Bank intelligent printing device and consumable management platform thereof

Abstract

This invention relates to the field of artificial intelligence technology and discloses a bank intelligent printing device and its consumable management platform. The platform includes: an intelligent monitoring module for real-time monitoring of the status of each bank's intelligent printing device, tracking consumable reserves, and adjusting for environmental adaptation; an AI prediction module for using a multimodal spatiotemporal fusion model to predict consumable usage data from a preset time period prior to the current time period, outputting a consumable demand list and failure risk for the future preset time period; and a dynamic inventory management module for automatically placing consumable orders when inventory reaches a critical value and performing barcode verification upon consumable receipt to intercept mismatched consumables. Through these methods, this invention achieves fully automated intelligent management and control of bank intelligent printing devices, effectively reducing failure rates and inventory costs.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to a smart printing device for banks and its consumables management platform. Background Technology

[0002] Currently, bank tellers need to print vouchers, customers need to print bank statements themselves, and back-office staff need to print reports when handling transactions. Traditional dot matrix and laser printers have the following problems in these scenarios: high equipment failure rate, with frequent paper jams and broken needles, requiring tellers to pause operations to handle the issues, which frustrates customers. In terms of cost, printer ribbons wear out quickly, and with the large volume of daily transactions in banks, consumable expenses are substantial. Maintenance costs are also high, especially for older printers, where spare parts are difficult to find. Environmental issues are increasingly important. Traditional printing uses so much paper and requires the disposal of waste ribbon cartridges and toner cartridges, which is both environmentally unfriendly and incurs additional disposal costs.

[0003] Therefore, the management of consumables for bank printing equipment directly relates to supply chain efficiency and cost control. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention provide a bank intelligent printing device and its consumable management platform to solve the problems of low intelligence, low efficiency and high cost of bank printing devices in the prior art.

[0005] According to one aspect of the present invention, a consumables management platform for a bank's smart printing equipment is provided, the platform comprising:

[0006] The intelligent monitoring module is used to detect the status of each bank's intelligent printing equipment in real time, track the remaining consumables, and adjust for environmental adaptation.

[0007] The AI ​​prediction module is used to predict consumable usage data from a preset time period prior to the current time period using a multimodal spatiotemporal fusion model, outputting a list of consumable demand and fault risks for the future preset time period. The multimodal spatiotemporal fusion model includes a fault prediction sub-model, a spatiotemporal convolutional network, a graph neural network, an attention mechanism, a feature embedding layer, a spatiotemporal fusion layer, and a multi-task prediction head. The multimodal spatiotemporal fusion model is trained using training data, including historical print volume time-series data, historical business calendar data, branch network topology data, equipment status data, and external factor data. Before training, the historical usage rate, business calendar data, branch network topology data, equipment status data, and external factor data are differentially encrypted according to their sensitivity levels. The sensitivity levels are dynamically adjusted in each training round through feature contribution analysis. The external factor data includes weather impact data.

[0008] The dynamic inventory management module is used to automatically place an order for consumables when the inventory reaches a critical value, and to perform barcode verification when consumables are received into the warehouse to intercept mismatched consumables.

[0009] In an alternative embodiment, the platform further includes a training module for:

[0010] Differential encryption is applied to the historical print volume time-series data, historical business calendar data, branch topology data, equipment status data, and external factor data to obtain encrypted historical print volume time-series data, encrypted historical business calendar data, encrypted branch topology data, encrypted equipment status data, and encrypted external factor data.

[0011] Encrypted historical print volume time-series data, encrypted historical business calendar data, encrypted branch topology data, encrypted equipment status data, and encrypted external factor data are processed by feature engineering to obtain feature-enhanced historical print volume time-series data, feature-enhanced historical business calendar data, feature-enhanced branch topology data, feature-enhanced equipment status data, and feature-enhanced external factor data.

