A model packaging and automated deployment method of a business processing agent

Abstract

The application relates to the technical field of artificial intelligence and cloud computing deployment, in particular to a model packaging and automatic deployment method of a business processing intelligent agent, which comprises the following steps: mapping a dynamic running context and a dependency topology into a cognitive manifold with an initial metric tensor, extracting a harmonic differential form as a core topology skeleton by using a Hodge Laplacian operator decomposition, packaging the core topology skeleton and basic operator codes into a topology image package and sending the topology image package to a target node, constructing a physical grid space by using a video memory allocation table, evolving the initial metric tensor to an isometric metric isomorphism by using a Ricci flow partial differential equation to obtain an isometric mapping matrix, injecting the core topology skeleton and the basic operator codes into the physical video memory according to indexes and instantiating the core topology skeleton and the basic operator codes into a running addressing state, constructing a Lyapunov energy function according to a memory addressing deviation and a positive definite weighting matrix when receiving business traffic, blocking the traffic and recalculating the isometric mapping matrix based on the current physical grid space if a time derivative is greater than or equal to zero.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and cloud computing deployment technology, and in particular to a model encapsulation and automated deployment method for a business processing intelligent agent. Background Technology

[0002] With the development of large language models, business processing agents with dynamic cognitive capabilities often need to perform dynamic migration and automated deployment between heterogeneous processor nodes. However, the dynamic runtime context and dependency topology generated during the agent's runtime exhibit high-dimensional non-Euclidean Riemannian manifold characteristics, while the available GPU memory of the target computing node exhibits flat Euclidean grid characteristics. Traditional deployment components use a linear physical memory copy scheme, ignoring the spatial dimension difference between the two. During hot loading, this easily tears apart the geometric connectivity of non-Euclidean data, resulting in massive cross-page memory fragmentation within the GPU memory. With continuous input of high-load business traffic, the physical accumulation of GPU memory fragmentation causes the memory addressing trajectory of runtime attention queries to diverge. Conventional linear methods such as orchestration quotas and space expansion cannot perceive deep non-Euclidean topological relationships and lack self-healing repair mechanisms for topological dimensions, ultimately causing a surge in computational latency, leading to severe context collapse, silent packet loss of business requests, and computational channel blockage and crashes. Summary of the Invention

[0003] To overcome the above shortcomings, this invention provides a model encapsulation and automated deployment method for business processing agents. It aims to improve the extraction of the topological skeleton and the combination of Ricci flow evolution and Lyapunov control to achieve equidistant mapping of video memory and address self-healing. This solves the problem that traditional agent deployment mostly uses linear physical copying, which leads to topological tearing due to ignoring spatial dimension differences, resulting in runtime computing channel blockage and crash.

[0004] In a first aspect, the present invention provides the following technical solution: a method for model encapsulation and automated deployment of a business processing intelligent agent, comprising the following steps:

[0005] S1. Map the dynamic runtime context and dependency topology of the business processing agent to a cognitive manifold with an initial metric tensor. Use the Hodge Laplace operator to perform differential form decomposition on the cognitive manifold and extract the harmonic differential form as the core topological skeleton.

[0006] S2. Package the core topology skeleton and the basic operator code of the business processing agent into a topology image package and send it to the target node.

[0007] S3. Construct a physical grid space from the memory allocation table of the target node, and use the Ricci flow partial differential equation to evolve the initial metric tensor of the core topology skeleton until the initial metric tensor is isomorphic to the flatness metric of the physical grid space, thus obtaining the isomorphic mapping matrix.

[0008] S4. According to the index of the equidistant mapping matrix, inject the core topology skeleton and the basic operator code into the physical memory of the target node and instantiate it into a running addressing state.

[0009] S5. When receiving service traffic in the running addressing state, construct a Lyapunov energy function based on the memory addressing bias and the positive definite weighting matrix. If the time derivative of the Lyapunov energy function is greater than or equal to zero, block the service traffic and recalculate the isometric mapping matrix based on the current physical grid space.

[0010] By adopting the above technical solution, the topological skeleton is extracted and combined with Ricci flow evolution and Lyapunov control, thereby realizing equidistant mapping of video memory and addressing self-healing. This solves the problem that traditional agent deployment mostly uses linear physical copying, which leads to topological tearing due to ignoring spatial dimension differences, resulting in runtime computing channel blockage and crash.

[0011] The present invention has the following beneficial effects:

[0012] 1. In this invention, by extracting the topological skeleton and combining Ricci flow evolution with Lyapunov control, equidistant mapping of video memory and addressing self-healing are achieved, thereby improving the problem that traditional agent deployment mostly adopts linear physical copying, which causes topological tearing due to ignoring spatial dimension differences, resulting in runtime computing channel blockage and crash.

[0013] 2. In this invention, the cognitive manifold is decomposed into a differential form by the Hodge Laplace operator to extract the harmonic differential form and perform environmental isolation packaging, thereby reducing the transmission load and establishing the component boundary. This improves the problem that traditional intelligent agent packaging mostly uses full cache packaging, which causes dynamic distribution to be easily interrupted and model weights to be tampered with due to the large image size and lack of isolation.

