Adaptive region Chinese text compression display method and system thereof

Abstract

The application belongs to the technical scheme of human-computer interaction interface optimization, and particularly relates to a self-adaptive area Chinese text compression display method and system. Step one: dynamic display parameters are collected, and the collected parameters are preprocessed; step two: the input English text is subjected to semantic structure analysis through a Chinese semantic analysis module; step three: based on the display parameters preprocessed in step one and the Chinese text subjected to semantic structure analysis in step two, the text compression level is calculated based on a space density evaluation matrix, and a compression strategy is implemented; step four: the original text data and character rendering are stored through a reversible compression marking system. The application is used to solve the core problems existing in the field of intelligent terminal text display, such as semantic integrity damage, cross-platform adaptation error, unreliable data restoration and lagging behind of technology evolution.

Description

Technical Field

[0001] This invention pertains to human-computer interaction interface optimization technology, specifically relating to an adaptive regional Chinese text compression display method and system. Background Technology

[0002] Current text display technology for smart terminals faces multi-dimensional technical bottlenecks, creating an irreconcilable contradiction between the limited physical display space and the need to retain complete information.

[0003] Traditional text compression methods generally employ rule-based deletion or stop word filtering mechanisms. These linear processing methods based on word frequency statistics have significant drawbacks in professional fields such as medicine and law: the probability of erroneous deletion of key indicators in medical diagnostic texts exceeds 18%, the loss rate of obligation clauses in legal contract terms due to broken logical chains is as high as 23.6%, and the unique function word structure of Chinese leads to a distortion of the tone of interrogative sentences exceeding 40% under conventional compression algorithms, which seriously weakens the effectiveness of information transmission.

[0004] In terms of display adaptation, the existing technical solutions are not adapted to the characteristics of Chinese characters, which has caused a chain of problems. The recognition error rate of complex Chinese characters on smartwatches has increased to 25% due to the stroke sticking caused by the scaling of font size. The fixed line spacing setting causes 30%-45% of the display space to be wasted in the portrait to landscape mode. The density evaluation model based on Chinese corpus has a calculation deviation of more than 1.8 entropy units for Chinese information entropy, which directly leads to inaccurate display decisions.

[0005] The reliability flaws of the data restoration system further exacerbated the technical predicament, with traditional hash tags experiencing collision probabilities exceeding 10 in long medical text processing. -4 The scale of these issues is sufficient to trigger major medical malpractice incidents. Furthermore, the failure rate of Region of Interest (ROI) technology in restoring nested semantic structures in Chinese reaches 32%, frequently resulting in semantic breaks in complex grammatical scenarios such as conditional sentences and adversative sentences. The lag in cross-platform data synchronization mechanisms causes semantic tag weights to be lost during transmission, leading to a rendering misalignment rate consistently exceeding 15% between smart terminals, severely restricting the user experience in multi-device collaborative scenarios. The disconnect between the speed of technological evolution and the pace of hardware innovation creates a new point of conflict. Semantic models trained on Chinese corpora have a 19.7% lower accuracy rate in processing Chinese function words compared to dedicated models. Differences in pixel arrangement on OLED screens cause 12%-18% character deformation, while current Chinese information processing standards have not yet incorporated stroke compensation rules for mobile display scenarios, resulting in a systemic misalignment between industry technical specifications and real-world application scenarios.

[0006] The essence of these technical defects stems from the structural limitations of the existing technological system: over 92% of patented solutions focus solely on the single-dimensional optimization of physical display density, neglecting the dynamic evaluation of semantic information density; 78% of algorithms use static compression thresholds, failing to integrate real-time parameters such as ambient light intensity and device orientation; and mainstream display frameworks exhibit less than 64% display consistency in cross-device collaborative scenarios, creating a break in the technological ecosystem. The interplay of these problems makes it difficult for current technologies to meet the dual demands of smart wearable devices, augmented reality terminals, and other new interactive carriers for both spatial efficiency and semantic fidelity. There is an urgent need to build a next-generation text display system with Chinese semantic understanding capabilities, support for dynamic environment adaptation, and accurate text reproduction. Summary of the Invention

[0007] This invention provides an adaptive regional Chinese text compression display method to solve core problems in the field of text display on smart terminals, such as semantic integrity destruction, inaccurate cross-platform adaptation, unreliable data restoration, and lagging technological evolution.

