Intelligent toolbox full life cycle management and control method based on data cockpit

Abstract

The invention discloses an intelligent toolbox full-life-cycle management and control method based on a data cockpit, and relates to the technical field of artificial intelligence and micro-service operation and maintenance. The method comprises the following steps: constructing an agent identity and access boundary graph based on multi-source interaction data to generate a pre-deployment strategy; in a multi-agent execution task, collecting a natural language output sequence, mapping the natural language output sequence into a continuous medium field, and calculating a fluid dynamic parameter containing an entropy coupling semantic Reynolds number; inputting the parameters into a data cockpit to render a flow field distribution view; and if it is determined that the agent has cognitive drift, injecting system resistance to the agent based on a preset orbit attenuation resistance function to control decommissioning. The method is used for solving the problems that traditional discrete monitoring is difficult to quantify and recognize agent implicit cognitive drift, and architecture cascade avalanche is easily caused by forced fusing of abnormal nodes.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and microservice operation and maintenance technology, and more specifically, to a method for full lifecycle management of an intelligent toolbox based on a data cockpit. Background Technology

[0002] Large language model agents have been widely encapsulated as intelligent toolkits and deeply integrated into enterprise-level microservice architectures and automated business processes. In complex distributed computing environments, multi-agent collaborative execution of high-concurrency tasks has become the norm. This requires the underlying management system not only to ensure robust scheduling of computing resources, but also to precisely manage the access boundaries, execution logic, and state evolution of agents throughout their entire lifecycle in order to maintain high availability and business continuity of the global system.

[0003] Currently, conventional lifecycle management solutions for intelligent agents or microservice clusters mainly rely on role-based access control models for static permission division, combined with gateway rate limiting and circuit breaking mechanisms and basic physical resource alerts to maintain operation. At the anomaly detection and node retirement level, the industry typically relies on preset regular expressions to extract structured error logs. Once a rule threshold is triggered, a forced termination command is directly issued to instantly sever the container process and associated network session connections.

[0004] However, the above solutions have significant technical limitations. First, traditional monitoring systems based on discrete error codes are almost ineffective when dealing with the unstructured natural language output of large models, completely unable to extract features and quantify instability identification of hidden logical illusions and implicit "cognitive drift" in real time. Second, in massive waterfall logs, the system struggles to intuitively distinguish between isolated local errors and cascading errors that could trigger a global avalanche, and also lacks the ability to predict the infection wavefront for unauthorized access detection and malicious injection. Finally, the "one-size-fits-all" forced circuit breaker approach for anomalous agents is highly prone to causing large-scale link suspensions and distributed transaction rollbacks in highly coupled microservice networks, lacking a resilient soft-landing retirement control mechanism, making the existing architecture extremely vulnerable to the unstructured semantic risks unique to large models. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a full lifecycle management method for an intelligent toolbox based on a data cockpit. This method constructs an identity and access boundary graph for pre-emptive management, maps the natural language output of a large model to a continuous medium field, and calculates fluid dynamic parameters to visualize and accurately identify cognitive drift. Simultaneously, it combines an orbital decay resistance function to implement flexible retirement of abnormal agents. This addresses the technical problems of traditional discrete log monitoring's inability to capture implicit logical illusions of agents in real time and the tendency of forced circuit breaking of abnormal nodes to easily trigger a cascading avalanche in microservice architectures.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for full lifecycle management of an intelligent toolkit based on a data cockpit includes the following steps: constructing an agent identity and access boundary map based on multi-source system interaction data; generating a pre-deployment strategy based on the agent identity and access boundary map and distributing it to the target computing node to control multiple agents to execute automated tasks; during the execution of automated tasks by multiple agents, collecting natural language output sequences and mapping the output sequences to a continuous medium field, and calculating the fluid dynamic parameters of the continuous medium field including entropy-coupled semantic Reynolds numbers; inputting the fluid dynamic parameters into the data cockpit to render a flow field distribution view; if it is determined from the flow field distribution view that the target agent has cognitive drift, then injecting system resistance into the target agent based on a preset orbital attenuation resistance function to control the target agent to perform a decommissioning operation.

[0007] In a preferred embodiment, the calculation of hydrodynamic parameters of a continuous medium field including the entropy-coupled semantic Reynolds number includes: converting a natural language output sequence into semantic vectors in a multidimensional vector space using a preset word embedding model; calculating the semantic flow velocity of the continuous medium field based on the rate of change of the semantic vectors within a preset time window; calculating the semantic fluid density of the continuous medium field based on the degree of aggregation of the semantic vectors in the feature space; extracting the logarithmic probability distribution during the generation process of the natural language output sequence and calculating the semantic information entropy increment of the current time window compared to the baseline time window; obtaining the decoding temperature parameter when the preset large language model generates the natural language output sequence and mapping it to a model temperature compensation coefficient; and calculating the entropy-coupled semantic Reynolds number based on the semantic flow velocity, semantic fluid density, semantic information entropy increment, model temperature compensation coefficient, and a preset semantic viscosity coefficient.

