Classifications

• • A—HUMAN NECESSITIES
  • A63—SPORTS; GAMES; AMUSEMENTS
  • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
  • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
  • A63F13/45—Controlling the progress of the video game
  • A63F13/48—Starting a game, e.g. activating a game device or waiting for other players to join a multiplayer session
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F13/00—Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
  • G06F13/38—Information transfer, e.g. on bus
  • G06F13/42—Bus transfer protocol, e.g. handshake; Synchronisation
  • G06F13/4282—Bus transfer protocol, e.g. handshake; Synchronisation on a serial bus, e.g. I2C bus, SPI bus
• • A—HUMAN NECESSITIES
  • A63—SPORTS; GAMES; AMUSEMENTS
  • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
  • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
  • A63F13/30—Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers
  • A63F13/35—Details of game servers
  • A63F13/358—Adapting the game course according to the network or server load, e.g. for reducing latency due to different connection speeds between clients
• • A—HUMAN NECESSITIES
  • A63—SPORTS; GAMES; AMUSEMENTS
  • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
  • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
  • A63F13/60—Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor
  • A63F13/67—Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F13/00—Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
  • G06F13/38—Information transfer, e.g. on bus
  • G06F13/382—Information transfer, e.g. on bus using universal interface adapter
  • G06F13/385—Information transfer, e.g. on bus using universal interface adapter for adaptation of a particular data processing system to different peripheral devices
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
  • G06F21/60—Protecting data
  • G06F21/602—Providing cryptographic facilities or services
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
  • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Definitions

