Video stream-based transaction backtracking method and device, and storage medium

Abstract

The application discloses a video stream-based transaction backtracking method and device and a storage medium, and relates to the field of financial technology. The method comprises the following steps: under the authorization of a user, when detecting that the user performs a touch operation in a transaction interface once, collecting a screenshot image of the transaction interface and recording a collection time; after the user ends a transaction through N touch operations, generating an image sequence from the collected N screenshot images and generating a time sequence from N collection times corresponding to the N screenshot images; deleting a target screenshot image in the image sequence according to the time sequence, and taking an image sequence composed of the remaining screenshot images as a target image sequence; generating a video stream according to the target image sequence; and backtracking transaction behavior information of the user according to the video stream. The application solves the technical problem that, in the prior art, a large amount of redundant images exist in a transaction recording video, the data volume of the recording video is excessively large, and the analysis efficiency of transaction backtracking is low.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and specifically to a transaction backtracking method, device and storage medium based on video streaming. Background Art

[0002] Currently, a growing number of scenarios in industries such as banking, finance, insurance, retail, and e-commerce require the retrospective analysis of customer behavior. For example, when customers withdraw cash from bank ATMs or purchase goods at 24-hour convenience stores, their behavior and spending records need to be recorded. These behaviors and records can be reviewed and analyzed through video playback.

[0003] Traditional video playback cannot capture or retrieve customer behavior during a transaction. However, transaction replay video can record customer behavior throughout the transaction. For example, when withdrawing cash from an ATM, this can include recording the customer entering their PIN, inserting their bank card, entering the withdrawal amount, and withdrawing banknotes. This video playback clearly captures the entire withdrawal process, including the PIN and withdrawal amount, rather than just the final withdrawal result. However, traditional technology often uses redundant photos to record customer behavior during transactions, resulting in excessive video data volume and inefficient transaction replay analysis.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] The main purpose of this application is to provide a transaction backtracking method, device and storage medium based on video streaming, so as to at least solve the technical problem in the prior art that due to the presence of a large number of redundant images in the transaction screen recording video, the amount of screen recording video data is too large, resulting in low analysis efficiency of transaction backtracking.

[0006] To achieve the above-mentioned purpose, according to one aspect of an embodiment of the present application, a transaction backtracking method based on video stream is provided, comprising: with user authorization, each time a touch operation of the user is detected in the transaction interface, a screenshot image of the transaction interface is captured and the capture time is recorded; after detecting that the user ends the transaction through N touch operations, an image sequence is generated from the N captured screenshot images, and a time sequence is generated from the N capture times corresponding to the N screenshot images, wherein N is an integer greater than 1; the target screenshot image in the image sequence is deleted according to the time sequence, and the image sequence composed of the remaining screenshot images is used as the target image sequence, wherein the target screenshot image is determined by the capture interval duration of at least two adjacent screenshot images in the image sequence; a video stream is generated according to the target image sequence; and the user's transaction behavior information is backtracked based on the video stream.

[0007] Optionally, after detecting that the user completes the transaction through N touch operations, the N captured screenshot images are used to generate an image sequence, and the N capture moments corresponding to the N screenshot images are used to generate a time sequence, including: using the screenshot image captured when the user performs the j-th touch operation as the j-th screenshot image in the image sequence, where j is a positive integer less than or equal to N; and using the capture moment corresponding to the j-th screenshot image as the j-th moment in the time series.

[0008] Optionally, before deleting the target screenshot image in the image sequence according to the time sequence and taking the image sequence composed of the remaining screenshot images as the target image sequence, the transaction backtracking method based on video stream further includes: calculating the interval duration between each two adjacent moments in the time sequence to obtain N-1 interval durations; forming a first time sequence with the N-1 interval durations and the value 0, wherein the value 0 is the first element of the first time sequence, and the kth interval duration among the N-1 interval durations is the k+1th element in the first time sequence, wherein k is a positive integer less than or equal to N-1; and determining the target screenshot image in the image sequence according to the first time sequence.

[0009] Optionally, determining a target screenshot image in an image sequence based on a first time series includes: performing multiple target operations on the first time series until all first N-1 elements in the first time series participate in the target operation, wherein each target operation is used to calculate the sum of the first element and the second element of the first time series, and determining to delete the second element or move the first element and the second element to a target set based on the sum result; moving the last element in the first time series to the target set; and determining the target screenshot image in the image sequence based on the target set.

[0010] Optionally, the target screenshot image in the image sequence is determined according to the target set, including: when the target set includes the g-th element of the first time sequence, taking the g-th screenshot image in the image sequence as the non-target screenshot image, where g is a positive integer less than or equal to N; when the target set does not include the g-th element in the first time sequence, taking the g-th screenshot image in the image sequence as the target screenshot image.

[0011] Optionally, each target operation includes: calculating the sum of the first element and the second element of the first time series; detecting whether the sum is greater than a preset threshold; if the sum is greater than the preset threshold, deleting the second element and updating the first time series; if the sum is less than or equal to the preset threshold, moving the first element and the second element to the target set and updating the first time series.

[0012] Optionally, the transaction backtracking method based on video streaming also includes: after obtaining the first time sequence updated after the xth target operation, starting the x+1th target operation according to the first time sequence, where x is an integer greater than 1 and the xth target operation is not the last target operation.