[0008] This invention provides an adaptive regional Chinese text compression display system, which is used to implement an adaptive regional Chinese text compression display method.

[0009] This invention is achieved through the following technical solution:

[0010] An adaptive regional Chinese text compression display method, the method comprising the following steps:

[0011] Step 1: Collect dynamic display parameters and preprocess the collected parameters;

[0012] Step 2: Perform semantic structure analysis on the input Chinese text using the semantic segmentation module;

[0013] Step 3: Based on the display parameters after preprocessing in Step 1 and the Chinese text after semantic structure analysis in Step 2, calculate the text compression level based on the spatial density evaluation matrix and adopt a three-level progressive compression strategy.

[0014] Step 4: Store the original text data and text rendering using a reversible compression tagging system.

[0015] Furthermore, step one specifically includes the following steps:

[0016] Step 11: Data collection via a standardized interface; Step 11 specifically includes:

[0017] The screen physical size data comes from the device hardware parameter library, with a collection accuracy of ±0.1mm, and is collected from a single initialization.

[0018] The pixel density data is derived from screen resolution ÷ physical size, with precise calculation to integer digits, and is collected in a single initialization data session.

[0019] The line-of-sight distance data comes from a ToF sensor / front-facing camera, with an acquisition accuracy of ±5cm, and is collected once every 10Hz.

[0020] Ambient light intensity data is obtained from a light sensor with a sampling accuracy of 1-100,000 Lux (sampled every 5 Hz).

[0021] The motion data of the device comes from a six-axis IMU, with an acquisition accuracy of ±0.001g for acceleration and ±0.05° / s for angular velocity, and is acquired once every 100Hz.

[0022] The screen orientation data comes from an orientation sensor, with a collection accuracy of ±1° tilt angle, and is collected once every 10Hz.

[0023] Steps 1 and 2: Preprocess the data from Step 1;

[0024] Step 13: Calculation of dynamic minimum font size threshold;

[0025] Step 14: Output the threshold calculated in Step 13.

[0026] Furthermore, steps one and two specifically refer to:

[0027] Outlier filtering: Kalman filtering is used to eliminate jitter noise in IMU data, and sliding window mean smoothing is used for ToF distance data;

[0028] Unit standardization: Convert screen size from inches to millimeters, and luminous intensity (Lux) value to a logarithmic scale. The formula is:

[0029] L log =log 10 (Lux+1)

[0030] Motion compensation: When the equipment acceleration is >2m / s² 2 When the text is in motion, the minimum font size weight is automatically increased by 10%-15%.

[0031] The formula for calculating the dynamic minimum font size threshold in step one three is as follows:

[0032]

[0033] Where: k is the ambient light compensation coefficient; PPI is the pixel density; L d For real-time line-of-sight distance; L s The length of the screen diagonal; This is the ratio of the current IMU acceleration to the maximum range.

[0034] Furthermore, step one four specifically includes:

[0035] Dynamic range constraint: Limits the calculation results to the range of 12px×12px to 24px×24px to prevent display abnormalities in extreme environments;

[0036] Orientation adaptation: Automatically increases font size and width weight by 20% in landscape mode;

[0037] Historical data cache: Retain the 10 most recent threshold data sets for mutation detection.

[0038] Furthermore, step two specifically includes the following steps:

[0039] Step 21: Input preprocessing: including text normalization and noise filtering;

[0040] Step 22: Dual-channel parsing: The lexical layer adopts a hybrid word segmentation strategy, based on dynamic word segmentation of the BERT-Base model and forced segmentation using an industry terminology database; the syntactic layer constructs a dependency tree, uses the LTP4.0 toolkit to perform Chinese dependency syntax analysis and extract the core subject-verb-object structure, and marks modifiers.

[0041] Steps 2 and 3: Semantic tag generation: including entity recognition and function tags.

[0042] Furthermore, step three specifically includes the following steps:

[0043] Step 31: Receive the display area parameters and text features from the semantic parsing module collected via the standardized interface;

[0044] Step 32: Calculate the Chinese information entropy using the improved Chinese information entropy formula;

[0045] The improved Chinese information entropy formula is as follows:

[0046]

[0047] Among them, S a S represents the actual display area. t N represents the theoretical required area. c The stroke complexity factor. α is the dynamic density ratio, β is the stroke compensation coefficient, and β is the device orientation weight.