[0008] In a preferred embodiment, determining that the target agent has cognitive drift based on the flow field distribution view includes: if the entropy-coupled semantic Reynolds number corresponding to the target agent is not greater than a preset threshold, then the semantic flow of the target agent is determined to be in a laminar state, and the target agent does not have cognitive drift; if the entropy-coupled semantic Reynolds number is greater than the threshold, then a vortex characterization image is generated in the region corresponding to the target agent in the flow field distribution view, and the target agent is determined to have cognitive drift.

[0009] In a preferred embodiment, injecting system resistance into the target agent based on a preset orbital decay resistance function to control the target agent to perform a retirement operation includes: obtaining the target agent's business activity index and dependency weights for downstream business nodes; substituting the activity index and dependency weights into the orbital decay resistance function to calculate a resistance allocation coefficient; injecting time delay into the target agent's API call chain according to the resistance allocation coefficient, and truncating the target agent's non-core read and write permissions according to the corresponding ratio; and destroying the target agent's authentication token when the number of concurrent connections of the target agent is detected to have decayed to zero.

[0010] In a preferred embodiment, the process of multiple agents executing the automated task further includes: capturing the full-link execution log of the multiple agents when executing the automated task; and mapping the automated operation flow in the full-link execution log to a lattice growth process in three-dimensional space. If an abnormal execution result is detected in the end-to-end execution log, a corresponding crystallographic defect map is generated during the lattice growth process; and a three-dimensional crystal strain view containing the crystallographic defect map is synchronously rendered in the data cockpit.

[0011] In a preferred embodiment, generating the corresponding crystallographic defect mapping during lattice growth includes: if the abnormal execution result is a local logic error of a single computing node, generating a point defect mapping at the corresponding position in three-dimensional space; if the abnormal execution result is a cascading error caused by cross-system data contamination, generating a line defect dislocation mapping in three-dimensional space and calculating the strain tensor distortion corresponding to the line defect dislocation mapping.

[0012] In a preferred embodiment, the method further includes: displaying the flow field distribution view and the three-dimensional crystal strain view in a multi-dimensional linkage in a data cockpit; when a drilling command is received for a specific vortex characterization image in the flow field distribution view, highlighting the line defect dislocation mapping in the three-dimensional crystal strain view that has the same time series label as the specific vortex characterization image region.

[0013] In a preferred embodiment, before generating the pre-deployment strategy based on the agent identity and access boundary map, the method further includes: calculating the basic infection number of each node in the identity and access boundary map when it encounters malicious instruction injection based on a preset epidemiological model; and rendering a permission infection heatmap in the data cockpit based on each basic infection number to predict the cascading spread probability of security risks.

[0014] In a preferred embodiment, the step of generating a pre-deployment strategy based on agent identity and access boundary graph includes: obtaining the API call frequency and resource consumption period of the agent to be deployed to construct multi-dimensional niche features; calculating the feature overlap between the multi-dimensional niche features and the features of the already running agent based on a preset niche overlap index model; if the feature overlap is greater than a preset overlap threshold, generating a niche competition phase graph in the data cockpit and rejecting the generation of a pre-deployment strategy containing the agent to be deployed.

[0015] The technical effects and advantages of the present invention's intelligent toolbox full lifecycle management method based on a data cockpit are as follows: This invention achieves pre-emptive security control and precise scheduling boundary delineation for concurrent tasks by constructing an identity and access boundary map of intelligent agents and issuing pre-deployment strategies. During the task execution phase, unstructured natural language output sequences are mapped to continuous medium fields, and fluid dynamic parameters containing entropy-coupled semantic Reynolds numbers are extracted. This reduces the dimensionality of hidden logical changes within large language models and transforms them into mathematical and physical indicators that can be accurately quantified in real time. Combined with the flow field distribution view rendering in the data cockpit, it breaks through the limitations of traditional text log monitoring and achieves intuitive visualization detection of high-dimensional cognitive states. When cognitive drift of a target intelligent agent is accurately captured, the system resistance is injected using an orbital decay resistance function to guide its decommissioning. This replaces the traditional hard circuit breaker mechanism, which is prone to causing downtime in underlying business links, and achieves flexible degradation and smooth disconnection of abnormal nodes. This greatly improves the operational resilience of the intelligent toolbox and the security of the global architecture throughout its entire lifecycle. Attached Figure Description

[0016] Figure 1 A schematic diagram of the process for the full lifecycle management of an intelligent toolbox based on a data cockpit, provided in an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of the heat distribution of access-based infection based on epidemiological model simulation, provided for an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram illustrating the simulation comparison of multidimensional niche feature overlap quantification provided in an embodiment of the present invention.

