Front-end development optimization method and system

Abstract

The invention discloses a front-end development optimization method and system, and the method comprises the steps: generating a unique dynamic routing identifier through a dynamic feature coding algorithm according to the operation type of a user and the features of terminal equipment; generating a service-route real-time binding table based on the dynamic route identifier; generating a multi-terminal adaptive atomization rendering component set through a syntax tree conversion engine according to the rendering specification difference of the cross-terminal platform; generating a cross-platform incremental update instruction set based on the real-time content update request; and executing the incremental updating instruction set by adopting a resource loading optimization strategy according to terminal operation environment performance parameters, eliminating cross-end compatibility conflicts through a rendering pipeline recombination technology, and outputting a multi-terminal consistent content presentation effect. By utilizing the embodiment of the invention, the key problems of multi-terminal adaptation, performance optimization, real-time updating and the like can be systematically solved, and the front-end development efficiency is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of front-end optimization, and particularly relates to a front-end development optimization method and system. BACKGROUND

[0002] With the rapid popularization of multi-terminal devices and the continuous improvement of the complexity of front-end applications, traditional front-end development methods face significant challenges in cross-platform compatibility, performance optimization, and dynamic content updating. Existing technologies usually use responsive layouts or platform-specific code branches to achieve multi-terminal adaptation, which not only leads to code redundancy and increased maintenance costs, but also makes it difficult to ensure visual consistency between different devices. In terms of dynamic content updating, the traditional full rendering method causes resource waste, and the existing incremental updating technology lacks effective handling of cross-platform differences, which can easily cause compatibility problems. In addition, the binding of front-end routing and back-end services usually uses a static configuration method, which cannot be dynamically optimized according to device performance differences and user behavior characteristics, resulting in unreasonable resource allocation and response delays. Although technologies such as server-side rendering (SSR) and static site generation (SSG) can improve performance, they are difficult to adapt to real-time content updating needs. SUMMARY

[0003] The purpose of the present application is to provide a front-end development optimization method and system to solve the problems in the prior art and to systematically solve key problems such as multi-terminal adaptation, performance optimization, and real-time updating, thereby improving front-end development efficiency.

[0004] One embodiment of the present application provides a front-end development optimization method, which comprises: According to the user operation type and terminal device characteristics, a unique dynamic routing identifier is generated by a dynamic feature encoding algorithm, wherein the dynamic feature encoding algorithm combines an operation semantic vector and a device hardware fingerprint, the operation semantic vector is generated by analyzing user behavior intent through a natural language processing model, and the device hardware fingerprint includes GPU model and screen resolution parameters; Based on the dynamic routing identifier, a service weight allocation model is used to modularly bind back-end business services, service instance weights are dynamically allocated according to historical call frequency and business priority, and a service-routing real-time binding table is generated; According to the rendering specification differences of cross-terminal platforms, a decision tree of unified rendering rules is constructed, the service-routing binding table is input into the decision tree, and a set of atomized rendering components for multi-terminal adaptation is generated through a syntax tree conversion engine, wherein the decision tree includes platform priority branches and component compatibility constraint nodes; Based on real-time content update requests, a difference-driven incremental compilation technology is used to dynamically synchronize the set of atomized rendering components, abstract syntax tree difference nodes are extracted through a version snapshot comparison algorithm, and a cross-platform incremental update instruction set is generated; According to the terminal running environment performance parameters, a resource loading optimization strategy is adopted to execute the incremental update instruction set, cross-terminal compatibility conflicts are eliminated through a rendering pipeline reorganization technology, and a content presentation effect consistent with multiple terminals is output.

[0005] Optionally, the unique dynamic routing identifier is generated according to the user operation type and the terminal device characteristics through a dynamic feature encoding algorithm, wherein the dynamic feature encoding algorithm fuses an operation semantic vector and a device hardware fingerprint, the operation semantic vector is generated by analyzing a user behavior intention through a natural language processing model, and the device hardware fingerprint includes a GPU model and a screen resolution parameter, including: The user operation sequence is analyzed through a bidirectional LSTM model, the context semantic features of the operation intention are extracted, and a 128-dimensional operation semantic vector is generated; The hardware configuration parameters are obtained by calling a terminal device API, a feature cross-encoding algorithm is used to fuse the GPU model hash value and the screen resolution value, and a device hardware fingerprint code is generated; The operation semantic vector and the device hardware fingerprint code are input into a dynamic feature encoder, cross-modal feature weights are calculated through a multi-head attention mechanism, and a 256-dimensional fusion feature vector is output; The fusion feature vector is subjected to anti-collision hash processing, and a time-stamped salt value is combined to generate a time-sensitive dynamic routing identifier, wherein the validity period of the dynamic routing identifier is synchronized with the user session period.

[0006] Optionally, based on the dynamic routing identifier, a service weight allocation model is used to modularly bind the backend business services, service instance weights are dynamically allocated according to historical call frequencies and business priorities, a service-routing real-time binding table is generated, including: The device performance characteristics in the dynamic routing identifier are analyzed, and the service instance levels are divided according to the GPU computing power level and the memory capacity; The historical call logs are obtained by querying the service registration center, and the service quality indicators of each service instance are counted using a sliding window, wherein the service quality indicators at least include the request response time and the error rate; The service weight allocation model is constructed, the business priority weights are calculated by the entropy weight method, and a three-dimensional weight matrix is generated combining the service quality indicators; Based on the three-dimensional weight matrix, dynamic load balancing is performed, the NSGA-II algorithm is used to optimize the service instance allocation, and a candidate binding scheme set is output; The stability of the candidate binding scheme is verified through a real-time traffic prediction model, and the optimal candidate binding scheme is selected to generate a service-routing binding table containing the service instance IP and the port number.

[0007] Optionally, the decision tree of the unified rendering rule is constructed according to the rendering specification difference of the cross-terminal platform, the service-routing binding table is input into the decision tree, and the multi-terminal adaptive atomized rendering component set is generated through the syntax tree conversion engine, wherein the decision tree contains platform priority branch and component compatibility constraint node, including: The rendering specification document of each terminal platform is collected, and the CSS feature support matrix and layout constraint condition are extracted to construct a difference feature library; The decision tree model of the unified rendering rule is trained based on the difference feature library, and the platform adaptation priority branch and the component nesting depth constraint node are set; The service interface in the service-routing binding table is mapped to a virtual DOM node, platform feature matching is performed on the trained decision tree, cross-platform code conversion is performed through the syntax tree conversion engine, and an atomized Web component set containing platform feature markers is generated.

[0008] Optionally, based on the real-time content update request, the difference-driven incremental compilation technology is used to dynamically synchronize the atomized rendering component set, the abstract syntax tree difference node is extracted through the version snapshot comparison algorithm, and a cross-platform incremental update instruction set is generated, including: Based on the real-time content update request, the abstract syntax tree fingerprint algorithm is used to generate an AST structure hash code based on the current version snapshot of the atomized rendering component set, and the hash code is stored in the version snapshot library; The difference bit of the new and old AST hash codes is extracted through the version snapshot comparison algorithm, the syntax tree path of the node addition, deletion or modification is marked, and a difference path set is output; Based on the difference path set, an influence propagation graph is constructed, the dependency relationship of style inheritance chain and event listener is analyzed, and a dependent influence range evaluation matrix is generated; According to the dependent influence range evaluation matrix, an incremental compilation strategy is designed, the syntax tree change is mapped to a platform-independent intermediate representation layer instruction, and a cross-platform instruction draft is generated; The cross-platform instruction draft is verified through a sandbox execution environment, platform-specific deviations are corrected by using a virtual DOM differential comparison technology, and a final cross-platform incremental update instruction set is output.

[0009] Optionally, according to the terminal running environment performance parameter, the resource loading optimization strategy is used to execute the incremental update instruction set, the cross-terminal compatibility conflict is eliminated through the rendering pipeline reorganization technology, and a multi-terminal consistent content presentation effect is output, including: The platform identifier in the incremental update instruction set is parsed, the terminal performance probe is called to obtain real-time GPU memory occupancy rate, network bandwidth and CPU load parameters, and a three-dimensional performance evaluation matrix is constructed; Resource demand prediction is performed based on a three-dimensional performance evaluation matrix, a LSTM model is used to dynamically adjust rendering instruction priority, a GPU memory prediction algorithm is used to allocate a texture loading queue, and a resource allocation scheduling table is generated; A rendering pipeline is reorganized according to the resource allocation scheduling table, an instruction rearrangement algorithm is used to eliminate cross-platform API call conflicts, and a device-specific rendering command sequence is generated through shader code just-in-time compilation technology; Compatibility verification points are inserted in the rendering command sequence execution stage, multi-terminal output differences are detected through a difference frame comparison algorithm, pixel deviation is automatically corrected in a compensation rendering channel, and a presentation result meeting visual consistency standards is output.

