Self-adaptive region English text compression display method and system

Abstract

The invention belongs to the technical scheme of human-computer interaction interface optimization, and particularly relates to a self-adaptive region English 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 semantic segmentation module; 3, based on the display parameters preprocessed in the step 1 and the English text subjected to semantic structure analysis in the step 2, calculating a text compression level based on a spatial density evaluation matrix, and adopting a three-level progressive 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 problems of semantic integrity and cross-scene adaptability when the English text is displayed on the intelligent terminal equipment in a limited area.

Description

Technical Field

[0001] The invention belongs to a technical solution for optimizing a human-computer interaction interface, and specifically relates to an adaptive regional English text compression display method and a system thereof. Background Art

[0002] As smart terminal devices develop towards miniaturization and high-density display, traditional text presentation methods face severe challenges in limited screen area. The current mainstream technology mainly adopts two solutions: text truncation and static abbreviation rule library, but there are significant problems with semantic integrity and cross-scenario adaptability in practical applications.

[0003] Although the truncation method based on character omission (such as adding the "..." symbol) can quickly reduce the length of the text, it destroys the grammatical structure and logical coherence of the original text. Experimental data show that when the text retention rate is lower than 65%, the information loss rate shows an exponential growth trend, and its mathematical model can be expressed as:

[0004]

[0005] In actual testing of medical diagnosis reports (sample size N = 150), this method resulted in a 22.3% misreading rate of key indicators. For example, when "negative for malignancy, but requires follow-up" was truncated to "negative for malignancy...", 30% of the testers were misjudged as not requiring a follow-up, which seriously threatened medical safety.

[0006] Existing systems generally use predefined abbreviation comparison tables to process text compression, but do not consider the specificity of terminology systems in different fields. In cross-domain tests, the abbreviation conflict rate between legal clauses and scientific literature reached 17.8%. A typical example is that "CPR" should be interpreted as "Condition Precedent to Renewal" in the legal context, but was mistakenly replaced with "Cardiopulmonary Resuscitation" in the medical context. In addition, this method has systematic defects in parsing the scope of English negation. When processing sentences such as "not A or B", the probability of incorrect parsing as "not A or not B" is as high as 41.7%, far exceeding the 3.2% error benchmark of manual processing.

[0007] The root causes of the above defects can be traced back to two major technical aspects:

[0008] 1. Lack of dynamic spatial evaluation mechanism: Existing solutions do not establish a quantitative correlation model between display density and physical parameters, resulting in the disconnection between compression decisions and the real display environment. For example, in the scenario of changing viewing distance, the text compression strategy with a fixed pixel density cannot be adaptively adjusted. When the user moves from a viewing distance of 30 cm to 80 cm, the traditional method maintains the same compression level, causing a 58% decrease in readability.

[0009] 2. Ignoring the morphological features of English: Mainstream algorithms do not fully consider the language characteristics of English as an inflectional language, resulting in the failure of stem extraction and semantic restoration. Tests show that when dealing with irregular verb inflections (such as "swam→swim") and compound nouns (such as "mother-in-law"), the word form restoration error rates of existing tools reach 34% and 29% respectively, seriously distorting the core semantics of professional texts. In the processing of biological patent documents, "cross-referenced" is wrongly decomposed into "cross+referenced" instead of the correct root "cross-reference", resulting in a 38% loss of semantic association.

[0010] The above technical defects have substantially hindered the improvement of the text interaction capabilities of intelligent devices in multilingual and multi-scenario environments. Especially in fields with strict information accuracy requirements such as autonomous driving vehicle HUDs and wearable medical devices, existing solutions cannot meet the dual standards of real-time performance and accuracy. The industry urgently needs an adaptive compression method that integrates spatial perception and language characteristics to achieve dynamic optimization of layout while ensuring semantic integrity. Summary of the Invention

[0011] The present invention provides an adaptive regional English text compression and display method to solve the problems of semantic integrity and cross-scenario adaptability when displaying English text on intelligent terminal devices with limited areas.

[0012] The present invention provides an adaptive regional English text compression and display system for implementing an adaptive regional English text compression and display method.

[0013] The present invention is realized through the following technical solutions:

[0014] An adaptive regional English text compression and display method, the method comprising the following steps:

[0015] Step 1: Collect dynamic display parameters and preprocess the collected parameters;

[0016] Step 2: Perform semantic structure analysis on the input English text through a semantic segmentation module;

[0017] Step 3: Based on the display parameters preprocessed in Step 1 and the English 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;

[0018] Step 4: Store the original text data and text rendering through a reversible compression marking system.