[0019] Figure 4 This is a schematic diagram illustrating the temporal evolution of entropy-coupled semantic Reynolds number provided in an embodiment of the present invention.

[0020] Figure 5 This is a schematic diagram of three-dimensional crystal strain and defect simulation provided in an embodiment of the present invention.

[0021] Figure 6A schematic diagram of computational fluid dynamics simulation mapping of the data cockpit flow field distribution view provided in an embodiment of the present invention.

[0022] Figure 7 The curve showing the change of comprehensive indicators during the track decay and decommissioning intervention process provided in this embodiment of the invention. Detailed Implementation

[0023] The technical solutions of 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1, Figure 1 This invention presents a method for full lifecycle management of an intelligent toolbox based on a data cockpit, comprising the following steps: Step S1: Construct an identity and access boundary map of intelligent agents based on multi-source system interaction data.

[0025] It should be noted that traditional access control mechanisms typically rely on statically configured role-based access control (RBAC) models. This conventional approach often fails to dynamically capture the hidden unauthorized attempts or instruction deviations of large language model agents with highly autonomous characteristics, variable logical chains, and unpredictable behavior. This results in severely lagging permission control boundaries and easily creates hidden security blind spots. To address these issues, the method in this embodiment abandons the fixed access control list judgment mode and achieves an adaptive three-dimensional characterization of the activity boundaries of multiple agents in complex network domains by dynamically capturing real-world network interaction footprints.

[0026] In this embodiment, distributed link tracing probes (such as proxy components based on the OpenTelemetry standard) pre-deployed on the business side and a mirrored traffic capture mechanism bypassing the API gateway cluster are used to collect microkernel call interaction relationships and authentication credential transfer logs between enterprise intranet computing nodes and microservice container clusters in a non-intrusive, 24 / 7 manner. This high-frequency time-series data is then used as the multi-source system interaction data. For each Remote Procedure Call (RPC) or RESTful API request initiated by the agent, its microsecond-level timestamp, source and destination IP addresses, Uniform Resource Identifier (URI), and declared features in the JSON Web Token (JWT) are extracted. Subsequently, a Labeled PropertyGraph (LPG) data structure is used to perform topological modeling on the discrete interaction data. Specifically, in the constructed graph structure... In, node set It includes various large-scale intelligent agents, microservice instances, and underlying data table resources. Each node encapsulates a unique node identifier, its current operational security level, and static and dynamic attributes representing its core position within the mesh, such as network centrality (i.e., total downstream business dependencies or out-degree connection weight); a set of directed edges. For each real request route between mapping nodes, the starting and ending points of the edges correspond to the request initiator and receiver, respectively. The attribute labels attached to the edges not only contain the API communication protocol type and the amount of payload data in the call, but also encapsulate time-series attribute labels generated based on microsecond-level timestamps and call frequency weights. The underlying data is persistently stored with high concurrency through the adjacency list index of a distributed graph database, thereby accurately reconstructing the real call chains of each agent in different service meshes and potential unauthorized access probing tendencies.

[0027] Furthermore, before constructing the agent identity and access boundary map based on multi-source system interaction data, the method further includes: introducing a preset epidemiological susceptibility-exposure-infection-recovery model into the data cockpit; calculating the basic reproduction number of each node in the access boundary map when encountering malicious command injection based on the epidemiological susceptibility-exposure-infection-recovery model; and rendering a permission infection heatmap in the data cockpit based on each of the basic reproduction numbers to dynamically predict and display the cascading spread probability of security risks. In the specific cross-border mapping logic of network security attack and defense, the complex IT microservice environment and the epidemiological propagation process are homomorphically abstracted: "Susceptible node" is defined as an old API node that has not installed the latest security patch, holds an overly broad Identity and Access Management (IAM) policy, or has not enabled a strict input parameter verification mechanism; "Exposed node" is defined as a suspended node that has received abnormal request data packets containing malicious prompt words (Prompt Injection), but has not yet been successfully triggered to escape memory or perform actual unauthorized operations due to internal buffering or delay; "Infectious node" is defined as a high-risk node whose defense has been breached, has experienced substantial unauthorized calls or has had its authentication token hijacked, and has the ability to move laterally to other nodes with lower security levels on the internal network; finally, "Recovered node" is defined as a de-identified node that has been cut off and isolated by zero-trust security components, has had its access token reset, or has completed a hot-fix release.

