A media resource incremental mining and dynamic updating method and system, and a medium

Abstract

This invention relates to the field of data processing and information transmission, and discloses a method, system, and medium for incremental mining and dynamic updating of media resources, comprising the following steps: The invention acquires incremental activity logs from a financial business system; it invokes a data exploration agent to perform rule matching, mines target business tags that meet thresholds, and determines popularity weights; based on these tags, it triggers a media generation agent to call a large model interface to generate candidate media resources; it performs compliance review and transfer of the resources through a collaborative routing module, and obtains confirmation logs; it writes the resources into a media library and dynamically updates the front-end panel according to the popularity weights. This invention drives large model generation through underlying data aggregation and quantification of weights, and combines pixel-level anti-counterfeiting watermarking and asynchronous network routing mechanisms to construct a system-level compliance closed loop, effectively solving the technical problems of traditional methods such as high reliance on manual labor, delayed updates, and low security in transfer.

Description

Technical Field

[0001] This invention relates to the field of data processing and information transmission technology, specifically to a method, system, and medium for incremental mining and dynamic updating of media resources. Background Technology

[0002] With the rapid development of digital finance, financial institutions heavily rely on front-end display panels (such as event banners, short video promotional slots, and dynamic posters) to reach users and drive business conversion. Currently, internal management and resource updates in financial marketing typically depend on operations staff manually exporting and analyzing access logs or transaction records from the underlying business systems, subjectively judging recent marketing trends based on experience, and then having the design team manually create corresponding media materials. Once the materials are created, they are usually circulated and approved among multiple departments such as business, compliance, and risk control through conventional instant messaging tools or basic office automation (OA) systems. After all nodes confirm that everything is correct, development or operations staff manually log into the material library backend to hard-code or statically update the front-end media resources.

[0003] However, this traditional data flow and control model, heavily reliant on manual intervention, reveals serious time lags and underlying architectural flaws when facing rapidly changing and strictly regulated financial business scenarios. Specifically, manual data exploration based on offline reports creates a significant time gap between the generation of business logs and the extraction of trending tags. The system cannot perform millisecond-level incremental mining based on actual user interaction frequency, resulting in display space resources remaining stagnant for extended periods. Simultaneously, the traditional manual creation model can no longer handle the massive, high-frequency iteration demands of media materials, easily leading to disruptions in marketing resource supply. Even more critically, in the extremely crucial compliance review process, the traditional online flow lacks system-level state isolation and underlying anti-tampering mechanisms. Candidate media resources transmitted between different terminals in plaintext or as regular attachments not only face extremely high risks of unauthorized tampering and data leakage, but their discrete review status also cannot be linked with the rendering logic of the front-end system at the underlying level. This loose workflow, characterized by sluggish data perception, low capacity, and a lack of physical-level security loops, ultimately leads to extremely low efficiency in updating display resources in financial business systems, accompanied by uncontrollable risks of irregularities in data transfer. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method, system, and medium for incremental mining and dynamic updating of media resources. This addresses the serious lag in underlying data exploration of existing financial business display resources, the low capacity for manual material production, and the lack of system-level state isolation and physical anti-tampering mechanisms in the circulation and review process. As a result, the dynamic updating efficiency of media resources is extremely low and accompanied by uncontrollable compliance management risks.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides a method for incremental mining and dynamic updating of media resources, applied to an internal management system for financial marketing, the method comprising: Obtain incremental activity logs from the financial business system; The preset dynamic data exploration agent is invoked to perform rule matching on the incremental activity logs, to discover target financial business tags that meet the preset conversion rate threshold, and to determine the popularity weight corresponding to the target financial business tags. Based on the target financial business tag, a media generation agent is triggered to call an external generative large model interface to generate incremental candidate media resources according to the target financial business tag. The incremental candidate media resources are subjected to compliance review and circulation through a preset collaborative routing module, and the review confirmation receipt log is obtained. After obtaining the review confirmation log, the incremental candidate media resources are written into the financial industry material library system, and the front-end display panel in the financial industry material library system is dynamically updated according to the popularity weight.

[0006] In one possible implementation, obtaining incremental activity logs from the financial business system includes: based on a configured general data collection node, pulling incremental access log data from the financial business system during the marketing campaign in real time; synchronously extracting rights distribution counter data from the financial business system; and performing data cleaning and structured splicing on the incremental access log data and the rights distribution counter data to construct the incremental activity logs.