[0048] Step 33: Generate compression instructions for levels 0-3.

[0049] Furthermore, the method for generating level 0-3 compression instructions in step three is as follows:

[0050] if E zh ≥3.0:

[0051] Level 0: No compression;

[0052] elif 2.5≤E zh <3.0:

[0053] First-level compression removes redundant particles and modal particles while retaining the core semantics;

[0054] elif 1.8≤E zh <2.5:

[0055] Two-level compression, calling a thesaurus to replace long phrases, and supporting dialect standardization;

[0056] else:

[0057] Three-level compression generates a reversible GBK tag.

[0058] Furthermore, step four specifically includes the following steps:

[0059] Step 41: Receive hierarchical compression instructions and execute compression mark generation; compress the original text using the LZ77 algorithm to generate a binary data stream, then use the SHA-256 hash algorithm to generate a 16-byte compression mark, and finally establish a mapping table between the compression mark and the original text, which is stored in the device's local encrypted database.

[0060] Step 42: Rendering proxy. It uses regular expressions to detect compression markers in the text stream in real time, then calls the decryption API to restore the original text based on the mapping table, and finally determines the final text based on the current display area S. a Choose one of the following display modes: Full Render Mode, Floating Marker Mode, or Permanent Marker Mode;

[0061] Step 43: Cross-platform synchronization. The mapping table is encoded into a JWT token using the RFC 7519 standard, and the token is digitally signed using the device fingerprint to achieve synchronization of rendering states across Android, iOS, and Web platforms.

[0062] An adaptive region Chinese text compression display system, the system using the adaptive region Chinese text compression display method described above, the system comprising:

[0063] Acquisition and Preprocessing Module: Acquires dynamic display parameters and preprocesses the acquired parameters;

[0064] Chinese text semantic structure analysis module: Performs semantic structure analysis on the input Chinese text through the Chinese semantic parsing module;

[0065] Text compression module: Based on the preprocessed display parameters and the Chinese text after semantic structure analysis, it calculates the text compression level based on the spatial density evaluation matrix and implements the compression strategy;

[0066] Text storage and rendering module: Stores raw text data and renders text through a reversible compression tagging system.

[0067] A limited-area Chinese text display system for a smart terminal device, using the method described above.

[0068] The beneficial effects of this invention are:

[0069] This invention provides a cross-platform text storage and rendering method including a reversible compression tagging system, which is suitable for screen space optimization scenarios of smart terminals (mobile phones / watches / AR devices) and realizes semantic integrity preservation and dynamic display adaptation of text information.

[0070] This invention is particularly applicable to smart terminal devices with limited display areas (such as vehicle HUDs, smartwatches, and industrial control panels).

[0071] This invention achieves intelligent reduction and semantic preservation of Chinese text by integrating deep learning and linguistic rules. Attached Figure Description

[0072] Figure 1 This is a flowchart of the method of the present invention.

[0073] Figure 2 This is a flowchart of the method for dynamic acquisition and preprocessing of display parameters according to the present invention.

[0074] Figure 3 This is a flowchart of the method by which the Chinese semantic parsing module of the present invention performs semantic structure analysis on input text.

[0075] Figure 4 This is a flowchart of the method for calculating text compression level based on spatial density assessment and implementing compression strategy according to the present invention.

[0076] Figure 5 This is a flowchart of the method for storing original text data and text rendering using a reversible compression marking system as described in this invention. Detailed Implementation

[0077] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0078] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0079] It should also be understood that the terminology used in this application specification is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this application specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0080] The following is in conjunction with the appendix to this application specification. Figure 1-5 The technical solutions in the embodiments of this application are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0081] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0082] Implementation Method 1

[0083] This embodiment provides an adaptive regional Chinese text compression display method, such as... Figure 1 As shown, the method includes the following steps:

[0084] Step 1: Collect dynamic display parameters and preprocess the collected parameters;

[0085] Step 2: Perform semantic structure analysis on the input Chinese text using the Chinese semantic parsing module;

[0086] Step 3: Based on the display parameters after preprocessing in Step 1 and the Chinese text after semantic structure analysis in Step 2, calculate the text compression level based on the spatial density evaluation matrix and implement the compression strategy.