[0028] To accurately quantify the cascading diffusion process of the aforementioned abnormal permission calls, an evolutionary calculation model for the basic infection number of node propagation dynamics is constructed. Combining the node and edge attributes extracted from the aforementioned graph structure, an evolutionary calculation model is developed for any node in the access boundary graph. The formula for calculating its basic reproduction number is: (1) In the formula, For the first The basic infection number of a computing node reflects the average expected number of surrounding downstream nodes that may be directly affected and damaged after the agent node is maliciously injected. This is the fundamental lateral propagation rate constant in the network topology, calibrated based on historical penetration test data; in engineering implementation, it is usually taken as an empirical value. between; Nodes extracted during the graph construction process The network centrality or out-degree connection weight represents the total effective downstream business dependencies of the node within the microservice registry. Let represent the probability function of a node transitioning from a vulnerable state to an exposed state. This probability is proportional to the data read / write exposure size of the target agent and the success rate of malicious commands bypassing the regularization interceptor. Its value space is . ; denominator This is the node natural recovery rate, which corresponds to the rate at which the underlying system's regular heartbeat self-checks and eliminates nodes or the natural expiration of tokens due to timeout. The proactive response intervention coefficient reflects the current blocking enforcement strength of the cluster security policy engine; Indicates targeting the node The reciprocal of the mean time to recovery (MTTR) when encountering an abnormal state.

[0029] After obtaining the basic infection rate assessment results for all nodes in the link, WebGL and programmable rendering pipeline technologies are used to transform the abstract mathematical matrix into a visual model on the digital twin canvas of the front-end data cockpit. The underlying logic is as follows: the basic nodes in the topology graph are rendered as polygonal geometric primitives in three-dimensional space, and the calculated values ​​are... This is mapped to fragment color interpolation and bloom intensity parameters in the shader material properties. When a node in a certain region... Exceeding the preset critical threshold At that time, the geometric primitives of the area will be rendered as a high-frequency breathing, flickering, deep red glowing halo. Simultaneously, the underlying particle engine will be invoked to simulate the dynamic radiation wavefront of malicious data packets flowing towards surrounding downstream vulnerable nodes. This achieves an immersive rendering display of high-dimensional clustering of hidden threats and a heatmap of permission-based infection at the spatial level. For example... Figure 2 The figure shows a schematic diagram of the heat map of permission-based infection simulation based on an epidemiological model. The diagram simulates the RPC call chain between microservice clusters in the form of a two-dimensional topological grid, with the color intensity of the grid nodes strictly mapping the calculated basic reproduction number. As can be seen, in the central node (source) area that has encountered malicious prompt injection, the node is displayed in dark red, representing a high-risk state (i.e., The simulation results show a wavefront decay trend, with the infected nodes gradually radiating outwards towards the lighter-colored susceptible nodes, along the out-degree connection. This simulation visually verifies the ability of this invention to "predict" the cascading spread probability of safety risks in a large model using an infectious disease model.

[0030] The aforementioned cross-disciplinary introduction of epidemiological model preprocessing mechanisms, combined with the precise characterization of underlying attribute graph data structures, completely breaks through the security limitations of traditional RBAC static permission graphs, which can only perform post-event tracking and review after irreversible unauthorized operations. By integrating underlying topological features such as graph network centrality into the micro-calculation of basic reproduction numbers, and combining this with deep integration of macro-level heatmap rendering, this embodiment achieves a significant breakthrough in IT network operations and maintenance: even when facing unknown composite risks such as zero-day vulnerability-type malicious prompt injection from large language models and uncontrollable logical illusion generalization, the connectivity of the graph can be used to predict in advance the cascading diffusion probability and the infection wavefront path where security defenses are breached. This mechanism effectively avoids core computing nodes getting deadlocked, avoids the catastrophic consequences of illegal bulk extraction of critical data assets and the avalanche of downtime in downstream core business systems, and buys a valuable spatiotemporal response window for the automated operations and maintenance interception system.

[0031] Step S2: Generate a pre-deployment policy based on the agent identity and access boundary map, and distribute it to the target computing node to control multiple agents to execute automated tasks.

[0032] It should be noted that traditional intelligent agent or microservice scheduling schemes typically employ first-come, first-served (FIFO) or simple round-robin allocation algorithms. This passive scheduling mechanism, in multi-agent concurrent scenarios, often lacks global coordination of underlying computing resources and interface call quotas. When multiple large language model intelligent agents with high throughput requirements compete for the same database table write lock or external gateway bandwidth within a similar timeframe, it can easily lead to serious failures such as resource exhaustion, request queuing timeouts, and even microservice deadlocks. To address these technical pain points, the method in this embodiment introduces a high-dimensional pre-conflict detection mechanism before actual scheduling occurs, achieving a leapfrog optimization from passive queuing to proactively avoiding internal friction.

[0033] In this embodiment, to ensure the robust operation of the underlying computing engine, strict pre-conflict detection is required before formally issuing scheduling instructions. Further, the generation of pre-deployment strategies based on agent identity and access boundary graphs includes: obtaining the API call frequency and resource consumption periods of the agent to be deployed to construct multi-dimensional niche features; calculating the feature overlap between the multi-dimensional niche features and the features of already running agents based on a preset niche overlap index model; if the feature overlap is greater than a preset overlap threshold, a niche competition phase graph is generated in the data cockpit, and the generation of a pre-deployment strategy containing the agent to be deployed is rejected. Specifically, the concept of niche is introduced into the information technology architecture system, and through bypass acquisition and performance benchmark testing, the dependence of the agent to be deployed on various resources at the underlying cluster during its operational lifecycle is quantified into a high-dimensional feature vector. This multidimensional niche feature not only includes the basic distribution of peak CPU computing power during specific periods and the size of the memory resident set (RSS), but also rigorously covers the contention frequency of row-level write locks for core business tables in specific relational databases (such as MySQL / PostgreSQL), as well as IT engineering parameters such as the utilization rate of the rate limit quota for high-frequency calls to external third-party components.