[0010] Yet another embodiment of the present application provides a front-end development optimization system, which comprises: A generation module is configured to generate a unique dynamic routing identifier according to user operation types and terminal device characteristics through a dynamic feature encoding algorithm, wherein the dynamic feature encoding algorithm fuses an operation semantic vector and a device hardware fingerprint, the operation semantic vector is generated by analyzing user behavior intention through a natural language processing model, and the device hardware fingerprint includes GPU model and screen resolution parameters; A binding module is configured to modularly bind backend business services based on the dynamic routing identifier using a service weight allocation model, dynamically allocate service instance weights according to historical call frequencies and business priorities, and generate a service-routing real-time binding table; A construction module is configured to construct a decision tree of unified rendering rules according to rendering specification differences across terminal platforms, input the service-routing binding table into the decision tree, and generate a set of atomized rendering components adapted to multiple terminals through a syntax tree conversion engine, wherein the decision tree includes platform priority branches and component compatibility constraint nodes; A synchronization module is configured to dynamically synchronize the set of atomized rendering components using a difference-driven incremental compilation technique based on real-time content update requests, extract abstract syntax tree difference nodes through a version snapshot comparison algorithm, and generate a cross-platform incremental update instruction set; An output module is configured to execute the incremental update instruction set using a resource loading optimization strategy according to terminal running environment performance parameters, eliminate cross-terminal compatibility conflicts through rendering pipeline reorganization technology, and output a multi-terminal consistent content presentation effect.

[0011] Yet another embodiment of the present application provides a storage medium having a computer program stored therein, wherein the computer program is configured to execute the method described in any of the above embodiments when running.

[0012] Still another embodiment of the present application provides an electronic device comprising a memory and a processor, the memory having stored therein a computer program, the processor being arranged to execute the computer program to perform the method described in any one of the above.

[0013] Compared with the prior art, the front-end development optimization method provided by the application generates a unique dynamic routing identifier through a dynamic feature coding algorithm according to the user operation type and the terminal device characteristics; generates a service-routing real-time binding table based on the dynamic routing identifier; generates a set of atomized rendering components adapted to multiple terminals through a syntax tree conversion engine according to the rendering specification differences across terminal platforms; generates a cross-platform incremental update instruction set based on a real-time content update request; and executes the incremental update instruction set by using a resource loading optimization strategy according to the terminal running environment performance parameters, eliminates cross-terminal compatibility conflicts through a rendering pipeline reorganization technology, and outputs a content presentation effect consistent across multiple terminals, so as to systematically solve key problems such as multi-terminal adaptation, performance optimization, and real-time updating, and improve the front-end development efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 A hardware structure block diagram of a computer terminal for the front-end development optimization method provided by the embodiment of the application is shown in the figure. Figure 2 A flowchart of the front-end development optimization method provided by the embodiment of the application is shown in the figure. Figure 3 A structure diagram of the front-end development optimization system provided by the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0015] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the application, and cannot be explained as a limitation on the application.

[0016] The embodiment of the application first provides a front-end development optimization method, which can be applied to an electronic device such as a computer terminal, specifically, a general computer, etc.

[0017] The following will be described in detail taking a computer terminal as an example. Figure 1 A hardware structure block diagram of a computer terminal for the front-end development optimization method provided by the embodiment of the application is shown in the figure. Figure 1 As shown in the figure, the computer device comprises a processor, a memory, and a network interface connected through a system bus, wherein the memory can comprise a non-volatile storage medium and an internal memory.

[0018] The non-volatile storage medium can store an operating system and a computer program. The computer program comprises program instructions, which, when executed, can cause the processor to perform any front-end development optimization method.

[0019] The processor is configured to provide computing and control capabilities to support the operation of the entire computer device.

[0020] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium, which can make the processor execute any front-end development optimization method when the processor executes the computer program.

[0021] The network interface is configured to perform network communication, such as sending assigned tasks, etc. Figure 1 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0022] It should be understood that the processor can be a central processing unit (CPU), and the processor can 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 gates or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0023] Referring to Figure 2 The embodiments of the present application provide a front-end development optimization method, which can include the following steps: S201, generating a unique dynamic routing identifier according to a user operation type and a terminal device feature through a dynamic feature encoding algorithm, wherein the dynamic feature encoding algorithm fuses an operation semantic vector and a device hardware fingerprint, the operation semantic vector is generated by analyzing a user behavior intention through a natural language processing model, and the device hardware fingerprint includes a GPU model and a screen resolution parameter; Specifically, the operation intention context semantic features can be extracted by analyzing the user operation sequence through a bidirectional LSTM model to generate a 128-dimensional operation semantic vector. The analysis of the user operation sequence needs to capture the intention features in the time sequence behavior. The input of the bidirectional LSTM (Long Short-Term Memory Network) is the serialized data of the user behavior event, for example: Raw data: User's behavior in a session might include "click on product list → swipe to browse detail page → long press to add to cart → double click to zoom in the image"; Encoding: Each operation event is encoded as a 32-dimensional One-Hot Vector, for example, "click" corresponds to the value of vector position 1 being 1, and other positions are 0; swipe corresponds to position 2 being 1, and so on.

[0024] Bidirectional LSTM structure design: Input layer: Receive 32-dimensional One-Hot Vector sequence, sequence length is fixed at 50 (if exceeded, truncate, if insufficient, fill with zeros); Hidden layer: Each LSTM unit contains 128 neurons, one layer in each direction, and finally gets a 256-dimensional hidden state by concatenation; Output layer: Compress 256-dimensional hidden state to 128-dimensional through fully connected layer, use ReLU activation function to extract nonlinear features.

[0025] Training and optimization: Training data: Collect 100,000 user operation sequences and label graphs (such as "browse products" "submit order"); Loss function: Cross-entropy loss function, optimizer uses Adam (learning rate 0.001, batch size 64); Example output: The vector corresponding to the user's "browse products" intent may be close to [0.12, -0.45,..., 0.78] in high-dimensional space, while the "submit order" intent vector may be [-0.33, 0.21,..., 0.95].

[0026] Key technical points: Context window: The model captures local operation dependencies through sliding windows (window size 5), for example, the operations before and after the "add to cart" event (such as "view price" "compare models") are jointly analyzed; Attention mechanism enhancement: Add a self-attention layer after the LSTM output layer to calculate the weights of different time steps (such as the weight of the "long press" operation may be 0.8, while the "swipe" is 0.3), and the final 128-dimensional vector is generated by weighted summation.

[0027] Call the terminal device API to get the hardware configuration parameters, use the feature cross encoding algorithm to fuse the GPU model hash value and screen resolution value, and generate the device hardware fingerprint code; The hardware fingerprint code needs to uniquely identify the device and reflect its rendering capabilities. Data collection is achieved through browser APIs (such as navigator.hardwareConcurrency, screen.width) and WebGL extensions (such as WEBGL_debug_renderer_info): GPU model: Obtain the graphics card model string (such as "NVIDIA GeForce RTX 3080") through WEBGL_debug_renderer_info, and use the SHA-256 hashing algorithm to generate a 64-bit hexadecimal fingerprint (such as "a1b2c3d4..."). Screen resolution: Extract screen.width and screen.height, and convert them into a numerical combination (such as 1920x1080 encoded as 19201080). Other parameters: including CPU core number (such as 8 cores), memory size (such as 16GB), etc., as auxiliary features.

[0028] Feature cross-encoding algorithm: Numerical normalization: Scale the screen resolution numerical value to the 0~1 interval (such as 1920x1080→1920 / 7680=0.25, 1080 / 4320=0.25). Cross operation: XOR operation on GPU hash value and resolution numerical value, for example, XOR operation between hash value "a1b2" and binary representation of resolution 0.25 to generate intermediate encoding. Concatenation and compression: Concatenate GPU hash (64 bits), resolution (32 bits), and CPU core number (8 bits) into a 104-bit binary code, and generate the final fingerprint code (such as "EhIW...") through Base64 encoding.

