Reusable management layer enterprise digital twin service packaging and dynamic arrangement method

Abstract

The application relates to an enterprise digital twin service encapsulation and dynamic arrangement method of a reusable management layer, and the core scheme is as follows: by extracting and normalizing the multi-dimensional features such as the meta-information, service contract and collaborative topology of the management layer, a unique high-dimensional semantic fingerprint is generated, embedded through a light feedforward network, and the static capabilities of each layer are highly distinguished. In combination with the real-time context of the business, the environment state discrete code is converted into a semantic weight through a non-parameter mapping mechanism, and according to the key layer strong constraint bit, the structure and weight expression of the scheduling vector are optimized, and the priority discrimination accuracy in the dynamic scene is improved. Through dot product matching and normalization algorithm, cross-layer and traceable execution instructions are output, so as to drive the efficient allocation of computing resources and state synchronization. The method has the advantages of efficient representation of heterogeneous layers, adaptive expansion of service scheduling and support of hot updating, and effectively improves the intelligent level and robustness of the digital twin system scheduling.

Description

Technical Field

[0001] This invention relates to the field of service orchestration and scheduling optimization technology in enterprise digital twin systems, and in particular to a method for encapsulating and dynamically orchestrating enterprise digital twin services with reusable management layers. Background Technology

[0002] With the rapid development of enterprise digital twin technology, more and more large enterprises are continuously increasing their investment in the research and development and deployment of reusable management layers and service orchestration systems in their core management aspects such as production operation and maintenance, energy consumption management, and process optimization. Existing enterprise-level digital twin systems typically build multi-layered management views, including equipment status management layer, energy consumption monitoring layer, and production scheduling layer, etc., to achieve unified mapping and dynamic linkage between underlying physical entities and upper-layer business scenarios through highly reusable layer services. To cope with complex and frequently changing production and operation and maintenance scenarios, service orchestration systems generally adopt predefined orchestration processes, BPEL-based workflow parsing, QoS-driven rule-based priority assignment, or partially combine model-driven and policy-optimized scheduling engines to achieve collaborative control and resource scheduling of multiple layers and multiple service instances.

[0003] Current mainstream technologies exhibit the following development trends: First, customized process orchestration widely employs graph-based business process modeling, embedding service instances into specific process nodes, but it suffers from slow response times in terms of context awareness and real-time decision-making. Second, scheduling schemes based on QoS metrics and service levels determine the scheduling order of critical services through fixed priority ratios and dynamic adjustment of some weights. This approach suffers from insufficient adaptability and difficulty in adequately addressing sudden changes in multi-source heterogeneous contexts. In recent years, some systems have attempted to introduce artificial intelligence methods such as reinforcement learning and multi-agent decision-making, iteratively optimizing strategies based on full context data and behavioral feedback. While this can improve adaptability and complex decision-making capabilities, it often incurs high model training costs, posing significant challenges to real-time performance, interpretability, and lightweight online deployment. Currently, the industry lacks a context-driven service prioritization system that is tailored to the characteristics of management layer services and combines high adaptability with extremely low scheduling latency.

[0004] In enterprise digital twin management scenarios, service orchestration systems need to support highly dynamic context environments such as equipment status fluctuations, operating condition changes, and event linkages, and achieve high-response, low-latency, and highly controllable service scheduling for critical business functions (such as security monitoring, emergency response, and production line emergency takeover). However, existing technologies rely on model training or strategy optimization based on full context features, lacking a lightweight decision-making mechanism at the "service-context semantic mapping level." Specifically, existing orchestration systems have the following prominent shortcomings: First, most existing solutions struggle to dynamically prioritize services based on real-time business context. Traditional process-based orchestration requires pre-setting service ordering and cannot intelligently adjust the queue structure as the context changes. This can lead to critical response services being "blocked" or delayed by low-priority services during unexpected situations or extreme events, impacting system stability and business continuity.

[0005] Secondly, while complex optimization schemes based on full data perception can provide a certain degree of adaptability, the model training and iteration cycles are long, making it difficult to complete efficient sorting under millisecond-level business switching cycles. Moreover, the model inference process is highly black-box, with weak interpretability and auditability, which does not meet the transparent control requirements of key business scenarios of enterprises.

[0006] Secondly, the existing solution is a bottleneck in terms of system scalability. For example, adding a new management layer or context dimension often requires redoing all feature mappings, retraining the model, or refactoring the process, resulting in high maintenance costs and issues with service migration and compatibility during continuous business evolution.

[0007] In addition, traditional service priority scheduling generally relies on static weight tables, hierarchical and weighted authentication, and single QoS function aggregation, which makes it difficult to capture the key information of the collaborative relationship and dynamic coupling between management layers, and lacks the ability to accurately map complex business intentions and dynamic scheduling requirements.

[0008] In summary, current technologies do not yet provide a digital twin management layer service for enterprises that can efficiently and automatically map upper-layer business semantics, service capabilities, and multi-dimensional contexts based on lightweight features without relying on full-scale complex context modeling, while also ensuring a dynamic service priority automatic sorting and resource allocation mechanism with transparent scheduling, deterministic order, and flexible expansion in real-time scenarios. Therefore, the industry urgently needs a new technological system that can achieve low-latency, high-determinism, and highly adaptive intelligent sorting and automatic scheduling of service priorities in scenarios with rapid fluctuations in multi-source business contexts and dynamic expansion and contraction of heterogeneous layer services. This system would fundamentally compensate for the technical shortcomings of existing enterprise digital twin service orchestration systems in areas such as dynamic priority adjustment, decision response latency, and scheduling interpretability, laying the foundation for high stability and robust service assurance capabilities in critical business scenarios. Summary of the Invention

[0009] This application provides a method for encapsulating and dynamically orchestrating enterprise digital twin services with reusable management layers, aiming to solve one of the problems or issues of the prior art mentioned in the background section.

[0010] The enterprise digital twin service encapsulation and dynamic orchestration method with reusable management layers provided in this application specifically includes: S1: Obtain the structured features of each reusable management layer in the enterprise digital twin system, normalize and concatenate them, and then input them into the feedforward network to generate layer semantic fingerprints. The structured features include layer collaborative topology features. S2: Receive standardized intermediate representation data from the unified context agent, extract key dimension status codes, and construct a lightweight context package; S3: Based on the key dimension status codes in the lightweight context package, each key dimension status code is converted into a preset semantic weight factor using a parameterless state mapping mechanism, and combined with the layer collaborative topological features in the layer semantic fingerprint to activate the weighted combination logic and generate a context distillation vector. S4: Read the preset key response layer marker information, locate the strong constraint bit in the layer semantic fingerprint, and perform a truncation operation on the context distillation vector according to the strong constraint bit to generate a corrected context distillation vector; S5: Based on the semantic fingerprint of the layer and the corrected context distillation vector, perform dot product matching calculation, quantify the semantic alignment degree between each layer service instance and the current context environment, and generate a real-time priority score sequence; S6: Arrange the layer service instances in descending order according to the real-time priority score sequence to construct a dynamic service scheduling queue; S7: Based on the dynamic service scheduling queue, issue a resource allocation instruction to the enterprise digital twin operation framework to drive the high-priority layer service instance to occupy computing resources and complete the state synchronization task; S8: Monitor the structured features of newly registered layers or the status code mapping rules of newly added context dimensions, and hot update the weight parameters of the feedforward network or the mapping table of the parameterless state mapping mechanism without restarting the scheduling core, so as to realize the adaptive expansion of the dynamic service orchestration system.

[0011] The enterprise digital twin service encapsulation and dynamic orchestration method with reusable management layers provided in this application has the following beneficial effects: (1) This application significantly improves the real-time performance and determinism of multi-management layer service scheduling in a digital twin system by constructing a lightweight semantic alignment mechanism between layer semantic fingerprints and context distillation vectors. Traditional scheduling methods rely on dynamic policy reasoning or complex QoS optimization models. When facing layers such as frequently updated equipment status, energy consumption monitoring, and production line scheduling, they often fail to meet the hard real-time requirements in industrial scenarios due to high computational overhead and high response latency. This solution abandons the heavy-load decision-making path based on online learning or search, and instead adopts layer semantic fingerprints generated by fusing three types of structured features: meta-information, service contracts, and collaborative topology, as a static abstract representation of layer service capabilities. At the same time, a context distiller is introduced to transform the original context stream into a lightweight context packet containing only key discrete status codes, and generates distillation vectors expressing priority tendencies through parameterless mapping. The two perform dot product matching in a unified semantic space, achieving millisecond-level reordering of hundreds of layers without model reasoning, which greatly reduces scheduling latency, effectively avoids scheduling jitter and unreproducible results problems that occur in high-concurrency environments in traditional methods, and significantly enhances the auditability and operational stability of the system.

[0012] (2) The layer intent anchoring mechanism and hot-swappable extension architecture proposed in this application effectively solve the technical bottlenecks of the existing technology, such as the difficulty in guaranteeing business intent and the lack of system flexibility. In actual operation, key layers such as security monitoring and emergency response must always maintain the highest scheduling priority. However, traditional scheduling strategies based on weighted scoring or multi-objective optimization are easily affected by context fluctuations and have the risk of wrong weight reduction. This solution ensures that once a layer is marked as a "key response layer", its priority is not affected by any non-fault context changes, and the rigid binding of business intent is reliably achieved. In addition, the system supports the rapid registration of new layers and the flexible expansion of context dimensions: new layers only need to configure their meta-information, service contracts and collaborative relationships to automatically generate semantic fingerprints and be included in the scheduling system. Adding new context dimensions only requires adding the corresponding status code mapping rules in the distiller, without retraining the model or modifying the core scheduling logic. This design greatly reduces the system maintenance cost and deployment threshold, supports the smooth evolution of enterprise digital twin platforms in dynamic scenarios such as production line changes and system upgrades, and has good engineering applicability and long-term scalability.

[0013] In summary, this application constructs a new paradigm for automated scheduling of enterprise digital twin management layers through a collaborative design of layer semantic fingerprint modeling, context distillation vector generation, and lightweight semantic alignment computation. This solution achieves high efficiency, determinism, and predictability in scheduling decisions without relying on complex model training, online policy optimization, or process parsing. Figure 1It achieves consistency, taking into account both system performance and business controllability, forming a replicable, auditable, and scalable technical closed loop, providing solid support for the stable operation and agile response of enterprise-level digital twin systems. Attached Figure Description

[0014] Figure 1 This is the main flowchart of a method for encapsulating and dynamically orchestrating enterprise digital twin services with reusable management layers; Figure 2 It is a sub-flowchart of a method for encapsulating and dynamically orchestrating enterprise digital twin services with reusable management layers; Figure 3 This is another sub-flowchart of the enterprise digital twin service encapsulation and dynamic orchestration method that allows for reusable management layers. Detailed Implementation

[0015] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0016] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0017] like Figure 1 As shown, this application provides a method for encapsulating and dynamically orchestrating enterprise digital twin services with reusable management layers, specifically including: S1: Obtain the structured features of each reusable management layer in the enterprise digital twin system, normalize and concatenate them, and input them into the feedforward network to generate layer semantic fingerprints. The structured features include layer collaborative topology features, layer metadata features, and layer service contract features.

[0018] S2: Receives standardized intermediate representation data from the unified context agent, extracts key dimension status codes, and constructs a lightweight context package containing discretized state information.

[0019] The key dimension status codes include: current production cycle level, main control system load status, abnormal alarm density, and data freshness of associated layers.