[0034] To quantify the conflict probability of different agents across the aforementioned resource dimensions, a precise calculation is performed based on a pre-defined niche overlap index model. Let the agent to be deployed be A, and any existing agent on the currently running target computing node be B. Extract the resource occupancy weight distribution vectors of both agents across n independent resource dimensions, and calculate the feature overlap between them. The mathematical formula for this calculation is as follows: (2) In the formula, The overlap of niche features between agent A to be deployed and agent B already in operation is essentially a cosine similarity transformation (Pianka overlap index) in multidimensional space. The value ranges from 0 to 1. The closer the value is to 1, the more homogeneous the two are in terms of resource demands, and the higher the probability of resource overlap. n is the total number of extracted IT basic resource dimensions. In specific engineering implementations, it is usually configured as n=12 or n=24 core monitoring dimensions. The normalized resource consumption weight of the agent A to be deployed in the j-th resource dimension is the proportion of the resource consumption of this single entity to its total resource consumption benchmark throughout its entire life cycle. The normalized occupancy weight of the existing running intelligent agent B on the same j-th resource dimension.

[0035] The highest feature overlap is obtained after calculation by the background scheduler. When the overlap exceeds the preset overlap threshold (the critical threshold is set to 0.85), it indicates that forcibly deploying the resource will likely cause resource starvation. In this case, the scheduling center will proactively execute interception logic, rejecting the deployment work order, and simultaneously extracting the feature data corresponding to the resource dimension with high overlap. Using the graphics rendering library at the data dashboard front end (such as the WebGL-based ECharts framework), the high-dimensional tensor is reduced to a three-dimensional coordinate system, and a phase diagram of niche competition that intuitively displays the focus of resource contention is generated. This diagram is then used by architects or automated operation and maintenance scripts for targeted orchestration and adjustment. Figure 3 The figure shown is a comparison of simulation results for the quantification of overlapping multidimensional niche features. Figure 3 The image uses polygonal radar projection to visually display the feature weights of the agent to be deployed (A) and the already running agent (B) across 12 core dimensions, including peak CPU computing power, MySQL write lock frequency, and external API concurrency quota. It is clearly observable that the projected areas of the write lock contention dimension and the gateway bandwidth dimension of the two agents highly overlap in the third time period (based on calculated extreme values). The value reached 0.89, significantly exceeding the threshold of 0.85. Based on this, the phase diagram generated by the system displays a warning dark red cross-shaded area in the corresponding dimension. This chart data intuitively demonstrates that the present invention, through its niche overlap calculation model, can accurately detect and intercept high-consumption work orders before physical deployment, demonstrating extremely high engineering feasibility.

[0036] This step, by introducing the niche competition theory from the field of bionics, completely breaks the lagging response mode of traditional microservice architecture based on runtime queuing theory and post-event degradation circuit breaking. This control strategy, which significantly advances the conflict resolution mechanism to the deployment stage, effectively avoids implicit resource consumption, database connection pool exhaustion, and complex microservice deadlock problems caused by multiple agents concurrently calling homogeneous underlying resources within the same time window. Isolating the operational trajectories of highly overlapping agents at the system foundation level not only greatly improves the throughput efficiency of the entire business flow of the target computing node but also ensures the global success rate and determinism when multiple agents collaboratively execute automated tasks.

[0037] Step S3: During the process of multi-agent execution of automated tasks, the natural language output sequence of each agent is collected in real time, and the natural language output sequence is mapped to a continuous medium field to calculate the hydrodynamic parameters of the continuous medium field.

[0038] It should be noted that traditional intelligent agent operation monitoring methods are usually limited to performance indicator alarms at the basic physical resource level (such as CPU load and memory usage), or rely on preset regular expressions and structured error codes for passive interception. This conventional mechanism is prone to falling into a "monitoring blind spot" of security and logic when facing high-dimensional, unstructured text generated by large language models: when an intelligent agent experiences cognitive drift or falls into logical illusion, its output sequence remains highly fluent in terms of syntax and network protocol, and does not trigger traditional application exception throwing mechanisms. To solve the above problems, this invention maps discrete text character streams to a high-dimensional continuous medium field, and by extracting the physical dynamic parameters of the underlying generation features, achieves accurate capture and quantification tracking of implicit cognitive shifts.