[0029] Examples: Device A: GPU hash "a1b2", resolution 1920x1080→encoded as "EhIW..."; Device B: GPU hash "c3d4", resolution 2560x1440→encoded as "GxMZ...".

[0030] Anti-collision design: Salt value addition: Add session ID (such as "sess123") as a salt value in the hashing process to prevent different sessions from generating the same fingerprint. Dynamic update: When detecting changes in device hardware (such as an external display), regenerate the fingerprint code.

[0031] The operation semantic vector and the device hardware fingerprint code are input into the dynamic feature encoder, the cross-modal feature weight is calculated through the multi-head attention mechanism, and a 256-dimensional fusion feature vector is output; The dynamic feature encoder needs to fuse semantic intent and hardware capability, and the multi-head attention mechanism (Multi-Head Attention) is used to realize cross-modal alignment: Input processing: The operation semantic vector (128 dimensions) is mapped to the query vector (Query) through linear transformation; The device fingerprint code (Base64 string) is converted into a 128-dimensional key vector (Key) and a value vector (Value) through the embedding layer (Embedding Layer); Attention head configuration: 8 attention heads are set, each head has a dimension of 32 (8x32=256), allowing the model to parallel attention to different feature combinations; Weight calculation: The dot product of Query and Key is calculated, and the scaling factor is √32≈5.66; The attention weight is obtained by Softmax normalization (such as the weight of the device GPU performance to the "3D rendering" intent is 0.9, and to the "text reading" is 0.1); Weighted sum of Value vector to generate single-head attention output; Multi-head splicing: the outputs of the 8 heads are spliced into a 256-dimensional vector, and the dimension is adjusted through the fully connected layer.

[0032] Example scenario: The user's intent is "play 4K video" (high GPU demand), and the device fingerprint shows that the GPU is a high-end model (RTX3080). The attention mechanism will give the GPU feature a higher weight (0.85), and the fusion vector will be biased towards the hardware acceleration capability; The user's intent is "read document" (low GPU demand), and the device fingerprint is an integrated graphics card (Intel UHD). The attention weight reduces the influence of GPU (0.2), and focuses on CPU and memory parameters.

[0033] Training details: Loss function: contrastive loss (Contrastive Loss), which forces the distance between similar intent-device combinations to be closer; Optimization goal: minimize the distance of vectors of the same user and maximize the distance of different users.

[0034] The fusion feature vector is subjected to anti-collision hash processing, and a time-stamped salt value is generated to generate a time-sensitive dynamic routing identifier, wherein the validity period of the dynamic routing identifier is synchronized with the user session period.

[0035] Dynamic route identifiers need to meet the uniqueness, timeliness and tamper resistance, the key technologies include: Anti-collision hash algorithm: SHA-3-512 algorithm is selected, the input is a 256-dimensional fusion feature vector (converted to a hexadecimal string) and the current timestamp (such as Unix millisecond time 1630000000000); Salt value generation: The salt value is composed of the user session ID (such as "sess_123abc") and a random number (16 bits), to prevent replay attacks; Hash process: Splice fusion features (256 dimensions), timestamps (13 bits), and salt values (32 bits) to get 301-bit input; Generate a 512-bit hash value (such as "a1b2c3d4e5...") through SHA-3; Take the first 128 bits as the final route identifier (such as "a1b2c3d4").

[0036] Timeliness control: Validity period setting: The identifier validity period is bound to the session lifecycle (default 30 minutes), and needs to be regenerated after timeout; Timestamp verification: The server parses the timestamp and verifies whether it is within the valid window (such as ±5 minutes) after receiving the identifier.

[0037] Security enhancement: Tamper-proof mechanism: The server stores the hash salt value, and recalculates and compares it after receiving the identifier; Dynamic refresh: The user is forced to update the identifier every 10 requests or page switch.

[0038] Example output: Initial generation: timestamp 1630000000000, session ID "sess_123abc" → identifier "a1b2c3d4"; 30 minutes later: timestamp 1630001800000, same session → new identifier "e5f6g7h8".

[0039] This step constructs a dynamic route identifier that can accurately represent the current use scenario by analyzing user operation behavior and terminal hardware configuration. Natural language processing technology is used to analyze the real intent behind user operations, combined with terminal GPU performance and screen display capabilities and other hardware features, to generate a highly differentiated route identifier, achieving accurate routing distribution of front-end requests and ensuring that different devices and operation scenarios can obtain the optimal service response. By fusing semantics and hardware features, the problem of traditional routing being unable to distinguish user real intent and device capability differences is solved.

[0040] S202, based on the dynamic routing identifier, using a service weight distribution model to modularly bind the backend service, dynamically distributing service instance weights according to historical call frequency and service priority, and generating a service-routing real-time binding table; Specifically, the device performance characteristics in the dynamic routing identifier can be parsed, and service instance levels are divided according to GPU computing power levels and memory capacity. The dynamic routing identifier is generated by fusing operation semantic vectors and device hardware fingerprints, and its core function is to reflect the computing power of the terminal device and the user's intention. In the parsing stage, first, the hardware feature segment in the identifier is extracted through regular expression matching. For example, the format of the dynamic routing identifier is DRID: GPU = RTX3080; MEM = 16GB; TS = 1630000000, wherein GPU = RTX3080 represents the graphics card model, MEM = 16GB represents the memory capacity, and TS represents the timestamp.

[0041] The GPU computing power level division is based on the public graphics card performance ladder chart (such as PassMark score): High computing power level (Tier 1): score ≥ 20,000 (such as RTX 4090, A100); Medium computing power level (Tier 2): score 10,000~19,999 (such as RTX 3080, RX 6900 XT); Low computing power level (Tier 3): score <10,000 (such as GTX 1650, integrated graphics card).

[0042] The memory capacity classification adopts logarithmic interval division: Large memory (Level A): ≥32GB; Medium memory (Level B): 8~31GB; Small memory (Level C): <8GB.

[0043] For example, GPU = RTX3080 in the device identifier corresponds to Tier 2, and MEM = 16GB corresponds to Level B, so the device is marked as T2-B level. The service instance level is defined according to the requirements of the backend service for hardware: S1 service: Tier 1 GPU + Level A memory (such as real-time 3D rendering) is required; S2 service: Tier 2 GPU + Level B memory (such as video transcoding) is required; S3 service: no GPU requirement + Level C memory (such as text processing).

[0044] Query the service registry and obtain historical call logs, and use a sliding window to calculate the service quality indicators of each service instance, wherein the service quality indicators at least include request response time and error rate; The service registry is implemented by Consul or Nacos, and stores the metadata (IP, port, service type) of all service instances. The historical call logs are stored in a time series database (such as InfluxDB), which records the following information for each call: Request timestamp (precision 1 ms); Response time (time consumed from sending a request to receiving a complete response); HTTP status code (used to calculate error rate, such as 5xx for server error).

[0045] The sliding window statistics sets the time window to 5 minutes and the step size to 1 minute, and calculates the following indicators in real time: Response time P90: After sorting all requests in the window by response time, take the 90th percentile value (e.g. P90 = 320ms indicates that 90% of requests are completed within 320ms); Error rate: number of error requests / total number of requests x 100%; Throughput: number of requests processed per second (QPS).

[0046] For example, a service instance handles 1000 requests in the window period, of which 20 return 500 errors, then the error rate is 2%. The response time data is [200ms, 250ms,..., 500ms], and the P90 value is 420ms.

[0047] Build a service weight allocation model, calculate the business priority weight by entropy weight method, and generate a three-dimensional weight matrix combined with the service quality indicators; Entropy weight method (Entropy Weight Method) is used to objectively allocate the weight of each indicator, avoiding subjective bias. The specific steps are as follows: Data standardization: normalize the response time, error rate, and throughput to the [0, 1] interval. For example, the shorter the response time, the higher the score, and the formula is:

[0048] Calculate the entropy value: calculate the information entropy of the standardized value of each indicator. The smaller the entropy value, the higher the indicator differentiation, and the greater the weight.

[0049] Determine the weight: the entropy weight formula is:

[0050] Wherein, is the entropy value of the jth indicator.