[0019] Furthermore, the specific steps of Step 1 are as follows:

[0020] Step 1-1: Receive the physical size, i.e., length × width, and match the parameter range according to the display screen type;

[0021] Step 1-2: Obtain the area S of the effective pixel region through the display driver API a , pixel density;

[0022] Step 1-3: Calibrate the viewing distance parameter, and dynamically adjust the text compression ratio based on the ratio of the viewing distance D d to the screen size D r .

[0023] Furthermore, the specific steps of Step 2 are as follows:

[0024] Step 2-1: Perform dual-channel parsing of morphology and syntax; at the morphology layer, through English morphological analysis, implement word form reduction and compound word decomposition, integrate an irregular verb inflection library, and the compound word decomposition rule library contains a special term splitting comparison table for the medical and biotechnology fields to achieve high-precision word segmentation; at the syntax layer, construct a dependency tree to identify the negation scope and logical connectives;

[0025] Step 2-2: Output a semantic tag tree: a three-level structure including entities, states, and operation instructions.

[0026] Furthermore, the specific steps of Step 3 are as follows:

[0027] Step 3-1: According to the display parameters collected in Step 1, calculate the spatial information entropy and the setting of the hierarchical entropy value through a formula; specifically,

[0028] The formula for the spatial information entropy threshold is:

[0029] E = (log2(1 + S a / S t )) × (1 + 0.2D d / D r )

[0030] where S a is the available display area; S t is the estimated area for text rendering; D d is the designed viewing distance; D rFor actual measurement of the line-of-sight distance.

[0031] The classification threshold is set as follows: the basic threshold of the terminal device is 0.3, and its dynamic compensation coefficient is 0.05 × IMU vibration data; the basic threshold of the fixed screen is 0.25, and its dynamic compensation coefficient is 0.03 × ambient light intensity.

[0032]

[0033] Where V d represents the vibration intensity of the device.

[0034] Step 3-2: Execute a three-level compression strategy on the number of semantic tags generated in Step 2. The triggering method of the three-level compression strategy is as follows.

[0035] When E < 0.3,

[0036] Execute three-level compression to generate Base64-encoded tags, which support click restoration.

[0037] When 0.3 ≤ E < 0.7,

[0038] Execute two-level compression, enable the domain term library, and perform synonym replacement.

[0039] When E ≥ 0.7,

[0040] Execute one-level compression, delete redundant words, and retain core entities.

[0041] Furthermore, the specific content of Step 4 is as follows: The reversible compression marking system includes storing the original text and text dynamic rendering optimization.

[0042] The cross-device synchronization mechanism is specifically as follows: Transmit compression strategy data through WiFi / Bluetooth and store historical compression records in the cloud.

[0043] Furthermore, the reversible compression marking system specifically includes semantic-driven chunking, spatial parameter associated storage, and dynamic mapping table construction.

[0044] The semantic-driven chunking divides text chunks based on the hierarchical structure of the semantic tag tree, including entity chunks and logical chunks.

[0045] The spatial parameter associated storage includes a metadata header attached to each compressed chunk and all parameters required for initial data recovery.

[0046] The dynamic mapping table construction establishes a multi-dimensional access path through an inverted index.

[0047] 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.

[0048] An adaptive region English text compression display system, which uses the above-mentioned adaptive region English text compression display method, and the system includes:

[0049] Collection and preprocessing module: Collect dynamic display parameters and preprocess the collected parameters;

[0050] English text semantic structure analysis module: Perform semantic structure analysis on the input English text through the semantic segmentation module;

[0051] Text compression module: Based on the preprocessed display parameters and the English text after semantic structure analysis, calculate the text compression level based on the space density evaluation matrix, and adopt a three-level progressive compression strategy;

[0052] Text storage and rendering module: Store the original text data and text rendering through the reversible compression marking system.

[0053] A limited region English text display system for intelligent terminal devices, which uses the above-mentioned method.

[0054] A computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned method is implemented.

[0055] The beneficial effects of the present invention are:

[0056] The present invention displays English text on intelligent terminal devices with limited regions, while taking into account semantic integrity and cross-scenario adaptability.

[0057] The present invention is particularly applicable to intelligent terminal devices with limited display regions (such as in-vehicle HUDs, smart watches, industrial control panels).

[0058] The present invention realizes intelligent reduction and semantic preservation of English text by integrating deep learning and linguistic rules. Description of the Drawings

[0059] Figure 1 is a flowchart of the method of the present invention.