[0007] In one possible implementation, the step of invoking a preset dynamic data exploration agent to perform rule matching on the incremental activity logs, mining target financial business tags that meet a preset conversion rate threshold, and determining the popularity weight corresponding to the target financial business tags includes: The incremental activity logs are input into the dynamic data exploration agent; The incremental activity logs are aggregated and statistically analyzed using the preset dynamic SQL exploration script in the dynamic data exploration agent to calculate the real-time conversion rate of each financial activity panel. Extract the target financial activity panel whose real-time conversion rate is greater than or equal to the preset conversion rate threshold, determine the attribute tag corresponding to the target financial activity panel as the target financial business tag, and use the real-time conversion rate as the popularity weight corresponding to the target financial business tag.

[0008] In the above steps, the core innovative principle of the dynamic data exploration agent lies in transforming the disordered log stream into structured decision-making data through an underlying aggregation algorithm. Specifically, a time observation window is configured for each financial activity panel, and the current time observation window is set as follows: The total number of incremental access log records obtained by the general data collection node for a certain financial activity panel within this window is [number missing]. The value of the rights distribution counter extracted synchronously is The real-time conversion rate Obtained through the following aggregation calculation model: in, A minimum constant smoothing factor is preset for the system to prevent calculation anomalies where the denominator is zero.

[0009] When dynamic data probing intelligent agent detects comparison ( When a preset conversion rate threshold is reached, the system determines that the financial activity panel has triggered a high-frequency interaction event, and then identifies its corresponding attribute tag as the target financial business tag. Simultaneously, the system uses a weight mapping function to assign corresponding popularity weights to the real-time conversion rate. The mapping logic is as follows: This algorithm mechanism ensures that the heat index weight extracted by the system maintains a strict linear correlation with the actual concurrent interaction volume of the financial activity panel, providing data support for the subsequent dynamic scheduling of the front-end display panel.

[0010] In one possible implementation, the step of triggering a media generation agent based on the target financial business tag, and calling an external generative large model interface according to the target financial business tag to generate incremental candidate media resources, including: Extract the financial application scenarios and basic business parameters associated with the target financial business tag from the financial business system; The intelligent agent generated by the media splices and assembles the target financial business label, the financial application scenario and the basic business parameters into a target prompt word template. The target prompt word template is sent to the external generative large model interface, and the resource data containing financial marketing copy or video data returned by the external generative large model interface based on the target prompt word template is received as the incremental candidate media resource.

[0011] In one possible implementation, the step of performing compliance review and routing of the incremental candidate media resources through a preset collaborative routing module and obtaining review confirmation receipt logs includes: When the incremental candidate media resource is video data, the preset video collaborative review agent in the collaborative routing module is invoked; The video collaborative review agent automatically injects tamper-proof digital watermarks into the incremental candidate media resources and generates encrypted review slice links based on authentication tokens. The encrypted review slice link is sent to the corresponding collaborative review terminal, and the review confirmation log returned by the collaborative review terminal based on the encrypted review slice link is received.

[0012] The underlying principle of how the video collaborative review agent injects tamper-proof digital watermarks into video data is as follows: The system extracts video frame sequences from incremental candidate media resources. A pseudo-random code sequence is generated based on the authentication token as the key base. The device identifier and compliance verification code are encoded into a watermark signal sequence using a modulation mechanism. For a given video frame The output data frame after watermarking Follow the image signal superposition model as follows: in, These are the pixel coordinates in the two-dimensional matrix of the current video frame. An adaptive embedding strength coefficient is used to balance the watermark's concealment and robustness. This refers to the spread spectrum modulation operator. This mechanism ensures that when generating encrypted review slice links, the video data flowing to the collaborative review terminal possesses a unique non-repudiable characteristic, meeting stringent compliance audit requirements.

[0013] In one possible implementation, before receiving the review confirmation log returned by the collaborative review terminal based on the encrypted review slice link, the method further includes: If the collaborative review terminal receives a verification failure instruction returned based on the encrypted review slice link, the human-machine collaborative task routing mechanism is triggered. The incremental candidate media resources and the verification failure instruction are encapsulated into a dynamic media optimization requirement order; Based on preset business attribute configuration matching rules, the dynamic media optimization demand orders are automatically dispatched to the target internal processing terminal; The system receives the revised finalized resource uploaded by the target's internal processing terminal, uses the revised finalized resource as the incremental candidate media resource, and re-triggers the compliance review process for the incremental candidate media resource.