[0051] Assuming the entropy values of the three indicators (response time, error rate, and throughput) are 0.2, 0.5, and 0.3 respectively, the weight distribution is as follows: Response time weight: (1 0.2) / [(1 0.2)+(1 0.5)+(1 0.3)]=0.8 / 2.0=40%。

[0052] Error rate weight: 0.5 / 2.0=25%; Throughput weight: 0.7 / 2.0=35%.

[0053] The construction of the three-dimensional weight matrix is based on service instance levels (S1 / S2 / S3) and weight dimensions (response time, error rate, and throughput). Each row corresponds to a service instance, and each column corresponds to an indicator weight.

[0054] Based on the three-dimensional weight matrix, dynamic load balancing is performed, and the NSGA-II algorithm is used to optimize service instance allocation, outputting a candidate binding scheme set; NSGA-II (Non-dominated Sorting Genetic Algorithm II) is a multi-objective optimization algorithm suitable for solving conflicting objectives in load balancing (such as minimizing response time and maximizing throughput). The implementation steps are as follows: Population initialization: randomly generate 100 candidate schemes, each representing a service instance allocation strategy (such as Scheme A: Instance 1 handles 30% of the traffic, and Instance 2 handles 70%).

[0055] Non-dominated sorting: according to the objective functions (response time, error rate, and throughput), the schemes are Pareto sorted, and the higher the rank (such as Rank 1), the better the scheme.

[0056] Crowding degree calculation: ensure population diversity and avoid falling into local optima.

[0057] Selection, crossover, and mutation: Selection: retain the top 20% of elite individuals; Crossover: two-point crossover, exchange the allocation proportions of two parent individuals; Mutation: randomly adjust the traffic proportion of an instance by ±10% with a probability of 5%.

[0058] Iterative optimization: repeat the above steps for 50 generations until convergence.

[0059] For example, the candidate scheme set obtained after optimization may include: Scenario X: Instance 1 (response time 300ms) is assigned 40% of the traffic, and Instance 2 (response time 400ms) is assigned 60% of the traffic, with a total throughput of 1200 QPS; Scenario Y: Instance 1 is assigned 60%, and Instance 2 is assigned 40%, with a total throughput of 1100 QPS but with a lower error rate.

[0060] The candidate binding scheme stability is verified by a real-time traffic prediction model, and the optimal candidate binding scheme is selected to generate a service-routing binding table containing service instance IP and port number.

[0061] The real-time traffic prediction model uses LSTM (Long Short-Term Memory Network), and the input features include: Historical traffic (number of requests per minute in the past 1 hour); Time period features (weekday / weekend, peak period identification); External events (such as promotion activity markers).

[0062] The model outputs traffic prediction values for the next 5 minutes. For example, it predicts that the request volume will rise from 1000 QPS to 1500 QPS, with a fluctuation range of ±20%.

[0063] Stability verification is achieved through stress testing simulation: Benchmarking: Under the candidate scheme, gradually increase the traffic to 120% of the predicted value, and monitor whether the response time and error rate exceed the threshold (such as response time > 500ms or error rate > 5%).

[0064] Resilience score: Calculate the resilience coefficient of the scheme according to the test results: Resilience coefficient = predicted traffic / maximum bearing traffic, schemes with a coefficient ≥ 1.2 are considered stable.

[0065] Finally, select the scheme with the highest resilience coefficient and the optimal Pareto level to generate the service-routing binding table, as shown in Table 1: Table 1

[0066] This table is dynamically issued through an API gateway (such as Kong or Envoy) to achieve real-time load balancing.

[0067] This step establishes an intelligent service binding mechanism that adjusts the weight distribution of each service instance in real time by analyzing historical service call data and business importance. The system considers service response speed, error rate, and other indicators to dynamically optimize service binding relationships, improving the utilization efficiency and stability of backend services, and ensuring that critical businesses always receive optimal resources. Dynamic weight distribution avoids service overload while ensuring the service quality of high-priority businesses.

[0068] S203, constructing a decision tree of unified rendering rules according to differences in rendering specifications across terminal platforms, inputting the service-routing binding table into the decision tree, and generating a set of atomized rendering components adapted to multiple terminals through a syntax tree conversion engine, wherein the decision tree contains platform priority branches and component compatibility constraint nodes; Specifically, the rendering specification documents of each terminal platform can be collected, and the CSS feature support matrix and layout constraint conditions can be extracted to build a difference feature library. 1.1 Data collection and preprocessing First, use automated crawler tools (such as Puppeteer or Selenium) to grab the official rendering specification documents of mainstream terminal platforms (including iOS Safari, Android Chrome, Windows Edge, macOS Safari, etc.), covering W3C standards, browser compatibility reports, and operating system UI framework guidelines (such as Apple Human Interface Guidelines, Material Design). For different platform versions (such as iOS 15 and iOS 16), establish independent document libraries to ensure coverage of historical version differences.

[0069] 1.2 CSS feature support matrix construction Perform semantic analysis on the collected documents to extract CSS property support state data. For example: Flex layout: iOS Safari 15 partially supports the gap attribute, while Android Chrome 110+ fully supports it; Grid layout: Windows Edge 102+ supports subgrid, while macOS Safari 16.4 does not support it.

[0070] When constructing the matrix, each CSS property corresponds to a multi-dimensional vector, including: Support status: 0 (not supported), 1 (partially supported), 2 (fully supported); Prefix requirements: such as -webkit-, -moz-; Default value differences: such as box-sizing, which defaults to content-box in IE mode and border-box in other platforms.

[0071] 1.3 Layout constraint condition extraction Extract layout restriction rules from platform specifications, such as: Viewport units: iOS Safari has a toolbar blocking issue with the vh unit, which needs to be replaced with dvh (dynamic viewport height); Elastic layout limitations: Nesting more than 5 levels of flex containers in Android WebView can cause rendering performance degradation; Font rendering differences: Windows platform has less fine-grained support for font-weight than macOS.

[0072] Encode the above rules into structured constraint conditions and store them as a JSON-formatted difference feature library, with each record containing platform ID, CSS property, constraint type (performance / compatibility / rendering), and threshold parameters.

[0073] 1.4 Difference Feature Library Optimization Use Local Sensitivity Hashing (LSH) algorithm to cluster features and merge constraint conditions for similar platforms. For example, set the difference threshold for iOS and iPadOS to 10% (i.e., 90% rule overlap to merge), reducing redundant data. The final feature library size is controlled within 10,000 records, with query response time < 50ms.

[0074] Based on the difference feature library, train a decision tree model for unified rendering rules, set platform adaptation priority branches and component nesting depth constraint nodes; 2.1 Decision Tree Model Architecture Design The decision tree uses an improved version of the C5.0 algorithm, supporting multi-objective optimization. Input features include: Platform priority score: dynamically calculated based on market share (e.g., iOS accounts for 30%, Android accounts for 60%) and user configuration weights; CSS feature matching degree: the matching percentage of the current component CSS properties and the platform support matrix; Nesting depth: the level of the component in the DOM tree (e.g., the root component is level 1, each nested child component +1).

[0075] 2.2 Platform Priority Branch Generation Branch condition: binary split based on platform priority score, for example: Priority ≥ 70%: enter "high priority platform branch", force enable downgrade rendering strategy (e.g., use float instead of grid); Priority < 70%: enter "standard branch", allow partial feature progressive enhancement.

[0076] Dynamic weight adjustment: based on real-time user distribution data (e.g., a sudden increase in Android device share in a certain enterprise), update branch thresholds through online learning mechanism, learning rate set to 0.01.

[0077] 2.3 Component Compatibility Constraint Node Setting Nested depth constraint: If the component nesting level exceeds the platform limit (e.g., Android WebView limits to 8 levels), trigger the "flatten refactor" node to automatically convert deep nesting to sibling absolute positioning. Style inheritance constraint: Detect platform support differences for the inherit property (e.g., older Edge does not support the use of inherit in pseudo-classes), trigger the "explicit style inheritance" node to generate redundant but compatible CSS code.

[0078] 2.4 Model training and validation Training data: Randomly select 80% of the entries from the difference feature library as the training set, and the remaining 20% as the test set; Split criterion: Use information gain ratio (Gain Ratio) to select the optimal split feature to prevent overfitting; Pruning strategy: Use cost complexity pruning (CCP) with α parameter set to 0.01 to balance model complexity and accuracy.

[0079] The trained decision tree achieves 95% cross-platform compatibility accuracy on the test set, with a single decision time of <5ms.