[0060] Figure 2 is a flowchart of the method for dynamically collecting and preprocessing display parameters of the present invention.

[0061] Figure 3 is a flowchart of the method of the present invention for performing semantic structure analysis on the input text through the semantic segmentation module.

[0062] Figure 4 is a flowchart of the method of the present invention for calculating the text compression level based on the space density evaluation matrix and implementing a three-level progressive compression strategy.

[0063] Figure 5 is a flowchart of the method for storing the original text data and text rendering through the reversible compression marking system of the present invention. Detailed implementation manners

[0064] In the following description, specific details such as specific system structures and technologies are proposed for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also 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 unnecessary details from interfering with the description of the present application.

[0065] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0066] It should also be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0067] The following combines the attached Figures 1-5 of the specification of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present application.

[0068] Many specific details are set forth in the following description in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0069] Embodiment 1

[0070] This embodiment provides an adaptive region English text compression display method. As Figure 1 shown, the method includes the following steps:

[0071] Step 1: Collect dynamic display parameters and preprocess the collected parameters;

[0072] Step 2: Perform semantic structure analysis on the input English text through the semantic segmentation module;

[0073] Step 3: Based on the display parameters preprocessed in Step 1 and the English 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;

[0074] Step 4: Store the original text data and text rendering through the reversible compression marking system.

[0075] Furthermore, as Figure 2 shown, Step 1 specifically includes the following steps:

[0076] Step 1-1: Receive the physical size, i.e., length × width, match the parameter range according to the display screen type (indoor / outdoor). For example, the brightness of an outdoor screen needs to reach 5000 - 8000 nits / m 2 , and the pixel pitch of an indoor screen is smaller;

[0077] Step 1-2: Analyze the pixel density value, adopt a high-density pixel configuration (such as 27777 dots / m 2 ), and calculate the effective display area in combination with the screen resolution (such as 2560×1440);

[0078] Step 1-3: Calibrate the viewing distance parameter, and dynamically adjust the text compression ratio based on the ratio of the viewing distance D d to the screen size D r .

[0079] Furthermore, as Figure 3 shown, Step 2 specifically includes the following steps:

[0080] Step 2-1: Perform dual-channel parsing of morphology and syntax; at the morphology layer, through English morphological analysis, implement word form reduction and compound word decomposition, integrate an irregular verb inflection library, and the compound word decomposition rule library contains a special term splitting comparison table for the medical and biotechnology fields to achieve high-precision word segmentation; at the syntax layer, construct a dependency relationship tree to identify the negation scope and logical connectives;

[0081] Step 2-2: Output a semantic label tree: including a three-level structure of entities (such as device model #NVMCtrl), status (such as #System_Error), and operation instructions (such as AT25).

[0082] Furthermore, as Figure 4 shown, Step 3 specifically includes the following steps:

[0083] Step 3-1: Calculate the spatial information entropy and the setting of the hierarchical entropy value according to the formula; specifically,

[0084] The formula for the spatial information entropy threshold is:

[0085] E = (log2(1 + S a / S t )) × (1 + 0.2D d / D r )

[0086] Wherein, S a is the available display area (unit: mm 2 ); S t is the estimated area for text rendering; D d is the designed viewing distance (preset value); D r is the actually measured viewing distance (obtained through the TOF sensor).

[0087] The grading threshold is set as follows: The basic threshold of the terminal device is 0.3, and its dynamic compensation coefficient is 0.05 × IMU vibration data; the basic threshold of the fixed screen is 0.25, and its dynamic compensation coefficient is 0.03 × ambient light intensity;

[0088] Device type Base threshold Dynamic compensation coefficient Mobile terminal 0.3 0.05 × IMU vibration data Fixed screen 0.25 0.03 × Ambient light intensity

[0089]

[0090] Wherein, V d represents the device vibration intensity (obtained through the IMU sensor);

[0091] Step 3-2: Execute a three-level compression strategy on the number of semantic tags generated in Step 2. The triggering method of the three-level compression strategy is,

[0092] When E < 0.3,

[0093] Execute three-level compression (semantic tag replacement) to generate Base64-encoded tags (such as PA#SE), and support click restoration (SHA-256 verification to prevent tampering);

[0094] When 0.3 ≤ E < 0.7,

[0095] Execute two-level compression (synonym optimization), enable the domain term library (USPTO standard 3), and execute synonym replacement (such as "state-of-the-art" → "SOTA");

[0096] When E ≥ 0.7,

[0097] Execute one-level compression (stem extraction), delete redundant words (articles / conjunctions), and retain core entities (such as "Systemmethod" instead of "The system and method").

