Self-adaptive region Chinese text compression display method and system

Abstract

The invention belongs to the technical scheme of human-computer interaction interface optimization, and particularly relates to an adaptive region Chinese text compression display method and system. The method comprises the following steps of: 1, acquiring dynamic display parameters, and preprocessing the acquired parameters; 2, performing semantic structure analysis on the input English text through a Chinese semantic analysis module; 3, based on the display parameters preprocessed in the step 1 and the Chinese text subjected to semantic structure analysis in the step 2, calculating a text compression level based on a spatial density evaluation matrix, and implementing a compression strategy; and 4, original text data and character rendering are stored through a reversible compression marking system. The method is used for solving the core problems that semantic integrity is damaged, cross-platform adaptation is not accurate, data reduction is unreliable, and technical evolution lags behind in the field of intelligent terminal text display.

Description

Technical Field

[0001] The present invention belongs to a technical solution for optimizing a human-computer interaction interface, and specifically relates to a method and system for adaptively displaying Chinese text in a regional compression mode. Background Art

[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 use rule-based deletion or stop word filtering mechanisms. This type of linear processing method based on word frequency statistics has exposed significant defects in professional fields such as medicine and law: the probability of accidental deletion of key indicators in medical diagnosis texts exceeds 18%, and the loss rate of obligation clauses in legal contracts due to broken logical chains is as high as 23.6%. The unique function word structure of Chinese causes the distortion of the tone of question sentences to exceed 40% when processed by conventional compression algorithms, seriously weakening the effectiveness of information transmission.

[0004] At the display adaptation level, the existing technical solutions are insufficiently adapted to the characteristics of Chinese characters, which leads to a chain of problems. The recognition error rate of complex Chinese characters on smart watches rises to 25% due to the sticking of strokes caused by pure font size scaling. The fixed line spacing setting causes 30%-45% of display space to be wasted in the vertical screen to landscape screen scenario. The density assessment model built based on Chinese corpus has a calculation deviation of Chinese information entropy of more than 1.8 entropy units, which directly leads to inaccurate display decisions.

[0005] The reliability defects of the data restoration system further exacerbated the technical difficulties. The collision probability of traditional hash tags in medical long text processing exceeded 10. -4 The magnitude is enough to cause major medical liability accidents, and the failure rate of regional interest tag (ROI) technology in restoring the nested semantic structure of Chinese characters reaches 32%. Semantic disconnection frequently occurs in complex grammatical scenarios such as conditional sentences and transitional sentences. The lag of the cross-platform data synchronization mechanism causes the semantic tag weight to be lost during the transmission process, resulting in a rendering misalignment rate between smart terminals that continues to be higher than 15%, seriously restricting the user experience in multi-device collaboration scenarios. The disconnect between the speed of technological evolution and the pace of hardware innovation has formed a new contradiction. The semantic model trained based on Chinese corpus has a 19.7% lower accuracy rate in processing Chinese function words than the dedicated model. The difference in pixel arrangement on OLED screens causes 12%-18% character deformation. The current Chinese information processing standards have not yet incorporated stroke compensation rules for mobile display scenarios, resulting in a systematic misalignment between industry technical specifications and real application scenarios.

[0006] The root cause of these technical deficiencies stems from the structural limitations of the existing technology ecosystem: over 92% of patent solutions focus solely on optimizing physical display density, neglecting the dynamic assessment of semantic information density; 78% of algorithms employ static compression thresholds, failing to integrate real-time parameters such as ambient light intensity and device spatial orientation; and mainstream display frameworks exhibit less than 64% display consistency across multiple devices, creating a gap in the technology ecosystem. The intertwining effects of these issues make it difficult for current technology to meet the dual demands of spatial efficiency and semantic fidelity for new interactive platforms such as smart wearables and augmented reality terminals. There is an urgent need to develop a next-generation text display system capable of understanding Chinese semantics, supporting dynamic environmental adaptation, and achieving accurate restoration. Summary of the Invention

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

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

[0009] The present invention is achieved through the following technical solutions:

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

[0011] Step 1: Collect dynamic display parameters and pre-process the collected parameters;

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

[0013] Step 3: Based on the display parameters pre-processed in step 1 and the English text after semantic structure analysis in step 2, the text compression level is calculated based on the spatial density evaluation matrix, and a three-level progressive compression strategy is adopted;

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

[0015] Furthermore, the step 1 specifically includes the following steps:

[0016] Step 11: Data collection through a standardized interface; the specific steps are:

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

[0018] The pixel density data is derived from the screen resolution divided by the physical size. The acquisition accuracy is integer bit accurate calculation, and the data is collected once for initialization.