[0014] 3. In this invention, a physical grid space is constructed by analyzing the video memory allocation table and performing Ricci flow curvature flattening evolution, thereby adapting to the differences in heterogeneous hardware space. This improves the problem that traditional deployment scheduling mostly adopts general segmented paging allocation, which cannot be compatible with non-Euclidean structural differences, thus causing cross-page video memory addressing interruption during the instantiation stage.

[0015] 4. In this invention, a dynamic state vector is constructed by collecting memory page hit rate deviations and solving continuous algebraic stability equations, thereby establishing addressing divergence boundaries and triggering self-healing reconstruction. This improves the problem of long-term silent packet loss caused by the lack of a space fragment repair mechanism in traditional runtime monitoring, which mostly uses deterministic threshold triggering. Attached Figure Description

[0016] Figure 1This is a flowchart of a method for model encapsulation and automated deployment of a business processing intelligent agent proposed in this invention;

[0017] Figure 2 This is a flowchart of cognitive manifold feature decomposition and topological mirror package generation for a model encapsulation and automated deployment method for a business processing intelligent agent proposed in this invention.

[0018] Figure 3 This is a flowchart illustrating the physical grid space and Ricci flow evolution of a model encapsulation and automated deployment method for a business processing intelligent agent proposed in this invention.

[0019] Figure 4 This is a flowchart of homeomorphism mapping and physical memory injection for a model encapsulation and automated deployment method for a business processing intelligent agent proposed in this invention.

[0020] Figure 5 This is a flowchart illustrating the runtime Lyapunov monitoring and feedback refactoring process of a business processing intelligent agent model encapsulation and automated deployment method proposed in this invention. Detailed Implementation

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1:

[0023] In a first embodiment of the present invention, the present invention provides a method for model encapsulation and automated deployment of business processing intelligent agents, such as... Figures 1-2 As shown, it includes the following steps:

[0024] S1. Map the dynamic runtime context and dependency topology of the business processing agent to a cognitive manifold with an initial metric tensor. Use the Hodge Laplace operator to decompose the cognitive manifold into differential form and extract the harmonic differential form as the core topological skeleton.

[0025] Furthermore, in S1, the dynamic runtime context and dependency topology of the business processing agent are mapped to a cognitive manifold with an initial metric tensor, including:

[0026] Extract context features from the currently active inference thread to generate a key-value cache tensor as a dynamic runtime context, and parse the external interface call relationship of the control layer to generate a tool call graph as a dependency topology;

[0027] Construct a local metric tensor based on the correlation strength between the key-value cache tensor and the tool call graph;

[0028] Using the local metric tensor as the initial metric tensor, the key-value cache tensor and the tool call graph are jointly constructed into a high-dimensional closed Riemannian manifold, and the high-dimensional closed Riemannian manifold is defined as a cognitive manifold.

[0029] In S1, the Hodge Laplace operator is used to perform differential form decomposition on the cognitive manifold, and the harmonic differential form is extracted to form the core topological skeleton, including:

[0030] We construct generalized eigenvalue equations for different differential forms on cognitive manifolds using the Hodge Laplace operator.

[0031] Solve the generalized eigenvalue equations and select the differential forms corresponding to the zero eigenvalues ​​to form the harmonic differential forms;

[0032] The harmonic differential form is defined as the core topological skeleton.

[0033] Specifically, in step S1, the system input data consists of the underlying inference logs and system call records of the target business processing agent on the source computing node. The deployment engine extracts the attention mechanism key-value pairs from the aforementioned underlying inference logs as context features and aggregates them to generate corresponding key-value cache tensors. The system synchronously parses the routing request directions and dependencies in the aforementioned system call records to construct a tool call graph representing the connectivity of external actions.

[0034] The system calculates association strength based on the overlap of time slices between the key-value cache tensor and the tool call graph within the same inference cycle. The calculated association strength is used to define local metric tensors for each dimension of the topological metric space. The key-value cache tensor with attached local metric tensors and the tool call graph are projected onto the topological space to complete the mathematical modeling of the high-dimensional closed Riemannian manifold, generating a cognitive manifold. This generated cognitive manifold serves as the input source object for subsequent dimensionality reduction processing.

[0035] After obtaining the cognitive manifold, the deployment engine retrieves the graph structure adjacency matrix constructed from the key-value cache tensor and the tool call graph. With node degree matrix The system first constructs a discretized combinatorial Laplace matrix through an algebraic computation unit. Combined Laplace matrix The mapping is represented by a discrete Hodge Laplace matrix, which is used to construct generalized eigenvalue algebraic equations for differentiable forms of cognitive manifolds in computer memory space. The system's built-in mathematical model is as follows:

[0036] ;

[0037] And it satisfies the definition of differential operator decomposition:

[0038] ;

[0039] in Let denote the Hodge Laplace operator for the configuration of a cognitive manifold, with a dimension of 1; Representing the differentiable forms of each order on the cognitive manifold, used to represent the microscopic distribution state of the agent's memory tensor field; This represents the eigenvalue scalar obtained by solving the generalized eigenvalue equation. When performing manifold feature extraction in this technical solution, it is set... The value of is strictly equal to 0; This represents an exterior differential operator that performs a geometric difference operation that maps a lower-order differential form to a higher-order form. This represents the codifferential operator, and performs the inverse adjoint mapping operation on the exterior differential operator.