[0014] In one possible implementation, the step of performing compliance review and routing of the incremental candidate media resources through a preset collaborative routing module and obtaining review confirmation receipt logs includes: The incremental candidate media resources are packaged into a compliance review message with mandatory confirmation attribute by using the preset notification engine routing node in the collaborative routing module. The compliance review message is pushed to the designated administrator terminal, and the incremental candidate media resources are locked in the status of the collaborative routing module. When a confirmation instruction is received from the designated administrator terminal regarding the compliance audit message, the status lock is released, and the audit confirmation receipt log is generated.

[0015] In one possible implementation, after obtaining the review confirmation receipt log, the incremental candidate media resources are written into the financial industry material library system, and the front-end display panel in the financial industry material library system is dynamically updated according to the popularity weight, including: Real-time monitoring of the state transition events of the incremental candidate media resources; When the status of the incremental candidate media resource is changed to the "deployed" status based on the review confirmation receipt log according to the status transition event, the incremental candidate media resource is officially and persistently written into the financial industry material library system. Based on the popularity weight, the incremental candidate media resources are dynamically replaced in the preset carousel display area or recommended resource display area in the front-end display panel, thus completing the dynamic update of the front-end display panel.

[0016] A second aspect of this invention provides an incremental mining and dynamic updating system for media resources, comprising: Memory, used to store computer programs; A processor, configured to perform the steps of the method as described in any of the first aspect of the invention and its various possible implementations when executing the computer program.

[0017] A third aspect of the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a processor, perform the steps of the method as described in any one of the first aspects of the present invention and its various possible implementations.

[0018] This invention provides a method, system, and medium for incremental mining and dynamic updating of media resources. It has the following beneficial effects: 1. This invention introduces a dynamic data exploration agent and combines it with underlying SQL aggregation calculations to transform the massive, disordered access logs and rights issuance counter data in financial business systems into real-time conversion rates with business guidance value. This mechanism not only eliminates invalid data noise but also keenly captures truly high-frequency interactive target financial business tags during marketing campaigns. This allows the system to accurately define marketing hotspots in a purely data-driven manner, fundamentally solving the problems of subjective lag and audience bias inherent in traditional manual material selection.

[0019] 2. Based on the structured reorganization of application scenarios and basic business parameters by the media generation intelligent agent, the system innovatively constructs a target prompt word template that is highly compatible with external large model interfaces. This solution cleverly bypasses the high computing power cost and long training cycle of building complex deep learning models internally, and uses standardized prompt word engineering to directly drive the generative large model to produce customized incremental candidate media resources, which greatly increases the underlying production throughput and iteration rate of financial marketing assets.

[0020] 3. In response to the extremely stringent compliance audit requirements of the financial industry for audio and video assets, this application incorporates an anti-tampering digital watermark injection and encrypted slice distribution mechanism into the collaborative routing module. By modulating and superimposing a pseudo-random code sequence containing device and verification information into the underlying pixel matrix, the system endows the circulating slices with non-repudiable security attributes. This not only ensures the confidentiality of sensitive unpublished materials during cross-terminal collaborative circulation but also provides a solid technical means of establishing ownership for any subsequent compliance traceability.

[0021] 4. In the design of the workflow approval chain, the system breaks through the traditional unidirectional linear workflow and constructs a human-machine collaborative task routing network that includes state locking and rollback reconstruction mechanisms. When a lower-level node triggers a verification failure instruction, the system can automatically encapsulate the defective asset into a dynamic media optimization demand order and accurately dispatch it to the corresponding internal processing terminal. This closed-loop asynchronous error correction logic effectively avoids the risk of misdelivering unqualified marketing materials and significantly reduces cross-team communication losses.

[0022] 5. The persistent storage of assets and their front-end display are cleverly linked to a real-time listener based on state transition events. Once the review receipt confirms that the material has been put into use, the system not only automatically completes the physical storage of the underlying files, but also dynamically replaces the carousel or recommendation area of ​​the front-end display panel without the user noticing, based on the popularity weight calculated in the early stages. This fully automated process completely breaks down the barriers between underlying data insights and top-level UI presentation, enabling the marketing panel to adaptively update in real time in response to market feedback. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the overall network architecture of the financial marketing internal management system of the present invention; Figure 3 This is a data flow diagram of the present invention; Figure 4 This is a schematic diagram illustrating the principle of video anti-tampering digital watermark injection in this invention; Figure 5 This is a timing diagram of the cooperative routing approval process with a state locking mechanism according to the present invention. Figure 6 This is a schematic diagram of the hardware structure of a computer system according to the present invention. Detailed Implementation