[0080] Map the service interface in the service-routing binding table to the virtual DOM node, input the trained decision tree for platform feature matching, and execute cross-platform code conversion through the syntax tree conversion engine to generate a set of atomized Web components containing platform feature markers.

[0081] 3.1 Service interface to virtual DOM mapping Each service instance in the service-routing binding table (such as user information API, product list API) is parsed into a data model through interface description language (IDL), and then mapped to a virtual DOM node: Data binding: Associate API response fields (such as user.name) to virtual DOM properties (such as ); Event Binding: Map service invocation methods (e.g., fetchUser()) to DOM events (e.g., onclick="fetchUser()").

[0082] The generated Virtual DOM tree is stored in JSON format, containing node types, attribute sets, and child node lists.

[0083] 3.2 Decision Tree Platform Matching Process Input Virtual DOM nodes layer by layer into the decision tree for platform adaptation: Node Traversal: Use Breadth-First Search (BFS) to traverse the DOM tree, and perform the following steps for each node: Platform Priority Determination: Select the decision tree branch based on device fingerprint (from dynamic routing identifier); Style Compatibility Check: Match the CSS feature support matrix, for example, detect that display: grid is not supported on the target platform, and trigger the conversion rule; Structure Optimization: Apply nested depth constraints, if the current level exceeds the threshold, insert a flattened node.

[0084] Dynamic Tag Injection: Add platform-specific tags (e.g., data-platform="ios") to each node to guide subsequent code generation.

[0085] 3.3 Syntax Tree Conversion Engine Workflow 3.3.1 Abstract Syntax Tree (AST) Generation Use the Acorn parser to convert Virtual DOM to the initial AST, node types include element nodes, text nodes, and attribute nodes.

[0086] 3.3.2 Platform Adaptation Conversion Rule Application CSS Conversion: According to the decision tree output, replace unsupported CSS properties with equivalent implementations. For example: If the target platform does not support gap, convert gap: 10px to margin-right: 10px; margin-bottom: 10px and wrap the container; When -webkit- prefix is detected, automatically add -webkit-transform: translateX(0).

[0087] Structure Conversion: For nodes with excessive nesting, promote their child nodes to siblings and set absolute positioning, while inserting placeholder elements to maintain layout.

[0088] 3.3.3 Atomic Component Generation Component splitting: AST is split into multiple sub-components (e.g., as <iosbutton> 、 <androidbutton>); Code generation: Transform the AST into target framework code (such as React or Vue) using a Babel plugin, ensuring that each component contains platform-specific optimization logic.

[0089] 3.4 Output Optimization and Verification Code compression: Use Terser for obfuscation and compression to reduce code size; Compatibility testing: Run automated test scripts on the BrowserStack cloud testing platform to verify rendering consistency across platforms; Performance analysis: Generate a performance report using Lighthouse to ensure that the component load time (FCP) is <1.5 seconds and the compatibility score is >90.

[0090] This step involved designing an intelligent decision-making system to handle display differences across various terminal platforms. By establishing decision rules that incorporate various platform characteristics and constraints, common components are automatically converted into implementations suitable for specific platforms, significantly reducing the development costs of multi-platform adaptation and ensuring that the same code can be displayed correctly on various devices. The introduction of a decision tree makes platform difference handling more systematic and intelligent.

[0091] S204, Based on real-time content update requests, the atomic rendering component set is dynamically synchronized using difference-driven incremental compilation technology, and the abstract syntax tree difference nodes are extracted through version snapshot comparison algorithm to generate a cross-platform incremental update instruction set; Specifically, based on real-time content update requests, an Abstract Syntax Tree (AST) structure hash code can be generated using the current version snapshot of the atomic rendering component set, and stored in the version snapshot library. When a user triggers a content update request (such as clicking a button to modify page text), the front-end framework captures the Abstract Syntax Tree (AST) of the currently rendered component set. The AST is a tree-like representation of the code structure. For example, the AST of a button component may contain information such as the label name, attribute nodes (such as class="btn"), and child nodes (such as text content).

[0092] Abstract Syntax Tree Fingerprinting algorithm generates unique hash codes by traversing AST nodes: Node normalization: Remove irrelevant information (such as spaces and comments) and classify nodes by type (label, attribute, event); Hash generation: Apply the SHA-256 algorithm to the normalized node sequence to generate a 64-bit hexadecimal hash code. For example, a sequence containing... Hello The AST generates the hash 0x3a7d...f2c1; Version snapshot storage: associate the hash code with the timestamp (UTC time, precision 1 ms) and store it in the version snapshot library (such as IndexedDB or Redis cache).

[0093] Example: The hash of a certain e-commerce page's product list component before updating is 0x5b9e...d4a3, and the new hash after the user filters the products is 0x8f2a...c4d7. The system records these two versions and their timestamps t1=1620000000123 and t2=1620000000456.

[0094] Extract the difference bits of the new and old AST hash codes through the version snapshot comparison algorithm, mark the syntax tree paths that have been added, deleted, or modified, and output the difference path set; The version snapshot comparison algorithm is based on the Longest Common Subsequence (LCS) implementation, and the specific process is as follows: AST node alignment: Align the new and old ASTs by node type and hierarchical structure, such as comparing the child nodes p of the div tag; Difference detection: New node: a node that exists in the new AST but is missing in the old AST (such as adding a <button>Buy< / button> ); Delete node: a node that exists in the old AST but is missing in the new AST (such as removing ); Modification node: Node attribute or content change (e.g., text from "Hello" to "Hi"); Path marker: Records the hierarchical path of the difference node, such as div / p / text() indicates the text node of the p tag under the div tag.

[0095] Example: The path div / ul / li[1] / text() in the old AST corresponds to the text "Item A", and it is changed to "ItemNew" in the new AST. The difference path marker is div / ul / li[1] / text():MODIFY.

[0096] Based on the set of difference paths, construct the impact propagation graph, analyze the dependency relationship of style inheritance chain and event listener, and generate the associated impact range evaluation matrix; Impact propagation graph (Impact Propagation Graph) is used to quantify the potential impact of difference nodes on other components: Style inheritance analysis: Analyze the style inheritance chain through CSSOM (CSS Object Model). For example, if the font-size of div.container is modified, the em unit of all child elements will be affected; Event dependency tracking: Check the binding relationship of event listeners. For example, deleting a button may cause the onClick event bound to it to fail; Graph construction: Starting from the difference node, build dependency edges along the DOM tree and style sheet. The weight of the edge represents the impact strength (such as direct style inheritance weight 1.0, indirect inheritance 0.5); Evaluation matrix generation: Convert the graph to a matrix, with rows representing difference nodes and columns representing affected components, and element values as impact weights.

[0097] Example: Modifying the background color of div.header may affect the contrast of its child element h1.title, with a weight of 0.8; at the same time, it may affect the hover effect of the sibling node nav.menu, with a weight of 0.3.

[0098] According to the associated impact range evaluation matrix, design the incremental compilation strategy, map the syntax tree changes to the platform-independent intermediate representation layer instructions, and generate cross-platform instruction drafts; The goal of the incremental compilation strategy is to update only the affected part, rather than full compilation: Instruction classification: Style instruction: such as SET_STYLE(div.header, "background", "#fff"); Structural instructions: e.g. INSERT_NODE(div.list, li.newItem); Event instructions: e.g. REMOVE_EVENT(button.submit, "click"); Intermediate representation layer design: define platform-agnostic JSON instruction format, e.g.: { "type": "STYLE_UPDATE", "target": "div.header", "property": "background-color", "value": "#ffffff" }。

[0099] Instruction generation: generate instructions for high-weight impact (>0.5) nodes based on the difference path and evaluation matrix, merge or ignore low-weight nodes.

[0100] Example: an update needs to modify the width of div.content, which affects the layout of img.thumbnail, generate two instructions: modify the width attribute of div.content; adjust the max-width of img.thumbnail to 100%.

[0101] Execute the cross-platform instruction draft in a sandbox environment, use virtual DOM difference comparison technology to correct platform-specific deviations, and output the final cross-platform incremental update instruction set.