[0013] In order to achieve the above-mentioned purpose, according to another aspect of an embodiment of the present application, a transaction backtracking device based on video stream is further provided, including: an acquisition unit, which, with user authorization, acquires a screenshot image of the transaction interface and records the acquisition time each time it detects that the user performs a touch operation in the transaction interface; a first generation unit, which, after detecting that the user ends the transaction through N touch operations, generates an image sequence with the N acquired screenshot images, and generates a time sequence with the N acquisition times corresponding to the N screenshot images, wherein N is an integer greater than 1; a first processing unit, which deletes the target screenshot image in the image sequence according to the time sequence, and uses the image sequence composed of the remaining screenshot images as the target image sequence, wherein the target screenshot image is determined by the acquisition interval duration of at least two adjacent screenshot images in the image sequence; a second generation unit, which generates a video stream according to the target image sequence; and a second processing unit, which backtracks the user's transaction behavior information based on the video stream.

[0014] According to another aspect of an embodiment of the present application, an electronic device is further provided, including: a memory storing an executable program; and a processor for running the program, wherein the above-mentioned video stream-based transaction backtracking method is executed when the program is running.

[0015] According to another aspect of an embodiment of the present application, a computer-readable storage medium is further provided, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned video stream-based transaction backtracking method.

[0016] According to another aspect of the embodiments of the present application, a computer program product is further provided, comprising computer instructions, which, when executed by a processor, implement the steps of the above-mentioned video stream-based transaction backtracking method.

[0017] In an embodiment of the present application, first, with user authorization, each time a user is detected to perform a touch operation in the transaction interface, a screenshot image of the transaction interface is captured and the capture time is recorded. Then, after detecting that the user has completed the transaction through N touch operations, the N captured screenshot images are used to generate an image sequence, and the N capture times corresponding to the N screenshot images are used to generate a time sequence, where N is an integer greater than 1. Then, according to the time sequence, the target screenshot image in the image sequence is deleted, and the image sequence composed of the remaining screenshot images is used as the target image sequence, where the target screenshot image is determined by the capture interval duration of at least two adjacent screenshot images in the image sequence. Then, a video stream is generated according to the target image sequence, and finally, the user's transaction behavior information is traced back based on the video stream.

[0018] As can be seen from the above, this application ensures that it runs only after the user has explicitly authorized it, respecting user privacy and data security. During the user's interaction with the transaction interface, every touch screen event is monitored and captured in real time. Whenever a touch operation is detected, a screenshot operation is immediately executed, capturing an image of the current transaction interface (screenshot image) and recording the corresponding capture time, ensuring the real-time recording of user behavior and providing a data foundation for subsequent video generation and backtracking.

[0019] Next, the user's touch operations are continuously monitored until a transaction completion signal is detected. At this point, all N collected screenshots are combined to form an image sequence in the order of the operations. Simultaneously, the capture times of each screenshot are also combined to form a time series, where N is the total number of touch operations performed by the user during the transaction, which must be an integer greater than 1. The combination of image and time series provides the necessary temporal and spatial information for subsequent data processing and video synthesis.

[0020] Furthermore, to further optimize the amount of video data, a data filtering operation was introduced. First, the duration between each adjacent moment in the time series was calculated to form a collection of screenshot time differences. An algorithm then analyzed these time differences to identify and filter out frequently occurring screenshots within a short period of time. This reduced the amount of meaningless duplicate images and significantly reduced the video file size. After filtering, the remaining screenshots were combined into a target image sequence, preparing selected data for video generation.

[0021] Finally, a continuous, playable video stream is generated using the target image sequence and its corresponding display duration information. This video stream not only captures all key user actions, but also, through preliminary filtering steps, ensures smoothness and comprehensibility while significantly compressing the data volume. The generated video stream can be further used to review and analyze user transaction behavior, providing businesses with an effective regulatory and auditing tool while significantly improving user experience and data management.

[0022] It can be seen that through the technical solution of the present application, the purpose of efficiently and accurately recording the user's operation trajectory during the transaction process while ensuring the integrity and clarity of the video backtracking is achieved, thereby realizing the technical effect of high-precision recording and video backtracking of touch operations during the transaction process, and thus solving the technical problem in the prior art that due to the presence of a large number of redundant images in the transaction screen recording video, the amount of screen recording video data is too large, resulting in low analysis efficiency of transaction backtracking. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0024] Figure 1 A hardware structure block diagram of a computer terminal for implementing a transaction backtracking method based on video stream is shown;

[0025] Figure 2 This is a flowchart of an optional video stream-based transaction backtracking method according to an embodiment of the present application;

[0026] Figure 3 This is a structural composition flow chart of an optional video stream-based transaction backtracking method according to an embodiment of the present application;

[0027] Figure 4 This is a flowchart of an optional method for generating a retrospective video based on a time series operation trajectory according to an embodiment of the present application;

[0028] Figure 5 This is a flowchart of the steps of sensing, collecting and filtering controls in an optional video stream-based transaction backtracking device according to an embodiment of the present application;

[0029] Figure 6 Schematic diagram of a transaction backtracking device based on video streaming provided according to an embodiment of the present application;

[0030] Figure 7 This is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] It should also be noted that the collected information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are 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 portals for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions to provide users with corresponding operation portals for users to choose to agree or refuse the automated decision-making results; if the user chooses to refuse, the expert decision-making process will be entered.