[0020] S3: Based on the key dimension status codes in the lightweight context package, each key dimension status code is converted into a preset semantic weight factor using a parameterless state mapping mechanism, and combined with the layer collaborative topology features in the layer semantic fingerprint to activate weighted combination logic, thereby generating a context distillation vector that represents the layer scheduling tendency at the current moment.

[0021] S4: Read the preset key response layer tag information, locate the strong constraint bit in the layer semantic fingerprint, and perform a truncation operation on the context distillation vector according to the strong constraint bit to eliminate the interference components that weaken the weight of the key layer, and generate the modified context distillation vector after intention anchoring.

[0022] S5: Based on the semantic fingerprint of the layer and the modified context distillation vector, perform dot product matching calculation, quantify the semantic alignment degree between each layer service instance and the current context environment, and generate a real-time priority score sequence.

[0023] S6: Arrange the service instances of each layer in descending order according to the real-time priority score sequence, construct a dynamic service scheduling queue that reflects the urgency of current business needs, and establish the order of invocation of service instances of each layer within the execution cycle.

[0024] S7: Based on the call order determined by the dynamic service scheduling queue, issue resource allocation instructions to the enterprise digital twin runtime framework to drive high-priority layer service instances to occupy computing resources first and complete the state synchronization task.

[0025] S8: Monitor the structured features of newly registered layers or the status code mapping rules of newly added context dimensions, and hot update the weight parameters of the feedforward network or the mapping table of the parameterless state mapping mechanism without restarting the scheduling core, so as to realize the adaptive expansion of the dynamic service orchestration system.

[0026] Step S1: Obtain the layer metadata features, layer service contract features, and layer collaborative topology features of each reusable management layer in the enterprise digital twin system. Then, normalize and concatenate these three types of structured features and input them into a lightweight feedforward network to generate a layer semantic fingerprint that uniquely identifies the static capabilities of each layer. Specifically, this includes: S1.1: Perform multi-dimensional parsing on the original definition data of reusable management layers to obtain layer meta-information features including layer spatial granularity, time update frequency, data source type, and lifecycle stage of the bound business entity. At the same time, extract layer service contract features covering the sensitive domain of service call interface input parameters, output result time decay curve, and resource consumption fluctuation baseline. Collect layer collaborative topology features describing the weight of data flow between the layer and other layers within the twin, the event trigger coupling strength, and the state synchronization dependency depth to form a multi-source heterogeneous structured feature set.

[0027] When performing multi-dimensional parsing on the original definition data of reusable management layers, the input object must first be identified as the layer registration metadata set from the enterprise digital twin system's operating framework. This set contains the basic attributes, interface agreement information, and internal collaboration relationship indexes that are fixed during the business modeling phase of each layer. Based on the registration metadata set, structural field decomposition operations are performed to extract the original field blocks describing spatial scale, sampling interval, and data source type. The lifecycle stage fields of the bound business entities are then enumerated and encoded to form preliminary layer metadata feature components. Based on the service interface definition portion of the same set, the sensitive input parameter field, the effective duration field of the output result, and the resource consumption curve parameter field are located. These are then numerically converted and discretized using a parameter type parser to generate layer service contract feature components with call sensitivity, result time-lapse characteristics, and resource consumption fluctuation patterns. For the collaboration relationship index carried in the metadata, an association matrix is ​​constructed based on the event triggering links and data flow paths between layers. The link weight values, trigger coupling strength values, and state synchronization dependency depth values ​​are mapped to a directed graph's numerical adjacency matrix, and further, collaboration topology feature components are derived according to topology parsing rules. The multi-source feature alignment module merges the three types of components into a multi-source heterogeneous structured feature set, providing an input benchmark for subsequent dimensional unification and normalization processing. Through this parsing and extraction link, the original definition data is transformed into a feature set with both structural integrity and multi-source coverage, ensuring that the feature information is sufficient to support the subsequent computation of a lightweight feedforward network to generate a unique semantic fingerprint.

[0028] For example, when a company's digital twin system registers a production line scheduling layer, its original definition data includes a spatial granularity field of "workstation level," a time update frequency field of "5 seconds," a data source type of "PLC signal," and a business entity lifecycle stage field coded as "running stage." In the service interface definition, the input parameter sensitivity domain covers production line cycle control commands, the output result timeliness curve exhibits exponential decay, and the resource consumption fluctuation baseline is CPU usage in the 20%-40% range. In the collaboration relationship index, the data flow weight between this layer and the energy consumption monitoring layer is 0.8, the event trigger coupling strength value is 0.6, and the state synchronization dependency depth value is 3. After structural field decomposition, spatial granularity, time update frequency, and data source type are encoded as numerical identifiers 1, 5, and 3, respectively, with a lifecycle stage enumeration value of 2. Sensitive domain fields are mapped to a security level coefficient of 0.9 by a type resolver. The timeliness curve is discretized to generate a decay parameter vector of length 10, and the resource consumption baseline is represented by mean and fluctuation amplitude. Cooperative topological features are constructed using an adjacency matrix to form a 3×3 matrix containing the aforementioned weights and depth indicators. The multi-source feature alignment module concatenates all numerical components in a preset order to generate a feature set covering spatial, temporal, resource, and topological multidimensional attributes. This set, after subsequent range normalization and tensor concatenation, can be input into a lightweight feedforward network, forming a dense, low-dimensional layer semantic fingerprint after nonlinear embedding, providing a computational basis for scheduling decisions.

[0029] S1.2: Based on the multi-source heterogeneous structured feature set, perform dimensional unification and numerical range standardization processing, and use the range normalization algorithm to map the numerical values ​​with different physical meanings in the layer meta-information features, layer service contract features and layer collaborative topology features to the same preset interval, eliminate the interference of dimensional differences on subsequent neural network calculations, and generate a normalized feature vector sequence with mathematical consistency.

[0030] Input condition filtering is performed on the multi-source heterogeneous structured feature set output by the preceding sub-step S1.1. The numerical components of meta-information, including layer space granularity, time update frequency, data source type, and life cycle stage of the bound business entity, the numerical components of contract features, including sensitive domain of service call interface input parameters, output result time decay curve, and resource consumption fluctuation baseline, as well as the numerical components of collaborative topology features describing data flow weight, event trigger coupling strength, and state synchronization dependency depth, are used as the original objects for dimensional unification processing.

[0031] For the original objects mentioned above, unit system matching is performed, converting all geographical length units involving spatial granularity to uniform metric values, unifying the time update frequency to second system, converting data source type fields to integer encoding for subsequent normalization, and unifying energy consumption and resource consumption-related indicators to kilowatt-hour energy consumption values.

[0032] The range normalization algorithm is used to standardize the numerical range of each feature component, the minimum and maximum values ​​of each feature component are calculated, and they are mapped to the preset interval [0,1] according to the following formula: ; Where x is the original value of the feature, x i min Let x be the minimum value of the i-th eigencomponent. i max It represents the maximum value of the i-th feature.

[0033] Normalization operations are performed on feature components with different physical meanings, and a unified numerical scale mapping table is established during the normalization process to ensure that each component of the subsequent neural network input vector is operated on the same mathematical scale, thus eliminating computational interference caused by differences in the original dimensions.

[0034] The consistency of each normalized feature component is verified, and any outliers (such as values ​​less than 0 or greater than 1) in the normalization result are removed to form a normalized feature vector sequence with mathematical consistency, which serves as the sole input source for subsequent tensor concatenation.

[0035] By using normalization algorithms and dimensional mapping processing, the multi-source heterogeneous structured feature set from the previous step is transformed into normalized feature vector data with consistent dimensions and uniform intervals, thus enabling high-precision computational conditions for subsequent neural network embedding.

[0036] S1.3: Perform tensor concatenation operation on the normalized feature vector sequence, and connect the layer meta-information feature components, layer service contract feature components and layer collaborative topology features end to end according to the preset feature fusion order to construct a fixed-length high-dimensional fusion feature vector that reflects the full-dimensional static attributes of the layer, which serves as the sole input data source for the lightweight feedforward network.

[0037] The generated normalized feature vector sequence is subjected to index parsing processing. Based on the preset feature fusion order table, the start and end positions of the layer meta-information feature components, layer service contract feature components, and layer collaborative topology features in the sequence are determined to ensure accurate component positioning in the subsequent splicing process.

[0038] Data buffer reading operations are performed on the identified feature components to store them as contiguous memory blocks. Preset separator bits are inserted between the memory blocks to maintain feature category boundaries during tensor splicing and prevent semantic distortion caused by cross-category data mixing.

[0039] A tensor splicing operator is used to perform head-to-tail connections along the feature dimension, sequentially splicing the three types of feature components into a fixed-length high-dimensional feature vector. The dimensional order of the splicing result is verified to ensure that it meets the structural requirements of the lightweight feedforward network input interface.

[0040] Position encoding injection is performed on the concatenated high-dimensional feature vector. The starting position index of each feature category is combined with the category identifier mapping table to form a position encoding vector, which is then element-wise added to the concatenated vector, thereby giving the fused feature vector the ability to recover category localization.

[0041] A global normalization process is performed on the result of the fusion feature and the positional encoding superposition to eliminate the numerical scale difference caused by the positional encoding superposition during the stitching process of different feature categories, forming a fixed-length high-dimensional fusion feature vector with consistent mathematical scale and reflecting the static attributes of the layer in all dimensions, which serves as the sole input data source for the lightweight feedforward network.

[0042] By using the tensor structured splicing and normalization methods described above, the results of the previous step are transformed into high-dimensional fusion feature data with a unified dimensional structure and locatable category boundaries, thus achieving the integrity and computability of the input data source.

[0043] For example, in the device status layer of an enterprise digital twin system, the normalized layer metadata feature component has a length of 12, the service contract feature component has a length of 8, and the collaborative topology feature component has a length of 15. The preset feature fusion order is metadata - service contract - collaborative topology. The index parsing determines the component positions (0-11), (12-19), and (20-34). Data buffer reading is performed with continuous storage and the category boundaries are separated by the identifier bits 0xAA and 0xBB. The tensor concatenation operator performs a connection at dimension 0, resulting in a fused feature vector of length 35. The position encoding vector is a sequence of integers of length 35, with the category starting positions being 0, 12, and 20, corresponding to identifier codes 1, 2, and 3. After encoding superposition, global normalization is performed. This processing outputs a high-dimensional fused feature vector of length 35 with uniform dimensions. After being input into a lightweight feedforward network, the network structure check passes, and it can be directly used to generate subsequent embedding representations. The execution performance is stable, and no feature misalignment or numerical anomalies occur.

[0044] S1.4: Input the fixed-length high-dimensional fused feature vector into the pre-trained lightweight feedforward network to perform nonlinear embedding transformation processing. Use the activation function of the hidden layer of the network to perform multi-level feature abstraction and dimensionality reduction mapping on the input data, compress redundant information and retain key semantic discriminativeness, and output a low-dimensional dense layer static capability embedding representation.

[0045] A fixed-length high-dimensional fused feature vector is used as the sole input data source and fed into the input layer of a pre-trained lightweight feedforward network. Matrix multiplication is then performed between the weight matrix and the fused feature vector to generate the first layer's weighted summation result.

[0046] The weighted summation result of the first layer is subjected to a predefined nonlinear activation function for element-wise transformation. Functions such as ReLU, Leaky ReLU, or Sigmoid are used to enhance the ability to distinguish between sparse features and medium-amplitude features, and the nonlinear feature representation of the first stage of the hidden layer is output.

