponding network point status information based on the regional transfer coefficient includes: When the first area transfer coefficient is greater than a first preset threshold, first network point status information is issued, where the first network point status information is used to indicate that the service area is busy.

5. The method according to claim 4, characterized in that The calculating the area transfer coefficient based on the first human flow, the second human flow and the third human flow also includes: Calculating a second area transfer coefficient based on the first human flow and the third human flow; The issuing of corresponding network point status information based on the regional transfer coefficient also includes: When the second area transfer coefficient is greater than a second preset threshold, second network point status information is issued, where the second network point status information is used to indicate that the network point is idle.

6. The method according to claim 5, characterized in that The calculating the area transfer coefficient based on the first human flow, the second human flow and the third human flow also includes: Calculating a third area transfer coefficient based on the second human flow and the third human flow; The issuing of corresponding network point status information based on the regional transfer coefficient also includes: When the third area transfer coefficient is less than a third preset threshold, third network point status information is issued, and the third network point status information is used to indicate that the service area is busy.

7. The method according to claim 1, characterized in that After counting the first person flow, the second person flow, and the third person flow based on the video data, the method further includes: When any one of the first human flow, the second human flow or the third human flow exceeds the corresponding preset human flow threshold, fourth network point status information is issued, and the fourth network point status information is used to indicate that the network point is busy.

8. The method according to any one of claims 1 to 3 and 5 to 7, characterized in that: The network point status statistics method is applied to the network point local server.

9. A network point status statistics device, characterized in that: The device comprises: A video acquisition module, used to collect video data of network points within a preset period; A people flow statistics module, used for counting a first people flow, a second people flow and a third people flow based on the video data, wherein the first people flow is the total people flow of the outlet, the second people flow is the people flow of the first target area, and the second people flow is the people flow of the second target area; a coefficient statistics module, configured to calculate a regional transfer coefficient based on the first human flow, the second human flow, and the third human flow, wherein the regional transfer coefficient is used to characterize a transfer coefficient of human flow between different regions, and N is a positive integer; and The network point status prompt module is used to issue corresponding network point status information based on the area transfer coefficient.

10. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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