[0034] According to an embodiment of the present application, a method embodiment of a transaction backtracking method based on video streaming is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0035] It should be noted that a video stream-based transaction backtracking system can serve as the execution entity of the video stream-based transaction backtracking method provided in the embodiments of this application. It is understood that the video stream-based transaction backtracking method provided in the embodiments of this application can also be executed by other systems or devices, and this embodiment of the application does not specifically limit this.

[0036] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or mobile device) for implementing a transaction backtracking method based on video streaming is shown. Figure 1As shown, the computer terminal 10 (or mobile device) may include one or more (illustrated as 102a, 102b, ..., 102n in the figure) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0037] It should be noted that the one or more processors 102 and / or other data processing circuits mentioned above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be fully or partially integrated into any of the other components in the computer terminal 10 (or mobile device). As in the video stream-based transaction backtracking method involved in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0038] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the video stream-based transaction backtracking method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the above-mentioned video stream-based transaction backtracking method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0039] The transmission device 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0040] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0041] Under the above operating environment, this application provides Figure 2 The transaction backtracking method based on video streaming is shown. Figure 2 FIG is a flowchart of an optional video stream-based transaction backtracking method according to an embodiment of the present application. Figure 2 As shown, the method includes the following steps:

[0042] Step S201 : With user authorization, every time a touch operation of the user in the transaction interface is detected, a screenshot of the transaction interface is captured and the capture time is recorded.

[0043] Optionally, the first condition for a transaction backtracking system based on video streaming is to obtain the explicit authorization of the end user. This step strictly complies with data protection and privacy regulations to ensure that the user is aware of and agrees to the monitoring and data collection of his or her touch interaction behavior in the transaction interface.

[0044] Optionally, whenever the video stream-based transaction backtracking system detects a user performing a touch operation in the transaction interface, it immediately triggers the screenshot function to capture the current state of the transaction interface with high fidelity to form a screenshot image.

[0045] Optionally, a transaction backtracking system based on video streaming accurately records the capture moment of the screenshot image, that is, the exact time point when the event occurred, and saves the timestamp information with millisecond-level accuracy, providing crucial raw data for subsequent event backtracking and behavior analysis, ensuring the complete recording and high-precision playback of the operation trajectory.

[0046] Step S202 , after detecting that the user has completed the transaction through N touch operations, generates an image sequence from the N captured screenshot images, and generates a time sequence from the N capture moments corresponding to the N screenshot images, where N is an integer greater than 1.

[0047] Optionally, when the video stream-based transaction backtracking system detects that the user has completed the transaction process through a series of N consecutive touch operations, marking the logical end point of the transaction event, the system integrates the N previously captured screenshots into an ordered image sequence. This image sequence provides a structured point-in-time image dataset for subsequent data processing and video synthesis, where N is defined as an integer greater than 1, ensuring the completeness and richness of the transaction operation sequence.

[0048] Optionally, a full set of screenshot images (ie, N screenshot images): P1, P2, P3...P(i-1), P(i), P(i+1)...Pn.

[0049] Optionally, a video-stream-based transaction backtracking system generates a time series consisting of N precise acquisition moments, corresponding to the capture instants of N screenshots. Each element of the time series is recorded with millisecond accuracy, providing a highly precise time base for subsequent analysis. This combination of time series and image sequences constructs a spatiotemporal coordinate system for transaction behavior, where N is greater than 1. This ensures a detailed record of multi-touch operations and time points, providing critical time clues for accurate backtracking and event reconstruction.

[0050] Optionally, a full set of screenshot moments (ie, N acquisition moments generate a time series): T1, T2, T3...T(i-1), T(i), T(i+1)...Tn.

[0051] Step S203 : deleting the target screenshot images in the image sequence according to the time sequence, and taking the image sequence composed of the remaining screenshot images as the target image sequence.

[0052] Optionally, the video stream-based transaction backtracking system removes redundant target screenshots from the image sequence based on the distribution of the intervals between the screenshot capture times in the time series. The system analyzes the time differences between consecutive screenshot capture times to determine the changes between the images, ensuring that the deleted images do not contain substantive operational information.

[0053] Optionally, after filtering, the remaining screenshots form a refined image sequence containing key operational information, formally defined as the target image sequence. This target image sequence not only removes redundancy, preserving the dynamics of transaction behavior and key moments of user operations, but also significantly reduces data volume, improving video replay efficiency and resource utilization.

[0054] Step S204: Generate a video stream according to the target image sequence.

[0055] Alternatively, a video stream-based transaction backtracking system can efficiently generate a continuous, low-data-volume video stream based on the target image sequence. This process leverages a streamlined image sequence, where each frame represents a key moment in the user's transaction behavior and time series analysis ensures that substantial changes between images are present.

[0056] Optionally, the system accurately and seamlessly connects a series of images in chronological order to form a smooth video stream, while significantly compressing the overall data volume of the video through early optimization strategies.

[0057] Step S205: trace back the user's transaction behavior information based on the video stream.

[0058] Optionally, the transaction backtracking system based on video stream utilizes the generated video stream with low data volume and high information density to accurately backtrack the user's transaction behavior information.