[0047] The nonlinear feature representation of the first stage of the hidden layer is input into the weight matrix of the second hidden layer, and matrix multiplication and bias superposition operations are performed. While ensuring dimensionality reduction, the coupling strength difference information in the collaborative topological features is preserved, so that the output has hierarchical feature abstraction.

[0048] A normalization operation is applied to the output of the second hidden layer. The distribution of each feature component is stabilized through the batch normalization mechanism, which reduces the internal covariate shift in gradient propagation and avoids the scale inconsistency problem in the dense embedded representation of the subsequent output.

[0049] By combining the nonlinear transformation results after the second hidden layer, target dimension mapping is performed to compress the high-dimensional input into a preset low-dimensional dense space, while maintaining the semantic distinguishability of the layer meta-information features and the layer service contract features, thus forming a static capability embedding representation of the layer.

[0050] Through the above nonlinear embedding transformation, the fixed-length high-dimensional fusion feature vector from the previous step is transformed into low-dimensional dense data suitable for subsequent unique identifier generation, thereby achieving efficient characterization of the static capabilities of multi-source heterogeneous layers in the embedding space.

[0051] S1.5: Based on the layer static capability embedding representation, perform a unique identifier generation operation, directly define the network output vector as a layer semantic fingerprint that is not updated with changes in runtime context, establish the layer semantic fingerprint as the static capability identifier of each layer service instance in the service encapsulation module, and complete the closed-loop construction from the original layer definition to the computable semantic fingerprint.

[0052] Based on the static capability embedding representation of the layers generated by the lightweight feedforward network, a unique hash check is performed on the numerical values ​​of each dimension in the embedding representation to verify the sufficiency of differences between embeddings of different layers. Coordinate space quantization is performed on the verified embedding representations, mapping each dimension value to a fixed-precision value bucket according to a preset quantization step size, ensuring the reproducibility of vector construction under the same system version. A global index injection mechanism is applied to the quantized embedding representation, embedding the unique index code assigned to the layer within the system into specific high-order bits of the vector to form a capability identifier vector with static retrieval capabilities. Irreversible encoding operations are performed on the globally indexed embedding representation, and a salted one-way hash function is used to calculate the feature summary. ; Where H is the final layer semantic fingerprint, V is the quantized embedding representation, and S is the injected salt constant. The generated layer semantic fingerprint is stored in the capability index table of the service encapsulation module and its version number is marked for subsequent consistency verification. Through irreversible encoding and global index injection, the embedding representation of the previous step is transformed into a permanent, static, and computable layer semantic fingerprint, realizing the full-chain closed-loop construction of layer static capability identifiers in the service encapsulation module.

[0053] During the operation of the system, the semantic fingerprint of this layer is used to quickly match the context distillation vector, which significantly reduces the scheduling latency. Furthermore, the irreversibility and static nature of the fingerprint ensures the auditability and security of the scheduling strategy.

[0054] Step S2: Receive standardized intermediate representation data from the unified context agent, extract key dimension status codes such as current production cycle level, main control system load status, abnormal alarm density, and data freshness of associated layers, and construct a lightweight context package containing discretized status information. Specifically, this includes: S2.1: Perform protocol parsing on the standardized intermediate representation data output by the unified context proxy to separate the multi-source heterogeneous data stream sequence containing timestamp normalization markers, ensuring that subsequent extraction operations are based on a time-aligned data benchmark.

[0055] The unified context agent is a dedicated software module in an enterprise digital twin system responsible for integrating, aligning, and standardizing multi-source heterogeneous real-time data streams. Through a built-in protocol adapter, it collects raw data in real time from different data sources such as sensors, MES systems, and monitoring platforms, performs timestamp alignment, unit unification, and format conversion, and ultimately outputs a standardized intermediate representation data stream with a consistent structure and timing.

[0056] For the standardized intermediate representation data output by the unified context proxy, load its binary control frame and perform bit segment decoding according to the preset protocol field length to extract the original data frame set containing the source system identifier, data type identifier and timestamp normalization mark.

[0057] The original data frame set is subjected to a multi-source data stream splitting operation. Based on the source system identifier, control frames from different service sources are mapped to the corresponding protocol parsing channels to form a data stream subset with source domain division.

[0058] In each data stream subset, the message structure analysis module of the protocol parser is invoked. Based on the field mapping table, the payload fragments corresponding to the data type identifiers are extracted item by item and structured field records are formed.

[0059] The timestamps of structured field records are normalized, and the time synchronization operator is called to convert heterogeneous source timestamps to a unified global reference clock domain, and a standard time index is calculated. The calculated normalized time index is backfilled into the corresponding structured field record, replacing the original timestamp position, forming a multi-source heterogeneous data stream sequence containing time alignment markers.

[0060] Through the above protocol parsing and normalization processing, the original intermediate representation data generated in the previous step is transformed into a multi-source heterogeneous data stream sequence with temporal consistency and source domain division, thereby ensuring a strict temporal benchmark for subsequent key dimension feature extraction.

[0061] S2.2: Perform dimensional feature matching operation based on the multi-source heterogeneous data stream sequence to identify and locate the original numerical field set of four key dimension status codes: current production cycle level, main control system load status, abnormal alarm density, and data freshness of associated layers.

[0062] Channel decomposition is performed on the multi-source heterogeneous data stream sequence containing timestamp normalization markers. Each data stream is mapped to a fixed-width buffer according to its source category (sensor stream, log stream, business event stream) to ensure that the subsequent key dimension feature localization processing is based on source consistency.

[0063] Perform a field scanning operation on the decomposed data stream buffer, and identify numerical fields containing production cycle level semantics according to a preset dimension matching pattern. This pattern is based on the cycle unit time output count definition output by the production monitoring module to ensure that the extracted fields correspond to the actual production rhythm.

[0064] Perform field position index construction operations on the log stream in the buffer to locate the indicator fields that record the load status of the main control system. The location of the load status field is based on a combination of signals such as CPU utilization, memory utilization, and the number of active threads in the thread pool. The storage location of the original load status value is bound to the index mapping table.

[0065] An event counting aggregation algorithm is used to collect abnormal alarm events. The statistical field of abnormal alarm entries that occur within the last 5 minutes is extracted as the original abnormal alarm density value. This extraction relies on the matching relationship between the event trigger time field and the normalized timestamp to ensure the consistency of the alarm statistics within the time window.

[0066] Perform data sampling time difference calculation on the data stream from the associated layer, use the double timestamp difference to determine the freshness of the layer data, and extract the corresponding delay in seconds as the original numerical field of the freshness of the associated layer data. The formula for calculating this difference is: ; Where Δt is the delay in seconds, T is the current sampling timestamp, and T0 is the data generation timestamp.

[0067] By using the above three extraction and positioning methods, the original production cycle level values, main control system load status values, abnormal alarm density values, and related layer data freshness values ​​in the multi-source heterogeneous data stream sequence are combined into a set of original numerical fields to achieve accurate positioning of key dimension identifiers.

[0068] S2.3: The original numerical field set is subjected to interval quantization processing using a preset discretization threshold mapping table to convert continuously changing physical quantities or statistics into a discrete set of key dimension status codes with clear semantic boundaries, thereby eliminating the interference of original data noise on scheduling decisions.

[0069] The discretization threshold mapping table is a pre-defined, structured data transformation rule table. Its core function is to map continuous physical quantities or statistical quantities into a finite number of discrete status codes with clear business semantics. This table predefines several threshold intervals for each key dimension to be processed, such as the load status of the main control system, and assigns a unique status code identifier to each interval. For example, three intervals are defined for the load status: low (<30%), medium (30%-70%), and high (>70%), along with their corresponding codes. During data processing, the system compares the collected raw values ​​with the thresholds of the corresponding dimensions in the table. Whichever interval the value falls into, the corresponding status code is output. Through this mechanism, continuous values ​​that are difficult to use directly for logical judgment are converted into stable, noise-resistant, and semantically clear discrete signals, a key configuration for ensuring the input quality of subsequent intelligent decision-making processes such as parameterless state mapping.

[0070] S2.4: Based on the business scenario constraint rules of the enterprise digital twin system, the discrete key dimension status code set is validated and redundantly removed to generate a simplified status code subset containing only valid key dimension information, ensuring the compactness of the context package.

[0071] The business scenario constraint rules of the enterprise digital twin system are a set of predefined logical judgment conditions that reflect the enterprise's specific processes, safety standards, and operational strategies. During the operation of the digital twin, this rule set is used to dynamically verify the validity and necessity of various status codes extracted from real-time data in the current specific business scenario. For example, in an equipment maintenance scenario, the status code for the production cycle may be deemed invalid and removed; or during steady-state operation, status codes of consecutively occurring identical abnormal alarms may be merged into a single valid alarm to eliminate information redundancy. Its core purpose is to ensure that the context information ultimately input into the intelligent scheduling or decision-making model is highly refined, fully aligned with the current business objectives, and free of logical conflicts, thereby guaranteeing the quality and reliability of data-driven decision-making from a business logic perspective.

[0072] S2.5: Perform a structured encapsulation operation based on the simplified state code subset to construct a lightweight context packet containing fixed-length discretized state information, which serves as the direct input object for driving the subsequent parameterless state mapping mechanism to generate context distillation vectors.

[0073] like Figure 2 As shown, step S3: Based on the key dimension state codes in the lightweight context package, each key dimension state code is converted into a preset semantic weight factor using a parameterless state mapping mechanism, and combined with the layer collaborative topology features in the layer semantic fingerprint to activate weighted combination logic, generating a context distillation vector representing the layer scheduling tendency at the current moment. Specifically, this includes: S3.1: Parse the discrete key dimension status codes contained in the lightweight context package, such as the current production cycle level, main control system load status, abnormal alarm density, and data freshness of associated layers, to extract the enumeration index value of each key dimension status code, forming the original status code sequence to be mapped, which serves as the direct input object for the parameterless status mapping mechanism.

[0074] S3.2: Based on the enumerated index values ​​in the original state code sequence, a key-value matching operation is performed using a preset parameterless state mapping lookup table to directly map each discrete key dimension state code to the corresponding preset semantic weight factor, eliminating the complex model reasoning process and generating an initial set of semantic weight factors containing the independent semantic contribution of each dimension.

[0075] The original status code sequence to be mapped is read by index value to form an intermediate index array containing each enumerated index, ensuring that each index value corresponds to a fixed dimension position in the lightweight context package.

[0076] The intermediate index array is subjected to key-value matching processing. A preset parameterless state mapping lookup table is called, and each enumerated index value is used as a lookup key to locate the corresponding semantic weight value field.

[0077] The pre-configured parameterless state mapping lookup table is a static, pre-configured key-value pair mapping table. Its core function is to directly and computationally convert discretized key dimension state codes into corresponding semantic weight factors. For each possible state code enumeration value, such as production cycle level: high, a fixed weight value reflecting its business importance is predefined. During processing, the system only needs to perform a simple key-value matching lookup in the table based on the input original state code sequence to quickly output an initial set of semantic weight factors composed of weight factors for each dimension. This mechanism avoids complex real-time parameter calculations, ensuring the consistency, interpretability, and efficiency of weight allocation, and providing a foundation for subsequent generation of context distillation vectors incorporating prior business knowledge.

[0078] The semantic weight value range obtained by the search is numerically decoded, and the internal encoding constants are converted into floating-point weight values ​​that can be directly used in subsequent calculations, forming a set of dimension-independent weight components.