[0019] The visual distance data comes from the ToF sensor / front camera, with an acquisition accuracy of ±5cm and is collected every 10Hz;

[0020] The ambient light intensity data comes from the light sensor, with an acquisition accuracy of 1-100,000 Lux (collected every 5 Hz;

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

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

[0023] Step 12: Preprocess the data from step 11;

[0024] Step 13: Dynamic minimum font size threshold calculation;

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

[0026] Furthermore, the steps one and two are specifically as follows:

[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 convert light intensity Lux value to logarithmic scale using the following formula:

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

[0030] Motion compensation: When the device acceleration is greater than 2m / s 2 When the text is in motion, it is determined to be in mobile state and the minimum font size weight is automatically increased by 10%-15%;

[0031] The formula for calculating the dynamic minimum font size threshold in step 13 is:

[0032]

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

[0034] Furthermore, the steps 1 to 4 are specifically as follows:

[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] Direction adaptation: Automatically increase the font size and width by 20% in landscape mode;

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

[0038] Furthermore, the step 2 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 uses a hybrid word segmentation strategy, based on the dynamic word segmentation of the BERT-Base model and mandatory segmentation based on the industry terminology library. The syntactic layer constructs a dependency tree, using the LTP4.0 toolkit to implement Chinese dependency syntax analysis, extract the subject-verb-object core structure, and mark modifiers.

[0041] Steps 2 and 3: Semantic label generation: including entity recognition and function labeling.

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

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

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

[0045] The improved Chinese information entropy formula is:

[0046]

[0047] Among them, 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;

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

[0049] Furthermore, the method for generating the level 0-3 compression instructions in step 33 is:

[0050] if E zh ≥3.0:

[0051] Level 0: no compression;

[0052] elif 2.5≤E zh <3.0:

[0053] First-level compression: remove redundant particles and modal words to retain the core meaning;

[0054] elif 1.8≤E zh <2.5:

[0055] Secondary compression, using a synonym library to replace long phrases, supports dialect standardization;

[0056] else:

[0057] Three-level compression, generating GBK reversible mark.

[0058] Furthermore, the step 4 specifically includes the following steps:

[0059] Step 41: 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. Finally, establish a mapping relationship table between the compression tag and the original text and store it in the device's local encrypted database;

[0060] Step 42: Rendering agent, detects compression marks in text stream in real time through regular expressions, then calls decryption API according to the mapping table to restore the original text, and finally calculates the decryption result based on the current display area S. a Select one of the three display modes: full rendering mode, marker suspension mode, and permanent marker mode;

[0061] Step 43: Cross-platform synchronization: Encode the mapping relationship table into a JWT token using the RFC 7519 standard, digitally sign the token using the device fingerprint, and synchronize the rendering status of Android, iOS, and Web.

[0062] A regional adaptive Chinese text compression and display system, the system using the regional adaptive Chinese text compression and display method as described above, the system comprising:

[0063] Acquisition and preprocessing module: collect dynamic display parameters and preprocess the collected parameters;

[0064] Chinese text semantic structure analysis module: The Chinese semantic parsing module performs semantic structure analysis on the input English text;

[0065] Text compression module: Based on the pre-processed display parameters and the Chinese text after semantic structure analysis, the text compression level is calculated based on the spatial density evaluation matrix and the compression strategy is implemented;

[0066] Text storage and rendering module: stores original text data and text rendering through a reversible compression marking system.

[0067] A limited area Chinese text display system for an intelligent terminal device uses the above method.

[0068] The beneficial effects of the present invention are:

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

[0070] The present invention is particularly suitable for smart terminal devices with limited display areas (such as vehicle-mounted HUDs, smart watches, and industrial control panels).