[0059] Optionally, based on the close association between each frame image in the video stream and the time series, through frame-by-frame analysis of the video stream and synchronous playback of the time base, the user's operation trajectory in the transaction interface can be reconstructed, including the position of the touch point, the order of operations and the duration of each operation step.

[0060] It can be seen from the contents of steps S201 to S205 that in an embodiment of the present application, first, with the user's authorization, each time the user is detected to perform a touch operation in the transaction interface, a screenshot image of the transaction interface is collected and the collection time is recorded. Then, after it is detected that the user has completed the transaction through N touch operations, the N collected screenshot images are used to generate an image sequence, and the N collection times corresponding to the N screenshot images are used to generate a time sequence, where N is an integer greater than 1. Then, according to the time sequence, the target screenshot image in the image sequence is deleted, and the image sequence composed of the remaining screenshot images is used as the target image sequence, where the target screenshot image is determined by the collection interval duration of at least two adjacent screenshot images in the image sequence. Then, a video stream is generated according to the target image sequence, and finally, the user's transaction behavior information is traced back based on the video stream.

[0061] As can be seen from the above, this application ensures that it runs only after the user has explicitly authorized it, respecting user privacy and data security. During the user's interaction with the transaction interface, every touch screen event is monitored and captured in real time. Whenever a touch operation is detected, a screenshot operation is immediately executed, capturing an image of the current transaction interface (screenshot image) and recording the corresponding capture time, ensuring the real-time recording of user behavior and providing a data foundation for subsequent video generation and backtracking.

[0062] Next, the user's touch operations are continuously monitored until a transaction completion signal is detected. At this point, all N collected screenshots are combined to form an image sequence in the order of the operations. Simultaneously, the capture times of each screenshot are also combined to form a time series, where N is the total number of touch operations performed by the user during the transaction, which must be an integer greater than 1. The combination of image and time series provides the necessary temporal and spatial information for subsequent data processing and video synthesis.

[0063] Furthermore, to further optimize the amount of video data, a data filtering operation was introduced. First, the duration between each adjacent moment in the time series was calculated to form a collection of screenshot time differences. An algorithm then analyzed these time differences to identify and filter out frequently occurring screenshots within a short period of time. This reduced the amount of meaningless duplicate images and significantly reduced the video file size. After filtering, the remaining screenshots were combined into a target image sequence, preparing selected data for video generation.

[0064] Finally, a continuous, playable video stream is generated using the target image sequence and its corresponding display duration information. This video stream not only captures all key user actions, but also, through preliminary filtering steps, ensures smoothness and comprehensibility while significantly compressing the data volume. The generated video stream can be further used to review and analyze user transaction behavior, providing businesses with an effective regulatory and auditing tool while significantly improving user experience and data management.

[0065] It can be seen that through the technical solution of the present application, the purpose of efficiently and accurately recording the user's operation trajectory during the transaction process while ensuring the integrity and clarity of the video backtracking is achieved, thereby realizing the technical effect of high-precision recording and video backtracking of touch operations during the transaction process, and thus solving the technical problem in the prior art that due to the presence of a large number of redundant images in the transaction screen recording video, the amount of screen recording video data is too large, resulting in low analysis efficiency of transaction backtracking.

[0066] In an optional embodiment, after detecting that the user has completed the transaction through N touch operations, the video stream-based transaction backtracking system first uses the screenshot image captured when the user performs the j-th touch operation as the j-th screenshot image in the image sequence, where j is a positive integer less than or equal to N, and then uses the capture moment corresponding to the j-th screenshot image as the j-th moment in the time series.

[0067] Optionally, when a user performs the jth touch operation, the video stream-based transaction backtracking system instantly responds and captures the current state of the transaction interface, generating a screenshot image. The screenshot image is then clearly identified and inserted into the image sequence as the jth screenshot image. Through this process, all N touch operations can be found in the image sequence. Where N is defined as an integer greater than 1, representing the total number of touch operations, and j is a positive integer less than or equal to N, ensuring a precise correspondence between the image sequence construction and the touch events.

[0068] Optionally, each time the user performs the jth touch operation and triggers screenshot image capture, the transaction backtracking system based on the video stream synchronously records the exact capture moment of the operation, and the capture moment information is then assigned to the time series as the jth moment.

[0069] Optionally, by closely associating the touch operation moment with the screenshot image capture moment, each image not only represents the state of the transaction interface, but also carries the timestamp of its generation moment, thereby constructing a precise time coordinate system.

[0070] From the above content, it can be seen that the video stream-based transaction backtracking system constructs an image sequence and time series that are highly consistent with the operation sequence and time attributes by limiting the one-to-one correspondence between N touch operations, N screenshot images in the image sequence, and N acquisition moments in the time series, providing solid data support for the efficient recording and backtracking of transaction behaviors.

[0071] In an optional embodiment, before deleting the target screenshot image in the image sequence according to the time sequence and taking the image sequence composed of the remaining screenshot images as the target image sequence, the transaction backtracking system based on the video stream first calculates the interval duration between each two adjacent moments in the time sequence to obtain N-1 interval durations, and then combines the N-1 interval durations and the value 0 to form a first time sequence, wherein the value 0 is the first element of the first time sequence, and the kth interval duration among the N-1 interval durations is the k+1th element in the first time sequence, wherein k is a positive integer less than or equal to N-1, and then determines the target screenshot image in the image sequence according to the first time sequence.