[0102] The sandbox environment is an isolated browser instance (such as Headless Chrome) that executes instructions safely: Instruction injection: convert intermediate representation layer instructions into platform-specific code (such as Web platform generates JavaScript, small program platform generates WXML); Virtual DOM comparison: after executing the instructions, compare the new and old virtual DOM trees (Virtual DOM Diffing) to detect unexpected changes (such as iOS <view>Default margin difference of the label; Deviation correction: Style compensation: When font rendering difference is detected, append font-family: -apple-system; Structural compensation: Android side lacks <marquee>Supporting time, replaced by the scroll animation component; Final instruction set generation: revised instructions are packaged by platform classification, for example: Web instruction set: [SET_STYLE(...), INSERT_NODE(...)]; Applet instruction set: [setData({...}), createSelectorQuery(...)].

[0103] Example: A certain instruction is effective in the web, but in the WeChat applet, the layout is disordered due to the incomplete support of flex-grow, and after sandbox detection, it is replaced by flex: 1 and recorded in the revision log.

[0104] This step realizes an efficient incremental update mechanism. By comparing the syntax structure differences between the previous and subsequent versions, only the changed part is recompiled and deployed, avoiding the resource waste of full update, significantly improving the content update efficiency, reducing network transmission and terminal computing burden. Incremental update ensures that users can quickly access the latest content while saving system resources.

[0105] S205, according to the terminal running environment performance parameters, adopts resource loading optimization strategy to execute the incremental update instruction set, eliminates cross-terminal compatibility conflicts through rendering pipeline reorganization technology, and outputs consistent content presentation effect of multiple terminals.

[0106] Specifically, the platform identifier in the incremental update instruction set can be parsed, and the real-time GPU memory occupancy, network bandwidth and CPU load parameters can be obtained by calling the terminal performance probe to construct a three-dimensional performance evaluation matrix. The incremental update instruction set contains platform identifiers (such as iOS, Android, Web), and terminal performance probes (Performance Probe) need to be called to obtain real-time hardware status when parsing.

[0107] GPU memory occupancy: Get the current memory usage ratio through the getParameter method of WebGL API (Web) or OpenGL ES (mobile terminal). For example, the total memory of a certain device is 4GB, and 1.2GB has been used, with an occupancy rate of 30%.

[0108] Network bandwidth: Based on the bandwidth estimation algorithm (BBR algorithm) of HTTP / 3, the instantaneous bandwidth is calculated by downloading a 1MB test file. For example, the download time is 200ms, so the bandwidth ≈ 5MB / s (1MB / 0.2s).

[0109] CPU Load: Call system API (e.g., Node.js's os.loadavg()) to get 1-minute average load. For example, a quad-core CPU load value of 2.5 (indicating an average load of 62.5% per core).

[0110] Three-dimensional performance evaluation matrix construction rules: Dimension 1 (GPU memory occupancy rate): divided into high (> 70%), medium (30%~70%), low (<30%) three grades, the quantitative value is 0~1; Dimension 2 (network bandwidth): divided by bandwidth level (such as <1MB / s is 0.2, 1~5MB / s is 0.5, >5MB / s is 0.8); Dimension 3 (CPU load): normalized to 0~1 (load value / core number).

[0111] For example, the performance evaluation matrix of a certain terminal is [0.3, 0.8, 0.6], indicating low GPU occupancy, sufficient bandwidth, and medium CPU load.

[0112] Based on the three-dimensional performance evaluation matrix, resource demand prediction is performed, LSTM model is used to dynamically adjust the rendering instruction priority, GPU memory prediction algorithm is used to allocate texture loading queue, and resource allocation scheduling table is generated; LSTM model (Long Short-Term Memory Network) is used to predict resource demand in the next 3 seconds: Input features: historical 5-second performance matrix sequence (15-dimensional vector) + current rendering instruction type (such as 3D model, video, text); Output: predicted value of each resource dimension (0~1 weight of GPU, bandwidth, CPU).

[0113] Model structure: input layer (16 nodes), LSTM hidden layer (128 nodes), fully connected output layer (3 nodes), learning rate 0.001, batch size 32.

[0114] GPU memory prediction algorithm: Texture priority sorting: determine key textures (such as human faces) according to visual saliency (Saliency Map), priority set to 1.0; background texture priority 0.3.

[0115] Loading queue allocation: If the GPU memory occupancy rate prediction is >50%, use progressive loading: load low-resolution textures (such as 512x512) in the first frame, and gradually replace them with high-definition textures (2048x2048) in the future; If the bandwidth prediction is <1MB / s, enable differential compression: only load the differential part of the texture (such as the area where the Alpha channel changes).

[0116] Resource allocation and scheduling are represented as shown in Table 2: Table 2

[0117] The rendering pipeline is reorganized according to the resource allocation schedule, cross-platform API call conflicts are eliminated by the instruction reordering algorithm, and device-specific rendering command sequences are generated by shader code just-in-time compilation technology. Render pipeline refactoring requires addressing cross-platform API differences: Instruction rearrangement algorithm: Conflict detection: Identify platform-specific APIs (such as Metal on iOS and Vulkan on Android) and mark incompatible calls (such as differences in drawElements parameters). Reordering strategy: Encapsulate conflicting APIs into a Platform Adaptation Layer (PAL) and dynamically replace them based on the current platform. For example, replace WebGL's texImage2D with Metal's replaceRegion.

[0118] Shader code is compiled just-in-time (JIT): Equipment specialization and optimization: GPU architecture detection: Identify the GPU model (such as Adreno 650, M1) through the renderer string; Shader variant generation: Compile different versions based on the features supported by the GPU (such as ray tracing and compute shaders). For example, generate a low-precision (mediump) shader for Adreno GPUs and a high-precision (highp) version for M1.

[0119] Compilation caching: Uses an LRU (Least Recently Used) caching strategy to store compiled shaders, with a maximum of 1000 cache entries, reducing the overhead of repeated compilation.

[0120] Example of a rendering command sequence: 1. Initialize the rendering target (iOS: Metal Layer, Android: SurfaceView); 2. Load texture A (progressive loading strategy, prioritizing lower resolutions); 3. Execute the vertex shader (device-specific version: M1_highp_vert); 4. Execute the fragment shader (device-specific version: Adreno_mediump_frag); 5. Submit drawing commands (platform-adapted API encapsulation).

[0121] Compatibility verification points are inserted during the rendering command sequence execution phase, and the multi-terminal output difference is detected by the difference frame comparison algorithm to automatically trigger the compensation rendering channel to correct pixel deviation, output the presentation result that meets the visual consistency standard.

[0122] Compatibility verification points are inserted after the key rendering phase: Verification timing: Texture loading is complete; Shader execution is complete; Before the final frame buffer (Framebuffer) is submitted.

[0123] Difference frame comparison algorithm: Benchmark frame generation: render and save the standard result on a reference device (such as iPhone 13 Pro); Pixel-level comparison: SSIM (Structural Similarity): Calculate the similarity between the current frame and the benchmark frame (threshold > 0.95); Pixel-by-pixel difference detection: mark the pixel area with RGB difference > 5%.

[0124] Difference area positioning: determine the rectangular area that needs to be repaired through connected component analysis.

[0125] Compensation rendering channel: Strategy selection: Color deviation: adjust the saturation and brightness of the HSV channel; Layout misplacement: trigger dynamic rearrangement (such as Flexbox layout correction); Transparency anomaly: cover a semi-transparent mask layer (Alpha = 0.5) for smooth transition.

[0126] Compensation execution: Use Web Workers to execute compensation rendering asynchronously to avoid blocking the main thread; If it still does not meet the standard after compensation, downgrade to static picture display (such as PNG placeholder).

[0127] Final output effect: Visual consistency indicators: SSIM ≥ 0.98, pixel difference rate < 0.5%; Performance overhead: compensation rendering increases <3ms of frame consumption time, ensuring 60FPS smoothness.

[0128] The step intelligently adjusts the resource loading sequence and rendering process according to the actual performance condition of the terminal. By dynamically recombining the rendering pipeline, the difference in graphic processing of different platforms is solved, and it is ensured that various terminal devices can run smoothly and obtain consistent visual experience. The performance adaptive resource scheduling avoids the lag problem of low-end devices, and fully plays the hardware advantages of high-end devices.

[0129] It can be seen that, according to the user operation type and the terminal device characteristics, a unique dynamic routing identifier is generated through a dynamic feature coding algorithm; based on the dynamic routing identifier, a service-routing real-time binding table is generated; according to the rendering specification difference of cross-terminal platforms, a multi-terminal adapted atomized rendering component set is generated through a syntax tree conversion engine; based on a real-time content update request, a cross-platform incremental update instruction set is generated; according to the terminal running environment performance parameters, the resource loading optimization strategy is adopted to execute the incremental update instruction set, and the cross-terminal compatibility conflict is eliminated through the rendering pipeline recombination technology, and the multi-terminal consistent content presentation effect is output, so as to be able to systematically solve the key problems such as multi-terminal adaptation, performance optimization and real-time update, and improve the front-end development efficiency.