[0071] The present invention achieves intelligent reduction and semantic preservation of Chinese text by integrating deep learning and linguistic rules. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 is a flow chart of the method of the present invention.

[0073] Figure 2 It is a flow chart of the method for dynamic acquisition and preprocessing of display parameters of the present invention.

[0074] Figure 3 It is a flow chart of a method in which the Chinese semantic parsing module of the present invention performs semantic structure analysis on an input text.

[0075] Figure 4 It is a flow chart of the method for calculating text compression level based on spatial density evaluation and implementing compression strategy of the present invention.

[0076] Figure 5 It is a flow chart of the method for storing original text data and text rendering through a reversible compression markup system of the present invention. DETAILED DESCRIPTION

[0077] In the following description, specific details such as specific system structures and technologies are provided for illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present application with unnecessary details.

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

[0079] It should also be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this 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 attached to this application specification Figure 1-5 , clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of this application.

[0081] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0082] Implementation Method 1

[0083] This embodiment provides a method for displaying Chinese text in an adaptive region. Figure 1 As shown, the method comprises the following steps:

[0084] Step 1: Collect dynamic display parameters and pre-process the collected parameters;

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

[0086] Step 3: Based on the display parameters pre-processed in step 1 and the Chinese text after semantic structure analysis in step 2, the text compression level is calculated based on the spatial density evaluation matrix and a compression strategy is implemented;

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

[0088] Further, such as Figure 2 As shown, the step 1 specifically includes the following steps:

[0089] Step 11: Data collection through a standardized interface; the specific steps are:

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

[0091] The pixel density data is derived from the screen resolution (unit: pixel) divided by the physical size (unit: inch). The acquisition precision is integer bit accurate calculation, and the data is collected once for initialization.

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

[0093] The ambient light intensity data comes from the light sensor (Lux value), with an acquisition accuracy of 1-100,000 Lux (10-level dynamic range) and is collected every 5 Hz;

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

[0095] Screen orientation data comes from the orientation sensor (landscape / portrait state), with an accuracy of ±1° for the tilt angle and is collected every 10Hz.

[0096] Step 12: Preprocess the data from step 11;

[0097] Step 13: Dynamic minimum font size threshold calculation;

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

[0099]

[0100] Furthermore, the steps one and two are specifically as follows:

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

[0102] Unit standardization: Convert the screen size from inches to millimeters (1 inch = 25.4 mm) and the light intensity Lux value to a logarithmic scale using the following formula:

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

[0104] Motion compensation: When the device acceleration is greater than 2m / s 2 When the text is in motion, it is determined to be in mobile state and the minimum font size weight is automatically increased by 10%-15%;

[0105] The formula for calculating the dynamic minimum font size threshold in step 13 is:

[0106]

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

[0108] Furthermore, the steps 1 to 4 are specifically as follows:

[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] Direction adaptation: Automatically increase the font width by 20% in landscape mode (because horizontal arrangement of Chinese characters can easily cause visual compression);

[0111] Historical data cache: retains the most recent 10 sets of threshold data for sudden change detection (such as enabling gradual transition animation when the distance changes suddenly).

[0112] Further, such as Figure 3 As shown, the step 2 specifically includes the following steps:

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

[0114] Step 22: Dual-channel parsing: At the lexical level, 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); at the syntactic level, 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: It includes entity recognition (annotating person names, place names, and organization names) and functional labels (distinguishing categories such as operation instructions and status descriptions).

[0116] Furthermore, 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 0-3 level compression instructions (0 level: no compression; 3 level: maximum compression).

[0124] Furthermore, the method for generating 0-3 level compression instructions in Step 33 is as follows:

[0125] if E zh ≥3.0:

[0126] 0 level, 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, calling the synonym library to replace long phrases (such as "fast running" → "galloping"), and supporting dialect standardization (such as "know" → "know")

[0131] else:

[0132] Three-level compression, generating GBK reversible mark (such as "#SHA256:a1b2c3...").