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Please see the appendix Figures 1-6 This invention provides a method, system, and medium for incremental mining and dynamic updating of media resources. The method includes the following steps: acquiring incremental activity logs from a financial business system; calling a preset dynamic data exploration agent to perform rule matching on the incremental activity logs, mining target financial business tags that meet a preset conversion rate threshold, and determining the popularity weight corresponding to the target financial business tags; based on the target financial business tags, triggering a media generation agent to call an external generative large model interface to generate incremental candidate media resources; performing compliance review and transfer on the incremental candidate media resources through a preset collaborative routing module, and acquiring review confirmation receipt logs; after acquiring the review confirmation receipt logs, writing the incremental candidate media resources into a financial industry material library system, and dynamically updating the front-end display panel in the financial industry material library system according to the popularity weights.

[0026] Specifically, the process of acquiring incremental activity logs from the financial business system encompasses the real-time collection and standardized reorganization of underlying heterogeneous data. Based on configured general data collection nodes, the system pulls incremental access log data from the financial business system during marketing campaigns in real time.

[0027] The general data acquisition node is deployed in the front-end interaction layer and back-end routing layer of various applications in the financial business system. This acquisition node continuously captures user click, browsing, and dwell behavior events on various financial activity panels through a non-intrusive instrumentation mechanism or by listening to network traffic in a bypass manner, forming a coherent incremental access log data stream.

[0028] While retrieving incremental access log data, the system simultaneously extracts benefit distribution counter data from the financial business system. This benefit distribution counter data records the precise amount and frequency of financial benefits such as points, vouchers, or trial funds actually issued by the system during the marketing campaign.

[0029] Subsequently, the system cleanses and structurally concatenates the incremental access log data and the rights distribution counter data to construct the incremental activity log. The data cleaning process eliminates invalid machine probe traffic and redundant retry requests, removing interference from dirty data.

[0030] The structured data integration mechanism aligns access metrics and equity consumption metrics from different dimensions to the same timestamp and the same financial activity dashboard identifier. The processed data is transformed into a unified and standardized time-series log format, providing high-dimensional and high-quality underlying data support for subsequent intelligent exploration and rule matching.

[0031] Furthermore, invoking a pre-defined dynamic data exploration agent to perform rule matching on incremental activity logs is a crucial step in achieving purely data-driven marketing decisions. The system inputs the completed incremental activity logs into the dynamic data exploration agent. This agent, as a resident microservice process in memory, continuously listens for and receives structured log stream data.

[0032] By using pre-defined dynamic SQL probing scripts within the dynamic data probing agent, the system aggregates and statistically analyzes incremental activity logs, calculating the real-time conversion rate for each financial activity panel. The dynamic SQL probing scripts group discrete log records according to the unique identifier of each financial activity panel and perform aggregation operations within a defined time observation window.

[0033] In the underlying calculation logic of aggregate statistics, a corresponding time observation window is configured for each financial activity panel. Let the current time observation window be... The total number of incremental access log records obtained by the general data collection node for a certain financial activity panel within this window is [number missing]. The value of the rights distribution counter extracted synchronously is Real-time conversion rate Obtained through the following aggregation calculation model: in, A minimal constant smoothing factor is preset for the system. The technical significance of introducing a minimal constant smoothing factor is to prevent system-level crashes caused by the denominator being zero when network blocking anomalies occur or access volume is at an extreme low within the time observation window, thereby ensuring the high availability and computational robustness of the aggregation statistics mechanism in complex high-concurrency environments.

[0034] The system continuously extracts target financial activity panels with real-time conversion rates greater than or equal to a preset conversion rate threshold. When the comparison detects that the real-time conversion rate of a financial activity panel reaches or exceeds the preset conversion rate threshold, the system determines that the panel has triggered a high density of effective interaction events and possesses extremely high business conversion potential.

[0035] Subsequently, the system identifies the attribute tags corresponding to the target financial activity panel as the target financial business tags, and uses the real-time conversion rate as the popularity weight corresponding to the target financial business tags. Attribute tags represent the specific business domain to which the activity panel belongs, such as wealth management, credit financing, or payment settlement.