[0040] The discrete Hodge Laplace matrix is ​​subjected to sparse matrix eigenvalue decomposition by calling the internally pre-built Lanczos eigenvalue parallel solution algorithm via the algebraic solution module. The computation unit then extracts the eigenvalues. The discrete feature vector solution set corresponding to the absolute value of is less than the machine floating-point precision threshold, and is set as . The system defines the extracted discrete feature vector solution set as a discrete harmonic differential form, and uses the discrete harmonic differential form as the structural topological feature data of the final output of step S1, that is, to generate the core topological skeleton.

[0041] By mapping the discrete state data of the business processing agent during runtime to a cognitive manifold, and using the Hodge Laplace operator to perform differential form decomposition on the cognitive manifold to extract the harmonic differential form corresponding to the zero eigenvalue as the core topological skeleton, this technique improves upon the technical shortcomings of traditional automated deployment methods that directly physically package massive and loose runtime memory data, resulting in extremely high network input / output loads and the easy truncation of context topological connectivity during data serialization. This avoids the specific problem of irreversible logical breaks in the attention mechanism of large models caused by state context collapse after the business processing agent is instantiated by migrating to heterogeneous computing nodes.

[0042] S2. Package the core topology skeleton and the basic operator code of the business processing intelligent agent into a topology image package and send it to the target node.

[0043] Furthermore, in S2, the core topology skeleton and the basic operator code of the business processing intelligent agent are packaged into a topology image package and sent to the target node, including:

[0044] Identify and remove high-frequency cognitive noise data that does not correspond to zero eigenvalues ​​in the cognitive manifold;

[0045] The core topology skeleton, basic operator code, and underlying basic model weights are injected into the isolated namespaces of the lightweight container engine.

[0046] The environment dependency configuration of the locked namespace is used to generate a topology image package, which is then distributed to the target nodes via a communication link.

[0047] Specifically, the system receives cognitive manifold data with full feature value mapping relationships as the initial input object. The deployment engine performs traversal screening logic on the initial input object, extracting feature value parameters bound to each differential form. The deployment engine sends instructions to the memory reclamation module to physically erase all high-frequency cognitive noise data associated with non-zero feature values, preventing redundant state data from flowing into the encapsulation pipeline.

[0048] The system invokes a lightweight container engine to create three parallel and independent namespaces within a memory sandbox. The deployment engine then performs low-level file handle redirection on the filtered and retained core topology skeleton, pre-installed basic operator code on local storage, and underlying basic model weights, injecting them independently into the three isolated namespaces. After injection, the system triggers a read-only attribute write command to lock the environment dependency configuration files for each namespace. The system uses a union file system to merge the independent namespaces into a unified read-only image layer, generating a topology image package. This topology image package is then transferred as output to the communication service module and distributed to target nodes via the underlying network communication link.

[0049] In the stage of stripping redundant data and performing aggregation and encapsulation, the deployment engine calculates and controls the final composition of the topology image package according to a preset load aggregation mathematical model. The specific definition of the load aggregation mathematical model is as follows:

[0050] ;

[0051] in This represents the total digital payload of the final topology image package, measured in megabytes. This represents the total number of differential form data blocks contained within the cognitive manifold, with a dimension of 1; Indicates the first The original digital payload of each differential form data block, in megabytes; This represents the binarization filtering control coefficient, with a dimension of 1. When the deployment engine executes the stripping instruction, if the... The eigenvalues ​​corresponding to each differential form data block are equal to 0, constrained to... The value is 1; if the corresponding eigenvalue is not equal to 0, it is limited. The value is 0; The numerical payload, measured in megabytes, represents the weights of the underlying base model within the independently injected namespace. This represents the numerical payload of the underlying operator code injected independently into the namespace, measured in megabytes.

[0052] By identifying and removing high-frequency cognitive noise data without corresponding zero eigenvalues ​​from the cognitive manifold, and injecting the retained core topology skeleton, basic operator code, and underlying basic model weights into the mutually isolated namespaces of a lightweight container engine to lock the configuration and generate a topology image package, this technique improves upon the traditional microservice image building mechanism's shortcomings. This mechanism mixes all runtime residual caches with the code environment, resulting in a geometric expansion of image size and a complete lack of physical security isolation boundaries between multi-level components. This avoids deployment interruptions caused by excessive network bandwidth load when business processing agents are distributed over long distances between heterogeneous computing power nodes, and eliminates the specific problem of unauthorized modification of underlying basic model weights due to memory pointer out-of-bounds access during system runtime.

[0053] like Figure 3 As shown, S3, construct the physical grid space from the memory allocation table of the target node, and use the Ricci flow partial differential equation to evolve the initial metric tensor of the core topological skeleton until the initial metric tensor is isomorphic to the flat metric of the physical grid space, thus obtaining the isometric mapping matrix.