[0133] Further, such as Figure 5 As shown, the step 4 specifically includes the following steps:

[0134] Step 41: 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. Finally, establish a mapping relationship table between the compression tag and the original text and store it in the device's local encrypted database;

[0135] Step 42: Rendering agent, detects compression marks in the text stream in real time through regular expression ( / #[A-F0-9]{4} / g), then calls decryption API according to the mapping table to restore the original text, and finally calculates the original text based on the current display area S. a Select one of the three display modes: full rendering mode, marker suspension mode, and permanent marker mode;

[0136] Step 43: Cross-platform synchronization: Encode the mapping relationship table into a JWT token using the RFC 7519 standard, digitally sign the token using the device fingerprint (DeviceID+IMEI), and synchronize the rendering status of Android, iOS, and Web.

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

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

[0139] The spatial parameters are stored in association, including metadata headers attached to each compressed block, and all parameters required for initial data recovery;

[0140] Example metadata structure:

[0141]

[0142]

[0143] The dynamic mapping table is constructed by establishing a multi-dimensional access path through an inverted index.

[0144]

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

[0146] The text dynamic rendering is specifically as follows:

[0147] Input: Block-compressed text data.

[0148] Processing flow: decompression (using LZ4 and other algorithms to decompress the original text stream), glyph generation (parsing the font file through the FreeType library to generate character bitmaps), layout calculation (automatically wrapping lines according to screen width, calculating line height and character position), pixel drawing (finally rendered bitmap)

[0149] Output: Final rendered bitmap.

[0150] The block compression technology is specifically designed as 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 between semantic tags and display parameters. Specifically, it includes semantic-driven block segmentation (dividing text blocks based on the hierarchical structure of the semantic tag tree) and spatial parameter association storage (each compressed block is attached with a metadata header containing all parameters required for original data recovery: such as block unique identifier, associated semantic tag, compression level, pixel density, minimum viewing distance, etc.). At the same time, a dynamic mapping table is constructed, and multi-dimensional access paths (such as semantic tag index and spatial parameter index) are established through inverted indexing.

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

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

[0153] Implementation Method 2

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

[0155] Acquisition and preprocessing module: collect dynamic display parameters and preprocess the collected parameters;

[0156] Chinese text semantic structure analysis module: performs semantic structure analysis on the input English text through the Chinese semantic analysis and modification module;

[0157] Text compression module: Based on the pre-processed display parameters and the Chinese text after semantic structure analysis, the text compression level is calculated based on the spatial density evaluation matrix and the compression strategy is implemented;

[0158] Text storage and rendering module: stores original text data and text rendering through a reversible compression marking system.

[0159] Implementation Method 3

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

[0161] You can use a text rendering acceleration chip that supports OpenCL 2.0 parallel computing;

[0162] A tactile feedback unit may be used in which vibration patterns are dynamically associated with Chinese compression levels.

[0163] Implementation Method 4

[0164] An embodiment of the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. The memory is used to store software programs and modules, and the processor executes various functional applications and data processing by executing the software programs and modules stored in the memory. The memory and processor are connected via a bus. Specifically, the processor implements any step of the first embodiment described above by executing the computer program stored in the memory.

[0165] It should be understood that in the embodiments of the present invention, the processor referred to may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

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

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

[0168] It should be understood that if the above-mentioned integrated modules / units are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The above-mentioned computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the above-mentioned computer program includes computer program code, and the above-mentioned computer program code can be in source code form, object code form, executable file or some intermediate form. The above-mentioned computer-readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the above-mentioned 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 will enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one 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 present invention. Therefore, the present invention is not limited to the embodiments shown herein, but is to be construed in the widest manner consistent with the principles and novel features disclosed herein.

[0170] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the above-mentioned 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 implementation method can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, and will not be repeated here.

[0171] It should be noted that the methods and detailed examples provided in the above embodiments can be combined with the devices and equipment provided in the embodiments, and references can be made to each other, and no further details will be given.

[0172] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

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

[0174] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for regional adaptive Chinese text compression display, characterized in that: The method comprises the following steps: Step 1: Collect dynamic display parameters and pre-process the collected parameters; Step 2: Perform semantic structure analysis on the input English text through the Chinese semantic analysis module; Step 3: Based on the display parameters pre-processed in step 1 and the Chinese text after semantic structure analysis in step 2, the text compression level is calculated based on the spatial density evaluation matrix and a compression strategy is implemented; Step 4: Store the original text data and text rendering through a reversible compression markup system.