[0087] Step 4: Store the original text data and text rendering using a reversible compression tagging system.

[0088] Furthermore, such as Figure 2 As shown, step one specifically includes the following steps:

[0089] Step 11: Data collection via a standardized interface; Step 11 specifically includes:

[0090] The screen physical size data comes from the device hardware parameter library (such as EDID information), with a collection accuracy of ±0.1mm (diagonal length), and is collected from a single initialization.

[0091] The pixel density data is derived from screen resolution (in pixels) ÷ physical size (in inches), with the acquisition precision calculated to integer digits, and data is acquired in a single initialization.

[0092] The visual distance data comes from the ToF sensor / front camera (estimated based on face detection algorithm), with a collection accuracy of ±5cm (range 20cm-100cm), and is collected once every 10Hz;

[0093] Ambient light intensity data is obtained from a light sensor (Lux value), with a sampling accuracy of 1-100,000 Lux (divided into 10 dynamic range levels), and is sampled every 5 Hz;

[0094] The motion data of the device comes from a six-axis IMU (accelerometer + gyroscope), with an acquisition accuracy of ±0.001g for acceleration and ±0.05° / s for angular velocity, and is acquired once every 100Hz.

[0095] The screen orientation data comes from the orientation sensor (landscape / portrait mode), with a collection accuracy of ±1° tilt angle, and is collected once every 10Hz;

[0096] Steps 1 and 2: Preprocess the data from Step 1;

[0097] Step 13: Calculation of dynamic minimum font size threshold;

[0098] Step 14: Output the threshold calculated in Step 13.

[0099]

[0100] Furthermore, steps one and two specifically refer to:

[0101] Outlier filtering: Kalman filtering is used to eliminate jitter noise in IMU data, and sliding window mean smoothing (window size = 5 frames) is used for ToF distance data;

[0102] Unit standardization: Convert screen size from inches to millimeters (1 inch = 25.4 mm), and convert luminous intensity (Lux) to a logarithmic scale. The formula is:

[0103] L log =log 10 (Lux+1)

[0104] Motion compensation: When the equipment acceleration is >2m / s² 2 When the text is in motion, the minimum font size weight is automatically increased by 10%-15%.

[0105] The formula for calculating the dynamic minimum font size threshold in step one three is as follows:

[0106]

[0107] Where: k is the ambient light compensation coefficient (k = 0.8 in low light, k = 1.2 in strong light, and k = 1.0 in normal conditions); PPI is the pixel density (unit: pixels per inch); L d Real-time visibility distance (unit: cm); L s The length of the screen diagonal (unit: cm); This is the ratio of the current IMU acceleration to the maximum range.

[0108] Furthermore, step one four specifically includes:

[0109] Dynamic range constraint: Limits the calculation results to the range of 12px×12px to 24px×24px to prevent display abnormalities in extreme environments;

[0110] Orientation adaptation: Automatically increase font size and width weight by 20% in landscape mode (because horizontal layout of Chinese characters can easily cause visual compression);

[0111] Historical data cache: Retain the 10 most recent threshold data sets for mutation detection (e.g., enabling gradual transition animation when distance changes abruptly).

[0112] Furthermore, such as Figure 3 As shown, step two specifically includes the following steps:

[0113] Step 21: Input preprocessing: including text normalization (full-width to half-width, traditional to simplified) and noise filtering (removing illegal characters based on regular expressions);

[0114] Step 22: Dual-channel parsing: For the lexical layer, a hybrid word segmentation strategy is adopted, including dynamic word segmentation based on the BERT-Base model (supporting out-of-vocabulary word recognition) and forced segmentation using an industry term library (e.g., "coronary atherosclerosis" in the medical field cannot be split); for the syntactic layer, a dependency relationship tree is constructed, and the LTP4.0 toolkit is used to implement Chinese dependency syntactic analysis and extract the core subject-predicate-object structure, marking the modifying components (time / place adverbials).

[0115] Step 23: Semantic label generation: including entity recognition (annotating person names, place names, and organization names) and functional labels (distinguishing categories such as operation instructions and status descriptions).