[0072] Optionally, each pair of adjacent acquisition moments in the user touch operation time series is precisely calculated to obtain the interval length therebetween, and generate N-1 specific values ​​reflecting the time interval of dynamic changes in the transaction interface.

[0073] Optionally, the screenshot time difference set (i.e., N-1 interval durations) is: TT1, TT2, TT3...TT(i-1), TT(i),...T(n-1).

[0074] Optionally, a first time series is constructed, starting with the value 0 as the first element of the sequence, representing the absolute time point at which the transaction began. Subsequently, the N-1 interval durations calculated above are sequentially inserted into the time series as the k+1th element, where k is a positive integer less than or equal to N-1. Through this structural reorganization and expansion, the first time series not only fully records the dynamic rhythm of the transaction behavior but also ensures a consistent number of elements with the image sequence, providing a unified time reference for subsequent data processing and video synthesis.

[0075] Optionally, the time difference set of the supplementary screenshots (ie, the first time series) is: TT0, TT1, TT2, TT3...TT(i-1), TT(i), TT(i+1)...T(n-1), in milliseconds.

[0076] Optionally, the transaction backtracking system based on video stream uses the quantitative time information in the first time series to analyze the target screenshot images in the image sequence, identify the differences between the images, and thus regard some images with very small differences as redundant information and eliminate them.

[0077] From the above content, it can be seen that the video stream-based transaction backtracking system identifies and eliminates repeated or slightly changed target screenshot images in the image sequence through quantification and sequence construction of time intervals, structured reorganization and expansion of time series, and analysis and identification of target images, thereby achieving simplification and optimization of the image sequence.

[0078] In an optional embodiment, the video stream-based transaction backtracking system first performs multiple target operations on the first time series until all the first N-1 elements in the first time series participate in the target operation, wherein each target operation is used to calculate the sum of the first element and the second element of the first time series, and determine whether to delete the second element or move the first element and the second element to the target set based on the sum result, then move the last element in the first time series to the target set, and then determine the target screenshot image in the image sequence based on the target set.

[0079] Optionally, the video stream-based transaction backtracking system introduces a cyclic iterative algorithm (i.e., multiple target operations) to perform in-depth analysis and structural optimization on the first time series to determine key frames in the image sequence.

[0080] Optionally, the video stream-based transaction backtracking system ensures that the last time interval element in the first time series is unconditionally transferred to the target set. For example, the target set can be ListTT: TT0, TT3...TT(i-1), TT(i+1)...T(n-1).

[0081] Alternatively, a video stream-based transaction backtracking system ultimately determines the target screenshots in the image sequence based on the time interval information in the target set. This achieves a precise mapping from time series to image sequence, ensuring that every frame in the video stream carries key information about changes in transaction behavior. Furthermore, through early iterative optimization, redundant images are eliminated, significantly reducing the amount of video data and improving the efficiency of information processing and storage.

[0082] From the above content, we can see that the video stream-based transaction backtracking system identifies and eliminates redundant image frames in the image sequence through a cyclic iterative process and analysis of time series, thereby retaining key visual information in the transaction process and ensuring the high quality and low data volume of the video stream.

[0083] In an optional embodiment, the transaction backtracking system based on video stream first uses the g-th screenshot image in the image sequence as a non-target screenshot image when the target set includes the g-th element of the first time series, where g is a positive integer less than or equal to N, and then uses the g-th screenshot image in the image sequence as a target screenshot image when the target set does not include the g-th element in the first time series.

[0084] Optionally, the video stream-based transaction backtracking system performs precise image sequence screening for the first time series element contained in the target set, i.e., the g-th element (where g is a positive integer less than or equal to N, and N is the total number of touch operations).

[0085] Alternatively, if the target set already contains the g-th time interval element, it indicates that the image change corresponding to that time point has been identified by the system as insignificant, or highly similar to the previous image. Therefore, the system marks the g-th screenshot as a non-target screenshot, that is, it is considered as redundant information that can be optimized and eliminated. Conversely, if the target set does not include the g-th element of the first time series, the system considers the g-th screenshot in the image sequence as a key dynamic change record, that is, the target screenshot.

[0086] Alternatively, for example, the target set may be ListTT: TT0, TT3, ..., TT(i-1), TT(i+1), ..., T(n-1). Then the filtered screenshot image set ListP (i.e., non-target screenshot images) may be: P1, P4, ..., P(i), P(i+2), ..., P(n). Among them, the process of determining the non-target screenshot image includes: "TT0" in the target set belongs to the first element in the first time sequence (TT0, TT1, TT2, TT3...TT(i-1), TT(i), TT(i+1)...T(n-1)), then the first screenshot image "P1" in the image sequence (P1, P2, P3...P(i-1), P(i), P(i+1)...P(n)) is determined as a non-target screenshot image; "TT3" in the target set belongs to the fourth element in the first time sequence (TT0, TT1, TT2, TT3...TT(i-1), TT(i), TT(i+1)...T(n-1)), then the fourth screenshot image "P4" in the image sequence (P1, P2, P3...P(i-1), P(i), P(i+1)...P(n)) is determined as a non-target screenshot image.