[0036] The system uses a weight mapping function to convert real-time conversion rates into popularity weights that the system can recognize. The underlying mathematical expression of this weight mapping logic is as follows: The aforementioned mechanism establishes a strict linear correlation between the popularity weights assigned to business tags by the system and the actual concurrent interaction volume of the financial activity panel. This quantitative weighting method completely eliminates the lag in traditional business personnel's subjective identification of popular marketing targets, and empowers the system to adaptively and accurately anchor target marketing resources based on real customer feedback data.

[0037] In this embodiment, based on the target financial business tags determined above, the system triggers a media generation agent to automatically construct incremental candidate media resources. This process relies on deep collaboration between pre-configured business logic and an external generative large model interface.

[0038] In this invention, the media-generating intelligent agent first extracts financial application scenarios and basic business parameters associated with the target financial business tag from the financial business system. These parameters constitute the business background of the media generation, including but not limited to the asset class preferences of the target customer group, specific financial product fee descriptions, and compliance constraint clauses.

[0039] Subsequently, the intelligent agent generated by the media performs semantic alignment and logical reorganization of the target financial business tags, financial application scenarios, and basic business parameters, assembling them into a target prompt word template. This template, through a pre-defined structured grammar, encapsulates complex financial business logic into a set of instructions that can be efficiently recognized by a generative large model.

[0040] In this embodiment, the system sends the target prompt word template to an external generative large model interface. Upon receiving the template, the large model interface performs inference calculations based on its internal semantic association rules and returns resource data containing financial marketing copy or video data as incremental candidate media resources. This API-based generation model allows the system to obtain high-quality marketing materials without deploying a large-scale computing cluster locally.

[0041] In this invention, for the generated incremental candidate media resources, the system performs compliance review and routing through a preset collaborative routing module. When the incremental candidate media resource is in the form of video data, the system calls a preset video collaborative review agent in the collaborative routing module to perform underlying security reinforcement on the video content.

[0042] Specifically, the video collaborative review agent automatically injects tamper-proof digital watermarks into the incremental candidate media resources. This watermark injection logic is not a simple visual overlay, but rather based on spatial transformation and signal modulation within the image domain.

[0043] In this embodiment, the underlying implementation principle of injecting tamper-proof digital watermarks into video data by the video collaborative review agent involves the splitting and processing of the video frame sequence. The system extracts the video frame sequence. It generates a pseudo-random code sequence as the key base based on the authentication token of the current task. .

[0044] Subsequently, the device identifier and compliance verification code are encoded into a watermark signal sequence using a modulation mechanism. For a given video frame The output data frame after watermarking Follow the image signal superposition model as follows: in, This represents the pixel coordinates in the two-dimensional matrix of the current video frame. This represents the adaptive embedding strength coefficient, which is automatically calculated by the system based on the texture complexity and luminance component of the current frame. It is used to balance the watermark's resistance to attacks and its visual concealment. This indicates the spread spectrum modulation operator, which extends the watermark signal to a wider frequency band, significantly improving the robustness of the watermark during video compression or editing.

[0045] In this embodiment, the video collaborative review agent further generates encrypted review slice links based on authentication tokens. These slice links divide the video data into segments according to a preset time length and attach a dynamically expired timestamp attribute.

[0046] Subsequently, the system sends encrypted review slice links to the corresponding collaborative review terminals. Because the slice links carry tamper-proof digital watermarks generated by the aforementioned overlay model, any unauthorized screen recording or screenshotting will carry traceable identity characteristics, thereby strengthening the security of the flow of financially sensitive materials during the review stage at the technical level.

[0047] In this embodiment, the collaborative routing module continuously monitors the feedback from the collaborative review terminal. If it receives a verification failure instruction returned by the collaborative review terminal based on the encrypted review slice link, it triggers the human-machine collaborative task routing mechanism.

[0048] The system encapsulates the current incremental candidate media resources and verification-failed instructions into a dynamic media optimization request order, and automatically dispatches the order to the target internal processing terminal based on preset business attribute configuration matching rules. This mechanism ensures that flawed materials can be accurately returned to professionals with the appropriate processing permissions for correction.

[0049] In this embodiment, the system receives the revised finalized resource uploaded by the target's internal processing terminal, updates it as an incremental candidate media resource, and then re-triggers the compliance review process. This closed-loop error correction path ensures that the materials ultimately entering the library system have 100% business accuracy and compliance.

[0050] In this invention, the collaborative routing module also utilizes preset notification engine routing nodes to exert strong control over the flow process. The system packages incremental candidate media resources into a compliance review message with mandatory confirmation attributes and pushes it to the designated administrator terminal.