[0116] Further, as Figure 4 shown, Step 3 specifically includes the following steps:

[0117] Step 31: Receive the display area parameters collected through the standardized interface and the text features of the semantic parsing module.

[0118] Step 32: Calculate the Chinese information entropy according to the improved Chinese information entropy formula.

[0119] The improved Chinese information entropy formula is:

[0120]

[0121] where S a is the actual display area, S t is the theoretical required area, N c is the stroke complexity factor, is the dynamic density ratio, α is the stroke compensation coefficient, and β is the device direction weight.

[0122]

[0123] Step 33: Generate compression instructions at levels 0-3 (Level 0: no compression; Level 3: maximum compression).

[0124] Further, the method for generating compression instructions at levels 0-3 in Step 33 is:

[0125] if E zh ≥3.0:

[0126] Level 0, no compression;

[0127] elif 2.5≤E zh <3.0:

[0128] Level 1 compression, delete redundant auxiliary words (de, di, de) and modal particles (ma, ba), and retain the core semantics;

[0129] elif 1.8≤Ezh <2.5:

[0130] Secondary compression, call the thesaurus to replace long and short phrases (e.g., "run fast" → "gallop"), support dialect standardization (e.g., "know" → "understand")

[0131] else:

[0132] Tertiary compression, generate a GBK reversible tag (e.g., "#SHA256:a1b2c3...").

[0133] Furthermore, as Figure 5 shown, step four specifically includes the following steps:

[0134] Step four - one: Receive the hierarchical compression instruction and execute compression tag generation; compress the original text using the LZ77 algorithm to generate a binary data stream, then use the SHA-256 hash algorithm to generate a 16-byte compression tag, and finally establish a mapping relationship table between the compression tag and the original text, which is stored in the local encrypted database of the device;

[0135] Step four - two: Rendering agent, detect the compression tags in the text stream in real time through regular expressions ( / #[A-F0-9]{4} / g), then call the decryption API to restore the original text according to the mapping relationship table, and finally select one of the three display modes of full rendering mode, tag suspension mode, and permanent tag mode according to the current display area area S a for display;

[0136] Step four - three: Cross-platform synchronization, encode the mapping relationship table into a JWT token through the RFC 7519 standard, and digitally sign the token using the device fingerprint (DeviceID + IMEI) to achieve Android / iOS / Web three-terminal rendering state synchronization.

[0137] Furthermore, the reversible compression tag system specifically divides the original text into multiple semantic / spatial logic units and establishes a dynamic mapping relationship with semantic tags and display parameters, specifically including semantic-driven chunking, spatial parameter associated storage, and dynamic mapping table construction;

[0138] The semantic-driven chunking divides text blocks based on the hierarchical structure of the semantic tag tree, including entity blocks (the smallest unit containing a complete named entity, such as medical device model #Device_NVMCTRL_2024) and logical blocks (bounded by the substructure of the dependency tree, such as a sentence component containing a complete negative scope);

[0139] The spatial parameter associated storage includes the metadata header attached to each compressed block and all the parameters required for initial data recovery;

[0140] Metadata structure example:

[0141]

[0142]

[0143] The dynamic mapping table is constructed by establishing multi-dimensional access paths through inverted indexes.

[0144]

[0145] Furthermore, the text dynamic rendering optimization includes using a polar coordinate layout algorithm on mobile devices and using a sound unit to synchronize audio prompts on fixed devices.

[0146] The dynamic text rendering specifically refers to...

[0147] Input: Chunked compressed text data.

[0148] Processing flow: Decompression (using algorithms such as LZ4 to decompress the raw text stream), glyph generation (parsing the font file using the FreeType library to generate character bitmaps), layout calculation (automatically wrapping lines based on screen width, calculating line height and character position), pixel drawing (final rendered bitmap).

[0149] Output: The final rendered bitmap.

[0150] The block compression technology is specifically a reversible data storage strategy. Its core is to segment the original text into multiple semantic / spatial logical units and establish a dynamic mapping relationship with semantic tags and display parameters. Specifically, it includes semantic-driven block segmentation (dividing text blocks based on a hierarchical structure of a semantic tag tree) and spatial parameter-associated storage (each compressed block is appended with a metadata header containing all the parameters needed to recover the original data, such as unique block identifiers, associated semantic tags, compression level, pixel density, minimum viewing distance, etc.). Simultaneously, a dynamic mapping table is constructed, and a multi-dimensional access path (e.g., semantic tag index, spatial parameter index) is established through an inverted index.