[0012] The feature-enhanced historical print volume time-series data is input into the spatiotemporal convolutional network, the feature-enhanced historical business calendar data is input into the attention mechanism, the feature-enhanced network topology relationship data is input into the graph neural network, and the feature-enhanced external factor data is input into the feature embedding layer. The spatiotemporal fusion layer receives the outputs of the spatiotemporal convolutional network, the attention mechanism, and the feature embedding layer, and performs feature fusion. Finally, the multi-task prediction head outputs the consumable prediction result, which includes the predicted consumable demand.

[0013] The enhanced equipment status data is input into the fault prediction sub-model to obtain the predicted fault risk results;

[0014] The loss function is calculated based on the consumables prediction results and the predicted failure risk results, the parameters are adjusted, and the feature contribution of historical print volume time series data, historical business calendar data, network topology relationship data, equipment status data and external factor data is calculated. The sensitivity level is adjusted based on the feature contribution.

[0015] Based on the adjusted parameters and the adjusted sensitivity level, iterative training continues until a well-trained multimodal spatiotemporal fusion model is obtained.

[0016] In one optional approach, the encryption of historical print volume time-series data, historical business calendar data, and external factor data using differentiated encryption processing to obtain encrypted historical print volume time-series data, encrypted historical business calendar data, encrypted branch topology data, encrypted equipment status data, and encrypted external factor data further includes:

[0017] Homomorphic encryption is applied to the historical print volume time-series data and historical business calendar data, with separate keys for each line, to obtain encrypted historical print volume time-series data.

[0018] The network topology data is encrypted using graph-structure obfuscation and central key custody to obtain encrypted network topology data.

[0019] The device status data is encrypted at the hardware level using a device chip fuse key mechanism to obtain the encrypted device status data.

[0020] Differential privacy computation is applied to the external factor data to obtain encrypted external factor data.

[0021] In one alternative approach, the feature-enhanced historical print volume time-series data is input into the spatiotemporal convolutional network, the feature-enhanced historical business calendar data is input into the attention mechanism, the feature-enhanced network topology data is input into the anonymous graph neural network, and the feature-enhanced external factor data is input into the feature embedding layer. The spatiotemporal fusion layer receives the outputs of the spatiotemporal convolutional network, the attention mechanism, and the feature embedding layer, and performs feature fusion. The multi-task prediction head then outputs a consumables prediction result, which includes a predicted consumables demand, and further includes:

[0022] When the feature-enhanced historical printing volume time-series data is input into the spatiotemporal convolutional network for training, a training method using encrypted state aggregation statistics is adopted.

[0023] After inputting the feature-enhanced historical business calendar data into the attention mechanism, it is trained using a training method that aggregates and statistically analyzes encrypted states.

[0024] Feature-enhanced network topology data is input into an anonymous graph neural network for training;

[0025] Before inputting the feature-enhanced external factor data into the feature embedding layer, the encrypted external factor data is locally decrypted at the edge nodes before training.

[0026] In an alternative embodiment, the intelligent monitoring module is further configured to:

[0027] By detecting heartbeats in real time, the status of each bank's smart printing device is collected at preset intervals, and abnormal bank smart printing devices are marked.

[0028] For the smart printing devices of various banks, the remaining percentage of ink cartridges or toner cartridges is updated every preset number of pages printed, and an alarm is issued when the toner level is lower than the preset level.

[0029] The printing speed and anti-paper jam mode are dynamically adjusted based on temperature and humidity data.

[0030] In an alternative embodiment, the dynamic inventory management module is further configured to:

[0031] When consumables are placed into the bank's smart printing device, the bank's smart printing device scans the NFC encryption tag of the consumables; wherein, the bank's smart printing device is equipped with an NFC encryption tag reading module;

[0032] The printhead of the bank's smart printing equipment verifies the NFC encrypted tag and matches the device model.

[0033] An alarm is issued when there is a mismatch to prevent mismatches.

[0034] In an alternative embodiment, the platform further includes a latent fault detection module, used for:

[0035] Collect the current operating parameters of the bank's smart printing equipment; the current operating parameters include mechanical vibration parameters, thermal distribution parameters, and circuit characteristic parameters.

[0036] Wavelet packet energy entropy analysis is used to determine whether the mechanical vibration parameters exceed a preset vibration threshold.