[0087] From the above content, it can be seen that when the g-th time interval element exists in the target set, the transaction backtracking system based on video stream regards the g-th screenshot image as a non-target screenshot image and eliminates it; otherwise, the image is regarded as a target screenshot image and retained, thus achieving the simplification of the image sequence and ensuring the high quality of the images and the integrity of the information in the video stream.

[0088] In an optional embodiment, the video stream-based transaction backtracking system first calculates the sum of the first element and the second element of the first time series, then detects whether the sum is greater than a preset threshold, and then, if the sum is greater than the preset threshold, deletes the second element and updates the first time series. Finally, if the sum is less than or equal to the preset threshold, moves the first element and the second element to the target set and updates the first time series.

[0089] Optionally, the first N-1 elements in the first time series are processed one by one by performing the target operation multiple times. Each target operation is to calculate the sum of the first element and the second element of the first time series. If the sum is greater than a preset threshold, the second element is deleted and the first time series is updated; if the sum is less than or equal to the preset threshold, the first element and the second element are moved to the target set and the first time series is updated.

[0090] Optionally, there is a calculation example:

[0091] Calculate TTT = TT i+TT (i+1) .

[0092] If TTT<300 (the preset threshold), filter out TT (i+1) , continue to calculate TTT=TTT+TT i+2 If TTT<300, filter out TT i+2 , and so on.

[0093] If TTT>=300, then TTT=0+TT i+2 , and so on. n-1 Unconditional reservation.

[0094] The final result set is: TT0, TT3...TT i-1 TT i+1 ...T n-1 .

[0095] As can be seen from the above, the video stream-based transaction backtracking system achieves scientific optimization of image sequences by calculating the sum of time intervals and comparing them with preset thresholds. This process is based on dynamic analysis of time series. Based on the threshold assessment, the system performs time series optimization operations based on the judgment results, including deleting or retaining time interval elements and transferring data to the target set. This not only achieves intelligent streamlining of image sequences, but also ensures the integrity and accuracy of the data required for video stream generation.

[0096] In an optional embodiment, after obtaining the first time sequence updated after the xth target operation, the video stream-based transaction backtracking system starts the x+1th target operation according to the first time sequence, where x is an integer greater than 1 and the xth target operation is not the last target operation.

[0097] Optionally, after the video stream-based transaction backtracking system completes the xth target operation, it obtains an updated first time series. To further improve the quality and data efficiency of the video stream, the system immediately initiates the next round, i.e., the x+1th target operation, based on this updated first time series, where x is an integer greater than 1.

[0098] Optionally, the first time series updated after the previous target operation is used as input information for the next target operation.

[0099] From the above content, it can be seen that the transaction backtracking system based on video streaming achieves continuous optimization and simplification of the first time series through a cyclic iterative algorithm. Each iteration is based on the results of the previous round of target operations. By recalculating the sum value and comparing it with the threshold, it determines whether to retain or delete the time interval elements, scientifically adjusts the structure of the first time series, and ensures the high quality and low data volume of the video stream.

[0100] In an optional embodiment, Figure 3 The following flowchart illustrates the structural composition of a video stream-based transaction backtracking method according to an embodiment of the present application. This provides a clearer understanding of the structure of the video stream-based transaction backtracking method: The device primarily comprises three components: a sensing and acquisition unit, a filtering unit (filtering algorithm), and a video synthesis unit. During operation, two types of intermediate data and one type of final data are generated: a full screenshot dataset, a filtered dataset, and synthesized video data.

[0101] In an optional embodiment, Figure 4 The flowchart of a method for generating a retrospective video based on an operation trajectory of a time series according to an embodiment of the present application is shown, which can more clearly understand the process of the transaction retrospective method based on video streaming: first, the customer's touch screen events are acquired in real time through the sensing and acquisition unit and screenshots are taken, while the time parameters between the events are recorded. Then, after the transaction operation is completed, the filtering unit analyzes the collected data and filters out high-frequency screenshot data. Then, the video synthesis unit synthesizes the filtered data into a video according to the corresponding display time data. Finally, the transaction terminal uploads the synthesized video to the backend server for archiving.

[0102] In an optional embodiment, Figure 5 The following is a flowchart showing the steps of the perception, acquisition and filtering control in a video stream-based transaction backtracking device according to an embodiment of the present application, which can more clearly understand the process of perception, acquisition and filtering:

[0103] The caller integrates and activates the transaction backtracking device;

[0104] The sensing and collection unit captures customer touch screen operations in real time and generates the following data:

[0105] Full screenshot collection: P1, P2, P3...P(i-1), P(i), P(i+1)...P(n);

[0106] Full screenshot time collection: T1, T2, T3...T(i-1), T(i), T(i+1)...T(n); unit is milliseconds;

[0107] The screenshot time difference set is calculated according to TT(i) = Ti-T(i-1). The screenshot time difference set is: TT1, TT2, TT3...TT(i-1), TT(i),...T(n-1); the unit is milliseconds; since the screenshot time difference set is 1 less than the number of screenshot images, a new TT0 is added with a value of 0 for one-to-one matching; the filling screenshot time difference set is: TT0, TT1, TT2, TT3...TT(i-1), TT(i), TT(i+1)...T(n-1); the unit is milliseconds;