[0151] The polar coordinate layout algorithm automatically adjusts the layout parameters according to the screen size to maintain visual consistency.

[0152] The fixed end uses a sound-generating unit to synchronize audio prompts, and through hardware clock synchronization and software protocol optimization, it achieves precise coordination of multiple sound-generating units.

[0153] Implementation Method 2

[0154] This embodiment provides an adaptive region Chinese text compression and display system. The system uses the adaptive region Chinese text compression and display method described in Embodiment 1. The system includes:

[0155] Acquisition and Preprocessing Module: Acquires dynamic display parameters and preprocesses the acquired parameters;

[0156] Chinese text semantic structure analysis module: Performs semantic structure analysis on the input Chinese text by modifying the Chinese semantic parsing module;

[0157] Text compression module: Based on the preprocessed display parameters and the Chinese text after semantic structure analysis, it calculates the text compression level based on the spatial density evaluation matrix and implements the compression strategy;

[0158] Text storage and rendering module: Stores raw text data and renders text through a reversible compression tagging system.

[0159] Implementation Method 3

[0160] This embodiment provides a limited-area Chinese text display system for a smart terminal device, which is implemented using an adaptive regional Chinese text compression display method.

[0161] A dedicated text rendering acceleration chip that supports OpenCL 2.0 parallel computing can be used;

[0162] A haptic feedback unit that dynamically correlates vibration modes with Chinese compression levels can be used.

[0163] Implementation Method 4

[0164] This invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor. The memory stores software programs and modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory and processor are connected via a bus. Specifically, the processor implements any step in Embodiment 1 by running the computer program stored in the memory.

[0165] It should be understood that, in the embodiments of the present invention, the processor may be a Central Processing Unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0166] Memory may include read-only memory, flash memory, and random access memory, and provides instructions and data to the processor. Some or all of the memory may also include non-volatile random access memory.

[0167] As can be seen from the above, the electronic device provided by the embodiments of the present invention can implement the adaptive regional Chinese text compression display method as described in Embodiment 1 by running a computer program, which integrates deep learning and linguistic rules and is suitable for the limited regional Chinese text display system of smart terminal devices.

[0168] It should be understood that if the integrated modules / units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods described above can also be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0169] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0170] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the above device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0171] It should be noted that the methods and detailed examples provided in the above embodiments can be incorporated into the apparatus and devices provided in the embodiments for mutual reference, and will not be repeated here.

[0172] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0173] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of modules or units described above is merely a logical functional division, and in actual implementation, it can be divided in other ways. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0174] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An adaptive regional Chinese text compression display method, characterized in that, The method includes the following steps: Step 1: Collect dynamic display parameters and preprocess the collected parameters; Step 2: Perform semantic structure analysis on the input Chinese text using the Chinese semantic parsing module; Step 3: Based on the display parameters after preprocessing in Step 1 and the Chinese text after semantic structure analysis in Step 2, calculate the text compression level based on the spatial density evaluation matrix and implement the compression strategy. Step 4: Store the original text data and text rendering using a reversible compression tagging system; Step three specifically includes the following steps: Step 31: Receive the display area parameters collected by the standardized interface and the text features from the semantic parsing module; Step 32: Calculate the Chinese information entropy using the improved Chinese information entropy formula; The improved Chinese information entropy formula is as follows: in, This refers to the actual display area. For the theoretical required area, The stroke complexity factor. For dynamic density ratio, This is the stroke compensation coefficient. For device orientation weights; Step 33: Generate compression instructions for levels 0-3; The method for generating level 0-3 compression instructions in step three is as follows: if ≥ 3.0: Level 0: No compression; elif 2.5 ≤ <3.0: First-level compression removes redundant particles and modal particles while retaining the core semantics; elif 1.8 ≤ <2.5: Two-level compression, calling a thesaurus to replace long phrases, and supporting dialect standardization; else: Three-level compression generates a reversible GBK tag.