[0054] Furthermore, in S3, the physical mesh space is constructed from the target node's memory allocation table, including:

[0055] The system probe reads the status information of the current free blocks and occupied blocks in the graphics processor of the target node to generate a video memory allocation table.

[0056] Extract free contiguous memory blocks and free non-contiguous memory blocks based on the memory allocation table;

[0057] Flatness measures are assigned to the extracted free contiguous memory blocks and free non-contiguous memory blocks. All memory blocks with flatness measures are then stitched together to form an Euclidean grid space, which is used as the physical grid space.

[0058] In S3, the initial metric tensor of the core topological skeleton is evolved using the Ricci flow partial differential equation, including:

[0059] Construct a Ricci flow partial differential equation containing virtual time variables and a Ricci curvature tensor within the scheduling space of the deployment engine;

[0060] The initial metric tensor of the core topological skeleton is substituted into the Ricci flow partial differential equation, and the solution is obtained by discrete iteration along the virtual time variable.

[0061] The local curvature parameters of the core topological skeleton are updated in the virtual time dimension by discrete iterative solution.

[0062] In S3, until the initial metric tensor is isomorphic to the flat metric of the physical grid space, the isometric mapping matrix is ​​obtained, including:

[0063] The maximum curvature norm of the core topological skeleton is calculated simultaneously during the discrete iterative solution process.

[0064] The maximum curvature norm is compared with a preset tolerance threshold. When the maximum curvature norm is determined to be less than the tolerance threshold, the discrete iterative solution is stopped, and the metric tensor at the time of stopping is extracted as the limit metric tensor.

[0065] Based on the deformation evolution trajectory from the initial metric tensor to the limit metric tensor, an isometric mapping matrix is ​​generated that maps from the core topological skeleton to the physical mesh space.

[0066] Specifically, in the physical layer input acquisition stage, the system's underlying microarchitecture probes collect real-time graphics processor (GPU) status data of the target node. The deployment engine analyzes the distribution boundaries of currently free and occupied blocks within the GPU status data, generating a memory allocation table to describe available memory addresses. The system extracts all free contiguous and non-contiguous GPU blocks from the memory allocation table. The system then forcibly allocates flatness metrics to all extracted free GPU blocks. These free GPU blocks, allocated flatness metrics, are logically concatenated to construct a Euclidean grid space. The completed Euclidean grid space is established as the physical grid space, serving as the physical layer benchmark point for subsequent topology evolution.

[0067] In the data evolution processing stage of cognitive manifolds, the deployment engine reads the pre-defined core topological skeleton and extracts its initial metric tensor. The deployment engine sets the virtual time step. The forward Euler numerical difference method is used to discretize the continuous Ricci flow partial differential equations in time and space, and the following discrete iterative state equations are established in the scheduling space of the deployment engine:

[0068] ;

[0069] For the directed graph network structure mapped in computer memory to the core topology skeleton, the deployment engine calls the discrete Ollivier-Ricci curvature calculation method to solve for the discrete Ricci curvature tensor between each graph node. Through the iterative calculation of the discrete state equations described above, the local curvature parameters of the core topological skeleton are driven to change at each virtual time step. Geometric deformation parameters are updated in each dimension until the convergence criterion is met. This represents the preset virtual time iteration step size scalar, with a dimension of 1. In actual deployment, it is preferred to set the value range to be between 0.001 and 0.01.

[0070] In the mapping matrix output generation stage, the scalar computation unit inside the deployment engine synchronously collects the maximum curvature norm at each iteration step. The system continuously compares the maximum curvature norm with an internally preset tolerance threshold. When the system determines that the maximum curvature norm is less than the preset tolerance threshold, it triggers an interrupt signal to stop the discrete iterative solution process. The system immediately extracts the metric tensor at the iteration stop time as the limit metric tensor. The deployment engine performs reverse address mapping based on the geometric deformation trajectory of the entire process from the initial metric tensor to the limit metric tensor, generating an isometric mapping matrix. The generated isometric mapping matrix serves as the final data output product of step S3 and is transferred to the subsequent instruction execution module for underlying memory allocation.

[0071] To achieve the homeomorphism from curved manifold to flat mesh space, the specific algebraic expression of the Ricci flow partial differential equation called at the deployment engine's underlying level is as follows:

[0072] ;

[0073] The convergence criterion model for triggering the system to stop discrete iterative computation is as follows:

[0074] ;

[0075] The flatness measure allocated to various types of free video memory blocks follows the following algebraic expression:

[0076] ;

[0077] in The dynamic metric tensor representing the core topological skeleton in the discrete iterative solution process has a dimension of 1. Its initial value corresponds to the aforementioned initial metric tensor, and its convergence and termination value corresponds to the aforementioned limit metric tensor. This represents a virtual time variable constructed in the geometric deformation evolution model, with a dimension of 1; The Ricci curvature tensor, which is dimensionless and is calculated from the partial derivative of the dynamic metric tensor, is used to characterize the degree of three-dimensional curvature of the local space of the core topological skeleton during the evolutionary stage. This represents the infinite norm of the Ricci curvature tensor, with a dimension of 1, which is the maximum curvature norm scalar mentioned in the steps and procedures. This represents the tolerance threshold parameter for controlling the convergence accuracy of curvature flattening, with a dimension of 1. In actual computing node deployment verification, to balance the efficiency of mapping matrix generation and spatial flatness, the specific value of this tolerance threshold is set to 0.00001; The Kronecker notation, representing the absolute flatness of the Euclidean grid space, has a dimension of 1. Its algebraic logic is that of a subscript scalar. scalar with subscript When the values ​​are equal, the value is 1; when the index is a scalar... scalar with subscript The value is 0 when the values ​​are not equal.