[0108] Calculate TTT = TT(i) + TT(i+1). If TTT < 300, filter out TT(i+1), and continue to calculate TTT = TTT + TT(i+2). If TTT < 300, filter out TT(i+2), and so on. If TTT > = 300, then TTT = 0 + TT(i+2), and so on. Tn-1 is retained unconditionally. The final result set is, for example: TT0, TT3...TT(i-1), TT(i+1)...T(n-1);

[0109] The image result set that needs to be retained for reverse matching. The format is as follows:

[0110] The filtered screenshot image set ListP: P1, P4, ...Pi, P(i+2) ...P(n); the filtered display result set ListTT: TT0, TT3 ...TT(i-1), TT(i+1) ...T(n-1);

[0111] The video synthesis unit synthesizes ListP: P1, P4, ...Pi, P(i+2) ...P(n) into a continuous playable video;

[0112] The caller uploads the video to the server for storage;

[0113] According to another aspect of the embodiment of the present application, a transaction backtracking device based on video stream is also provided, wherein: Figure 6 is a schematic diagram of an optional transaction backtracking device based on video stream according to an embodiment of the present application, such as Figure 6 As shown, the transaction backtracking device based on video stream includes: a collection unit 601, a first generation unit 602, a first processing unit 603, a second generation unit 604, and a second processing unit 605.

[0114] The acquisition unit 601, with user authorization, captures a screenshot image of the transaction interface and records the capture time each time it detects that the user performs a touch operation in the transaction interface; the first generation unit 602, after detecting that the user ends the transaction through N touch operations, generates an image sequence with the N captured screenshot images, and generates a time sequence with the N capture times corresponding to the N screenshot images, where N is an integer greater than 1; the first processing unit 603 deletes the target screenshot image in the image sequence according to the time sequence, and uses the image sequence composed of the remaining screenshot images as the target image sequence, where the target screenshot image is determined by the capture interval length of at least two adjacent screenshot images in the image sequence; the second generation unit 604 generates a video stream according to the target image sequence; the second processing unit 605 traces back the user's transaction behavior information based on the video stream.

[0115] Optionally, the video stream-based transaction backtracking device further includes: a third processing unit and a fourth processing unit. The third processing unit is configured to use the screenshot image captured when the user performs the jth touch operation as the jth screenshot image in the image sequence, where j is a positive integer less than or equal to N; and the fourth processing unit is configured to use the capture moment corresponding to the jth screenshot image as the jth moment in the time series.

[0116] Optionally, the video stream-based transaction backtracking device further includes: a calculation unit, a fifth processing unit, and a determination unit. The calculation unit is configured to calculate the interval duration between each two adjacent moments in the time series to obtain N-1 interval durations; the fifth processing unit is configured to combine the N-1 interval durations and the value 0 to form a first time series, wherein the value 0 is the first element of the first time series, and the kth interval duration among the N-1 interval durations is the k+1th element of the first time series, where k is a positive integer less than or equal to N-1; and the determination unit is configured to determine a target screenshot image in the image sequence based on the first time series.

[0117] Optionally, the determination unit includes: a first processing subunit, a second processing subunit, and a first determination subunit. The first processing subunit is used to perform multiple target operations on the first time series until the first N-1 elements in the first time series all participate in the target operation, wherein each target operation is used to calculate the sum of the first element and the second element of the first time series, and determine whether to delete the second element or move the first element and the second element to a target set based on the sum; the second processing subunit is used to move the last element in the first time series to the target set; and the first determination subunit is used to determine the target screenshot image in the image sequence based on the target set.

[0118] Optionally, the first determining subunit includes: a first processing module and a second processing module. The first processing module is configured to, if the target set includes the gth element of the first time series, use the gth screenshot image in the image sequence as a non-target screenshot image, where g is a positive integer less than or equal to N; and the second processing module is configured to, if the target set does not include the gth element of the first time series, use the gth screenshot image in the image sequence as a target screenshot image.

[0119] Optionally, the first processing subunit includes: a first calculation module, a first detection module, a third processing module, and a fourth processing module. The first calculation module is configured to calculate the sum of the first element and the second element of the first time series; the first detection module is configured to detect whether the sum is greater than a preset threshold; the third processing module is configured to delete the second element and update the first time series if the sum is greater than the preset threshold; and the fourth processing module is configured to move the first element and the second element to a target set and update the first time series if the sum is less than or equal to the preset threshold.

[0120] Optionally, the transaction backtracking device based on video streaming also includes: a fifth processing module, which is used to start the x+1th target operation according to the first time sequence after obtaining the first time sequence updated after the xth target operation, wherein x is an integer greater than 1 and the xth target operation is not the last target operation.

[0121] An embodiment of the present application may provide an electronic device, Figure 7 This is a structural block diagram of an electronic device according to an embodiment of the present application. Figure 7 As shown, the electronic device may include: one or more ( Figure 7 Only one is shown) processor, memory, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0122] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implementing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0123] It can be understood by those skilled in the art that Figure 7 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (MID), a PAD, or other terminal devices. Figure 7 It does not limit the structure of the above electronic device. For example, the electronic device may also include Figure 7More or fewer components (such as network interfaces, display devices, etc.) shown in, or with Figure 7 Different configurations shown.