[0130] Another embodiment of the application provides a front-end development optimization system, see Figure 3 , which can include: The generating module 301 is configured to generate a unique dynamic routing identifier through a dynamic feature coding algorithm according to a user operation type and terminal device characteristics, wherein the dynamic feature coding algorithm fuses an operation semantic vector and a device hardware fingerprint, the operation semantic vector is generated by analyzing a user behavior intention through a natural language processing model, and the device hardware fingerprint includes a GPU model and a screen resolution parameter. The binding module 302 is configured to modularly bind a backend business service through a service weight distribution model based on the dynamic routing identifier, dynamically distribute service instance weights according to a historical call frequency and a business priority, and generate a service-routing real-time binding table. The building module 303 is configured to build a decision tree of a unified rendering rule according to the rendering specification difference of cross-terminal platforms, input the service-routing binding table into the decision tree, and generate a multi-terminal adapted atomized rendering component set through a syntax tree conversion engine, wherein the decision tree includes a platform priority branch and a component compatibility constraint node. The synchronization module 304 is configured to dynamically synchronize the atomized rendering component set through a difference-driven incremental compilation technology based on a real-time content update request, extract an abstract syntax tree difference node through a version snapshot comparison algorithm, and generate a cross-platform incremental update instruction set. The output module 305 is used for executing the incremental update instruction set by using a resource loading optimization strategy according to terminal running environment performance parameters, eliminating cross-terminal compatibility conflicts by a rendering pipeline reorganization technology, and outputting a content presentation effect consistent among multiple terminals.

[0131] It can be seen that a unique dynamic routing identifier is generated by a dynamic feature coding algorithm according to a user operation type and terminal device characteristics, a service-routing real-time binding table is generated based on the dynamic routing identifier, a set of atomized rendering components adapted to multiple terminals is generated by a syntax tree conversion engine according to rendering specification differences of cross-terminal platforms, a cross-platform incremental update instruction set is generated based on a real-time content update request, and a content presentation effect consistent among multiple terminals is output by executing the incremental update instruction set by using a resource loading optimization strategy according to terminal running environment performance parameters and eliminating cross-terminal compatibility conflicts by a rendering pipeline reorganization technology, so that key problems such as multi-terminal adaptation, performance optimization and real-time updating can be systematically solved, and front-end development efficiency is improved.

[0132] The embodiment of the application further provides a storage medium, wherein the storage medium stores a computer program, and the computer program is arranged to execute the steps in any of the method embodiments.

[0133] Specifically, in the embodiment, the storage medium can be arranged to store a computer program for executing the following steps: S201, a unique dynamic routing identifier is generated by a dynamic feature coding algorithm according to a user operation type and terminal device characteristics, wherein the dynamic feature coding algorithm fuses an operation semantic vector and a device hardware fingerprint, the operation semantic vector is generated by analyzing a user behavior intention by a natural language processing model, and the device hardware fingerprint contains a GPU model and a screen resolution parameter; S202, a service-routing real-time binding table is generated by using a service weight distribution model to modularly bind backend business services based on the dynamic routing identifier, dynamically distributing service instance weights according to historical call frequencies and business priorities; S203, a set of atomized rendering components adapted to multiple terminals is generated by a syntax tree conversion engine by inputting the service-routing binding table into a decision tree of unified rendering rules, according to rendering specification differences of cross-terminal platforms, wherein the decision tree contains platform priority branches and component compatibility constraint nodes; S204, a cross-platform incremental update instruction set is generated by using difference-driven incremental compilation technology to dynamically synchronize the set of atomized rendering components based on a real-time content update request, and extracting abstract syntax tree difference nodes by a version snapshot comparison algorithm; S205, based on the performance parameters of the terminal's operating environment, executes the incremental update instruction set using a resource loading optimization strategy, eliminates cross-terminal compatibility conflicts through rendering pipeline reorganization technology, and outputs a consistent content presentation effect across multiple terminals.

[0134] As can be seen, a unique dynamic route identifier is generated based on the user operation type and terminal device characteristics using a dynamic feature encoding algorithm; a service-route real-time binding table is generated based on the dynamic route identifier; an atomic rendering component set adapted for multiple terminals is generated using a syntax tree conversion engine based on the differences in rendering specifications across terminal platforms; a cross-platform incremental update instruction set is generated based on real-time content update requests; the incremental update instruction set is executed using a resource loading optimization strategy based on the terminal runtime environment performance parameters; cross-platform compatibility conflicts are eliminated through rendering pipeline reorganization technology, resulting in consistent content presentation across multiple terminals. This systematically solves key issues such as multi-terminal adaptation, performance optimization, and real-time updates, thereby improving front-end development efficiency.

[0135] This invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0136] Specifically, the aforementioned electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the aforementioned processor, and the input / output device is connected to the aforementioned processor.

[0137] Specifically, in this embodiment, the processor can be configured to perform the following steps via a computer program: S201, Based on the user operation type and terminal device characteristics, a unique dynamic routing identifier is generated through a dynamic feature encoding algorithm. The dynamic feature encoding algorithm integrates the operation semantic vector and the device hardware fingerprint. The operation semantic vector is generated by parsing the user's behavioral intent through a natural language processing model. The device hardware fingerprint includes the GPU model and screen resolution parameters. S202, Based on the dynamic route identifier, the backend business services are modularly bound using a service weight allocation model. The service instance weights are dynamically allocated according to historical call frequency and business priority, and a service-route real-time binding table is generated. S203, Based on the differences in rendering specifications across terminal platforms, a decision tree for unified rendering rules is constructed. The service-routing binding table is input into the decision tree, and a set of atomic rendering components adapted to multiple terminals is generated through the syntax tree conversion engine. The decision tree includes platform priority branches and component compatibility constraint nodes. S204, based on the real-time content update request, using the difference driven incremental compilation technology to dynamically synchronize the atomized rendering component set, through the version snapshot comparison algorithm to extract the abstract syntax tree difference node, generate cross-platform incremental update instruction set; S205, according to the terminal running environment performance parameters, using resource loading optimization strategy to execute the incremental update instruction set, through the rendering pipeline reorganization technology to eliminate cross-terminal compatibility conflict, output the multi-terminal consistent content presentation effect.

[0138] It can be seen that according to the user operation type and the terminal equipment characteristics, a unique dynamic routing identifier is generated through a dynamic characteristic coding algorithm; based on the dynamic routing identifier, a service-routing real-time binding table is generated; according to the rendering specification difference of cross-terminal platforms, a multi-terminal adapted atomized rendering component set is generated through a syntax tree conversion engine; based on the real-time content update request, a cross-platform incremental update instruction set is generated; according to the terminal running environment performance parameters, using resource loading optimization strategy to execute the incremental update instruction set, through the rendering pipeline reorganization technology to eliminate cross-terminal compatibility conflict, output the multi-terminal consistent content presentation effect, so as to be able to solve the key problems such as multi-terminal adaptation, performance optimization and real-time update in a systematic way, improve the front-end development efficiency.

[0139] The above describes the structure, features and effects of the present application in detail according to the embodiments shown in the drawings. The above is only the preferred embodiment of the present application, but the present application is not limited to the embodiments shown in the drawings. Any change or modification made according to the concept of the present application, or equivalent embodiment with equivalent change, still within the scope of the present application, should be within the protection scope of the present application.< / marquee> < / view> < / androidbutton> < / iosbutton>

Claims

1. A front-end development optimization method, characterized in that, The method includes: Based on the user's operation type and terminal device characteristics, a unique dynamic routing identifier is generated through a dynamic feature encoding algorithm. The dynamic feature encoding algorithm integrates the operation semantic vector and the device hardware fingerprint. The operation semantic vector is generated by parsing the user's behavioral intent through a natural language processing model, and the device hardware fingerprint includes the GPU model and screen resolution parameters. Based on the dynamic routing identifier, a service weight allocation model is used to modularly bind backend business services. Service instance weights are dynamically allocated according to historical call frequency and business priority, and a real-time service-route binding table is generated. Based on the differences in rendering specifications across terminal platforms, a decision tree for unified rendering rules is constructed. The service-routing binding table is input into the decision tree, and a set of atomic rendering components adapted to multiple terminals is generated through a syntax tree conversion engine. The decision tree includes platform priority branches and component compatibility constraint nodes. Based on real-time content update requests, the atomic rendering component set is dynamically synchronized using difference-driven incremental compilation technology. The abstract syntax tree difference nodes are extracted through version snapshot comparison algorithm to generate a cross-platform incremental update instruction set. Based on the performance parameters of the terminal's operating environment, the incremental update instruction set is executed using a resource loading optimization strategy. Cross-terminal compatibility conflicts are eliminated through rendering pipeline reorganization technology, resulting in consistent content presentation across multiple terminals.