[0078] By constructing a physical grid space with a flatness metric by splicing together the memory allocation tables of the target nodes, and using the Ricci flow partial differential equation to drive the initial metric tensor of the core topology skeleton to evolve along the virtual time variable to flatten the curvature until an equidistant mapping matrix is ​​generated, this technique improves upon the traditional technical defect of scheduling components that ignore the dimensionality differences between the model's hardware and software topology spaces and forcibly perform segmented page memory copying, resulting in the violent physical tearing of non-Euclidean structure data. This avoids the specific problems of massive cross-page memory addressing interruptions and the resulting tensor computation channel blockage and crash during the instantiation and runtime of the business processing agent.

[0079] like Figure 4 As shown, S4, according to the index of the equidistant mapping matrix, inject the core topology skeleton and basic operator code into the physical memory of the target node and instantiate it into a running addressing state.

[0080] Furthermore, in S4, the core topology skeleton and basic operator code are injected into the physical memory of the target node according to the index of the equidistant mapping matrix, and instantiated into a runtime addressing state, including:

[0081] Analyze the equidistant mapping matrix to extract the corresponding non-linear address mapping index;

[0082] The direct memory access controller that controls the target node bypasses the system's standard memory copy channel and non-linearly allocates the core topology skeleton and basic operator code to sectors of physical video memory based on a non-linear address mapping index.

[0083] After the allocation is completed, the data in the physical video memory is mounted and the status is marked to form the running addressing state.

[0084] Specifically, in the data input acquisition and mapping execution phase, the deployment engine reads the previously generated equidistant mapping matrix and extracts the non-linear address mapping indexes encapsulated within the matrix. The deployment engine then sends a direct write command to the underlying Direct Memory Access Controller (DMI) of the target node. Upon receiving the command, the DMI takes over the system's underlying memory bus, bypassing the standard memory copy channel between the operating system kernel mode and user mode. Based on the non-linear address mapping indexes, the DMI performs non-linear hashing to write the core topology skeleton and basic operator code, allocating them directionally to the pre-defined sectors of each physical memory module.

[0085] In the data status marking and output phase, after all the core topology skeleton and basic operator code have been allocated, the deployment engine sends a permission mount command to the system kernel to execute the data mount operation in the physical memory. After the mount is completed, the system records the status liveness flag in the underlying page table, thereby establishing the running addressing state at the physical hardware layer. The establishment of the running addressing state represents the termination of the automated deployment process at the underlying hardware level and serves as the starting signal variable for triggering the subsequent business traffic access monitoring phase.

[0086] During the process of direct memory access controllers bypassing the system's standard memory copy channel to perform physical sector allocation, a non-linear physical address calculation model is needed to pinpoint the specific physical coordinates of the data being written. The non-linear physical address calculation model is defined as follows:

[0087] ;

[0088] in This represents the underlying target physical absolute address in the sectors of physical memory where the core topology skeleton and basic operator code are allocated; the unit is bytes. This parameter represents the starting point of the physical video memory base address allocated by the system to the target node, and its unit is bytes; Represents a nonlinear address mapping index tensor generated by the equidistant mapping matrix, with a dimension of 1, used to indicate the nonlinear deflection relationship from logical space to physical sector; This represents the original logical offset vector carried by the core topology skeleton and basic operator code in the topology mirror package form, with the dimension in bytes.

[0089] By using the index control of the equidistant mapping matrix to bypass the system's standard memory copy channel, the core topology skeleton and basic operator code are nonlinearly allocated to the physical memory sectors of the target node and their status is marked to instantiate the addressing state. This improves upon the traditional software deployment method's reliance on the central processing unit to perform multi-level segmented page-style linear copy operations from logical addresses to physical addresses, which leads to extremely high kernel-mode switching communication overhead and memory bus susceptibility to exclusive blocking. This avoids the specific problems of long-latency cold start waiting caused by the queuing transmission of massive model tensor data in the early stages of business processing agent instantiation and the waste of the underlying graphics processor's computing power when facing high-concurrency loading tasks.

[0090] like Figure 5 As shown, S5, when receiving service traffic in the running addressing state, construct the Lyapunov energy function based on the memory addressing deviation and the positive definite weighting matrix. If the time derivative of the Lyapunov energy function is greater than or equal to zero, block the service traffic and recalculate the isometric mapping matrix based on the current physical grid space.

[0091] Furthermore, in S5, the Lyapunov energy function is constructed based on the memory addressing bias and the positive definite weighting matrix, including:

[0092] During the business traffic input process, the memory page hit rate deviation parameter and the input / output latency jitter rate parameter are collected in real time when executing query operations, and the memory addressing deviation is constructed together.

