Intelligent display driving chip system based on distributed architecture

Abstract

This invention belongs to the field of driver chip technology, specifically a distributed architecture-based intelligent display driver chip system, comprising: multiple intelligent display driver chips responsible for image processing in their respective areas; each intelligent display driver chip includes an AI anomaly detection module for detecting the behavior patterns of the intelligent display driver chip, a fault prediction module for detecting the behavior patterns of the intelligent display driver chip, and a blockchain module for recording and synchronizing the task allocation status of the intelligent display driver chip; the AI ​​anomaly detection module includes an anomaly classification and response unit for processing the anomalies of the intelligent display driver chip. This system, by integrating the AI ​​anomaly detection module, fault prediction module, and blockchain module, achieves real-time monitoring and fault early warning of the intelligent display driver chip's behavior patterns. Simultaneously, through an intelligent sensing unit, it can collect environmental data in real time, sense user behavior, and analyze displayed content, thereby achieving more intelligent display control.

Description

Technical Field

[0001] This invention belongs to the field of driver chip technology, specifically a smart display driver chip system based on a distributed architecture. Background Technology

[0002] With the continuous development of display technology, the demand for high resolution, high dynamic range, and multi-scenario applications in intelligent display systems is increasing. Traditional display driver chip architectures mostly adopt a centralized processing approach. This approach is prone to performance bottlenecks when facing large-scale display systems, such as data processing latency, excessive power consumption, and the risk of single points of failure.

[0003] In recent years, distributed architecture has gradually become an important development direction in the display driver field. For example, the Hisense U8H TV adopts a 1+N distributed driver chip architecture, using 608 driver chips to achieve local dimming, significantly improving the display effect. In addition, the Voury Zhuohua network distributed control system also adopts a distributed architecture, solving the technical bottlenecks of traditional controllers such as limited bus bandwidth, high failure rate, and slow processing speed. However, existing distributed display driving systems still have the following shortcomings in terms of intelligence and reliability: traditional systems lack the ability to monitor and detect anomalies in the behavior patterns of driving chips in real time, making it difficult to provide early warnings and handle faults before they occur; in addition, existing systems are weak in terms of environmental perception, user behavior perception, and display content analysis, and cannot dynamically adjust display parameters according to real-time environment and user needs.

[0004] Therefore, a smart display driver chip system based on a distributed architecture is proposed to address the above problems. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by providing an intelligent display driver chip system based on a distributed architecture, thereby solving the technical problems mentioned in the background.

[0006] To address the above technical problems, the following technical solution is adopted: an intelligent display driver chip system based on a distributed architecture, comprising; Multiple intelligent display driver chips are used to handle image processing in their respective areas, while ensuring global display consistency through a high-speed communication network; It also includes an AI anomaly detection module for detecting the behavior patterns of intelligent display driver chips, a fault prediction module for detecting the behavior patterns of intelligent display driver chips, and a blockchain module for recording and synchronizing the task allocation status of intelligent display driver chips. The AI ​​anomaly detection module includes an anomaly classification and response unit for processing intelligent display driver chips; The high-speed communication network includes a communication protocol to support real-time data transmission and a topology to ensure direct communication between two smart display driver chips, avoiding a single fixed point. It also includes intelligent sensing, which includes an environmental sensing unit for real-time collection of environmental data, a user sensing unit for sensing user behavior, and a content sensing unit for analyzing displayed content.

[0007] Preferably, the anomaly classification and response unit of the intelligent display driver chip is as follows: Mild anomaly: The chip temperature is slightly high and the load is slightly high at this time; Mild anomaly response: The system automatically reduces the load on the intelligent display driver chip and assigns tasks to nearby intelligent display driver chips. At the same time, dynamic voltage and frequency adjustment is triggered to reduce the power consumption and temperature of the intelligent display driver chip. Moderate anomaly: At this point, the chip task completion time is significantly delayed; Moderate anomaly response: The system marks the smart display driver chip as suspicious and initiates in-depth diagnostics. At the same time, a nearby smart display driver chip takes over some of the chip's tasks to ensure that the displayed content is not affected. Serious anomaly: At this point, the chip loses its heartbeat signal and a hardware failure occurs; Severe anomaly response: The system immediately marks the intelligent display driver chip as faulty and takes over its tasks completely, while activating redundant intelligent display driver chips or reassigning tasks to neighboring intelligent display driver chips.

[0008] Preferably, the fault prediction module includes an AI model for predicting faults in the intelligent display driver chip and taking preventative measures. The AI ​​model is a time-series prediction model trained based on historical and real-time data from the intelligent display driver chip. The historical data includes: Historical operating temperature values: Historical operating load values; Historical runtime; The real-time data is: real-time operating temperature value; Real-time load management; Current runtime; When an AI model predicts that a certain smart display driver chip may fail, it can pre-assign tasks or activate redundant units.