[0039] In this embodiment, to achieve seamless interception and high-frequency real-time analysis of multi-agent output streams, the underlying infrastructure adopts a streaming listening sidecar mode under a service mesh architecture. Specifically, during the mounting lifecycle of each agent container on the target computing node, a lightweight listening component based on eBPF (Extended Berkeley Packet Filter) technology or Envoy high-performance proxy is injected in a bypass manner. This sidecar proxy does not directly participate in the execution and blocking of core business logic, but instead hooks the container's standard output stream, standard error stream, and application-layer remote procedure call protocols (such as gRPC streaming responses) to capture, in real-time, the text chunks and corresponding underlying metadata output token-by-token by the large language model during the inference operation phase, using micro-time slices at the 10-millisecond level, thereby constructing a complete and continuous natural language output sequence data stream.

[0040] Further, the step of mapping the natural language output sequence to a continuous medium field to calculate the hydrodynamic parameters of the continuous medium field includes: converting the natural language output sequence into semantic vectors in a multidimensional vector space using a word embedding model of a preset large-scale language model; calculating the semantic flow velocity of the continuous medium field based on the rate of change of the semantic vectors within a preset time window; calculating the semantic fluid density of the continuous medium field based on the degree of aggregation of the semantic vectors in the feature space; extracting the logarithmic probability distribution during the generation process of the natural language output sequence and calculating the semantic information entropy increment of the current time window compared to the baseline time window; obtaining the decoding temperature parameter of the preset large-scale language model when generating the natural language output sequence and mapping it to a model temperature compensation coefficient; and calculating the entropy-coupled semantic Reynolds number of the continuous medium field based on the semantic flow velocity, the semantic fluid density, the semantic information entropy increment, the model temperature compensation coefficient, and a preset semantic viscosity coefficient.

[0041] In the specific engineering implementation, the sidecar agent first calls a local lightweight feature extraction model (such as a quantized and pruned 768-dimensional BERT model) to convert the captured string into a 768-dimensional dense tensor representation in real time. For measuring the semantic flow speed, within a preset time window with a sliding step of 2 seconds, the cosine similarity change rate between the centroids of semantic vectors in adjacent time slices is continuously calculated. The higher this rate, the greater the jump in the topic of discussion by the agent, i.e., the faster the flow speed. For measuring the semantic fluid density, the degree of clustering is characterized by calculating the reciprocal of the average L2 norm distance of all semantic vectors within the current time window. The more focused and logically rigorous the text sequence, the higher its spatial density. Most importantly, the sidecar agent not only captures plaintext in the bypass mode but also uses the framework interface to extract the original logarithmic probability distribution (Logits) of the model's inference output layer. After transforming it into a probability space using the Softmax function, Shannon's theorem is introduced to calculate the current prediction information entropy. This allows us to obtain the semantic information entropy increment compared to the baseline window of normal dialogue. This allows for the precise quantification of drastic fluctuations in uncertainty within the model. Specifically, the formula for calculating the semantic information entropy increment is as follows: (3) (4) In the formula, The Shannon information entropy is the prediction of the next text token by the large language model within the current time window; N is the preset total dimensionality of the vocabulary of the large language model. and , respectively, are the original logits of the k-th and j-th candidate words in the model inference output layer; the fractional part in the formula represents the probability distribution between 0 and 1 that is normalized and mapped to the logits of the underlying layer by the Softmax function; This is the calculated semantic information entropy increment; Let be the expected value of the baseline information entropy of the target agent after exponential moving average (EMA) smoothing within a normal and hallucination-free historical dialogue baseline time window. This formula, by capturing the probability diffusion of the model when generating multiple-choice words at the microphysical layer (i.e., the probability distribution of the model becomes flatter when the model "hesits" or "makes up words," leading to a surge in entropy), can detect the outbreak of uncertainty in the agent's internal logic earlier than traditional application-layer character error reporting.

[0042] Specifically, the entropy-coupled semantic Reynolds number of the continuous medium field is calculated based on the semantic flow velocity, the semantic fluid density, the semantic information entropy increment, the model temperature compensation coefficient, and the preset semantic viscosity coefficient, using the following formula: (5) In the formula, The entropy-coupled semantic Reynolds number, which characterizes the current output stream state of a large language model, is used as a core criterion to quantitatively evaluate whether the text generation process is in a stable "laminar flow" or a chaotic "turbulent flow" (i.e., a state of severe cognitive drift or hallucination). The semantic fluid density of the continuous medium field characterizes the cohesion of text vectors in the feature space; Semantic flow speed represents the rate of change in text topics over time. The feature sequence length of the current natural language output sequence, i.e., the total number of tokens accumulated within the current analysis sliding window; The preset semantic stickiness coefficient is a physical quantity used to simulate the inherent logical inertia of the pre-training corpus of a large language model. In actual engineering configurations, a higher stickiness coefficient (e.g., 0.8) is usually assigned to specialized agents that have undergone fine-tuning in vertical domains, while a lower value (e.g., 0.30) is assigned to divergent general chatter models. This represents the semantic information entropy increment calculated using the original probability tensor. The model temperature compensation coefficient is directly linearly mapped from the current decoding temperature (Temperature) hyperparameter of the large language model instance (usually set to 0.1 to 1.2). This parameter is placed in the denominator to dynamically offset the expected natural entropy increase introduced by the business side's proactive high randomness generation strategy, thereby ensuring the objectivity and consistency of the Reynolds number calculation benchmark under different inference temperature settings.