2. The method according to claim 1, characterized in that The step 1 specifically includes the following steps: Step 11: Data collection through a standardized interface; the specific steps are: The data of the screen physical size comes from the device hardware parameter library, with an acquisition accuracy of ±0.1mm, and is collected for single initialization. The pixel density data is derived from the screen resolution divided by the physical size. The acquisition accuracy is integer bit accurate calculation, and the data is collected once for initialization. The visual distance data comes from the ToF sensor / front camera, with an acquisition accuracy of ±5cm and is collected every 10Hz; The ambient light intensity data comes from the light sensor, with an acquisition accuracy of 1-100,000 Lux (collected every 5 Hz; The device's motion data comes from a six-axis IMU with an accuracy of ±0.001g acceleration and ±0.05° / s angular velocity, collected every 100Hz. The screen orientation data comes from the orientation sensor, with an accuracy of ±1° for the tilt angle and is collected every 10 Hz. Step 12: Preprocess the data from step 11; Step 13: Dynamic minimum font size threshold calculation; Step 14: Output the threshold calculated in step 13.

3. The method according to claim 2, characterized in that The 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 convert light intensity Lux value to logarithmic scale using the following formula: L log =log 10 (Lux+1) Motion compensation: When the device acceleration is greater than 2m / s 2 When the text is in motion, it is determined to be in mobile 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 13 is: Where: k is the ambient light compensation coefficient; PPI is the pixel density; L d is the real-time visual distance; L s is the diagonal length of the screen; The ratio of the current IMU acceleration to the maximum range.

4. The method according to claim 2, characterized in that The steps 1 to 4 are specifically as follows: Dynamic range constraint: limits the calculation results to the range of 12px×12px to 24px×24px to prevent display abnormalities in extreme environments; Direction adaptation: Automatically increase the font size and width by 20% in landscape mode; Historical data cache: retains the most recent 10 sets of threshold data for mutation detection.

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

6. The method according to claim 1, characterized in that The step three specifically includes the following steps: Step 31: receiving display area parameters collected from the standardized interface and text features from the semantic parsing module; Step 32: Calculate Chinese information entropy according to the improved Chinese information entropy formula; The improved Chinese information entropy formula is: Among them, 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; Step 33: Generate level 0-3 compression instructions.

7. The method according to claim 6, characterized in that The method for generating level 0-3 compression instructions in step 33 is: if E zh ≥3.0: Level 0: no compression; elif 2.5≤E zh <3.0: First-level compression: remove redundant particles and modal words to retain the core meaning; elif 1.8≤E zh <2.5: Secondary compression, using a synonym library to replace long phrases, supports dialect standardization; else: Three-level compression, generating GBK reversible mark.

8. The method according to claim 6, characterized in that The step 4 specifically includes the following steps: Step 41: 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. Finally, establish a mapping relationship table between the compression tag and the original text and store it in the device's local encrypted database; Step 42: Rendering agent, detects compression marks in text stream in real time through regular expressions, then calls decryption API according to the mapping table to restore the original text, and finally calculates the decryption result based on the current display area S. a Select one of the three display modes: full rendering mode, marker suspension mode, and permanent marker mode; Step 43: Cross-platform synchronization: Encode the mapping relationship table into a JWT token using the RFC 7519 standard, digitally sign the token using the device fingerprint, and synchronize the rendering status of Android, iOS, and Web.

9. An adaptive regional Chinese text compression and display system, characterized in that: The system uses the adaptive regional Chinese text compression and display method according to any one of claims 1 to 8, and the system includes: Acquisition and preprocessing module: collect dynamic display parameters and preprocess the collected parameters; Chinese text semantic structure analysis module: The Chinese semantic parsing module performs semantic structure analysis on the input English text; Text compression module: Based on the pre-processed display parameters and the Chinese text after semantic structure analysis, the text compression level is calculated based on the spatial density evaluation matrix and the compression strategy is implemented; Text storage and rendering module: stores original text data and text rendering through a reversible compression marking system.

10. A limited area Chinese text display system for intelligent terminal equipment, characterized in that: Use the method according to any one of claims 1 to 8.

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