[0037] Convolutional temperature field reconstruction was used to analyze and determine whether the local temperature difference of the bank's smart printing equipment was abnormal.

[0038] Fourier spectrum kurtosis is used to detect current ripple in the circuit characteristic parameters to determine whether the circuit is abnormal.

[0039] In an alternative embodiment, the platform further includes a cost control module for:

[0040] Perform printing behavior detection, identify paper waste, and generate prompt messages;

[0041] The system calculates the cost information of each smart bank printing device according to a preset cycle, generates cost reports for each smart printing device, and alerts users of smart printing devices that exceed the target cost.

[0042] In one alternative approach, the platform further includes a blockchain audit token module for:

[0043] Record the circulation information of consumables in the blockchain to trace the circulation of ink cartridges or toner cartridges throughout their life cycle;

[0044] The difference between the actual printing quantity and the nominal value of consumables is recorded in the blockchain. When the difference exceeds a preset difference threshold, a claim is initiated.

[0045] Record consumable requisition information in the blockchain.

[0046] According to another aspect of the present invention, a smart printing device for banks is provided, comprising: the smart printing device for banks includes the aforementioned consumables management platform.

[0047] This invention employs an intelligent monitoring module to monitor the status of each bank's smart printing equipment in real time, track consumable reserves, and adjust for environmental adaptation. An AI prediction module uses a multimodal spatiotemporal fusion model to predict consumable usage data from a preset time period prior to the current time period, outputting a consumable demand list and failure risk for the future preset time period. A dynamic inventory management module automatically places an order for consumables when inventory reaches a critical value and performs barcode verification upon consumable receipt to intercept mismatched consumables. This achieves fully automated intelligent management and control of bank smart printing equipment, effectively reducing failure rates and inventory costs. Furthermore, considering the privacy of printing data in the smart bank printing system, hierarchical encryption is applied to the data acquisition and training process for model training, effectively preventing data leakage.

[0048] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0049] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0050] Figure 1 This invention illustrates a schematic diagram of the consumables management platform for a bank's intelligent printing equipment provided in an embodiment of the present invention.

[0051] Figure 2 This paper illustrates the architecture of a multimodal spatiotemporal fusion model in the consumables management platform of a bank's intelligent printing equipment provided in an embodiment of the present invention.

[0052] Figure 3 A schematic diagram of the structure of the bank's intelligent printing device provided in an embodiment of the present invention is shown;

[0053] Figure 4 This diagram illustrates the interaction between a bank's intelligent printing device and a consumables management platform, according to another embodiment of the present invention. Detailed Implementation

[0054] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0055] Figure 1 This diagram illustrates the structure of a consumables management platform 100 for a bank's intelligent printing device according to an embodiment of the present invention. The consumables management platform 100 can be, for example... Figure 3 As shown, the software system set in a centrally controlled bank smart printing device 10 can also be, for example... Figure 4 The image shows a cloud-based consumables management platform that interacts with multiple bank smart printing devices. For example... Figure 1 As shown, the platform includes the following modules:

[0056] The intelligent monitoring module 110 is used to monitor the status of each bank's intelligent printing equipment in real time, track the remaining consumables, and adjust for environmental adaptation.

[0057] The intelligent monitoring module 110 is specifically used to: collect the status of each bank's smart printing equipment at preset intervals through real-time heartbeat detection, and mark abnormal smart printing equipment. Specifically, it can collect the equipment status of each bank's smart printing equipment every minute: online / offline, print queue, and error code. Abnormal smart printing equipment is automatically marked. For example, if a bank's smart printing equipment remains offline for more than 15 minutes, it indicates that the smart printing equipment is abnormal.

[0058] The intelligent monitoring module 110 is specifically used for: updating the remaining percentage of ink cartridges or toner cartridges for each bank's intelligent printing equipment every preset number of pages printed, and issuing an alert when the toner level is lower than the preset level. In this embodiment of the invention, the remaining percentage of the toner cartridge / ink drum is updated every 50 pages printed, and a yellow alert icon flashes when the toner level is lower than 20%. The printing speed and anti-paper jam mode are dynamically adjusted based on temperature and humidity data. Specifically, by installing chips or sensors on the toner cartridge / ink drum, the usage of consumables is monitored in real time, and the number of pages printed is recorded by a counter built into the printer.