[0043] To further verify the accuracy of the above fluid dynamics mapping, such as Figure 4 As shown, a set of temporal simulation evolution diagrams of entropy-coupled semantic Reynolds number based on the output stream of a real large language model are provided. Figure 4 The middle horizontal axis represents the sliding time window (slicing precision is 10 milliseconds), and the left vertical axis represents the semantic information entropy increment. The right vertical axis represents the calculated Reynolds number. As can be seen from the chart data, during the normal inference phase of 0 to 15 seconds, It remains within a small fluctuation range below 0.1. The curve stabilized within the laminar flow band between 800 and 1200. At 15.5 seconds, when a confusing prompt was intentionally injected into the input to induce cognitive illusions in the target agent, the data domain could be observed to... A sudden surge of pulses occurs, and due to the nonlinear propagation of parameters, the coupled calculations are affected. The curve spiked within 1.2 seconds and rapidly surpassed the turbulence critical threshold of 2300. This time-series variation curve fully demonstrates the effectiveness of reducing abstract and discrete unstructured text to fluid dynamic parameters to achieve real-time anomaly interception of cognitive drift.

[0044] The above processing, by introducing classical fluid dynamics equations and Shannon's information entropy theory, achieves deep mathematical modeling of unstructured, implicitly divergent text. It successfully reduces the "cognitive illusions" and "logical shifts" of large models—which are difficult to capture by conventional IT monitoring scripts—into dimensionality-reduced and transformed into dynamic indicators that can be continuously calculated, quantified in real time, and possess physical significance. This mechanism completely breaks through the limitations of traditional microservice architectures, which heavily rely on low-level network error code responses and discrete application log checks. Without interfering with the normal business processes of the target computing nodes, it establishes a new paradigm for observing the operational status of large models with physical interpretability. This provides a robust data foundation for subsequent targeted intervention and fundamentally prevents downstream related business systems from experiencing logical cascading crashes or data pollution due to the continuous output of forged data or erroneous instructions by the intelligent agent.

[0045] In another aspect of this embodiment, to address the problem of "cognitive overload" for administrators caused by the waterfall logs generated by traditional distributed tracing, the data dashboard and underlying monitoring agent also introduce a visualization monitoring mechanism based on condensed matter physics. Specifically, during the execution of the automated task by multiple agents, the process further includes: capturing the full-link execution logs of the multiple agents while executing the automated task; mapping the automated operation flow in the full-link execution logs to a lattice growth process in three-dimensional space; if abnormal execution results are detected in the full-link execution logs, generating corresponding crystallographic defect mappings during the lattice growth process; and synchronously rendering a three-dimensional crystal strain view containing the crystallographic defect mappings in the data dashboard. In the specific spatial dimensionality reduction and reconstruction logic, the underlying rendering engine constructs a standard three-dimensional Cartesian coordinate system: mapping the timestamp to the growth extension direction of the X-axis to represent the temporal evolution of the task; mapping the topology call hierarchy of microservice nodes to the vertical stacking of the Y-axis to represent the depth of the service link; and mapping the size of the communication interface data payload to the spatial cross-sectional width of the Z-axis. Under normal business processes, such as when an agent initiates an HTTP 200 response or a gRPC remote call succeeds, the front-end rendering pipeline will generate a perfect cubic unit cell (such as a face-centered cubic Bravais lattice) with uniform side length in the three-dimensional coordinate system. As the automated task is executed smoothly, a large number of normal call logs will grow continuously in the three-dimensional space like crystals naturally crystallizing, into perfectly arranged and dense lattice blocks.