[0009] formula: , in: This is a historical transport data sequence for intelligent display driver chips, including historical operating temperatures. Historical operating load and historical runtime ; The hidden state at time step t; The memory state at time step t; For the predicted results; These are the parameters of the LSTM model.

[0010] Preferably, the blockchain module is used to record the task allocation information of each smart display driver chip on the blockchain to ensure data consistency and traceability. When one of the smart display driver chips is damaged, the system can quickly find its task allocation record through the blockchain and reassign the task to it. Task allocation is used to record and synchronize the task allocation status of the intelligent display driver chip, and the formula is as follows: , in: Record the task allocation for the intelligent display driver chip i, including its status. ,Task and timestamp ; The benefits of the intelligent display driver chip i include health status or task load.

[0011] Let i be the probability that the intelligent display driver chip i is selected as the block generator.

[0012] Preferably, the blockchain module is also used to realize the state synchronization between multiple smart display driver chips to ensure the consistency of the system. Specifically, one or more smart display driver chips periodically write their state information into the blockchain, and other smart display driver chips can read the state information of the smart display driver chips to understand the overall state of the system. The state information includes the chip's operating temperature, operating load, and operating time. The state synchronization is used to achieve state synchronization between intelligent display driver chips, and its formula is as follows: , in: This provides status information for the intelligent display driver chip i, including its operating temperature. Operating load and running time ; A block is a state information block on a blockchain; Valid indicates the valid validation result of the block.

[0013] Preferably, the state synchronization is achieved through a fast consensus algorithm, PoS.

[0014] Preferably, the communication protocol is a low-latency, high-bandwidth communication protocol.

[0015] Preferably, the topology is a mesh network structure, which enables direct communication between any two intelligent display driver chips, avoiding single points of failure.

[0016] Preferably, the intelligent sensing further includes: a multimodal sensing fusion unit for combining environmental, user and content sensing data, and a predictive adjustment unit for predicting user behavior and environmental changes and adjusting display parameters in advance; The multimodal perception fusion is used to combine environmental, user, and content perception data to optimize display parameters, and its formula is shown below: , in: For environmental sensing data; Data for user perception; Content-aware data: To display parameters; These are the optimized display parameters.

[0017] Preferably, the environmental sensing unit includes a light sensor and a temperature sensor; The user sensing unit includes a high-definition camera and an infrared sensor; The user behavior includes gaze point and distance; The content awareness includes static images and dynamic videos; Preferably, the predictive adjustment unit is used to predict user behavior and environmental changes, and adjust the display parameters in advance, as shown in the following formula: , in: For historical sensing data sequences; For the predicted results; These are the parameters of the prediction model.

[0018] The beneficial effects of this invention are: (1) The system integrates an AI anomaly detection module, a fault prediction module and a blockchain module to achieve real-time monitoring and fault warning of the behavior mode of the intelligent display driver chip. At the same time, the intelligent sensing unit introduced in this system can collect environmental data in real time, sense user behavior and analyze display content, thereby achieving more intelligent display control. In addition, the system adopts a high-speed communication network to support real-time data transmission and direct communication between intelligent display driver chips, avoiding the single-point failure risk of traditional topology.

[0019] (2) This system breaks through the limitations of traditional display driving systems by combining distributed architecture and intelligent modules, providing a more efficient and reliable solution for high-resolution, high dynamic range display systems.

[0020] (3) This system does not require a central controller. The task allocation information is jointly maintained by all the intelligent display driver chips. The high reliability of this system can be improved by setting up a blockchain module. The immutability of the blockchain ensures the accuracy of the task allocation information. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] In the attached diagram: Figure 1 This is a schematic diagram of the overall framework of the intelligent display driver chip system of the present invention; Figure 2 This is a schematic diagram of the hierarchical framework of the AI ​​anomaly detection module of the present invention; Figure 3 This is a schematic diagram of the intelligent sensing framework of the present invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0024] Specific implementation examples are given below.