[0051] Simultaneously with the message push, the collaborative routing module locks the incremental candidate media resource at the system level. Resources in a locked state cannot be accessed by third parties or prematurely added to the database. Upon receiving a confirmation command from the designated administrator terminal, the system unlocks the state and officially generates an audit confirmation receipt log.

[0052] In this embodiment, after obtaining the review confirmation log, the system performs persistent storage and front-end mapping of the assets. The system monitors the status transition events of incremental candidate media resources in real time. When the resource status changes from "under review" to "deployed", the resource is persistently written into the financial industry material library system.

[0053] Finally, based on the popularity weights calculated in the previous steps, the system dynamically updates the front-end display panel. The system dynamically replaces the incremental candidate media resources in the preset carousel display area or recommended resource display area of ​​the front-end display panel.

[0054] Because this update process allocates resources based on quantified popularity weights, materials with higher weights will be prioritized for placement in core display positions. This logic achieves a fully automated closed loop for financial marketing materials, from data mining and intelligent generation to security review and precise targeting.

[0055] In this embodiment, based on the aforementioned incremental mining and dynamic updating method for media resources, the present invention also provides a physical hardware system capable of implementing this method, namely, an incremental mining and dynamic updating system for media resources. The hardware architecture of this system is specifically designed for high-concurrency financial data processing and the secure transfer of multimedia files.

[0056] In this invention, the system physically comprises interconnected memory and processor. Data exchange and instruction transmission between the two occur via a high-speed internal system bus, ensuring seamless transfer of front-end log collection to back-end large model generation resources.

[0057] In this embodiment, the memory is used to store various heterogeneous data generated during the operation of computer programs and systems. Specifically, the memory not only has a dedicated instruction storage area to retain the executable code of dynamic data exploration agents, media generation agents, and cooperative routing modules, but also a protected data cache area for temporarily storing incremental activity logs, encrypted slice links, and audit confirmation receipt logs containing state machine locks in the financial business system.

[0058] In this invention, the processor, as the core computing power control hub of the system, is used to read and execute the computer program stored in the memory to implement all the steps of the aforementioned incremental mining and dynamic updating method for media resources. The processor transforms abstract business processes into specific electrical signal processing and logic gate operations through underlying hardware resource scheduling.

[0059] Specifically, when the processor performs a data probing task, its underlying arithmetic logic unit (ALU) directly participates in the conversion rate aggregation calculation model. The physical calculations. The processor retrieves the current time observation window from the memory cache. Total number of incremental access log records within Data value of rights distribution counter And in the arithmetic logic unit, a system-preset minimal constant smoothing factor is incorporated. The division command is executed to directly output the real-time conversion rate, which reflects the concurrent interaction volume of financial business tags. This serves as the hardware-level trigger for the system to execute subsequent heat weight scheduling.

[0060] In this embodiment, during the video compliance streaming stage, the processor's internal graphics computing core or dedicated multimedia instruction set is activated to execute the underlying image signal overlay model. The processor extracts video frames. Corresponding two-dimensional matrix pixel coordinates The pixel value at that location is read, and the adaptive embedding strength coefficient is read. Subsequently, the processor control logic gates combine the device identifier with the watermark signal sequence encoded by the compliance check code. After spreading modulation operator The key base generated with the pseudo-random code sequence After modulation, the data is physically superimposed onto the original pixel values ​​to generate and output a data frame with an injected anti-tampering watermark. This pixel-level operation at the processor instruction level gives the transfer slices an anti-counterfeiting property that makes them impossible to peel off.

[0061] Based on the same inventive concept, this embodiment also provides a computer-readable storage medium as a physical carrier for solidifying the above-mentioned innovative technical solution.

[0062] In this invention, the computer-readable storage medium stores computer instructions. These computer instructions are generated by a compiler from the source code implementing agent scheduling, external generative large model interface calling protocol, and state machine listening mechanism, and are persistently burned or recorded in the physical storage unit of the medium in the form of machine code or intermediate bytecode.

[0063] In this embodiment, when the computer instructions encapsulated in the computer-readable storage medium are read and executed by the processor within the electronic device, the underlying hardware of the electronic device can be forced to cooperate, thus fully implementing all steps of the aforementioned incremental mining and dynamic updating method for media resources. This medium is typically a non-volatile storage device, ensuring that complex financial marketing rule configurations and secure routing logic can still be restored and operate stably without loss after a system power outage or restart.