[0079] A structural verification operation is performed on the set of weighted components to ensure that all components meet the preset dimensional consistency and numerical range limits, thereby eliminating the interference of outliers on subsequent weighted combination logic.

[0080] The verified weight component set is backfilled and assembled according to the dimensional order in the original status code sequence to form an initial semantic weight factor set containing the independent semantic contribution of each dimension.

[0081] By using key-value matching and weight decoding, the results of the previous step are transformed into initial semantic weight factor data that can be used for topology-aware weighting, thus achieving rapid semantic weight generation under model-free inference conditions.

[0082] For example, in a dynamic orchestration scenario of an enterprise digital twin system, the original status code sequence input by the lightweight context packet is configured as {Production cycle time level index: 2, Main control system load status index: 1, Anomaly alarm density index: 3, Related layer data freshness index: 0}. A preset lookup table is called to map index 2 to a weight value of 0.75, index 1 to a weight value of 0.50, index 3 to a weight value of 0.90, and index 0 to a weight value of 0.65. The corresponding weight value for a single index is calculated using the following weight matching formula: ; Where w is the output weight value, T is the set of weight vectors stored in the lookup table, and i is the status code enumeration index value. Structural verification is performed on the four-dimensional weights to confirm that they are all within the closed interval of 0 to 1, and that the floating-point precision is maintained to two decimal places. The final output is the initial semantic weight factor set {0.75, 0.50, 0.90, 0.65}. This set is directly used in the next step of weighted combination logic combining collaborative topological features. In the test scenario, the execution time is less than milliseconds, significantly improving the timeliness and semantic accuracy of priority reordering.

[0083] S3.3: Decode the layer collaborative topological features in the generated layer semantic fingerprint to extract the topological connection matrix that describes the weight of data flow between the layer and other layers within the twin, the event triggering coupling strength, and the state synchronization dependency depth, forming a topological structure descriptor that reflects the dynamic dependency relationship between layers.

[0084] Decoding is performed on the layer collaborative topology features in the generated layer semantic fingerprint. The input object is a fixed-length numerical vector containing data flow weights, event trigger coupling strength, and state synchronization dependency depth. This vector is ensured to originate from fixed-length high-dimensional fusion features and be embedded and output by a lightweight feedforward network.

[0085] By employing a feature component localization method based on field labels, the collaborative topological feature components are extracted from the layer semantic fingerprint vector and mapped to the input buffer area of ​​the topological feature decoder, so that the component can be subsequently transformed into a matrix form of topological connection relationship description.

[0086] The field re-parsing operation is performed on the vector elements in the decoder input buffer area. The data flow weight components are moved into the horizontal weight slots of the matrix, the event trigger coupling strength components are moved into the vertical coupling slots of the matrix, and the state synchronization dependency depth components are filled into the diagonal dependency slots of the matrix, thus achieving a strict matrix feature arrangement.

[0087] A normalization calibration mechanism is adopted to map the matrix slot values ​​with different physical meanings according to the preset maximum and minimum values. Based on the calibrated matrix values, sparsity filtering is performed to set the connection relationships below the preset sparsity threshold to zero, so as to reduce redundant connections without actual dependence and maintain the salience of high coupling relationships.

[0088] The calibrated and sparsified matrix is ​​defined as a topology descriptor and stored in the scheduling logic as the direct basis for the activation of subsequent weighted combinational logic. This processing method establishes a precise binding relationship between the previous step status code mapping result and the dynamic dependency relationship between layers, thereby enhancing topology awareness during scheduling biased calculation.

[0089] For example, in a certain enterprise digital twin system, in the static capability embedding representation received by the layer collaborative topology feature decoder, the data flow weight component is a vector of length 9, the event triggering coupling strength component is a vector of length 9, and the state synchronization dependency depth is a vector of length 9. The decoder fills the three types of vectors into the horizontal, vertical, and diagonal slots of a 9×9 matrix, respectively, and sets x according to a normalization calibration mechanism. min =0, x max =10, and proportionally map the original slot values. For example, if the original event coupling strength is 7, the normalized value is calculated to be 0.7. Then, a sparsity threshold of 0.2 is set, and matrix elements below this value are set to zero to form a sparse matrix. The output topology descriptor can significantly increase the priority of layers with an association coupling strength ≥0.5 when weighted in scheduling optimization. System verification results show that this method significantly improves the response speed of highly coupled layers and significantly reduces scheduling latency in multi-source context high-frequency update scenarios.

[0090] S3.4: Based on the event-triggered coupling strength value in the topology descriptor, perform a dynamic weighted combination logic activation operation on the initial semantic weight factor set, automatically filter and amplify the semantic weight factors associated with the current high-coupling layer according to the coupling strength threshold, suppress interference components in low-coupling dimensions, and generate a topology-aware corrected semantic weight factor array.

[0091] The highly coupled layer refers to a specific data layer in the layer collaboration topology of a digital twin that has a strong event triggering and state linkage relationship with the current core business activities or abnormal events. It is identified by the quantified event triggering coupling strength value in the topology descriptor; when this value exceeds a preset threshold, the system determines it to be a highly coupled layer. In the dynamic weighting stage of generating the context distillation vector, the semantic weight factors associated with these layers are automatically filtered and significantly amplified to ensure that the final decision vector prioritizes the impact of key layers that will trigger chain reactions once the state changes, thereby allowing system resources and attention to be precisely focused on the most critical linkage links.

[0092] Based on the event-triggered coupling strength values ​​in the topological descriptor, the corresponding initial semantic weight factor set is selected as the input data array.

[0093] The coupling strength field in the topology connection matrix is ​​compared element by element with the preset coupling strength threshold to construct a high coupling association index list. This index list is used to locate the target component that needs to be amplified in the initial weight factor set.

[0094] For each position in the highly coupled associative index list, the weight amplification operator is invoked to multiply the original semantic weight value by a coupling amplification factor. The factor is calculated based on the ratio of the difference between the coupling strength and the threshold in the topology, such as: ; Where S is the event triggering coupling strength, T is the preset threshold, and k is the amplification factor.

[0095] For low-coupling associative index positions, the weight suppression operator is invoked, multiplying the original semantic weight values ​​by a suppression factor. The suppression factor is calculated inversely based on the ratio of coupling strength to a threshold, as shown in the formula: ; When r is less than 1, this component is attenuated.

[0096] The weight values ​​that have undergone amplification and suppression are merged, and position alignment checks are performed to ensure that they are consistent with the index mapping of the original initial set of weight factors.

[0097] By using a dynamic weighted combination logic activation method, the initial semantic weight factor set from the previous step is transformed into a weight factor array that has been corrected by topology awareness, thereby achieving a significant adjustment of layer priority driven by context.

[0098] For example, in a certain enterprise digital twin system, the event-triggered coupling strength of the topology connection matrix ranges from 0.2 to 1.0, with a preset coupling strength threshold of 0.7. The high-coupling association index list contains indices [2,5], and the low-coupling association index list contains indices [0,1,3,4]. The initial weight factor set is [0.3,0.5,0.6,0.4,0.8,0.2]. For index 2, the amplification factor k is calculated to be 0.2857, amplifying 0.6 to 0.6×(1+0.2857)=0.7714. For index 5, the amplification factor k is calculated to be 0.2143, amplifying 0.2 to 0.2×(1+0.2143)=0.2429. For index 0, its coupling strength is 0.4, and the suppression factor r is calculated to be 0.5714, suppressing 0.3 to 0.1714. Following this process, all index positions are adjusted, generating the corrected weight array [0.1714, 0.2857, 0.7714, 0.2286, 0.4571, 0.2429]. This array significantly improves the scheduling priority of highly coupled layers and suppresses interfering calls from low-coupled layers in subsequent context distillation vector reconstruction. Verification results show that the response latency of highly coupled layers is significantly reduced in real-time scheduling, and the system resource usage structure is more optimized.

[0099] S3.5: Perform vector reconstruction and normalization processing on the topology-aware modified semantic weight factor array, and concatenate each modified semantic weight factor into a dense vector of fixed length according to the preset dimensional arrangement order. Output a context distillation vector that represents the current layer scheduling tendency and does not contain the original values, thus completing the compressed representation from discrete state to continuous scheduling semantics.

[0100] like Figure 3 As shown, step S4: Read the preset key response layer marker information, locate the strong constraint bit in the layer semantic fingerprint, and perform a truncation operation on the context distillation vector according to the strong constraint bit to eliminate interference components that weaken the weight of the key layer, generating a corrected context distillation vector after intent anchoring. Specifically, this includes: S4.1: Obtain the pre-stored set of key response layer marker information, and use a bitmask parsing algorithm to decode the Boolean identifier field in the set to extract a list of specific layer indices that have been manually marked as security monitoring layers or emergency control layers, forming a key layer index sequence to be protected.

[0101] The pre-stored critical response layer tagging information set is a static configuration dataset loaded during system initialization. It uses compact Boolean identifier fields, such as bitmasks, to tag specific data layers in the digital twin that are pre-defined by humans as security monitoring layers or emergency control layers. This critical response layer tagging information set is the authoritative data source for all subsequent critical layer identification and protection logic. When the system needs to determine which layers belong to critical response layers, it calls a bitmask parsing algorithm to decode this set, quickly extracting the indices of all tagged layers, thereby generating a clear critical layer index sequence to guide subsequent strong-constraint bit localization.

[0102] S4.2: Based on the key layer index sequence to be protected, traverse the generated layer semantic fingerprints, use the feature vector offset to calculate the specific coordinate position of the strong constraint bit corresponding to each key layer index in a fixed-length vector, and generate a strong constraint bit coordinate mapping table containing the coordinates of all strong constraint bits.

[0103] Based on the index sequence of the key layers to be protected, an index traversal operation is performed on the generated layer semantic fingerprint to sequentially access the fixed-length feature vector of each layer service instance and extract its component location metadata.

[0104] For the accessed feature vector, load the predefined strong constraint bit offset baseline value in the mapping rule table, and calculate the absolute coordinate position of the corresponding strong constraint bit of the layer by numerical accumulation, combined with the current layer's sequential index in the set.

[0105] To ensure the rigor of offset calculations, the product of the layer order index and the offset baseline value is encapsulated into a formula, and the absolute coordinate position is defined using the following MathML formula: ; Where P represents the absolute coordinate position, I represents the sequential index of the layer in the collection, and O represents the offset baseline value.

[0106] After calculating the absolute coordinate position, the strong constraint bit coordinate parameters of the corresponding access layer feature vector are recorded and written into the data structure of the strong constraint bit coordinate mapping table to form a set of mapping table entries containing the strong constraint bit coordinates of all key layers.

[0107] Perform deduplication and sorting operations on the set of mapping table entries, remove duplicate coordinates and ensure that the coordinates are sorted in ascending order, so as to provide a data basis for slicing the distillation vector in coordinate order in the subsequent intention anchoring process.

[0108] By using index traversal and offset calculation, the index sequence of the key layers to be protected is transformed into a mapping table containing all strongly constrained bit coordinates, thereby achieving the expected technical effect of locating the dimension where the weight of the key layers is located.