[0025] Example Please see Figures 1-3 The present invention provides an intelligent display driver chip system based on a distributed architecture, comprising: Multiple intelligent display driver chips are used to handle image processing in their respective areas, while ensuring global display consistency through a high-speed communication network. In this embodiment, each intelligent display driver chip is an independent intelligent unit with the following functions: supporting real-time image rendering, color correction, and dynamic contrast enhancement; and integrating a lightweight AI accelerator, which is a mature existing technology and will not be described in detail here, and can support localized AI computing. It works in conjunction with other intelligent display driver chips through a high-speed communication interface to achieve data sharing and task allocation; Each intelligent display driver chip can autonomously optimize image quality based on the local display content, without relying on the central processing unit; It also includes an AI anomaly detection module for detecting the behavior patterns of intelligent display driver chips, a fault prediction module for detecting the behavior patterns of intelligent display driver chips, and a blockchain module for recording and synchronizing the task allocation status of intelligent display driver chips. The AI ​​anomaly detection module includes an anomaly classification and response unit for processing intelligent display driver chips; The high-speed communication network includes a communication protocol to support real-time data transmission and a topology to ensure direct communication between two smart display driver chips, avoiding a single fixed point. It also includes intelligent sensing, which includes an environmental sensing unit for real-time collection of environmental data, a user sensing unit for sensing user behavior, and a content sensing unit for analyzing displayed content.

[0026] Furthermore, the anomaly classification and response unit of the intelligent display driver chip is as follows: Mild anomaly: The chip temperature is slightly high and the load is slightly high at this time; Mild anomaly response: The system automatically reduces the load on the intelligent display driver chip and assigns tasks to nearby intelligent display driver chips. At the same time, dynamic voltage and frequency adjustment is triggered to reduce the power consumption and temperature of the intelligent display driver chip. Moderate anomaly: At this point, the chip task completion time is significantly delayed; Moderate anomaly response: The system marks the smart display driver chip as suspicious and initiates in-depth diagnostics. At the same time, a nearby smart display driver chip takes over some of the chip's tasks to ensure that the displayed content is not affected. Serious anomaly: At this point, the chip loses its heartbeat signal and a hardware failure occurs; Severe anomaly response: The system immediately marks the intelligent display driver chip as faulty and completely takes over its tasks, while activating redundant intelligent display driver chips or reassigning tasks to neighboring intelligent display driver chips. This allows the system to take different measures based on the severity of the anomaly, avoiding overreaction, and by combining real-time data to dynamically adjust the response strategy, the system's flexibility is improved. When the intelligent display driver chip is in the aforementioned abnormal state, this system needs to quickly take over its tasks and restore data. The specific process includes task takeover and data recovery. During task takeover, a nearby intelligent display driver chip or redundant unit takes over the tasks of the abnormal intelligent display driver chip. The redundant units in this system are reserved intelligent display driver chips. At the same time, this system quickly synchronizes the task status through a high-speed communication network. During data recovery, the display data of the abnormal intelligent display driver chip is restored from distributed storage or backup, while ensuring that the display content is not interrupted or has no significant delay. Through high-speed communication and intelligent algorithms, a smooth transition between tasks can be achieved. By utilizing distributed storage and backup technologies, it can be ensured that no data is lost.

[0027] During the self-healing or repair process of the intelligent display driver chip, the state of the intelligent display driver chip can be reloaded, fixed, or reset, and the cause of the anomaly can be analyzed through an AI model, thereby optimizing the operating parameters of the intelligent display driver chip.

[0028] Furthermore, the fault prediction module includes an AI model for predicting faults in the intelligent display driver chip and taking preventative measures. The AI ​​model is a time-series prediction model trained based on historical and real-time data from the intelligent display driver chip. The historical data includes: Historical operating temperature values: Historical operating load values; Historical runtime; The real-time data is: real-time operating temperature value; Real-time load management; Current runtime; When an AI model predicts that a certain smart display driver chip may fail, it can pre-assign tasks or activate redundant units.

[0029] formula: , in: This is a historical transport data sequence for intelligent display driver chips, including historical operating temperatures. Historical operating load and historical runtime ; The hidden state at time step t; The memory state at time step t; The predicted results include the probability of failure and the remaining lifespan of the chip.

[0030] These are the parameters of the LSTM model.

[0031] Furthermore, the blockchain module is used to record the task allocation information of each smart display driver chip on the blockchain to ensure data consistency and traceability. When one of the smart display driver chips is damaged, the system can quickly find its task allocation record through the blockchain and reassign the task to it. Task allocation is used to record and synchronize the task allocation status of the intelligent display driver chip, and the formula is as follows: , in: Record the task allocation for the intelligent display driver chip i, including its status. ,Task and timestamp ; The benefits of the intelligent display driver chip i include health status or task load.

[0032] Let i be the probability that the intelligent display driver chip i is selected as the block generator.

[0033] This system does not require a central controller. Task allocation information is maintained jointly by all the intelligent display driver chips. The blockchain module enhances the system's reliability, and the immutability of the blockchain ensures the accuracy of the task allocation information.