2. The method according to claim 1, characterized in that, Step one specifically includes the following steps: Step 11: Data collection via a standardized interface; Step 11 specifically includes: The screen physical size data comes from the device hardware parameter library, with a collection accuracy of ±0.1mm, and is collected from a single initialization. The pixel density data is derived from screen resolution ÷ physical size, with precise calculation to integer digits, and is collected in a single initialization data session. The line-of-sight distance data comes from a ToF sensor / front-facing camera, with an acquisition accuracy of ±5cm, and is collected once every 10Hz. Ambient light intensity data is obtained from a light sensor with a sampling accuracy of 1-100,000 Lux, and is collected every 5 Hz. The motion data of the device comes from a six-axis IMU, with an acquisition accuracy of ±0.001g for acceleration and ±0.05° / s for angular velocity, and is acquired once every 100Hz. The screen orientation data comes from an orientation sensor, with a collection accuracy of ±1° tilt angle, and is collected once every 10Hz. Steps 1 and 2: Preprocess the data from Step 1; Step 13: Calculation of dynamic minimum font size threshold; Step 14: Output the threshold calculated in Step 13.

3. The method according to claim 2, characterized in that, Steps one and two are specifically as follows: Outlier filtering: Kalman filtering is used to eliminate jitter noise in IMU data, and sliding window mean smoothing is used for ToF distance data; Unit standardization: Convert screen size from inches to millimeters, and luminous intensity (Lux) value to a logarithmic scale. The formula is: Motion compensation: When the device acceleration is greater than 2m / s², it is determined to be in a moving state, and the minimum font size weight is automatically increased by 10%-15%; The formula for calculating the dynamic minimum font size threshold in step one three is as follows: in: This is the ambient light compensation coefficient; Pixel density; Real-time visible distance; The length of the screen diagonal; This is the ratio of the current IMU acceleration to the maximum range.

4. The method according to claim 2, characterized in that, Step one four specifically refers to: Dynamic range constraint: Limits the calculation results to the range of 12px×12px to 24px×24px to prevent display abnormalities in extreme environments; Orientation adaptation: Automatically increases font size and width weight by 20% in landscape mode; Historical data cache: Retain the 10 most recent threshold data sets for mutation detection.

5. The method according to claim 1, characterized in that, Step two specifically includes the following steps: Step 21: Input preprocessing: including text normalization and noise filtering; Step 22: Dual-channel parsing: The lexical layer adopts a hybrid word segmentation strategy, based on dynamic word segmentation of the BERT-Base model and forced segmentation using an industry terminology database; the syntactic layer constructs a dependency tree, uses the LTP 4.0 toolkit to implement Chinese dependency syntax analysis and extract the core subject-verb-object structure, and marks modifiers. Steps 2 and 3: Semantic tag generation: including entity recognition and function tags.

6. The method according to claim 1, characterized in that, Step four specifically includes the following steps: Step 41: Receive hierarchical compression instructions and execute compression mark generation; compress the original text using the LZ77 algorithm to generate a binary data stream, then use the SHA-256 hash algorithm to generate a 16-byte compression mark, and finally establish a mapping table between the compression mark and the original text, which is stored in the device's local encrypted database. Step 42: Rendering proxy. It uses regular expressions to detect compression markers in the text stream in real time, then calls the decryption API to restore the original text based on the mapping table, and finally determines the final text based on the current display area. Choose one of the following display modes: Full Render Mode, Floating Marker Mode, or Permanent Marker Mode; Step 43: Cross-platform synchronization. The mapping table is encoded into a JWT token using the RFC 7519 standard, and the token is digitally signed using the device fingerprint to achieve synchronization of rendering states across Android, iOS, and Web platforms.

7. An adaptive regional Chinese text compression display system, characterized in that, The system uses the adaptive regional Chinese text compression display method as described in any one of claims 1-6, and the system includes: Acquisition and Preprocessing Module: Acquires dynamic display parameters and preprocesses the acquired parameters; Chinese text semantic structure analysis module: Performs semantic structure analysis on the input English text through the Chinese semantic parsing module; Text compression module: Based on the preprocessed display parameters and the Chinese text after semantic structure analysis, it calculates the text compression level based on the spatial density evaluation matrix and implements the compression strategy; Text storage and rendering module: Stores raw text data and renders text through a reversible compression tagging system.

8. A limited-area Chinese text display system for an intelligent terminal device, characterized in that, Use the method described in any one of claims 1-6.

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