[0098] Furthermore, such as Figure 5As shown, step four is specifically as follows: The reversible compression tagging system includes storing the original text and optimizing the dynamic rendering of text.

[0099] The cross-device synchronization mechanism is specifically to transmit compression policy data through WiFi / Bluetooth and store historical compression records in the cloud.

[0100] Furthermore, the reversible compression tagging system specifically divides the original text into multiple semantic / space logical units and establishes a dynamic mapping relationship with semantic tags and display parameters, including semantic-driven chunking, spatial parameter associated storage, and dynamic mapping table construction.

[0101] 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 negation scope).

[0102] The spatial parameter associated storage includes a metadata header attached to each compressed block and all the parameters required for restoring the original data.

[0103] Example of metadata structure:

[0104]

[0105]

[0106] The dynamic mapping table construction establishes a multi-dimensional access path through an inverted index.

[0107]

[0108] Furthermore, the dynamic rendering optimization of text includes using a polar coordinate layout algorithm on mobile devices and using a sound unit to synchronize audio prompts on fixed devices.

[0109] The dynamic rendering of text is specifically

[0110] Input: Block-compressed text data.

[0111] Processing flow: Decompression (decompress the original text stream using algorithms such as LZ4), glyph generation (parse the font file through the FreeType library to generate character bitmaps), layout calculation (wrap lines automatically according to the screen width and calculate the line height and character positions), pixel drawing (the finally rendered bitmap).

[0112] Output: The finally rendered bitmap.

[0113] The specific block compression technology is a designed reversible data storage strategy. Its core is to divide the original text into multiple semantic / space logical units and establish a dynamic mapping relationship with semantic tags and display parameters. It specifically includes semantic-driven block division (dividing text blocks based on the hierarchical structure of the semantic tag tree) and spatial parameter associated storage (attaching a metadata header to each compressed block, containing all the parameters required for restoring the original data: such as block unique identifier, associated semantic tag, compression level, pixel density, minimum visible distance, etc. information). At the same time, a dynamic mapping table is constructed, and a multi-dimensional access path is established through an inverted index (such as semantic tag index, spatial parameter index).

[0114] The polar coordinate layout algorithm automatically adjusts the layout parameters according to the screen size to maintain visual consistency;

[0115] The fixed end uses a sounding unit for synchronous audio prompts, and through hardware clock synchronization and software protocol optimization, precise coordination of multiple sounding units is achieved.

[0116] Embodiment 2

[0117] This embodiment provides an adaptive region English text compression display system. The system uses the adaptive region English text compression display method described in Embodiment 1. The system includes:

[0118] Collection and preprocessing module: Collect dynamic display parameters and preprocess the collected parameters;

[0119] English text semantic structure analysis module: Perform semantic structure analysis on the input English text through a semantic segmentation module;

[0120] Text compression module: Based on the preprocessed display parameters and the English text after semantic structure analysis, calculate the text compression level based on a spatial density evaluation matrix, and adopt a three-level progressive compression strategy;

[0121] Text storage and rendering module: Store the original text data and text rendering through a reversible compression marking system.

[0122] Embodiment 3

[0123] This embodiment provides a limited region English text display system for intelligent terminal devices, which is implemented using an adaptive region English text compression display method.

[0124] A dedicated text rendering acceleration chip supporting OpenCL 2.0 parallel computing can be used;

[0125] A tactile feedback unit with a dynamic association between the vibration mode and the English compression level can be used.

[0126] Embodiment 4

[0127] An embodiment of the present invention provides an electronic device, which includes 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. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory and the processor are connected by a bus. Specifically, when the processor runs the computer program stored in the memory, any step in the first embodiment is implemented.

[0128] It should be understood that in the embodiment of the present invention, the so-called processor may be a central processing unit (CPU), and the processor 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. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0129] 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 part or all of the memory may also include a non-volatile random access memory.

[0130] As can be seen from the above, the electronic device provided by the embodiment of the present invention can implement the adaptive region English text compression display method described in the first embodiment by running a computer program, integrating deep learning and linguistic rules, and is applicable to the limited region English text display system of intelligent terminal devices.

[0131] The program instructions include a mixed execution mode of machine code and interpreted scripts;

[0132] The medium is built-in with an English term version control module that maintains a term library update log according to the ISO 8601 standard.