[0109] For example, in the layer semantic fingerprint of a certain enterprise digital twin system, each feature vector has a fixed length of 96, an offset baseline value of 12, and a key response layer index sequence of [2, 5, 8]. Traversing the set, the feature vector at index 2 is accessed, and its absolute coordinate position is calculated as 2 × 12 = 24; the feature vector at index 5 is accessed, and its absolute coordinate position is calculated as 5 × 12 = 60; the feature vector at index 8 is accessed, and its absolute coordinate position is calculated as 8 × 12 = 96. [24, 60, 96] is recorded in a mapping table, and deduplication and sorting are performed. The mapping table output is {24, 60, 96}. During the verification phase, the system, based on this mapping table, can accurately extract the weight factors corresponding to each key layer when distilling vector slices in the context, ensuring that they are not weakened in subsequent scheduling priority calculations. Performance tests show that key layers are always at the top of the scheduling queue, significantly improving the response time and stability of key business requests.

[0110] S4.3: Receive the context distillation vector generated at the current moment, and extract a subset of the initial semantic weight factors corresponding to the key layer index using vector dimension slicing technology based on the strong constraint coordinates in the strong constraint bit coordinate mapping table, forming a key dimension weight factor array to be verified.

[0111] It receives the context distillation vector generated at the current moment as the input object, and clarifies that the vector is a fixed-length dense vector and that the indexes of each dimension maintain a one-to-one correspondence with the feature arrangement order of the layer service instance.

[0112] Based on each coordinate position in the strongly constrained bit coordinate mapping table, the mapping table is input as an index set to the dimension slicing operation control logic to ensure that the slicing operation only applies to the vector positions that precisely correspond to the key layer index.

[0113] In the slicing operation, a high-precision memory copy operator is called to extract the original values ​​at the specified index in the context distillation vector without loss, forming an intermediate weight factor cache containing the real values ​​of all target dimensions, while maintaining the extraction order consistent with the mapping table records.

[0114] The extracted intermediate weight factor cache is subjected to structured renaming processing, and each value is bound to its layer unique identifier and constraint bit information to construct a key dimension weight factor array with complete metadata so that the subsequent threshold comparison logic can accurately identify interference components.

[0115] By using the above vector dimension slicing and metadata binding processing methods, the context distillation vector generated in the previous step is transformed into a structured array containing only the weights of key layers, thereby achieving centralized and traceable storage of key dimension weight factors.

[0116] S4.4: Perform threshold comparison logic on the key dimension weight factor array to be verified, identify interference components whose values ​​are lower than the preset minimum protection threshold, and use the hard truncation reset operator to force the identified interference components to be assigned the maximum weight constant, thereby generating an intermediate correction weight factor array to eliminate negative interference.

[0117] The system receives an array of key dimension weight factors to be verified as input. A precise numerical scanning component sequentially reads the floating-point values ​​of each weight factor and compares them one-to-one with a preset minimum guarantee threshold, forming a marker matrix to record the indices of interference components below the threshold. Utilizing the logical relationship between this marker matrix and the threshold constant, a hard truncation reset operator module is invoked to replace the corresponding interference component values ​​in the marker matrix with the maximum weight constant. During the replacement process, a memory write protection mechanism is implemented to prevent numerical contamination of unmarked locations. A data consistency check is then performed again on the replaced weight factor sequence to confirm that all interference components below the threshold have been successfully reset and that the reset values ​​are completely consistent with the maximum weight constant. This chained operation generates an intermediate corrected weight factor array containing the ability to eliminate negative interference. This array is output in a fixed-length structured storage format to ensure that subsequent vector backfilling operations have a directly referenceable format and precision.

[0118] By using threshold comparison and hard truncation reset processing, the array of key dimension weight factors to be verified in the previous step is transformed into an intermediate corrected weight factor array that has removed low-weight interference components and has the maximum weight guarantee value, thereby achieving the expected technical effect of locking the priority weight of the key response layer.

[0119] For example, in a company's digital twin system, the array of weight factors for key dimensions to be verified contains 8 floating-point values. The preset minimum guarantee threshold is set to 0.75, and the maximum weight constant is set to 1.00. The numerical scanning component detects that the 2nd, 5th, and 7th elements are 0.60, 0.72, and 0.40 respectively, which are below the threshold of 0.75. These are marked as "1" in the labeling matrix, while the remaining positions are marked as "0". The hard truncation reset operator, based on the labeling matrix, replaces the above three elements with 1.00, while retaining the original values ​​in the other positions. The consistency check formula is then executed: ; Where w i M represents the value of the i-th weight factor. i This represents the value at the corresponding position in the marker matrix, and W represents the output array set. The detection results show that the 2nd, 5th, and 7th elements are all 1.00, and other elements retain their original values, verifying that the intermediate correction weight factor array conforms to the expected structure. After this step, the priority weight of the critical response layer is significantly improved, enabling it to stably occupy the head of the scheduling queue during dynamic orchestration, ensuring the immediate execution of high-response-requirement services.

[0120] S4.5: Based on the intermediate corrected weight factor array, the vector reconstruction algorithm is used to backfill it into the corresponding strong constraint bit coordinate position in the original context distillation vector, replacing the original low weight values, and outputting the intention-anchored corrected context distillation vector that keeps the overall semantic structure unchanged but locks the weights of key dimensions.

[0121] Based on the generated intermediate correction weight factor array, the localization module of the vector reconstruction algorithm is called to read the strongly constrained bit coordinate mapping table to determine the insertion position index of each element in the array in the original context distillation vector.

[0122] After locating the coordinates, the intermediate correction weight factor array is mapped one-to-one with the corresponding dimension of the original context distillation vector according to the index order, forming a set of coordinate-weight pairs that can be directly replaced.

[0123] The set is replaced by replacing the low-weight numerical dimensions in the original context distillation vector with the corresponding modified weight factors, while keeping the values ​​at the other non-strongly constrained bit coordinates unchanged, so as to maintain the consistency of the overall semantic structure.

[0124] After the replacement is completed, the vector consistency verification module is called to check the density and fixed length properties of the correction result, ensuring that the corrected context distillation vector is completely consistent with the uncorrected version in terms of dimensional arrangement and structural density.

[0125] By using vector reconstruction and fixed-point replacement, the intermediate correction weight factor array from the previous step is transformed into a context distillation vector that maintains the overall semantic structure but locks the weights of key dimensions, thus ensuring the priority scheduling intent of key response layers.

[0126] For example, in a security monitoring scenario, a company's digital twin system sets the key response layer indices to 4, 7, and 15, with corresponding positions in the strong constraint bit coordinate mapping table being (4,0), (7,0), and (15,0). In the intermediate correction weight factor array output by the system in S4.4, the three values ​​are 2.5, 3.0, and 2.8, while the other dimensions retain their original values. During the replacement process, the vector reconstruction algorithm directly writes these three values ​​into the corresponding positions in the original context distillation vector, replacing the original low-weight values ​​such as 1.2, 1.5, and 0.9. After the replacement, a consistency check is performed to confirm that the corrected context distillation vector is still a dense vector of length 32. The weight update in this process can be represented by the following formula: ; Where V′ is the corrected context distillation vector, V is the original context distillation vector, and M... I Let R be the mapping matrix, R be the array of corrected weight factors, and Q be the original weight array that is being replaced. After this update, the critical response layer achieves a significantly improved matching score in the subsequent priority reordering step, ensuring that it is at the head of the scheduling queue and has priority in occupying resources during the system execution cycle, thereby improving the real-time performance and stability of the security response.

[0127] Step S5: Based on the layer semantic fingerprint and the corrected context distillation vector, perform lightweight dot product matching calculation to quantify the semantic alignment between each layer service instance and the current context environment, and generate a real-time priority score sequence. Specifically, this includes: S5.1: Perform dimension consistency verification and vector space standardization on the input layer semantic fingerprint to eliminate the dimensional differences caused by the embedding process of the preceding lightweight feedforward network, and generate a standard layer semantic fingerprint matrix with a unified metric benchmark to ensure that subsequent dot product operations are performed on the same feature space scale.

[0128] S5.2: Based on the cooperative topological feature dimension in the standard layer semantic fingerprint matrix, extract the initial feature subspace corresponding to the activation status code in the current modified context distillation vector, use the mask mapping mechanism to filter out the noise interference components of irrelevant dimensions, generate an adaptive layer semantic fingerprint subset for the current context environment, and achieve accurate alignment between static capability identifiers and dynamic environment states.

[0129] Based on a standard layer semantic fingerprint matrix with a unified metric, column vectors belonging to the collaborative topological feature dimension of the matrix are selected as the initial feature subspace extraction objects to ensure that the processed features are limited to static capability association components that reflect the weight of data flow between layers, the strength of event triggering coupling, and the depth of state synchronization dependency.

[0130] The current intent-anchored modified context distillation vector is parsed using status code activation, generating a dimension mapping index table containing the index positions corresponding to each activation status code, which serves as the matching basis for subsequent mask mapping mechanisms.

[0131] The dimension mapping index table is matched with the initial feature subspace to construct a binary mask matrix, where the coordinates with a mask value of 1 represent coordinates that have a direct semantic relationship with the current context, and the coordinates with a mask value of 0 represent irrelevant dimensions that need to be masked.

[0132] Element-wise multiplication of the mask matrix is ​​performed on the initial feature subspace to zero out the numerical components of the irrelevant dimensions to eliminate noise interference components, and only the highly correlated feature components corresponding to the activation state codes in the modified context distillation vector are retained.

[0133] The feature subspace filtered by the mask is subjected to a denser reconstruction process. All zero-reset column vectors are removed while maintaining the order of the remaining column vectors consistent with the original standard layer semantic fingerprint matrix. This generates an adaptive layer semantic fingerprint subset for the current context environment, achieving accurate feature alignment between static capability identifiers and dynamic environment states.

[0134] By using masking and filtering and dense reconstruction, the result of the previous step is transformed into a subset of fingerprint data with context-specific relationships.

[0135] For example, in an enterprise digital twin system containing 200 layer service instances, the standard layer semantic fingerprint matrix has a dimension of 200×64, where the last 12 dimensions are the collaborative topology feature dimensions. The activation status code indices in the current corrected context distillation vector are dimensions 2, 5, 9, and 11. These four indices are mapped to the corresponding columns of the collaborative topology feature dimensions, generating a 12×1 binary mask column vector. The mask values ​​are 1 in columns 2, 5, 9, and 11, and 0 in other columns. This mask column vector is expanded to a 200×12 mask matrix and element-wise multiplied with the collaborative topology feature subspace, causing irrelevant dimensions to be directly zeroed out. For example, the eigenvalue 0.76 in the 3rd column of the original index row is zeroed out. The filtered matrix is ​​then densified, removing all zeroed-out columns and retaining the index column vector order of 2, 5, 9, and 11, forming a 200×4 subset of the adapted layer semantic fingerprint. When this subset is input into the next sub-step dot product matching calculation, the interference of irrelevant features is reduced, the dot product projection overlap is significantly improved, and the priority score is greatly improved in terms of adaptability to business scenarios.

[0136] S5.3: Perform element-wise multiplication and summation operations on the semantic fingerprint subset of the adapted layer and the modified context distillation vector after intent anchoring, and use a predefined lightweight dot product matching algorithm to calculate the projection overlap degree of the two in the multidimensional feature space to generate an original similarity scalar value that represents the degree of semantic alignment between a single layer service instance and the current business scenario.

[0137] S5.4: Apply a linear normalization transformation function to the original similarity scalar value to map the projection overlap of different orders of magnitude to the preset zero-to-one closed interval probability distribution domain, eliminate the score distribution offset caused by the fluctuation of the number of layers, generate a standardized real-time priority basic score with comparability, and establish the relative importance order of each layer service instance under the unified evaluation system.