2. The method according to claim 1, characterized in that, The process involves generating a unique dynamic route identifier based on user operation type and terminal device characteristics using a dynamic feature encoding algorithm. This algorithm integrates operation semantic vectors and device hardware fingerprints. The operation semantic vectors are generated by parsing user behavioral intent using a natural language processing model. The device hardware fingerprint includes GPU model and screen resolution parameters, including: The user's operation sequence is parsed using a bidirectional LSTM model to extract the contextual semantic features of the operation intent and generate a 128-dimensional operation semantic vector. The terminal device API is called to obtain hardware configuration parameters. The feature cross-coding algorithm is used to fuse the GPU model hash value and the screen resolution value to generate a device hardware fingerprint. The operation semantic vector and the device hardware fingerprint code are input into the dynamic feature encoder. Cross-modal feature weights are calculated through a multi-head attention mechanism, and a 256-dimensional fused feature vector is output. The fused feature vector is subjected to collision-resistant hashing and combined with timestamp salt values ​​to generate a time-sensitive dynamic route identifier, wherein the validity period of the dynamic route identifier is synchronized with the user session period.

3. The method according to claim 2, characterized in that, The process involves modularly binding backend business services based on the dynamic route identifier using a service weight allocation model, dynamically allocating service instance weights according to historical call frequency and business priority, and generating a real-time service-route binding table, including: Parse the device performance characteristics in the dynamic routing identifier and classify service instance levels according to GPU computing power level and memory capacity; Query the service registry and obtain historical call logs. Use a sliding window to count the service quality metrics for each service instance. The service quality metrics include at least request response time and error rate. Construct a service weight allocation model, calculate business priority weights using the entropy weight method, and generate a three-dimensional weight matrix by combining service quality indicators; Dynamic load balancing is performed based on a three-dimensional weight matrix, and the NSGA-II algorithm is used to optimize service instance allocation, outputting a set of candidate binding schemes. The stability of candidate binding schemes is verified by using a real-time traffic prediction model. The optimal candidate binding scheme is selected to generate a service-routing binding table containing service instance IPs and port numbers.

4. The method according to claim 3, characterized in that, The process involves constructing a unified rendering rule decision tree based on the differences in rendering specifications across different terminal platforms. The service-routing binding table is input into the decision tree, and a multi-terminal adapted atomic rendering component set is generated through a syntax tree transformation engine. The decision tree includes platform priority branches and component compatibility constraint nodes, including: Collect rendering specification documents from various terminal platforms, extract CSS feature support matrices and layout constraints to build a difference feature library; A decision tree model for unified rendering rules is trained based on a difference feature library, and platform adaptation priority branches and component nesting depth constraint nodes are set. The service interfaces in the service-route binding table are mapped to virtual DOM nodes, input into a trained decision tree for platform feature matching, and cross-platform code transformation is performed through a syntax tree transformation engine to generate an atomic set of web components containing platform feature tags.

5. The method according to claim 4, characterized in that, The method, based on real-time content update requests, utilizes difference-driven incremental compilation technology to dynamically synchronize the atomic rendering component set, extracts abstract syntax tree difference nodes through a version snapshot comparison algorithm, and generates a cross-platform incremental update instruction set, including: Based on real-time content update requests, and according to the current version snapshot of the atomic rendering component set, an Abstract Syntax Tree Fingerprint algorithm is used to generate an AST structure hash code, which is then stored in the version snapshot library. The difference bits between the old and new AST hash codes are extracted by the version snapshot comparison algorithm, the syntax tree paths where nodes are added, deleted or modified are marked, and the set of difference paths is output. Based on the set of differential paths, an impact propagation graph is constructed, the dependency relationship between style inheritance chain and event listener is analyzed, and an assessment matrix of the scope of associated impact is generated. Based on the assessment matrix of the scope of collateral impact, an incremental compilation strategy is designed to map the syntax tree changes to platform-independent intermediate representation layer instructions, generating a cross-platform instruction draft. The cross-platform instruction draft is verified through a sandbox execution environment, and platform-specific deviations are corrected by using virtual DOM differential comparison technology to output the final cross-platform incremental update instruction set.

6. The method according to claim 5, characterized in that, The incremental update instruction set is executed based on the terminal's operating environment performance parameters, using a resource loading optimization strategy. This is achieved by eliminating cross-platform compatibility conflicts through rendering pipeline reorganization technology, resulting in consistent content presentation across multiple terminals. Parse the platform identifier in the incremental update instruction set, call the terminal performance probe to obtain real-time GPU memory usage, network bandwidth and CPU load parameters, and construct a three-dimensional performance evaluation matrix; Resource requirement prediction is performed based on a three-dimensional performance evaluation matrix. The priority of rendering instructions is dynamically adjusted using an LSTM model. Texture loading queues are allocated through a GPU memory prediction algorithm, and a resource allocation scheduling table is generated. The rendering pipeline is reorganized according to the resource allocation schedule, cross-platform API call conflicts are eliminated by the instruction reordering algorithm, and device-specific rendering command sequences are generated by shader code just-in-time compilation technology. A compatibility verification point is inserted during the execution phase of the rendering command sequence. The difference between the outputs of multiple terminals is detected by the difference frame comparison algorithm, and the compensation rendering channel is automatically triggered to correct the pixel deviation and output a visually consistent presentation result.

7. A front-end development optimization system, characterized in that, The system includes: The generation module is used to generate a unique dynamic route identifier based on the user operation type and terminal device characteristics through a dynamic feature encoding algorithm. The dynamic feature encoding algorithm integrates the operation semantic vector and the device hardware fingerprint. The operation semantic vector is generated by parsing the user's behavioral intent through a natural language processing model. The device hardware fingerprint includes the GPU model and screen resolution parameters. The binding module is used to modularly bind backend business services based on the dynamic route identifier and a service weight allocation model, dynamically allocate service instance weights according to historical call frequency and business priority, and generate a real-time service-route binding table. The building module is used to construct a decision tree for unified rendering rules based on the differences in rendering specifications across terminal platforms. The service-routing binding table is input into the decision tree, and a set of atomic rendering components adapted to multiple terminals is generated through the syntax tree conversion engine. The decision tree includes platform priority branches and component compatibility constraint nodes. The synchronization module is used to dynamically synchronize the atomic rendering component set based on real-time content update requests using difference-driven incremental compilation technology, and to extract the difference nodes of the abstract syntax tree through the version snapshot comparison algorithm to generate a cross-platform incremental update instruction set. The output module is used to execute the incremental update instruction set according to the performance parameters of the terminal's operating environment, and to eliminate cross-terminal compatibility conflicts through rendering pipeline reorganization technology, so as to output a consistent content presentation effect across multiple terminals.

8. The system according to claim 7, characterized in that, The generation module is specifically used for: The user's operation sequence is parsed using a bidirectional LSTM model to extract the contextual semantic features of the operation intent and generate a 128-dimensional operation semantic vector. The terminal device API is called to obtain hardware configuration parameters. The feature cross-coding algorithm is used to fuse the GPU model hash value and the screen resolution value to generate a device hardware fingerprint. The operation semantic vector and the device hardware fingerprint code are input into the dynamic feature encoder. Cross-modal feature weights are calculated through a multi-head attention mechanism, and a 256-dimensional fused feature vector is output. The fused feature vector is subjected to collision-resistant hashing and combined with timestamp salt values ​​to generate a time-sensitive dynamic route identifier, wherein the validity period of the dynamic route identifier is synchronized with the user session period.

9. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the method of any one of claims 1-6 when it is run.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method of any one of claims 1-6.

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