[0059] The AI ​​prediction module 120 is used to make predictions based on the consumable usage data of a preset time period before the current time period using a multimodal spatiotemporal fusion model, and outputs a list of consumable demand and failure risks for the future preset time period.

[0060] Among them, such as Figure 2As shown, the multimodal spatiotemporal fusion model includes a fault prediction sub-model, a spatiotemporal convolutional network, a graph neural network, an attention mechanism, a feature embedding layer, a spatiotemporal fusion layer, and a multi-task prediction head. The multimodal spatiotemporal fusion model is trained using training data. The training data includes historical print volume time-series data, historical business calendar data, branch network topology data, equipment status data, and external factor data. Before training, based on the sensitivity level of the data, the historical usage rate, business calendar data, branch network topology data, equipment status data, and external factor data are subjected to differentiated encryption processing, and the sensitivity level is dynamically adjusted through feature contribution analysis during each training round. The external factor data includes weather impact data.

[0061] The platform also includes a training module.

[0062] The training module is used to encrypt historical print volume time-series data, historical business calendar data, branch topology data, equipment status data, and external factor data using differentiated encryption processes, resulting in encrypted historical print volume time-series data, encrypted historical business calendar data, encrypted branch topology data, encrypted equipment status data, and encrypted external factor data.

[0063] Specifically, historical print volume time-series data is accumulated, and printer historical logs are collected. For example, at least 60 days of continuous collection of bank smart printing equipment operation data are collected, including: daily total print volume and color print volume, consumable balance change curves, and equipment start-up and shutdown counts. The collected time-series data is standardized and missing values ​​are linearly imputed to construct a feature sequence with a 30-day period.

[0064] This includes collecting historical business calendar data from various banks' smart printing devices from the bank's core system, including: end-of-month settlement date markings, countdowns to new branch openings, and pre-booked printing tasks for marketing activities.

[0065] In this embodiment of the invention, a branch topology relationship data for all bank smart printing devices is constructed based on the location, device information, and quantity of the smart printing devices in each bank branch. The device status data here includes historical sensor data of the devices (including fuser temperature, gear vibration spectrum, power module current harmonics, etc.) and toner cartridge usage information (such as the number of pages printed). External factor data includes environmental humidity and ambient temperature information.

[0066] The training module is also used to: For the historical print volume time-series data and historical business calendar data, both of which are medium-sensitivity data, homomorphic encryption is used, and key management employs independent keys for each branch, respectively encrypting to obtain encrypted historical print volume time-series data and encrypted historical business calendar data. Specifically, for the branch topology relationship data, which has a high data sensitivity level, graph structure obfuscation (K-anonymization) encryption is used, and key management employs central key escrow, resulting in encrypted branch topology relationship data. For the device status data, hardware-level (SGX / TEE) encryption is used, as its data sensitivity level is extremely high; therefore, a device chip meltdown key mechanism is employed for encryption, and key management uses the device chip meltdown key, resulting in encrypted device status data through hardware-level encryption. For the external factor data, due to its low data sensitivity level, differential privacy computation can be used to obtain encrypted external factor data.

[0067] The training module is also used to: perform feature engineering on encrypted historical print volume time-series data, encrypted historical business calendar data, encrypted branch network topology data, encrypted equipment status data, and encrypted external factor data to obtain feature-enhanced historical print volume time-series data, feature-enhanced historical business calendar data, feature-enhanced branch network topology data, feature-enhanced equipment status data, and feature-enhanced external factor data. Specifically, the feature-enhanced branch network topology data is enhanced using graph structure encoding and adjacency matrix normalization. The encrypted historical business calendar data is processed using one-hot encoding and time-attenuation weighting. Furthermore, the encrypted equipment status data is subjected to wavelet transform and time-frequency domain feature extraction to obtain feature-enhanced equipment status data.