[0064] In this embodiment, based on the same inventive concept, the present invention also provides a computer-readable storage medium. This computer-readable storage medium, as a physical entity carrying the aforementioned logic for the flow and dynamic updating of financial marketing media resources, internally stores compiled and optimized computer instructions.

[0065] In this invention, the computer-readable storage medium specifically manifests as non-volatile memory, magnetic hard disk, solid-state drive, or a distributed storage array partitioned within a cloud server architecture. These physical media, through magnetic pole orientation, optical concavity / convexity characteristics, or the charge residence state of floating-gate transistors, transform high-dimensional business routing logic and underlying complex mathematical operations into persistently stored binary machine code or bytecode.

[0066] In this embodiment, when the computer instructions stored in the computer-readable storage medium are read and executed by the corresponding electronic device or processing unit, a dynamic data probing process is first instantiated in the system memory space. This instruction drives the hardware data interface to continuously retrieve incremental access log data and equity issuance counter data from the financial business system, and opens a time observation window in the processor's register. The instruction further guides the arithmetic logic unit to execute a predefined aggregation calculation model: in, This represents the total number of incremental access log data records extracted within the time observation window. The data value of the rights and benefits distribution counter is extracted synchronously. This is a very small constant smoothing factor pre-programmed into the media storage sectors. The execution of this calculation instruction enables the hardware to extract a real-time conversion rate reflecting the actual concurrent interaction volume of the business from the disordered log stream. And accurately map it to the heat weight that triggers the subsequent generation scheduling mechanism.

[0067] In this invention, the computer instructions further include an executable code library for triggering the media generation agent and the collaborative routing module. When this part of the instructions is executed, the system semantically concatenates the target financial business tags, financial application scenarios, and basic business parameters in memory to construct a target prompt word template and establish a secure communication socket with the external generative large model interface to pull incremental candidate media resources. Subsequently, the instructions force the system to enable state locking and human-machine collaborative task routing mechanisms in the internal network flow nodes to ensure that any media assets that have not received an audit confirmation receipt log are strictly isolated from the inbound queue of the financial industry material library system.

[0068] In this embodiment, to address the compliance and anti-counterfeiting requirements of video media data, the computer-readable storage medium specifically encapsulates an instruction set for image domain transformation. When this low-level anti-tampering processing instruction is executed, the processor extracts video frames one by one. and the pixel coordinates of its two-dimensional matrix And construct the key base based on the pre-set random number generation function in the medium. The instructions then drive the hardware arithmetic unit to execute the image signal superposition model: in, The embedding strength coefficient is adaptively calculated for the system. This is a watermark signal sequence containing device identification and compliance verification codes. This represents the spread spectrum modulation operator used for bandwidth expansion. By executing this mathematical operation instruction, the system outputs a data frame with an injected, tamper-proof digital watermark. This allows for the construction of encrypted review slice links, fundamentally locking the anti-counterfeiting and traceability chain in the material circulation process.

[0069] In this invention, when the computer instruction detects a state transition event triggered by an audit confirmation receipt log via the internal event bus, the instruction immediately drives the underlying disk controller to perform an asynchronous I / O write operation, formally and persistently writing the incremental candidate media resources into the financial industry material library system. Simultaneously, the instruction dynamically replaces document object model nodes or rendering data sources in the preset area of ​​the front-end display panel according to the popularity weights maintained in memory. This storage medium, through its meticulously arranged internal computer instructions, completely establishes an automated business loop from intelligent exploration of underlying heterogeneous data and external model-driven generation to adaptive updates of the top-level front-end.

[0070] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An incremental mining and dynamic analysis of media resources, characterized in that, The method, applied to an internal management system for financial marketing, includes: Obtain incremental activity logs from the financial business system; The preset dynamic data exploration agent is invoked to perform rule matching on the incremental activity logs, to discover target financial business tags that meet the preset conversion rate threshold, and to determine the popularity weight corresponding to the target financial business tags. Based on the target financial business tag, a media generation agent is triggered to call an external generative large model interface to generate incremental candidate media resources according to the target financial business tag. The incremental candidate media resources are subjected to compliance review and circulation through a preset collaborative routing module, and the review confirmation receipt log is obtained. After obtaining the review confirmation log, the incremental candidate media resources are written into the financial industry material library system, and the front-end display panel in the financial industry material library system is dynamically updated according to the popularity weight.