[0093] A weight matrix is ​​generated based on the variance fluctuation of the input characteristics of business traffic, and the state evolution matrix is ​​identified by performing autoregressive state space fitting of historical sequence on memory addressing bias.

[0094] The continuous algebraic stability equation is established by using the product of the transpose of the state evolution matrix and the positive definite weighting matrix to be found, plus the product of the positive definite weighting matrix and the state evolution matrix, the sum of which equals the negative weighting matrix. The continuous algebraic stability equation is then solved to obtain the positive definite weighting matrix.

[0095] Extract the quadratic form of the positive definite weighted matrix and the memory addressing bias to generate the Lyapunov energy function.

[0096] In S5, if the time derivative of the Lyapunov energy function is greater than or equal to zero, traffic is blocked and the isometric mapping matrix is ​​recalculated based on the current physical grid space, including:

[0097] The time derivative of the Lyapunov energy function is continuously calculated along the system's operating time axis;

[0098] When the time derivative of the monitoring judgment is greater than or equal to zero, it is confirmed that the current memory addressing deviation is in a divergent state, triggering a system kernel-level hardware interrupt to cut off the input channel of business traffic.

[0099] The latest memory allocation table of the target node after memory fragmentation is read to update the physical grid space. The metric tensor of the core topology skeleton and the updated physical grid space are substituted into the Ricci flow partial differential equation for iterative solution to reconstruct the isometric mapping matrix.

[0100] Specifically, in the data input and feature extraction process, during the continuous input phase of external business traffic, the underlying monitoring module collects in real time the memory page hit rate deviation parameter and the input / output latency jitter rate parameter when the large model performs attention query operations. The system algebraically concatenates the memory page hit rate deviation parameter and the input / output latency jitter rate parameter to jointly construct a memory addressing deviation vector.

[0101] The system's underlying state identification unit constructs a sliding time window of a preset length, continuously reading the historical discrete sequence of memory addressing deviation vectors within the sliding time window. The system uses the least squares parameter identification method to fit the state space of the historical discrete sequence to an autoregressive moving average model, thereby identifying and extracting the state evolution matrix representing the memory addressing evolution law under the current operating state online. The system synchronously extracts the statistical variance fluctuation of business traffic input characteristics and generates a matching dynamic weight matrix based on the variance fluctuation. .

[0102] In the stability model construction phase, the deployment engine introduces continuous algebraic stability equations to solve for system parameters and constructs the energy function accordingly. The algebraic expression of the continuous algebraic stability equations executed at the system's underlying level is as follows:

[0103] ;

[0104] Based on the solution to the stability equation, the system extracts the quadratic form of the product of the positive definite weighting matrix and the memory addressing bias vector to generate the Lyapunov energy function, the specific algebraic expression of which is as follows:

[0105] ;

[0106] The system continuously calculates the time derivative of the Lyapunov energy function along the hardware runtime axis, and its partial derivative algebraic expression is as follows:

[0107] ;

[0108] in The state evolution matrix represents the internal memory addressing evolution law of the system, and has a dimension of 1; This represents the transpose of the state evolution matrix, with a dimension of 1. This represents a dynamic weight matrix generated in real time based on the variance fluctuation of business traffic, with a dimension of 1. This represents a positive definite weighted matrix obtained by solving the continuous algebraic stability equation using the weight matrix, with a dimension of 1; This represents the memory addressing bias vector jointly constructed by the memory page hit rate bias parameter and the latency jitter rate parameter; The transpose of the memory addressing bias vector; This represents the scalar value of the Lyapunov energy function generated by the operation, with a dimension of 1; A time-axis scalar parameter representing the system's operational process; This represents the time derivative of the Lyapunov energy function with respect to the time axis scalar, and has a dimension of 1.

[0109] In the data output and reconstruction control flow, the kernel-mode scheduler determines the polarity of the time derivative value in real time. When the time derivative is greater than or equal to zero, the scheduler confirms that the current memory addressing deviation is in a divergent state. The system immediately triggers a kernel-level hardware interrupt instruction to forcibly cut off the physical input channel of external service traffic. After the interrupt instruction is executed, the deployment engine rereads the latest memory allocation table of the target node after memory fragmentation, and overwrites and updates the physical grid space according to the parameters of the latest memory allocation table. The deployment engine extracts the metric tensor of the core topology skeleton, and substitutes the metric tensor and the updated physical grid space back into the Ricci flow partial differential equation to trigger a second discrete iterative solution. This iterative solution process finally outputs the reconstructed isometric mapping matrix, which serves as the underlying execution basis for subsequent remounting of memory.

[0110] By constructing a Lyapunov energy function based on memory addressing bias and a positive definite weighted matrix during runtime, and triggering a hardware interrupt to block traffic when the time derivative is greater than or equal to zero, and then extracting the latest memory allocation table to update the physical grid space to reconstruct the equidistant mapping matrix, this technique improves upon the traditional technical defect of conventional microservice orchestration tools that lack an adaptive repair mechanism for hardware topology after deployment. This prevents the system from actively avoiding the risk of memory breakdown when facing bursts of high loads from long texts. This approach avoids the specific problem of attention mechanism computation trajectory deviation caused by physical accumulation of memory fragmentation during long-cycle operation of the business processing agent, and the resulting large-scale business requests encountering system-level silent packet loss.