[0138] The system receives a subset of semantic fingerprints from the adapted layers and a modified context distillation vector after intent anchoring, and uses the resulting raw similarity scalar values ​​generated through lightweight dot product matching as input data for normalization. Based on the target interval for normalization, a pre-defined linear mapping function parameter for a closed interval from zero to one is set, including a mapping slope coefficient and an offset constant, ensuring that the boundaries of the output value range are consistent with the physical meaning of the probability distribution. Each raw similarity scalar value is multiplied by the slope coefficient and the offset constant is added to construct an intermediate normalized vector before mapping, maintaining the linear monotonicity between input and output. An interval pruning operation is performed on the intermediate normalized vector before mapping, using a minimum value constraint to set values ​​less than zero to zero and a maximum value constraint to set values ​​greater than one to one, ensuring that the normalized score strictly falls within the pre-defined closed interval. The set of normalized values ​​after interval pruning is encapsulated into a standardized real-time priority base score array, and a numerical distribution consistency check is performed on it to confirm that changes in the number of layers under different scenarios do not cause a shift in the score distribution. By combining linear normalization transformation with consistency testing, the projection overlap result of the previous step is transformed into a standardized real-time priority base score with a unified evaluation benchmark that can be directly used for priority ranking, thereby realizing the probabilistic quantitative expression of the relative importance of service instances in each layer in real-time scheduling.

[0139] For example, in an enterprise digital twin production monitoring scenario, assuming the original similarity scalar value sequence obtained by lightweight dot product matching is [0.15, 0.48, 0.95], the preset normalized mapping interval is zero to one, the mapping slope coefficient is set to 1.05, and the offset constant is set to 0.02. The mapping calculation is performed using the following MathML formula: ; Where `score` is the normalized score and `sim` is the original similarity scalar value. Substituting the original value of 0.15 into the formula yields a pre-mapping value of 0.1775, substituting 0.48 yields 0.523, and substituting 0.95 yields 1.0175. Interval pruning is then performed on the pre-mapping values, setting values ​​less than zero to zero and values ​​greater than one to one, resulting in a normalized score array [0.1775, 0.523, 1]. This score array maintains a consistent evaluation benchmark across different numbers of layers, and the score range conforms to a closed interval of the probability distribution, ensuring the stability and comparability of subsequent priority ranking. In practical tests, this normalization process significantly improves the scheduling engine's ranking stability under high layer concurrency, reduces resource allocation errors caused by score range offsets, and ensures the response reliability of critical layers.

[0140] S5.5: Based on the standardized real-time priority base score and the preset intent anchoring gain coefficient, the score items marked as key response layers are weighted and enhanced to compensate for the weight loss that may be caused by the context distillation vector truncation operation. Finally, a real-time priority score sequence containing absolute priority weights is generated, completing the complete conversion from semantic alignment metric to executable scheduling instruction value.

[0141] The standardized real-time priority base score matrix is ​​element-wise matched with a preset intent anchoring gain coefficient matrix to establish a correspondence between score items and gain coefficients, ensuring the accuracy of matching key response layer index positions. A weighted calculation is then performed on the matched score items using the formula: ; Where S is the standardized real-time priority base score, G is the preset intent anchoring gain coefficient, and S′ is the enhanced priority score. The enhancement result is calculated element-by-element based on the key response layer position within the score matrix. Boundary clipping is performed on all weighted score items using the formula: ; To ensure the enhanced score does not exceed the upper limit of the closed interval (1.0), preventing numerical overflow that could lead to incorrect sorting permissions, a global consistency check is performed on the cropped score matrix. This verifies that the absolute value of the score for the critical response layer in the enhanced score sequence is higher than the highest score for any non-critical layer, forming a real-time priority score sequence containing absolute priority weights. Through the aforementioned weighted enhancement and boundary control processing, the semantic alignment measurement result from the previous step is transformed into a priority execution value with a critical response guarantee mechanism, achieving the expected technical effect of prioritizing critical service calls.

[0142] For example, in a digital twin production scheduling scenario for a certain enterprise, the standardized real-time priority base score matrix is ​​[0.72, 0.65, 0.58, 0.80], where index 0 corresponds to the security monitoring layer, and the preset intent anchoring gain coefficient matrix is ​​[0.20, 0.00, 0.00, 0.10]. Applying the formula to index 0 yields an enhanced score of 0.864 for the security monitoring layer and 0.88 for index 3, while the scores of the remaining indices remain unchanged. The matrix obtained after applying the boundary trimming formula is [0.864, 0.65, 0.58, 0.88]. Consistency checks confirm that the scores of indices 0 and 3 are higher than the other scores, forming a real-time priority score sequence [0.88, 0.864, 0.65, 0.58]. During the scheduling instruction set generation phase, the security monitoring and high-load control layers are located at the front of the queue, achieving a significant priority call effect for critical service instances.

[0143] Step S6: Arrange the service instances of each layer in descending order according to the real-time priority score sequence, construct a dynamic service scheduling queue that reflects the urgency of current business needs, and establish the invocation order of service instances of each layer within the execution cycle. Specifically, this includes: S6.1: Perform data structure parsing on the generated real-time priority score sequence to extract a set of key-value pairs containing the unique identifier of the layer service instance and the corresponding standardized real-time priority base score, forming a list of raw scheduling data to be sorted, which serves as the basic input object for constructing the dynamic service scheduling queue.

[0144] S6.2: Based on the standardized real-time priority base score values ​​in the original scheduling data list, perform multiple rounds of comparison and exchange operations using quicksort or heapsort algorithms to rearrange the disordered layer service instances according to the logical relationship of scores from high to low, generating an ordered service instance index sequence with strict monotonically decreasing characteristics.

[0145] The original scheduling data list to be sorted is indexed and initialized, and the unique identifier of each layer service instance is bound to the corresponding standardized real-time priority base score as a comparable unit.

[0146] A memory index mapping table is established for the initialized set of comparable units, and the score field in the key-value pair set is preloaded into the cache area to reduce the access latency of subsequent multi-round comparisons.

[0147] Based on the score data in the cache area, a quick sorting algorithm is used to partition the data. The median score is selected as the initial partitioning benchmark. Service instance indexes that are higher than the benchmark are placed in the partition to the left of the benchmark, and indexes that are lower than the benchmark are placed in the partition to the right of the benchmark.

[0148] The quicksort comparison and swap module is recursively called on each partition after it has been divided. The index with the higher score is moved forward in the memory-mapped table, and the index with the lower score is moved backward, until the length of each partition is less than the preset termination threshold.

[0149] For sets whose partition length exceeds a preset termination threshold, the heap sort algorithm is used to construct a max-heap structure. The high-priority instance index corresponding to the top element of the heap is output to the head of the ordered sequence, and heap adjustment operation is performed to maintain the heap order property until all elements have completed the heap removal operation.

[0150] By combining quicksort and heapsort, the disordered layer service instances are transformed into an ordered service instance index sequence with strict monotonically decreasing characteristics, achieving the expected technical effect of explicit arrangement of high priority instances at the head of the queue.

[0151] S6.3: Perform timestamp binding and status marking processing on the ordered service instance index sequence to inject the global timestamp of the current system running time and the business scenario context label into the metadata field of each layer service instance, and generate a weighted reorganized service instance linked list carrying timeliness verification information to ensure that the scheduling queue has time dimension traceability.

[0152] S6.4: Based on the weighted reorganization service instance linked list, perform queue structure encapsulation operation to construct a ring buffer structure with the head and tail connected according to the physical connection order of the linked list nodes, and map each node as a dynamic service scheduling queue unit with an independent execution context, forming a complete dynamic service scheduling queue that supports loop traversal and breakpoint resume.

[0153] S6.5: Perform eventual consistency verification and head pointer locking on the completed dynamic service scheduling queue to confirm that the layer service instance corresponding to the queue head node has the highest priority calling permission, and output the execution cycle scheduling instruction set containing a clear calling order to establish the resource occupancy priority of each layer service instance in the next execution cycle.

[0154] For the constructed dynamic service scheduling queue, the queue consistency verification module is invoked to receive metadata field information of each node in the circular buffer structure, including node index, unique identifier of the bound layer service instance, and global timestamp. Based on the metadata field information, the physical connection order of the queue is parsed, and a node topology verification table is generated using connection relationship mapping. This table is then compared element-by-element with the current ordered service instance index sequence to detect any abnormalities such as misalignment, missing indexes, or duplicate nodes. For detected abnormal queue states, an error correction operator is initiated to perform node relocation or missing node compensation operations. The repaired node list is then repackaged back into the circular buffer, ensuring complete consistency with the logical order of the real-time priority score sequence. After the consistency verification passes, the first node index value is extracted from the node topology verification table. A head pointer locking mechanism is used to fix the entry pointer of the circular buffer to the address of this node, preventing it from being preempted by lower-priority nodes during the scheduling cycle. The unique identifier of the layer service instance bound to the head pointer-locked node and its priority score are read. This information is used as the starting call unit and written into the execution cycle scheduling instruction set. The call information of other nodes is then appended sequentially according to the physical order of the circular buffer, constructing a complete scheduling instruction set containing explicit call order relationships. By using head pointer locking and consistency verification, the ordered queue structure from the previous step is transformed into a data instruction set with guaranteed call order and execution cycle marking, thereby ensuring the stable determination of the resource occupancy order of each layer service instance in the next execution cycle.

[0155] Step S7: Based on the calling order determined by the dynamic service scheduling queue, issue resource allocation instructions to the enterprise digital twin runtime framework, driving high-priority layer service instances to preferentially occupy computing resources and complete the state synchronization task. Specifically, this includes: S7.1: Perform deep parsing on the weighted reorganized service instance linked list in the dynamic service scheduling queue to extract the unique identifier of the layer service instance bound in each dynamic service scheduling queue unit, the standardized real-time priority base score, and the global timestamp, and construct a set of task descriptions to be scheduled containing complete execution context information, which serves as the original input data source for resource mapping calculation.

[0156] S7.2: Based on the standardized real-time priority base score values ​​in the set of tasks to be scheduled, a hierarchical quantization operation is performed using a preset multi-level resource granularity mapping algorithm to discretize the continuous priority scores into three resource allocation levels: exclusive computing core binding, shared thread pool scheduling, or background asynchronous execution, thereby generating a differentiated resource quota strategy list that characterizes the resource occupancy intensity of service instances in each layer.

[0157] Based on the standardized real-time priority base score values ​​in the set of tasks to be scheduled, the resource allocation calculation is input by quantifying the score of each record in the set into the mapping basis required for resource occupancy intensity. For the input score, a pre-defined multi-level resource granularity mapping algorithm parameter table is loaded, which contains three discretized level nodes and corresponding resource allocation strategy templates. Threshold segmentation is performed on the score, dividing the continuous score domain into three mutually exclusive intervals according to the boundary values ​​defined in the mapping algorithm: exclusive computing core binding level, shared thread pool scheduling level, and background asynchronous execution level. For score items belonging to the exclusive computing core binding level, the exclusive resource occupancy flag bit in the corresponding strategy template is assigned, and the computing core number and binding time parameter are recorded. For score items belonging to the shared thread pool scheduling level, the thread pool allocation rules in the strategy template are matched, and the maximum number of concurrent threads and polling interval parameters are set. For score items belonging to the background asynchronous execution level, the background task priority identifier and execution latency tolerance value in the strategy template are filled in. All mapping results are encapsulated into a differentiated resource quota strategy list, where each entry contains a layer service instance identifier, resource level code, and corresponding allocation parameter combination. Through hierarchical quantization and mapping processing, the real-time priority score from the previous step is transformed into executable resource quota data, achieving a precise correspondence between resource allocation strategies and scheduling queue priority order.