[0068] The training module is further configured to: input the feature-enhanced historical print volume time-series data into the spatiotemporal convolutional network; input the feature-enhanced historical business calendar data into the attention mechanism; input the feature-enhanced network topology relationship data into the graph neural network; input the feature-enhanced external factor data into the feature embedding layer; the spatiotemporal fusion layer receives the outputs of the spatiotemporal convolutional network, the attention mechanism, and the feature embedding layer, and after feature fusion, the multi-task prediction head outputs the consumable prediction result, which includes the predicted consumable demand. In this embodiment of the invention, a federated learning architecture is used to train the model on encrypted data.

[0069] The training module is also used to: input feature-enhanced equipment status data into the fault prediction sub-model to obtain the predicted fault risk result.

[0070] The training module is further used to: calculate loss functions based on the consumable prediction results and the predicted failure risk results, adjust parameters, and calculate the feature contribution of historical print volume time-series data, historical business calendar data, network topology data, equipment status data, and external factor data, and adjust the sensitivity level based on the feature contribution. In this embodiment of the invention, the contribution of key features is determined through SHAP value analysis. The main loss function can be a quantile loss function, and the auxiliary loss function can be a physical constraint loss function to ensure that the consumable conservation law is satisfied and a fluctuation smoothing penalty is used to suppress drastic fluctuations in the predicted values. This embodiment of the invention does not impose specific limitations.

[0071] The training module is also used to: continue iterative training based on the adjusted parameters and the adjusted sensitivity level until a trained multimodal spatiotemporal fusion model is obtained.

[0072] After training a multimodal spatiotemporal fusion model in the training module, the AI ​​prediction module 120 inputs consumable usage data from a preset time period prior to the current time period into the spatiotemporal fusion model to make predictions, obtaining a list of consumable needs and failure risks for the future preset time period. For example, it can provide a list of consumable needs for each bank's smart printing equipment for the next 7 days, including the quantity and model of toner cartridges or ink cartridges required, as well as the failure rate.

[0073] The dynamic inventory management module 130 is used to automatically place an order for consumables when the inventory reaches a critical value, and to perform barcode verification when consumables are received into the warehouse to intercept mismatched consumables.

[0074] When consumables are inserted into the bank's smart printing device, the device scans the NFC encryption tag on the consumables. The smart printing device is equipped with an NFC encryption tag reading module. The print head of the smart printing device verifies the NFC encryption tag and matches the device model. If there is a mismatch, an alarm is issued to avoid mismatch.

[0075] In this embodiment of the invention, the platform further includes a latent fault detection module, used to: collect the current operating parameters of the bank's smart printing equipment; the current operating parameters include mechanical vibration parameters, thermal distribution parameters, and circuit characteristic parameters. Wavelet packet energy entropy analysis is used to determine whether the mechanical vibration parameters exceed a preset vibration threshold. Convolutional temperature field reconstruction is used to analyze and determine whether the local temperature difference of the bank's smart printing equipment is abnormal. Fourier spectral kurtosis is used to detect current ripple in the circuit characteristic parameters to determine whether the circuit is abnormal.

[0076] The platform also includes a cost control module, which is used to detect printing behavior, identify paper waste, and generate prompts; it also calculates the cost information of each smart bank printing device according to a preset cycle, generates cost reports for each smart printing device, and prompts information on smart printing devices that exceed the target cost.

[0077] In this embodiment of the invention, the platform further includes a blockchain audit token module, used to record the circulation information of consumables in the blockchain, trace the circulation of ink cartridges or toner cartridges throughout their life cycle; record the difference between the actual printing volume and the nominal value of consumables in the blockchain, and initiate a claim when the difference is greater than a preset difference threshold; and record consumable collection information in the blockchain.

[0078] According to another aspect of the present invention, a smart printing device for banks is provided, comprising: the smart printing device for banks includes the aforementioned consumables management platform.