[0124] A person skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0125] According to another aspect of the present application, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium. When the computer program is executed, the device where the computer-readable storage medium is located executes the above-mentioned video stream-based transaction backtracking method.

[0126] According to another aspect of the present application, a computer program product is provided, wherein the computer program product includes computer instructions, wherein when the computer instructions are executed, the device where the computer program product is located executes the above-mentioned video stream-based transaction backtracking method.

[0127] The above-mentioned embodiments or examples disclosed in this application are not exhaustive, but are only illustrations of some embodiments or examples, and are not intended to be specific limitations on the scope of protection disclosed in this application. In the absence of contradiction, each step in a certain embodiment or example in this application can be implemented as an independent example, and the steps can be arbitrarily combined. For example, the solution after removing some steps in a certain embodiment or example can also be implemented as an independent example, and the order of the steps in a certain embodiment or example can be arbitrarily exchanged. In addition, the optional methods or optional examples in a certain embodiment or example can be arbitrarily combined; in addition, the various embodiments or examples can be arbitrarily combined. For example, some or all of the steps in different embodiments or examples can be arbitrarily combined, and a certain embodiment or example can be arbitrarily combined with the optional methods or optional examples of other embodiments or examples.

[0128] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0129] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0130] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0131] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0132] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0133] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0134] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A transaction backtracking method based on video streaming, characterized in that: include: With user authorization, each time a touch operation is detected on the transaction interface by the user, a screenshot of the transaction interface is captured and the capture time is recorded; After detecting that the user has completed the transaction through N touch operations, generating an image sequence from the N captured screenshot images, and generating a time sequence from the N capture moments corresponding to the N screenshot images, where N is an integer greater than 1; Calculating the interval duration between each two adjacent moments in the time series to obtain N-1 interval durations; combining the N-1 interval durations and a value of 0 to form a first time series, wherein the value 0 is the first element of the first time series, and the kth interval duration among the N-1 interval durations is the k+1th element in the first time series, wherein k is a positive integer less than or equal to N-1; Performing a target operation on the first time series multiple times until all first N-1 elements in the first time series participate in the target operation, wherein each target operation is used to calculate the sum of the first element and the second element of the first time series, and determining, based on the sum, to delete the second element or move the first element and the second element to a target set; and moving the last element in the first time series to the target set; When the target set includes the g-th element of the first time series, the g-th screenshot image in the image sequence is used as a non-target screenshot image, where g is a positive integer less than or equal to N; when the target set does not include the g-th element in the first time series, the g-th screenshot image in the image sequence is used as a target screenshot image; Deleting target screenshot images from the image sequence according to the time sequence, and using an image sequence consisting of the remaining screenshot images as a target image sequence, wherein the target screenshot images are determined by a capture interval duration between at least two adjacent screenshot images in the image sequence; generating a video stream according to the target image sequence; The transaction behavior information of the user is traced back based on the video stream.

2. The transaction backtracking method based on video stream according to claim 1 is characterized in that: After detecting that the user completes the transaction through N touch operations, generating an image sequence from the N captured screenshot images, and generating a time sequence from the N capture moments corresponding to the N screenshot images, including: Using the screenshot image captured when the user performs the j-th touch operation as the j-th screenshot image in the image sequence, where j is a positive integer less than or equal to N; The acquisition moment corresponding to the j-th screenshot image is taken as the j-th moment in the time series.

3. The transaction backtracking method based on video stream according to claim 1, characterized in that: Each target operation includes: Calculating the sum of the first element and the second element of the first time series; Detecting whether the summation result is greater than a preset threshold; When the sum result is greater than the preset threshold, deleting the second element and updating the first time series; When the sum result is less than or equal to the preset threshold, the first element and the second element are moved to the target set, and the first time series is updated.

4. The transaction backtracking method based on video stream according to claim 3 is characterized in that: The transaction backtracking method based on video stream also includes: After obtaining the first time sequence updated after the xth target operation, the x+1th target operation is started according to the first time sequence, where x is an integer greater than 1 and the xth target operation is not the last target operation.

5. A transaction backtracking device based on video stream, used to execute the transaction backtracking method based on video stream according to any one of claims 1 to 4, characterized in that: include: a collection unit, with user authorization, collecting a screenshot of the transaction interface and recording the collection time each time it detects that the user performs a touch operation on the transaction interface; a first generating unit, after detecting that the user ends the transaction through N touch operations, generating an image sequence from the N captured screenshot images, and generating a time sequence from N capture moments corresponding to the N screenshot images, where N is an integer greater than 1; a first processing unit, configured to delete target screenshot images from the image sequence according to the time sequence, and use an image sequence consisting of the remaining screenshot images as a target image sequence, wherein the target screenshot images are determined by a capture interval between at least two adjacent screenshot images in the image sequence; a second generating unit, generating a video stream according to the target image sequence; The second processing unit traces back the transaction behavior information of the user based on the video stream.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the computer-readable storage medium is located is controlled to execute the video stream-based transaction backtracking method according to any one of claims 1 to 4.

7. An electronic device, characterized in that: include: a memory storing an executable program; A processor is used to run the program, wherein when the program is running, the processor executes the video stream-based transaction backtracking method according to any one of claims 1 to 4.

8. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the steps of the video stream-based transaction backtracking method according to any one of claims 1 to 4 are implemented.

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