[0111] Example 2:

[0112] This system is applied to a multimodal compliance audit system in a cross-border financial sector. In this scenario, multiple large-scale audit agents responsible for different business flows are deployed. These agents need to intercept and process massive amounts of long-text financial statements, scanned copies of supply chain contracts, and other business traffic uploaded from branches around the world in real time. To cope with dynamic changes in regional policies and sudden audit peaks, these business processing agents are containerized and frequently automatically scheduled and dynamically migrated between cross-border cloud nodes containing heterogeneous GPU models.

[0113] During cross-node scheduling, the high-dimensional cognitive state originally carried by the agent, as a non-Euclidean manifold, is directly and forcibly written into the flat physical memory grid of the target node by the traditional low-level orchestrator using linear memory copying. This crude deployment mechanism severely tears the manifold topology, generating massive amounts of cross-page memory fragments during runtime, causing irreversible divergence in memory addressing trajectories. This not only leads to extremely high I / O latency in the computation of the underlying attention mechanism of large models, but also makes the agent highly susceptible to system-level stateful context collapse when processing high-concurrency, multimodal long texts. Ultimately, this manifests as large-scale silent packet loss and inference logic breaks in real-world online compliance audit business flows. To solve the above problems, this invention provides a model encapsulation and automated deployment method for business processing agents, the structure of which is as follows: Figure 1 As shown. The specific implementation process of this method is as follows:

[0114] By mapping the operational data of business processing agents to a cognitive manifold and extracting the core topological skeleton using the Hodge Laplace operator, and then using the Ricci flow partial differential equation at the deployment node to evolve the initial metric tensor of the core topological skeleton to generate an isometric mapping matrix and perform memory injection, and then constructing a Lyapunov energy function in conjunction with memory addressing bias at runtime to perform convergence monitoring and reconstruction updates, this technology improves upon the traditional technical shortcomings of microservice orchestration tools that ignore the differences in model and underlying hardware topology dimensions, rely solely on deterministic hardware indicators for physical copying leading to manifold tearing, and lack self-healing mechanisms at runtime. This avoids the specific problems of stateful context collapse after business processing agents are migrated and instantiated between heterogeneous computing power nodes, as well as the specific problems of attention mechanism computation trajectory deviation caused by memory fragmentation accumulation during runtime, resulting in system-level silent packet loss and computing channel blockage and crashes for a large number of business requests.

[0115] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for model encapsulation and automated deployment of a business processing intelligent agent, characterized in that, Includes the following steps: S1. Map the dynamic runtime context and dependency topology of the business processing agent to a cognitive manifold with an initial metric tensor. Use the Hodge Laplace operator to perform differential form decomposition on the cognitive manifold and extract the harmonic differential form as the core topological skeleton. S2. Package the core topology skeleton and the basic operator code of the business processing agent into a topology image package and send it to the target node. S3. Construct a physical grid space from the memory allocation table of the target node, and use the Ricci flow partial differential equation to evolve the initial metric tensor of the core topology skeleton until the initial metric tensor is isomorphic to the flatness metric of the physical grid space, thus obtaining the isomorphic mapping matrix. S4. According to the index of the equidistant mapping matrix, inject the core topology skeleton and the basic operator code into the physical memory of the target node and instantiate it into a running addressing state. S5. When receiving service traffic in the running addressing state, construct a Lyapunov energy function based on the memory addressing bias and the positive definite weighting matrix. If the time derivative of the Lyapunov energy function is greater than or equal to zero, block the service traffic and recalculate the isometric mapping matrix based on the current physical grid space.

2. The method for model encapsulation and automated deployment of a business processing intelligent agent according to claim 1, characterized in that, In S1, mapping the dynamic runtime context and dependency topology of the business processing agent to a cognitive manifold with an initial metric tensor includes: Extract the context features within the currently active inference thread to generate a key-value cache tensor as the dynamic runtime context, and parse the external interface call relationships of the control layer to generate a tool call graph as the dependency topology; A local metric tensor is constructed based on the correlation strength between the key-value cache tensor and the tool call graph; The local metric tensor is used as the initial metric tensor. The key-value cache tensor and the tool call graph are jointly constructed into a high-dimensional closed Riemannian manifold, and the high-dimensional closed Riemannian manifold is defined as the cognitive manifold.

3. The method for model encapsulation and automated deployment of a business processing intelligent agent according to claim 1, characterized in that, In S1, the step of using the Hodge Laplace operator to perform differential form decomposition on the cognitive manifold and extracting the harmonic differential form as the core topological skeleton includes: Using the Hodge Laplace operator, generalized eigenvalue equations for different differential forms on the cognitive manifold are constructed. Solve the generalized eigenvalue equation and select the differential forms corresponding to the zero eigenvalues ​​to form the harmonic differential form; The harmonic differential form is defined as the core topological skeleton.