[0079] This invention employs an intelligent monitoring module to monitor the status of each bank's smart printing equipment in real time, track consumable reserves, and adjust for environmental adaptation. An AI prediction module uses a multimodal spatiotemporal fusion model to predict consumable usage data from a preset time period prior to the current time period, outputting a consumable demand list and failure risk for the future preset time period. A dynamic inventory management module automatically places an order for consumables when inventory reaches a critical value and performs barcode verification upon consumable receipt to intercept mismatched consumables. This achieves fully automated intelligent management and control of bank smart printing equipment, effectively reducing failure rates and inventory costs. Furthermore, considering the privacy of printing data in the smart bank printing system, hierarchical encryption is applied to the data acquisition and training process for model training, effectively preventing data leakage.

[0080] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the content of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0081] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0082] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0083] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0084] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A consumables management platform for intelligent printing equipment in banks, characterized in that, The platform includes: The intelligent monitoring module is used to detect the status of each bank's intelligent printing equipment in real time, track the remaining consumables, and adjust for environmental adaptation. The AI ​​prediction module is used to predict consumable usage data from a preset time period prior to the current time period using a multimodal spatiotemporal fusion model, outputting a list of consumable demand and fault risks for the future preset time period. The multimodal spatiotemporal fusion model includes a fault prediction sub-model, a spatiotemporal convolutional network, a graph neural network, an attention mechanism, a feature embedding layer, a spatiotemporal fusion layer, and a multi-task prediction head. The outputs of the fault prediction sub-model, spatiotemporal convolutional network, graph neural network, attention mechanism, and feature embedding layer are connected to the spatiotemporal fusion layer, and the outputs of the spatiotemporal fusion layer and the fault prediction sub-model are connected to the multi-task prediction head. The multimodal spatiotemporal fusion model is trained using training data. The training data includes historical print volume time-series data, historical business calendar data, branch network topology data, equipment status data, and external factor data. Before training, the historical print volume time-series data, historical business calendar data, branch network topology data, equipment status data, and external factor data are differentially encrypted according to their sensitivity levels. The sensitivity levels are dynamically adjusted in each training round through feature contribution analysis. The external factor data includes weather impact data. The platform also includes a training module, used for: encrypting historical print volume time-series data, historical business calendar data, branch network topology data, equipment status data, and external factor data using differentiated encryption processing to obtain encrypted historical print volume time-series data, encrypted historical business calendar data, encrypted branch network topology data, encrypted equipment status data, and encrypted external factor data; performing feature engineering processing on the encrypted historical print volume time-series data, encrypted historical business calendar data, encrypted branch network topology data, encrypted equipment status data, and encrypted external factor data to obtain feature-enhanced historical print volume time-series data, feature-enhanced historical business calendar data, feature-enhanced branch network topology data, feature-enhanced equipment status data, and feature-enhanced external factor data; inputting the feature-enhanced historical print volume time-series data into the spatiotemporal convolutional network, and then processing the feature-enhanced historical business calendar data... Historical data is input into the attention mechanism, feature-enhanced network topology data is input into the graph neural network, and feature-enhanced external factor data is input into the feature embedding layer. The spatiotemporal fusion layer receives the outputs of the spatiotemporal convolutional network, the attention mechanism, the graph neural network, and the feature embedding layer, and performs feature fusion. The multi-task prediction head outputs the consumable prediction result, which includes the predicted consumable demand. Feature-enhanced equipment status data is input into the fault prediction sub-model to obtain the predicted fault risk result. The loss function is calculated based on the consumable prediction result and the predicted fault risk result, and the parameters are adjusted. The feature contribution of historical print volume time-series data, historical business calendar data, network topology data, equipment status data, and external factor data is calculated, and the sensitivity level is adjusted based on the feature contribution. Iterative training continues based on the adjusted parameters and the adjusted sensitivity level until a well-trained multimodal spatiotemporal fusion model is obtained. The dynamic inventory management module is used to automatically place an order for consumables when the inventory reaches a critical value, and to perform barcode verification when consumables are received into the warehouse to intercept mismatched consumables.