[0158] For example, in a production line scheduling scenario of an enterprise digital twin system, the set of tasks to be scheduled contains 15 layer service instances, whose standardized real-time priority base scores are distributed between 0.15 and 0.92. The threshold definition for the preset multi-level resource granularity mapping algorithm is: a score ≥ 0.8 maps to an exclusive computing core binding level, 0.5 ≤ a score < 0.8 maps to a shared thread pool scheduling level, and a score < 0.5 maps to a background asynchronous execution level. The score sequence is input into the hierarchical quantization processing module. For a security monitoring layer with a score of 0.85, an exclusive computing core binding template is matched, the computing core number is set to CPU#2, and the binding time is 240ms. For an energy optimization layer with a score of 0.65, a shared thread pool scheduling template is matched, the maximum number of concurrent threads is set to 4, and the polling interval is 120ms. For a historical data statistics layer with a score of 0.32, it is mapped to a background asynchronous execution template, the background priority identifier is set to Low, and the execution latency tolerance value is 1000ms. This hierarchical quantization processing involves formula calculation of the mapping interval; for example, the discretization function is defined as: ; Where L is the resource level code, s is the standardized real-time priority base score, and floor is the floor function. A score of 0.85 yields an L value of 2, which is mapped to an exclusive level. Verification shows that this differentiated resource quota strategy list can directly drive the subsequent S7.3 step to generate refined resource allocation instruction packages, and significantly improve the resource availability guarantee and timeliness of status synchronization for high-priority tasks within the production line scheduling execution cycle.

[0159] S7.3: Based on the exclusive computing core binding level item in the differentiated resource quota strategy list, combined with the state synchronization dependency depth feature in the layer semantic fingerprint, the size of the state data buffer required for each high-priority layer service instance and the network bandwidth reservation threshold are calculated using the memory prefetch window dynamic adjustment mechanism, and a fine-grained resource allocation instruction package containing specific hardware resource parameters is generated.

[0160] Based on the exclusive computing core binding level item in the differentiated resource quota strategy list, the corresponding high-priority layer service instance is selected as the computing object. Based on the state synchronization dependency depth feature of this instance in the layer semantic fingerprint, quantitative indicators reflecting the amount of data and synchronization frequency that need to be synchronized within the twin system are extracted to form the baseline value of state data buffer requirement.

[0161] The state synchronization dependency depth feature value is multiplied by the preset synchronization data block size parameter to calculate the initial buffer capacity estimate, and this value is used as the starting point for subsequent dynamic adjustments.

[0162] A dynamic memory prefetch window adjustment mechanism is used to predictively optimize the initial buffer capacity estimate, expanding the prefetch window size to cover the expected number of synchronized data blocks, thereby reducing runtime I / O wait latency. This adjustment process can be expressed as the following formula: ; Where B is the final buffer size, D is the state synchronization dependency depth, S is the data block size, and A is the additional capacity based on prefetch window prediction delay compensation.

[0163] Based on the dynamically adjusted buffer capacity and the average data transfer rate of the layer service instance during state synchronization, the required network bandwidth reservation threshold is calculated. This calculation can be expressed as: ; Where W is the network bandwidth reservation threshold, and T is the expected execution time of a single state synchronization task.

[0164] The dynamically adjusted buffer size and the calculated network bandwidth reservation threshold are encapsulated into a resource allocation instruction package according to a refined resource parameter format, and a unique identifier for the target layer service instance and a resource occupancy intensity level label are attached to achieve the mapping from the exclusive computing core binding level item to the specific hardware resource parameters.

[0165] By dynamically adjusting the memory prefetch window and calculating parameters as described above, the resource quota level from the previous step is transformed into quantitative buffer size and network bandwidth threshold data, achieving the expected technical effect of low latency and high stability for high-priority layer service instances during state synchronization.

[0166] S7.4: Perform protocol encapsulation and integrity verification processing on the refined resource allocation instruction package. In accordance with the remote control interface specification defined by the enterprise digital twin operation framework, encode the resource quota parameters, the unique identifier of the layer service instance, and the execution time constraint into a standardized binary control frame sequence to construct a low-level resource scheduling command flow with atomic execution characteristics.

[0167] Perform byte order formatting on the resource quota parameter field within the fine-grained resource allocation instruction package to ensure consistent parsing of binary data across different system architectures.

[0168] Based on the enterprise digital twin operation framework, the remote control interface specification calls the interface descriptor retrieval mechanism to obtain the command frame header structure definition corresponding to the interface, and fills the layer service instance unique identifier field into the reserved position of the frame header with a fixed length of bytes.

[0169] The execution time constraint field is encoded using time parameters. A timestamp compression algorithm is used to map the second-level precision time constraint value into a short integer representation as specified in the protocol, and then written into the corresponding control segment in the command frame.

[0170] Field dependency validation logic is used to verify the consistency of references among resource quota parameters, unique identifiers and execution time constraints, ensuring that there are no cross-field mismatches or null pointer references in the command flow.

[0171] A checksum segment is added to the encapsulated binary control frame sequence using the CRC (Cyclic Redundancy Check) encoding mechanism, and the checksum value is calculated using the following formula: ; Among them, C out To output the checksum, C in The cumulative value of the input data is Poly, which is the generator polynomial specified by the protocol and encoded using the XOR operator.

[0172] The generated checksum is appended to the end of the binary control frame to form the underlying resource scheduling command stream with atomic execution characteristics output in this step.

[0173] By using protocol encapsulation and integrity verification, the result of the previous step is transformed into low-level command data that can be directly recognized and executed by the kernel scheduler, thereby ensuring the secure issuance and execution of resource allocation instructions.

[0174] S7.5: Issue the underlying resource scheduling command stream to the kernel scheduler of the enterprise digital twin runtime framework, use the operating system-level process priority promotion primitive to drive high-priority layer service instances to immediately occupy the specified computing resources and trigger the full state synchronization task, and monitor the resource lock feedback signal to confirm the successful start of the state synchronization task, thus completing the closed-loop control from logical sorting to physical execution.

[0175] The underlying resource scheduling command stream, which has been constructed and verified in the previous step S7.4, is received as the input object. A binary control frame loading operation is performed at the kernel scheduler interface of the enterprise digital twin runtime framework. The frame sequence of the command stream is written into the scheduler buffer and a second frame sequence integrity check is performed to ensure that the resource quota parameters, the unique identifier of the layer service instance, and the execution time constraints are not distorted during transmission.

[0176] The priority primitive is called according to the preset process priority mapping table for the command frame sequence loaded in the buffer. The call operation determines the corresponding system primitive according to the priority level value marked in the command frame. For example, the priority boosting primitive used for CPU core binding is called to immediately raise the process scheduling priority to the exclusive level, so that the high priority layer service instance occupies the front execution slot in the scheduler.

[0177] The resource locking activation operation is performed on the target process that has been prioritized. The resource mutex lock of the kernel scheduler is used to achieve exclusive use of computing resources and network bandwidth. The state synchronization trigger function is called to set the start flag of the state synchronization thread in the layer service instance to active state, thereby officially starting the full state synchronization task at the physical resource level.

[0178] While triggering the synchronization task, a resource lock feedback listening thread is started on the scheduler side. The kernel-generated lock success signal is compared with the task start flag. If both are successful, the execution process is marked as completing the closed loop from queue logical sorting to physical execution start. If the feedback signal shows that the lock has failed, the rollback mechanism is immediately invoked to release the occupied part of the resources and the priority promotion process is retried.

[0179] After the call is completed, the resource usage record of this cycle is written. The priority primitive call record, resource locking status and synchronization task start time are written to the system log as the data basis for subsequent performance analysis and scheduling optimization. Through the precise combination of the underlying resource scheduling command flow and the operating system-level priority improvement primitive, the execution order of the scheduling queue generated by the S6 step is transformed into execution instructions that can take effect immediately at the physical resource level, realizing the deterministic start of the real-time operation and state synchronization of high-priority layer service instances.

[0180] For example, in a production line safety monitoring scenario of a company's digital twin system, the underlying resource scheduling command stream generated by S7.4 includes a unique identifier for a high-priority security monitoring layer service instance, a 200ms time constraint for security monitoring task execution, and resource quotas of exclusive CPU core binding and 100Mbps network bandwidth reservation. After the kernel scheduler loads the command stream, the priority mapping table maps the instance's priority level to the highest real-time level of the operating system. Exclusive locking of core 1 is achieved by calling the CPU core binding primitive, triggering the full state synchronization function of the security monitoring thread. After the resource lock feedback listening thread detects the "lock successful" signal from the kernel and the "start successful" flag from the synchronization thread, it executes a closed-loop marking. The log records: priority primitive call timestamp, CPU core locking time of 3ms, network bandwidth reservation success, and task startup time of 12ms. The total resource allocation and task startup process is completed within 15ms, significantly improving the immediate response capability of high-priority security monitoring tasks in emergency situations.

[0181] Step S8: Monitor the structured features of newly registered layers or the status code mapping rules of newly added context dimensions, and hot-update the weight parameters of the feedforward network or the mapping table of the parameterless state mapping mechanism without restarting the scheduling core, thereby achieving adaptive expansion of the dynamic service orchestration system. The status code mapping rules, in specific implementation, are manifested as a preset discretized threshold mapping table, specifically including: S8.1: Parse and process the new registration layer event or context dimension change event captured by the registration listener to extract the structured feature data of the target layer to be updated or the status code definition information of the context dimension to be added, and generate a hot update trigger instruction containing the change type identifier and the original configuration payload.

[0182] S8.2: Based on the change type identifier in the hot update trigger instruction, perform a positioning operation on the input layer weight matrix of the lightweight feedforward network or the state code mapping table of the parameterless state mapping mechanism to determine the target storage address range that needs to be injected with new parameters, and generate a parameter update pointer pointing to a specific model component.

[0183] S8.3: Utilize the parameters to update the target storage address range pointed to by the pointer, recalculate the normalization coefficients of the extracted target layer structured feature data, or discretize and encode the newly added context dimension status code definition information to generate a standardized incremental feature vector or an extended status code index key value that adapts to the current network structure.

[0184] Perform a data access initialization operation on the target storage address range pointed to by the parameter update pointer, so as to load the target layer structured feature data and the newly added context dimension status code definition information parsed in the previous sub-step while maintaining the running state of the scheduling core.

[0185] The extracted target layer structured feature data is input into the normalization coefficient recalculation module. Based on the current numerical range and physical dimensions of each feature component, the range normalization algorithm is used to calculate the new normalization coefficients according to a unified preset interval mapping, ensuring that the updated feature components maintain the same numerical scale as the existing network input layer.

[0186] The newly added context dimension status code definition information is input into the discretization encoding mapping module. Based on the preset interval threshold table, the continuous physical quantity or statistical quantity is converted into an integer index key value, and a unique index position is assigned to each key dimension status code to ensure that the parameterless status mapping mechanism can quickly find the corresponding semantic weight factor.

[0187] Perform the feature increment construction operation, merge the newly calculated normalized feature components and the newly added status code index in the feature vector generation module, and form a standardized incremental feature vector according to the existing feature arrangement order, maintaining a fixed length and consistent feature position mapping relationship.

[0188] Through the above recomputation and encoding mapping process, the result of the previous step is transformed into a standardized incremental feature vector or extended status code index key value that adapts to the current network structure and mapping table storage format, thereby realizing the computable semantic extension of dynamic layers and context dimensions.