[0034] Furthermore, the blockchain module is also used to synchronize the states of multiple smart display driver chips to ensure system consistency. Specifically, one or more smart display driver chips periodically write their state information into the blockchain, and other smart display driver chips can read the state information of the smart display driver chips to understand the overall state of the system. The state information includes the chip's operating temperature, operating load, and operating time. The state synchronization is used to achieve state synchronization between intelligent display driver chips, and its formula is as follows: , in: This provides status information for the intelligent display driver chip i, including its operating temperature. Operating load and running time ; A block is a state information block on a blockchain; Valid indicates the valid validation result of the block.

[0035] Furthermore, the state synchronization is achieved through a fast consensus algorithm, PoS.

[0036] Furthermore, the communication protocol is a low-latency, high-bandwidth communication protocol.

[0037] Furthermore, the topology is a mesh network structure, which enables direct communication between any two intelligent display driver chips, avoiding single points of failure.

[0038] Furthermore, the intelligent perception also includes: a multimodal perception fusion unit for combining environmental, user and content perception data, and a predictive adjustment unit for predicting user behavior and environmental changes and adjusting display parameters in advance; The multimodal perception fusion is used to combine environmental, user, and content perception data to optimize display parameters, and its formula is shown below: , in: This refers to environmental sensing data; specifically, data from light and temperature sensors.

[0039] For user-perceived data; this includes gaze point and distance.

[0040] Content-aware data: This includes both static images and dynamic data.

[0041] These are display parameters; they include brightness and contrast, among others.

[0042] These are the optimized display parameters.

[0043] Furthermore, the environmental sensing unit includes a light sensor and a temperature sensor; The user sensing unit includes a high-definition camera and an infrared sensor for sensing user behavior; The user behavior includes gaze point and distance; the distance is the distance between the user and the display of the system application.

[0044] The content awareness includes both static images and dynamic videos.

[0045] Furthermore, the predictive adjustment formula is used to predict user behavior and environmental changes, and adjust display parameters in advance. The formula is shown below: , in: For historical sensing data sequences; For predicting outcomes; this includes changes in user behavior and the environment.

[0046] These are the parameters of the prediction model.

[0047] Furthermore, task takeover and data recovery are used to quickly take over tasks and recover data when the intelligent display driver chip malfunctions. The formula is as follows: Wherein: IDDC is the intelligent display driver chip; Assignment of tasks after takeover; The restored display data; For distributed storage or backup of data in China.

[0048] Furthermore, the self-healing mechanism, based on reinforcement learning, is used to optimize the operating parameters of the intelligent display driver chip, and its formula is shown below: , in: This provides information on the status of the intelligent display driver chip, including operating temperature and load. This is a self-healing action; it includes restarting or reconfiguration.

[0049] The reward for performing the action includes recovery time and task completion rate. The learning rate; This is the discount factor.

[0050] In summary, this system integrates an AI anomaly detection module, a fault prediction module, and a blockchain module to achieve real-time monitoring and fault warning of the behavior patterns of intelligent display driver chips. Furthermore, the intelligent sensing unit introduced in this system can collect environmental data in real time, sense user behavior, and analyze the displayed content, thereby achieving more intelligent display control. Additionally, the system uses a high-speed communication network to support real-time data transmission and direct communication between intelligent display driver chips, avoiding the single-point failure risk of traditional topologies.

[0051] This system, through the combination of distributed architecture and intelligent modules, breaks through the limitations of traditional display driving systems, providing a more efficient and reliable solution for high-resolution, high dynamic range display systems.

[0052] In the description of this invention, it should be understood that the terms "front and back", "left and right", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0053] Of course, those skilled in the art should understand that in this technical solution, the term "a" should be understood as "at least one" or "one or more," that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple. The term "a" should not be construed as a limitation on the quantity.

[0054] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art under the technical guidance of the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A smart display driver chip system based on a distributed architecture, characterized in that: include; Multiple intelligent display driver chips are used to handle image processing in their respective areas, while ensuring global display consistency through a high-speed communication network; It also includes an AI anomaly detection module for detecting the behavior patterns of intelligent display driver chips, a fault prediction module for detecting the behavior patterns of intelligent display driver chips, and a blockchain module for recording and synchronizing the task allocation status of intelligent display driver chips. The AI ​​anomaly detection module includes an anomaly classification and response unit for processing intelligent display driver chips; The high-speed communication network includes a communication protocol to support real-time data transmission and a topology to ensure direct communication between two smart display driver chips, avoiding a single fixed point. It also includes intelligent sensing, which includes an environmental sensing unit for real-time collection of environmental data, a user sensing unit for sensing user behavior, and a content sensing unit for analyzing displayed content.