2. The platform according to claim 1, characterized in that, The historical print volume time-series data, historical business calendar data, branch network topology data, equipment status data, and external factor data are encrypted using differentiated encryption processing to obtain encrypted historical print volume time-series data, encrypted historical business calendar data, encrypted branch network topology data, encrypted equipment status data, and encrypted external factor data, further including: Homomorphic encryption is applied to the historical print volume time-series data and historical business calendar data to obtain encrypted historical print volume time-series data and encrypted historical business calendar data, respectively. The network topology data is encrypted using graph-structure obfuscation and central key custody to obtain encrypted network topology data. The device status data is encrypted at the hardware level using a device chip fuse key mechanism to obtain the encrypted device status data. Differential privacy computation is applied to the external factor data to obtain encrypted external factor data.

3. The platform according to claim 2, characterized in that, The process involves inputting the enhanced historical print volume time-series data into the spatiotemporal convolutional network, the enhanced historical business calendar data into the attention mechanism, the enhanced network topology data into the graph neural network, and the enhanced external factor data into the feature embedding layer. The spatiotemporal fusion layer receives the outputs from the spatiotemporal convolutional network, the attention mechanism, the graph neural network, and the feature embedding layer, and performs feature fusion. Finally, the multi-task prediction head outputs the consumables prediction result, which includes predicted consumables demand, and further includes: When the feature-enhanced historical printing volume time-series data is input into the spatiotemporal convolutional network for training, a training method using encrypted state aggregation statistics is adopted. After inputting the feature-enhanced historical business calendar data into the attention mechanism, it is trained using a training method that aggregates and statistically analyzes encrypted states. Feature-enhanced network topology data is input into an anonymous graph neural network for training; Before inputting the feature-enhanced external factor data into the feature embedding layer, the encrypted external factor data is locally decrypted at the edge nodes before training.

4. The platform according to claim 1, characterized in that, The intelligent monitoring module is further used for: By detecting heartbeats in real time, the status of each bank's smart printing device is collected at preset intervals, and abnormal bank smart printing devices are marked. For the smart printing devices of various banks, the remaining percentage of ink cartridges or toner cartridges is updated every preset number of pages printed, and an alarm is issued when the toner level is lower than the preset level. The printing speed and anti-paper jam mode are dynamically adjusted based on temperature and humidity data.

5. The platform according to claim 1, characterized in that, The dynamic inventory management module is further used for: When consumables are placed into the bank's smart printing device, the bank's smart printing device scans the NFC encryption tag of the consumables; wherein, the bank's smart printing device is equipped with an NFC encryption tag reading module; The printhead of the bank's smart printing equipment verifies the NFC encrypted tag and matches the device model. An alarm is issued when there is a mismatch to prevent mismatches.

6. The platform according to any one of claims 1-5, characterized in that, The platform also includes a latent fault detection module, used for: Collect the current operating parameters of the bank's smart printing equipment; the current operating parameters include mechanical vibration parameters, thermal distribution parameters, and circuit characteristic parameters. Wavelet packet energy entropy analysis is used to determine whether the mechanical vibration parameters exceed a preset vibration threshold. Convolutional temperature field reconstruction was used to analyze and determine whether the local temperature difference of the bank's smart printing equipment was abnormal. Fourier spectrum kurtosis is used to detect current ripple in the circuit characteristic parameters to determine whether the circuit is abnormal.

7. The platform according to any one of claims 1-5, characterized in that, The platform also includes a cost control module for: Perform printing behavior detection, identify paper waste, and generate prompt messages; The system calculates the cost information of each smart bank printing device according to a preset cycle, generates cost reports for each smart printing device, and alerts users of smart printing devices that exceed the target cost.

8. The platform according to any one of claims 1-5, characterized in that, The platform also includes a blockchain audit token module, used for: Record the circulation information of consumables in the blockchain to trace the circulation of ink cartridges or toner cartridges throughout their life cycle; The difference between the actual printing quantity and the nominal value of consumables is recorded in the blockchain. When the difference exceeds a preset difference threshold, a claim is initiated. Record consumable requisition information in the blockchain.

9. A smart printing device for banks, characterized in that, The bank's smart printing equipment includes a consumables management platform as described in any one of claims 1-8.

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