• the present invention belongs to the field of computer technologies, and specifically, relates to a serialization system for random data access.
• a to-be-next-accessed data set and its probability can be calculated according to a historical status and a current context of a program by using a machine learning model, and accordingly, random data access is represented in an approximately serial manner to implement preloading, predictive loading, and random reading of data.
• the present invention provides a serialization system for random data access, to "serialize" access to the pieces of data and serve data access in a manner similar to the streaming media, thereby perfectly resolving the problems in games, programs, and data distribution and access.
• a game can be clicked and played without waiting for installation or download.
• a program can also be clicked and opened, and can be quickly entered without waiting for download and installation. Big data can be accessed without waiting for download to the local, and random access at any time becomes possible without worrying about a local storage capacity.
• a serialization system for random data access includes a big data analysis and processing sub-system, a data access serialization server sub-system, a client runtime sub-system, and a prediction and instruction-combined data distribution sub-system.
• the big data analysis and processing sub-system includes a machine learning algorithm sub-system, a behavioral data processing and modeling sub-system, and a model evaluation sub-system.
• the machine learning algorithm sub-system selects machine learning algorithms, such as Aproiri, Naive Bayesian, Bayesian Network, K-Means, KNN, DBSCAN, SVM, LSTM, CNN, AdaBoost, GBDT, and RandomForest, according to feedbacks in researching and actual production, and uses one or more combinations of the machine learning algorithms to construct a machine learning algorithm pool.
• machine learning algorithms such as Aproiri, Naive Bayesian, Bayesian Network, K-Means, KNN, DBSCAN, SVM, LSTM, CNN, AdaBoost, GBDT, and RandomForest
• the behavioral data processing and modeling sub-system uses different algorithms in the machine learning algorithm pool to process collected user behaviors and program behaviors, and establishes a data access prediction model for different users and different data with reference to types and characteristics of the data.
• the models predict, based on a user behavior and a program behavior, data that a user needs next.
• the model evaluation sub-system predicts a success rate for each piece of data of each user in a model evaluation stage according to stability under samples, and calculates indicators, such as complexity, to select top models with best performance.
• the data access serialization server sub-system performs authentication on a user when receiving a data access request from a client, performs authentication on accessed data, and rejects access in a case of invalidity; selects, if the data access request is a preloading request, a corresponding model and generates a preloaded packet; directly searches for, if the data access request is a real-time reading request, a corresponding compressed data block, and returns the data block to the client after encrypting the data block; selects, if the data access request is a predicted download request, a corresponding model, inputs a user behavior and a program context transmitted from the client, calculates to-be-needed data, searches for a corresponding compressed data block, and returns the data block to the client after encrypting the data block.
• the client may negotiate with a server to select non-compressed and non-encrypted data.
• the server divides data into data blocks, selects compression algorithms with high compression ratios and less time consumption for decompression and compression parameters for
• the client runtime sub-system takes over the data access request and maps the data access request to data access to a cache and a server, satisfies a data access requirement through preloading, real-time reading, and predictive loading, is transparent to a user and a game (program) developer, records behaviors of the user during game playing (program using), including a key press, key press duration, a dwell time in each interface, a program response, a program running context, and information, such as data access, of a program, and reports the behaviors to the server for establishing a data access model after analysis;
• intercepts a data access request for a program maps data access to data access to a local cache and the server, and is transparent to a user;
• a local cache uses an algorithm with a high compression ratio to store compressed data, and uses a more aggressive cache invalidation algorithm to delete invalid caches.
• a greedier preloading and prediction policy is negotiated with the server, to reduce impact of network jitters.
• the prediction and instruction-combined data distribution system usually delivers, according to a user behavior and a program behavior and a model generated by a big data processing system, data used or to be used by a user.
• the prediction and instruction-combined data distribution system further supports a game (program) developer to implant an instruction into game (program) code or on the server according to a protocol, and when a game (program) is run to specific positions or a context satisfies a specified condition, the data distribution system executes a corresponding instruction, for example, an operation such as delivering specific data or deleting specific data in a cache, and under a combination of prediction and an instruction, data access achieves zero-network latency the same as access to local data
• the serialization system for random data access provided in the present invention is properly designed and includes the following advantages:
• the present invention greatly reduces capacity limits in game (program) development, and is transparent to both users and developers.
• a game developer can access a game resource of any capacity by accessing a local file.
• the present invention naturally supports an open-ended game, such as a metaverse game, and also greatly reduces the difficulty of game development.
• the present invention naturally supports interactive videos such as AR/VR.
• the present invention integrates access interfaces of cloud data and local data, to lower the technical threshold of the cloud storage + local computation mode, which really revitalizes cloud storage and makes the cloud storage more convenient.
• FIG. 1 is a schematic block diagram of a serialization system for random data access according to the present invention
• FIG. 2 is a block diagram of a big data analysis sub-system for universal data streaming of a serialization system for random data access according to the present invention
• FIG. 3 is a block flowchart of server data access processing of a serialization system for random data access according to the present invention
• FIG. 4 is a flowchart of client data access processing of a serialization system for random data access according to the present invention.
• FIG. 5 is a block diagram of a client runtime sub-system of a serialization system for random data access according to the present invention.
• orientations or positional relationships such as “center”, “upper”, “lower”, “left”, “right”, “vertical”, “horizontal”, “inner”, and “outer”, are based on the orientations or positional relationships shown in the accompanying drawings, are used merely for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated apparatuses or elements need to have specific orientations or be constructed and perform operations in specific orientations, and therefore, should not be construed as limitations on the present invention.
• first”, “second”, and “third” are used merely for the purpose of description, and should not be understood as indicating or implying relative importance.
• a serialization system for random data access includes a big data analysis and processing sub-system, a data access serialization server sub-system, a client runtime sub-system, and a prediction and instruction-combined data distribution sub-system.
• the big data analysis and processing sub-system includes a machine learning algorithm sub-system, a behavioral data processing and modeling sub-system, and a model evaluation sub-system.
• the machine learning algorithm sub-system selects machine learning algorithms according to feedbacks in researching and actual production, and uses one or more combinations of the machine learning algorithms to construct a machine learning algorithm pool.
• the behavioral data processing and modeling sub-system uses different algorithms in the machine learning algorithm pool to process collected user behaviors and program behaviors, and establishes a data access prediction model for different users and different data with reference to types and characteristics of the data.
• the models predict, based on a user behavior and a program behavior, data that a user needs next.
• the model evaluation sub-system predicts a success rate for each piece of data of each user in a model evaluation stage according to stability under samples, and calculates indicators, such as complexity, to select top models with best performance.
• the data access serialization server sub-system performs authentication on a user when receiving a data access request from a client, performs authentication on accessed data, and rejects access in a case of invalidity; selects, if the data access request is a preloading request, a corresponding model and generates a preloaded packet; directly searches for, if the data access request is a real-time reading request, a corresponding compressed data block, and returns the data block to the client after encrypting the data block; selects, if the data access request is a predicted download request, a corresponding model, inputs a user behavior and a program context transmitted from the client, calculates to-be-needed data, searches for a corresponding compressed data block, and returns the data block to the client after encrypting the data block.
• the client may negotiate with a server to select non-compressed and non-encrypted data.
• the server divides data into data blocks, selects compression algorithms with high compression ratios and less time consumption for decompression and compression parameters for
• the client runtime sub-system takes over the data access request and maps the data access request to data access to a cache and a server, satisfies a data access requirement through preloading, real-time reading, and predictive loading, is transparent to a user and a game (program) developer, records behaviors of the user during game playing (program using), including a key press, key press duration, a dwell time in each interface, a program response, a program running context, and information, such as data access, of a program, and reports the behaviors to the server for establishing a data access model after analysis;
• intercepts a data access request for a program maps data access to data access to a local cache and the server, and is transparent to a user;
• a local cache uses an algorithm with a high compression ratio to store compressed data, and uses a more aggressive cache invalidation algorithm to delete invalid caches.
• a greedier preloading and prediction policy is negotiated with the server, to reduce impact of network jitters.
• the prediction and instruction-combined data distribution system usually delivers, according to a user behavior and a program behavior and a model generated by a big data processing system, data used or to be used by a user.
• the prediction and instruction-combined data distribution system further supports a game (program) developer to implant an instruction into game (program) code or on the server according to a protocol, and when a game (program) is run to specific positions or a context satisfies a specified condition, the data distribution system executes a corresponding instruction, for example, an operation such as delivering specific data or deleting specific data in a cache, and under a combination of prediction and an instruction, data access achieves zero-network latency the same as access to local data.

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• General Health & Medical Sciences (AREA)
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• Computer Security & Cryptography (AREA)
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• Health & Medical Sciences (AREA)
• Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

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Citations (5)

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• 2021
  • 2021-12-15 CN CN202111532690.1A patent/CN114791893B/zh active Active
• 2022
  • 2022-12-15 WO PCT/CN2022/139426 patent/WO2023109924A1/en unknown

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