2. The intelligent display driver chip system based on a distributed architecture according to claim 1, characterized in that: The anomaly classification and response unit of the intelligent display driver chip is as follows: Mild anomaly: The chip temperature is slightly high and the load is slightly high at this time; Mild anomaly response: The system automatically reduces the load on the intelligent display driver chip and assigns tasks to nearby intelligent display driver chips. At the same time, dynamic voltage and frequency adjustment is triggered to reduce the power consumption and temperature of the intelligent display driver chip. Moderate anomaly: At this point, the chip task completion time is significantly delayed; Moderate anomaly response: The system marks the smart display driver chip as suspicious and initiates in-depth diagnostics. At the same time, a nearby smart display driver chip takes over some of the chip's tasks to ensure that the displayed content is not affected. Serious anomaly: At this point, the chip loses its heartbeat signal and a hardware failure occurs; Severe anomaly response: The system immediately marks the intelligent display driver chip as faulty and takes over its tasks completely, while activating redundant intelligent display driver chips or reassigning tasks to neighboring intelligent display driver chips.

3. The intelligent display driver chip system based on a distributed architecture according to claim 2, characterized in that: The fault prediction module includes an AI model for predicting faults in the intelligent display driver chip and taking preventative measures. The AI ​​model is a time-series prediction model trained based on historical and real-time data from the intelligent display driver chip. The historical data includes: Historical operating temperature values: Historical operating load values; Historical runtime; The real-time data is: real-time operating temperature value; Real-time load management; Current runtime; When an AI model predicts that a certain intelligent display driver chip may fail, it pre-allocates tasks or activates redundant units, as shown in the following formula: ， in: This is a historical transport data sequence for intelligent display driver chips, including historical operating temperatures. Historical operating load and historical runtime ; The hidden state at time step t; The memory state at time step t; For the predicted results; These are the parameters of the LSTM model.

4. The intelligent display driver chip system based on a distributed architecture according to claim 1, characterized in that: The blockchain module is used to record the task allocation information of each smart display driver chip on the blockchain to ensure data consistency and traceability. When one of the smart display driver chips is damaged, the system can quickly find its task allocation record through the blockchain and reassign the task to it. Task allocation is used to record and synchronize the task allocation status of the intelligent display driver chip, and its formula is as follows: ， in: Record the task allocation for the intelligent display driver chip i, including its status. ,Task and timestamp ; The rights and interests of the intelligent display driver chip i include, among other things, health status or task load; Let i be the probability that the intelligent display driver chip i is selected as the block generator.

5. The intelligent display driver chip system based on a distributed architecture according to claim 4, characterized in that: The blockchain module is also used to synchronize the states of multiple smart display driver chips to ensure system consistency. Specifically, one or more smart display driver chips periodically write their state information into the blockchain, and other smart display driver chips can read the state information of the smart display driver chips to understand the overall state of the system. The state information includes the chip's operating temperature, operating load, and operating time. The state synchronization is used to achieve state synchronization between intelligent display driver chips, and its formula is as follows: ， in: This provides status information for the intelligent display driver chip i, including its operating temperature. Operating load and running time ; A block is a state information block on a blockchain; Valid indicates the valid validation result of the block.

6. The intelligent display driver chip system based on a distributed architecture according to claim 5, characterized in that: The state synchronization is achieved through a fast consensus algorithm called PoS.

7. The intelligent display driver chip system based on a distributed architecture according to claim 1, characterized in that: The communication protocol is a low-latency, high-bandwidth communication protocol.

8. The intelligent display driver chip system based on a distributed architecture according to claim 1, characterized in that: The topology is a mesh network structure, which enables direct communication between any two intelligent display driver chips, avoiding single points of failure.

9. The intelligent display driver chip system based on a distributed architecture according to claim 1, characterized in that: The intelligent sensing also includes: a multimodal sensing fusion unit for combining environmental, user and content sensing data, and a predictive adjustment unit for predicting user behavior and environmental changes and adjusting display parameters in advance. The multimodal perception fusion is used to combine environmental, user, and content perception data to optimize display parameters, and its formula is shown below: ， in: For environmental sensing data; Data for user perception; Content-aware data: To display parameters; These are the optimized display parameters.

10. The intelligent display driver chip system based on a distributed architecture according to claim 9, characterized in that: The predictive adjustment unit is used to predict user behavior and environmental changes, and adjust the display parameters in advance, as shown in the following formula: ， in: For historical sensing data sequences; For the predicted results; These are the parameters of the prediction model.

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