[0189] For example, in a digital twin system of an industrial control enterprise, a newly registered energy consumption optimization layer has a spatial granularity of {"content":"4m^2"}, a time update frequency of 600 seconds, a source type of electricity metering instrument, and a life cycle stage indicator of production stage. When recalculating the normalization coefficients, let the historical minimum values ​​of each indicator be {"content":"2m^2"}, 300 seconds, type value 1, and stage value 1, and the maximum values ​​be {"content":"6m^2"}, 900 seconds, type value 3, and stage value 3, then the spatial granularity normalization coefficient is calculated to be 0.5, the time update frequency normalization coefficient is 0.5, and the normalization coefficients of other discrete indicators are calculated in the same way. A new context dimension, such as "device health index," has a continuous value range of 0 to 100. Discretization interval thresholds are set as [0,30), [30,70), and [70,100], mapped to indices 0, 1, and 2. When the input value is 85, it is encoded as index 2. All processing results are merged to form a standardized incremental feature vector with increased length. This vector is injected online into the feedforward network embedding layer and then hot-loaded to update the layer semantic fingerprint generation parameters. This allows the scheduling engine to accurately incorporate the new layer and context dimension without restarting, significantly improving the scheduling system's adaptability to new services.

[0190] S8.4: Based on the standardized incremental feature vector or extended status code index key value, perform an atomic write-replacement operation on the semantic weight factor lookup table of the embedded layer connection weights or parameterless state mapping mechanism of the lightweight feedforward network to complete the online hot loading of the layer semantic fingerprint generation parameters or status code mapping rules, and generate an effective dynamic configuration version identifier.

[0191] Perform data integrity verification on the generated standardized incremental feature vectors or extended status code index keys to ensure that their row and column dimensions in the current network structure completely match the expected parameter dimensions of the embedding layer connection weight matrix or semantic weight factor lookup table.

[0192] The verified standardized incremental feature vector is loaded into the temporary buffer, and the parameter update pointer pointing to the target storage address is called to locate the writable address segment of the lightweight feedforward network embedding layer connection weight matrix or the semantic weight factor lookup table of the parameterless state mapping mechanism.

[0193] An atomic write-replacement operation is performed within the located writable address segment, using a memory barrier mechanism to ensure that the feature vector or index key value written in a multi-threaded environment is not interrupted or overwritten by concurrent processes.

[0194] If the object to be updated is the embedding layer connection weight matrix, then the matrix row replacement pattern is used to fill the weight terms of the corresponding row with the standardized incremental feature vector. The writing process follows the calculation formula below: ; Where W represents the original embedding layer connection weight matrix, ΔW represents the weight increment matrix obtained by mapping the standardized incremental feature vector, and the formula output W′ is the new weight matrix after hot update.

[0195] If the object to be updated is a semantic weight factor lookup table, the weight factor of the corresponding slot is directly replaced using the index key value, and the structural integrity of the updated lookup table is checked by a hash check kernel.

[0196] After the write operation is completed, a dynamic configuration version identifier is generated, which includes the current timestamp, target storage address, and update data summary, to mark that this version parameter has taken effect in the runtime environment.

[0197] By using atomic write replacement, the results of the previous step are transformed into layer semantic fingerprint generation parameters or context status code mapping rules that have been loaded into the running memory and can be called immediately, achieving the effect of online hot loading technology without restarting the scheduling core.

[0198] S8.5: Based on the dynamic configuration version identifier that has taken effect, perform consistency verification on the runtime context environment of the dynamic service orchestration system to verify whether the newly generated layer semantic fingerprint or the corrected context distillation vector can be used to perform dot product matching calculation, and output a system state synchronization signal confirming successful adaptive expansion.

[0199] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

[0200] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the elements or objects preceding “comprising” or “including” encompass the elements or objects listed following “comprising” or “including” and their equivalents, and do not exclude other elements or objects. The “multiple” mentioned in the embodiments of this application refers to two or more. A and / or B indicate three possibilities: A; B; and A and B.

[0201] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for encapsulating and dynamically orchestrating enterprise digital twin services with reusable management layers, characterized in that: Specifically, it includes: S1: Obtain the structured features of each reusable management layer in the enterprise digital twin system, normalize and concatenate them, and then input them into the feedforward network to generate layer semantic fingerprints. The structured features include layer collaborative topology features. S2: Receive standardized intermediate representation data from the unified context agent, extract key dimension status codes, and construct a lightweight context package; S3: Based on the key dimension status codes in the lightweight context package, each key dimension status code is converted into a preset semantic weight factor using a parameterless state mapping mechanism, and combined with the layer collaborative topological features in the layer semantic fingerprint to activate the weighted combination logic and generate a context distillation vector. S4: Read the preset key response layer marker information, locate the strong constraint bit in the layer semantic fingerprint, and perform a truncation operation on the context distillation vector according to the strong constraint bit to generate a corrected context distillation vector; specifically, it includes: obtaining a pre-stored key response layer marker information set, using a bitmask parsing algorithm to decode the Boolean identifier field in the set to extract a specific layer index list and form a key layer index sequence; Based on the key layer index sequence, the generated layer semantic fingerprints are traversed, and the specific coordinate positions of the strong constraint bits corresponding to each key layer index in a fixed-length vector are determined by computer using the feature vector offset, thereby generating a strong constraint bit coordinate mapping table containing the coordinates of all strong constraint bits. Receive the context distillation vector generated at the current moment, and extract a subset of the initial semantic weight factors corresponding to the key layer index using vector dimension slicing technology based on the strong constraint coordinates in the strong constraint bit coordinate mapping table, forming a key dimension weight factor array to be verified. A threshold comparison logic is executed on the key dimension weight factor array to identify interference components whose values ​​are lower than the preset minimum protection threshold. The interference components are forcibly assigned the maximum weight constant using a hard truncation reset operator to generate an intermediate correction weight factor array to eliminate negative interference. The intermediate corrected weight factor array is backfilled into the original context distillation vector at the corresponding strong constraint bit coordinate position by the vector reconstruction algorithm, replacing the original low weight values, and the corrected context distillation vector is output. S5: Based on the semantic fingerprint of the layer and the corrected context distillation vector, perform dot product matching calculation, quantify the semantic alignment degree between each layer service instance and the current context environment, and generate a real-time priority score sequence; S6: Arrange the layer service instances in descending order according to the real-time priority score sequence to construct a dynamic service scheduling queue; S7: Based on the dynamic service scheduling queue, issue a resource allocation instruction to the enterprise digital twin operation framework to drive the high-priority layer service instance to occupy computing resources and complete the state synchronization task; S8: Monitor the structured features of newly registered layers or the status code mapping rules of newly added context dimensions, and hot update the weight parameters of the feedforward network or the mapping table of the parameterless state mapping mechanism.

2. The method for encapsulating and dynamically orchestrating enterprise digital twin services with reusable management layers according to claim 1, characterized in that, The key dimension status codes include: current production cycle level, main control system load status, abnormal alarm density, and data freshness of associated layers.

3. The method for encapsulating and dynamically orchestrating enterprise digital twin services with reusable management layers according to claim 2, characterized in that, Step S3 specifically includes: The current production cycle level, main control system load status, abnormal alarm density and related layer data freshness are parsed and processed to extract the enumeration index value of the status code of each key dimension, forming the original status code sequence to be mapped. Based on the enumerated index values ​​in the original status code sequence, a key-value matching operation is performed using a preset parameterless status mapping lookup table to map each discrete key dimension status code to a corresponding preset semantic weight factor, thereby generating an initial set of semantic weight factors. The layer collaborative topological features in the generated layer semantic fingerprint are decoded to extract topological descriptors that describe the collaborative relationships between layers; Based on the event-triggered coupling strength value in the topology descriptor, a dynamic weighted combination logic activation operation is performed on the initial semantic weight factor set. The semantic weight factors associated with the current highly coupled layer are automatically filtered and amplified according to the coupling strength threshold, and a modified semantic weight factor array is generated. The modified semantic weight factor array is subjected to vector reconstruction and normalization processing. The modified semantic weight factors are concatenated into a dense vector of fixed length according to a preset dimensional arrangement order, and the context distillation vector is output.

4. The method for encapsulating and dynamically orchestrating enterprise digital twin services with reusable management layers according to claim 3, characterized in that, The collaborative relationships between layers are characterized by a topological connectivity matrix that describes the data flow weights, event triggering coupling strength, and state synchronization dependency depth of the layers within the twin.

5. The method for encapsulating and dynamically orchestrating enterprise digital twin services with reusable management layers according to claim 4, characterized in that, The specific layer index list includes layer indexes that are manually marked as security monitoring layers or emergency control layers.

6. The method for encapsulating and dynamically orchestrating enterprise digital twin services with reusable management layers according to claim 1, characterized in that, Step S5 specifically includes: The layer semantic fingerprint is subjected to dimensional consistency verification and vector space standardization to generate a standard layer semantic fingerprint matrix with a unified metric benchmark. Based on the cooperative topological feature dimension in the standard layer semantic fingerprint matrix, an initial feature subspace corresponding to the activation state code in the current modified context distillation vector is extracted, and noise interference components of irrelevant dimensions are filtered out to generate an adapted layer semantic fingerprint subset. Element-wise multiplication and summation operations are performed on the adapted layer semantic fingerprint subset and the modified context distillation vector. The projection overlap of the two in the multidimensional feature space is calculated using a predefined lightweight dot product matching algorithm to generate an original similarity scalar value that represents the degree of semantic alignment between a single layer service instance and the current business scenario. Based on the original similarity scalar value, a linear normalization transformation function is applied to map the projection overlap of different orders of magnitude to a preset zero-to-one closed interval probability distribution domain, eliminate the score distribution shift caused by the fluctuation of the number of layers, generate a standardized real-time priority base score with comparability, and establish the relative importance order of each layer service instance under a unified evaluation system. Based on the standardized real-time priority base score and the preset intent anchoring gain coefficient, the score items marked as key response layers are weighted and enhanced to compensate for the weight loss that may be caused by the context distillation vector truncation operation. Finally, a real-time priority score sequence containing absolute priority weights is generated, completing the full conversion from semantic alignment metric to executable scheduling instruction value.

7. The method for encapsulating and dynamically orchestrating enterprise digital twin services with reusable management layers according to claim 6, characterized in that, The step of extracting an initial feature subspace corresponding to the activation state code in the current modified context distillation vector based on the cooperative topological feature dimension in the standard layer semantic fingerprint matrix, and filtering out noise interference components of irrelevant dimensions to generate an adapted layer semantic fingerprint subset includes: The column vectors belonging to the collaborative topological feature dimension in the standard layer semantic fingerprint matrix are selected as the initial feature subspace; Perform status code activation parsing on the modified context distillation vector to generate a dimension mapping index table containing the index positions corresponding to each activation status code; The dimension mapping index table is matched with the initial feature subspace to construct a binary mask matrix; Based on the binary mask matrix, perform element-wise multiplication of the mask matrix on the initial feature subspace to obtain the feature subspace filtered by the mask. The masked feature subspace is subjected to a densification reconstruction process, all zeroed column vectors are removed and the order of the remaining column vectors is kept consistent with the original standard layer semantic fingerprint matrix, and the adapted layer semantic fingerprint subset is generated.

8. The method for encapsulating and dynamically orchestrating enterprise digital twin services with reusable management layers according to claim 1, characterized in that, The structured features also include: layer metadata features and layer service contract features.

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