2. The method for incremental mining and dynamic updating of media resources according to claim 1, characterized in that, The acquisition of incremental activity logs from the financial business system includes: Based on the configured general data collection nodes, the incremental access log data of the financial business system during the marketing campaign is pulled in real time. Synchronously extract the rights issuance counter data from the financial business system; The incremental access log data and the rights distribution counter data are cleaned and structurally combined to construct the incremental activity log.

3. The method for incremental mining and dynamic updating of media resources according to claim 1, characterized in that, The process involves invoking a pre-defined dynamic data exploration agent to perform rule matching on the incremental activity logs, identifying target financial business tags that meet a pre-defined conversion rate threshold, and determining the popularity weight corresponding to the target financial business tags, including: The incremental activity logs are input into the dynamic data exploration agent; The incremental activity logs are aggregated and statistically analyzed using the preset dynamic SQL exploration script in the dynamic data exploration agent to calculate the real-time conversion rate of each financial activity panel. Extract the target financial activity panel whose real-time conversion rate is greater than or equal to the preset conversion rate threshold, determine the attribute tag corresponding to the target financial activity panel as the target financial business tag, and use the real-time conversion rate as the popularity weight corresponding to the target financial business tag.

4. The method for incremental mining and dynamic updating of media resources according to claim 1, characterized in that, Based on the target financial business tag, a media generation agent is triggered to call an external generative large model interface to generate incremental candidate media resources, including: Extract the financial application scenarios and basic business parameters associated with the target financial business tag from the financial business system; The intelligent agent generated by the media splices and assembles the target financial business label, the financial application scenario and the basic business parameters into a target prompt word template. The target prompt word template is sent to the external generative large model interface, and the resource data containing financial marketing copy or video data returned by the external generative large model interface based on the target prompt word template is received as the incremental candidate media resource.

5. The method for incremental mining and dynamic updating of media resources according to claim 1, characterized in that, The process of performing compliance review and transfer of the incremental candidate media resources through a preset collaborative routing module, and obtaining review confirmation receipt logs, includes: When the incremental candidate media resource is video data, the preset video collaborative review agent in the collaborative routing module is invoked; The video collaborative review agent automatically injects tamper-proof digital watermarks into the incremental candidate media resources and generates encrypted review slice links based on authentication tokens. The encrypted review slice link is sent to the corresponding collaborative review terminal, and the review confirmation log returned by the collaborative review terminal based on the encrypted review slice link is received.

6. The method for incremental mining and dynamic updating of media resources according to claim 5, characterized in that, Before receiving the review confirmation log returned by the collaborative review terminal based on the encrypted review slice link, the method further includes: If the collaborative review terminal receives a verification failure instruction returned based on the encrypted review slice link, the human-machine collaborative task routing mechanism is triggered. The incremental candidate media resources and the verification failure instruction are encapsulated into a dynamic media optimization requirement order; Based on preset business attribute configuration matching rules, the dynamic media optimization demand orders are automatically dispatched to the target internal processing terminal; The system receives the revised finalized resource uploaded by the target's internal processing terminal, uses the revised finalized resource as the incremental candidate media resource, and re-triggers the compliance review process for the incremental candidate media resource.

7. The method for incremental mining and dynamic updating of media resources according to claim 1, characterized in that, The process of performing compliance review and transfer of the incremental candidate media resources through a preset collaborative routing module, and obtaining review confirmation receipt logs, includes: The incremental candidate media resources are packaged into a compliance review message with mandatory confirmation attribute by using the preset notification engine routing node in the collaborative routing module. The compliance review message is pushed to the designated administrator terminal, and the incremental candidate media resources are locked in the status of the collaborative routing module. When a confirmation instruction is received from the designated administrator terminal regarding the compliance audit message, the status lock is released, and the audit confirmation receipt log is generated.

8. The method for incremental mining and dynamic updating of media resources according to claim 1, characterized in that, After obtaining the review confirmation log, the incremental candidate media resources are written into the financial industry material library system, and the front-end display panel in the financial industry material library system is dynamically updated according to the popularity weight, including: Real-time monitoring of the state transition events of the incremental candidate media resources; When the status of the incremental candidate media resource is changed to the "deployed" status based on the review confirmation receipt log according to the status transition event, the incremental candidate media resource is officially and persistently written into the financial industry material library system. Based on the popularity weight, the incremental candidate media resources are dynamically replaced in the preset carousel display area or recommended resource display area in the front-end display panel, thus completing the dynamic update of the front-end display panel.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 8.

10. A system for incremental mining and dynamic updating of media resources, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the method as described in any one of claims 1 to 8.

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