4. The method for model encapsulation and automated deployment of a business processing intelligent agent according to claim 1, characterized in that, In S2, the step of packaging the core topology skeleton and the basic operator code of the business processing agent into a topology image package and sending it to the target node includes: Identify and remove high-frequency cognitive noise data that does not correspond to zero eigenvalues ​​from the cognitive manifold; The retained core topology skeleton, basic operator code, and underlying basic model weights are injected into mutually isolated namespaces of the lightweight container engine. The environment dependency configuration of the namespace is locked to generate the topology image package, and the topology image package is distributed to the target node through the communication link.

5. The method for model encapsulation and automated deployment of a business processing intelligent agent according to claim 1, characterized in that, In S3, the construction of the physical mesh space from the memory allocation table of the target node includes: The video memory allocation table is generated by reading the status information of the current free blocks and occupied blocks in the graphics processor of the target node through the underlying system probe; Extract free contiguous memory blocks and free non-contiguous memory blocks according to the aforementioned memory allocation table; A flatness metric is assigned to the extracted free contiguous video memory blocks and the free non-contiguous video memory blocks. All video memory blocks with the flatness metric are then joined together to form an Euclidean grid space, which is used as the physical grid space.

6. The method for model encapsulation and automated deployment of a business processing intelligent agent according to claim 1, characterized in that, In S3, the evolution of the initial metric tensor of the core topological skeleton using the Ricci flow partial differential equation includes: Construct the Ricci flow partial differential equation containing virtual time variables and the Ricci curvature tensor within the scheduling space of the deployment engine; Substitute the initial metric tensor of the core topological skeleton into the Ricci flow partial differential equation and perform discrete iterative solution along the virtual time variable; The discrete iterative solution drives the local curvature parameters of the core topological skeleton to generate deformation parameter updates in the virtual time dimension.

7. The method for model encapsulation and automated deployment of a business processing intelligent agent according to claim 6, characterized in that, In S3, the process of obtaining an isometric mapping matrix until the initial metric tensor is isomorphic to the flatness metric of the physical grid space includes: The maximum curvature norm of the core topological skeleton is calculated simultaneously during the discrete iterative solution process. The maximum curvature norm is compared with a preset tolerance threshold. When the maximum curvature norm is determined to be less than the tolerance threshold, the discrete iterative solution is stopped, and the metric tensor at the time of stopping is extracted as the limit metric tensor. Based on the deformation evolution trajectory from the initial metric tensor to the limiting metric tensor, the isometric mapping matrix is ​​generated from the core topological skeleton to the physical grid space.

8. The method for model encapsulation and automated deployment of a business processing intelligent agent according to claim 1, characterized in that, In S4, the step of injecting the core topology skeleton and the basic operator code into the physical memory of the target node according to the index of the equidistant mapping matrix, and instantiating it into a running addressing state, includes: Parse the equidistant mapping matrix to extract the corresponding nonlinear address mapping index; The direct memory access controller controlling the target node bypasses the system's standard memory copy channel and non-linearly allocates the core topology skeleton and the basic operator code to sectors of the physical video memory according to the non-linear address mapping index. After the allocation is completed, the data in the physical video memory is mounted and the status is marked to form the running addressing state.

9. The method for model encapsulation and automated deployment of a business processing intelligent agent according to claim 1, characterized in that, In S5, the construction of the Lyapunov energy function based on the memory addressing bias and the positive definite weighting matrix includes: During the business traffic input process, the memory page hit rate deviation parameter and the input / output latency jitter rate parameter are collected in real time when executing query operations, and the memory addressing deviation is constructed together. A weight matrix is ​​generated based on the variance fluctuation of the input characteristics of the business traffic, and the memory addressing deviation is fitted to the historical sequence autoregressive state space to identify the state evolution matrix. The product of the transpose of the state evolution matrix and the positive definite weighting matrix to be found, plus the product of the positive definite weighting matrix and the state evolution matrix, with the sum equal to the negative weighting matrix, is used as an algebraic constraint to establish a continuous algebraic stability equation, and the continuous algebraic stability equation is solved to obtain the positive definite weighting matrix. Extract the quadratic product configuration of the positive definite weighted matrix and the memory addressing deviation to generate the Lyapunov energy function.

10. The method for model encapsulation and automated deployment of a business processing intelligent agent according to claim 1, characterized in that, In S5, the step of blocking service traffic and recalculating the isometric mapping matrix based on the current physical grid space if the time derivative of the Lyapunov energy function is greater than or equal to zero includes: The time derivative of the Lyapunov energy function is continuously calculated along the system's operating time axis; When the monitoring determines that the time derivative is greater than or equal to zero, it is confirmed that the current memory addressing deviation is in a divergent state, triggering a system kernel-level hardware interrupt to cut off the input channel of service traffic. The latest memory allocation table of the target node after memory fragmentation is read to update the physical grid space. The metric tensor of the core topology skeleton and the updated physical grid space are substituted into the Ricci flow partial differential equation for iterative solution to reconstruct the isometric mapping matrix.

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