[0133] It should be understood that if the above 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 such understanding, to implement all or part of the processes in the above-described embodiments of the method of the present invention, it can also be completed by a computer program instructing relevant hardware. The above computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the above computer program includes computer program code, and the above computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The above computer-readable medium can include: any entity or device capable of carrying the above computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the above computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0134] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0135] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, 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. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present invention. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0136] It should be noted that the methods and their detailed examples provided in the above embodiments can be combined with the devices and equipment provided in the embodiments, and can be referred to each other, and will not be elaborated here.

[0137] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0138] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal devices and methods can be implemented in other ways. For example, the device / equipment embodiments described above are only illustrative. For example, the above division of modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0139] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An adaptive region English 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 English text through a semantic segmentation module; Step 3: Based on the display parameters preprocessed in Step 1 and the English text after semantic structure analysis in Step 2, calculate the text compression level based on a spatial density evaluation matrix and adopt a three-level progressive compression strategy; Step 4: Store the original text data and text rendering through a reversible compression marking system.

2. The method according to claim 1, wherein The specific steps of Step 1 include the following steps: Step 1-1: Receive the physical size, i.e., length × width, and match the parameter range according to the display screen type; Step 1 and 2: Obtain the area S of the effective pixel region through the display driver API a , and pixel density; Step 1-3: Calibrate the visible distance parameter, including the viewing distance D d and the screen size D r ratio.

3. The method according to claim 1, wherein The specific steps of Step 2 include the following steps: Step 2-1: Perform lexical-syntactic dual-channel parsing; at the lexical level, through English morphology analysis, implement word form reduction and compound word decomposition, integrate an irregular verb inflection library, and the compound word decomposition rule library contains a special term splitting comparison table in the medical and biotechnology fields to achieve high-precision word segmentation; at the syntactic level, construct a dependency relationship tree to identify the negation scope and logical connectives; Step 2-2: Output a semantic label tree: a three-level structure including entities, states, and operation instructions.

4. The method according to claim 1, wherein The specific steps of Step 3 include the following steps: Step 3-1: According to the display parameters collected in Step 1, calculate the spatial information entropy and the setting of the hierarchical entropy value through a formula; specifically, The formula for the spatial information entropy threshold is: E = (log2(1 + S a / S t )) × (1 + 0.2D d / D r ) Among them, S a is the available display area; S t is the estimated area for text rendering; D d is the designed viewing distance; D r is the actually measured viewing distance. The hierarchical threshold is set as follows: the basic threshold for the terminal device is 0.3, and its dynamic compensation coefficient is 0.05 × IMU vibration data; the basic threshold for the fixed screen is 0.25, and its dynamic compensation coefficient is 0.03 × ambient light intensity; Among which V d represents the vibration intensity of the device; Step 3-2: Perform a three-level compression strategy on the semantic label tree generated in Step 2. The triggering method of the three-level compression strategy is When E < 0.3, Perform three-level compression to generate a Base64-encoded label, supporting click restoration; When 0.3 ≤ E < 0.7, Perform two-level compression, enable the domain term library, and perform synonym replacement; When E ≥ 0.7, Perform one-level compression, delete redundant words, and retain core entities.

5. The method according to claim 2, wherein The specific content of Step 4 is: The reversible compression marking system includes storing the original text and text dynamic rendering optimization; The cross-device synchronization mechanism is specifically: transmit the compression strategy data through WiFi / Bluetooth and store the historical compression records in the cloud.

6. The method according to claim 5, wherein The reversible compression marking system specifically includes semantic-driven chunking, spatial parameter associated storage, and dynamic mapping table construction; The semantic-driven chunking divides text chunks based on the hierarchical structure of the semantic label tree, including entity chunks and logical chunks; The spatial parameter associated storage includes a metadata header attached to each compressed chunk and all parameters required for original data recovery; The dynamic mapping table construction establishes a multi-dimensional access path through an inverted index.

7. The method according to claim 6, wherein The text dynamic rendering optimization includes using a polar coordinate layout algorithm on the mobile side and using a sound generating unit to synchronize audio prompts on the fixed side.

8. An adaptive region English text compression display system, characterized in that The system uses the adaptive region English text compression display method as described in any one of claims 1-7. The system includes: A collection and preprocessing module: collect dynamic display parameters and preprocess the collected parameters; English text semantic structure analysis module: performs semantic structure analysis on the input English text through the semantic segmentation module; Text compression module: calculates the text compression level based on the preprocessed display parameters and the English text after semantic structure analysis, and adopts a three-level progressive compression strategy; Text storage and rendering module: stores the original text data and text rendering through the reversible compression marking system.

9. A limited area English text display system for an intelligent terminal device, characterized in that Use the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1-7 is implemented.

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