[0046] Furthermore, to accurately distinguish the severity level of the fault, the generation of corresponding crystallographic defect maps during lattice growth includes: if the abnormal execution result is a local logic error of a single computing node, a point defect map is generated at the corresponding position in three-dimensional space; if the abnormal execution result is a cascading error caused by cross-system data pollution, a line defect dislocation map is generated in three-dimensional space, and the strain tensor distortion degree corresponding to the line defect dislocation map is calculated. In specific IT fault scenario mapping, when a local error isolated within the current microservice context, such as a null pointer exception thrown by an edge computing node, a single database query timeout, or an HTTP 500 internal server error, is captured, such an exception will not continue to propagate downstream. At this time, the rendering pipeline will generate a "point defect map" such as a vacancy or interstitial at the three-dimensional space coordinates corresponding to the calling node, which manifests as a missing square or an extra free heterogeneous node in the originally perfect lattice array. However, when cross-system data contamination occurs—for example, when a target large model agent experiences cognitive illusions and outputs a JSON structure with misaligned key-value pairs or distorted format—leading to a deserialization crash in the downstream parsing center and further causing write lock suspension in the underlying database and continuous circuit breakers in related microservices, this type of fault has extremely strong penetrating and destructive power. At this point, the rendering pipeline will generate a "line defect dislocation mapping" along the Y-axis (call hierarchy axis) in the corresponding region of the 3D crystal growth, consisting of edge dislocations or screw dislocations that continuously break down multiple unit cells. This visually represents the error tearing apart the entire microservice architecture.

[0047] like Figure 5 The figure shows a 3D simulation diagram of crystal strain and defects in the mapping of the entire execution log. A 3D physical coordinate system is constructed with microsecond-level timestamps as the X-axis, microservice topology layers as the Y-axis, and data payload as the Z-axis. Most areas in the figure consist of a perfect lattice array composed of regularly arranged, densely structured gray spheres (representing normal HTTP200 / RPC response logs); scattered missing spaces are visible within the lattice (corresponding to "point defects" formed by local logical errors in a single node); and slightly to the right of the center of the lattice, there is a line defect dislocation composed of multiple red highlighted spheres running along the Y-axis. This line defect visually reproduces the cross-system cascading error tear caused by distorted JSON output from a large model, proving that visual dimensionality reduction methods based on condensed matter physics can instantly expose structural system breakpoints in massive waterfall-style logs.

[0048] To accurately quantify the destructive severity of this cross-system cascade error, the underlying computational engine incorporates the Burgers vector theory from crystallography. By tracing the topological propagation path of the error stack, it calculates the strain tensor distortion corresponding to the dislocation mapping of the line defect. The specific calculation formula is as follows: (6) In the formula, The strain tensor distortion degree corresponding to the line defect dislocation mapping is a dimensionless parameter. The larger the value, the more severe the structural damage to the global business flow caused by the secondary cascade error. The Burgers vector magnitude calculated at the micro level is rigorously mapped in IT engineering to the cumulative number of bytes of the distorted payload that caused this data pollution (i.e., the volume of dirty data). It is the standard lattice constant under fault-free baseline conditions, specifically the scalar product of the historical average response time and average payload size of the service link under normal HTTP 200 conditions; The propagation depth of the cross-system cascading error, i.e., the total number of downstream microservice nodes that were actually infected by the distorted JSON data and caused the exception to be thrown; This is a vulnerability amplification factor for the current microservice network topology. This factor is preset by the system; the higher the coupling of the core microservice mesh, the greater its vulnerability. The larger the value.

[0049] This parallel mechanism creatively introduces the theories of lattice deformation and topological defects from condensed matter physics into distributed system log monitoring, successfully mapping the originally linearly exploding, massive amounts of obscure error text into intuitive three-dimensional physical deformations. This allows architects to instantly distinguish isolated single points of failure (point defects) from structural tear zones (line defects) that could lead to system collapse from complex microservice topologies, completely overturning the traditional troubleshooting model based on text retrieval and regular expression extraction, and significantly shortening fault response and root cause location time.

[0050] Step S4: Input the fluid dynamics parameters into the cockpit rendering flow field distribution view.

[0051] In this embodiment, to intuitively present the implicit state changes of the multi-agent output sequence, the front-end data cockpit introduces a fluid rendering engine based on computational fluid dynamics (CFD) visualization principles and the WebGL graphics interface. Specifically, the data access layer extracts the entropy-coupled semantic Reynolds number calculated in real-time by the backend. Semantic fluid density and semantic flow speed The multidimensional fluid dynamics parameters, including those from the image, are pushed to the front-end flow field renderer with low latency via a WebSocket long-lived connection. The rendering engine rigorously maps the abstract fluid dynamics parameters to the geometric and shading attributes of the front-end interface: semantic fluid density... Mapped to the particle aggregation and spatial streamline density in the flow field; semantic flow velocity. It is mapped to the velocity vector of the front-end particle; and the most core entropy is coupled with the semantic Reynolds number. This is mapped to color depth interpolation and turbulence perturbation coefficients in the shader. When monitoring an agent within the domain... When the flow rate increases rapidly, the particle streamlines in the corresponding coordinate region will quickly evolve from a smooth and orderly laminar flow state into a chaotic vortex, accompanied by a dynamic color gradient from cool to warm tones. This allows the cognitive drift or hallucination intensity of the target intelligent agent's current natural language output sequence to be represented in real time on the macroscopic digital twin screen.

[0052] like Figure 6 The image shown is a schematic diagram of the computational fluid dynamics (CFD) simulation mapping of the flow field distribution view at the front of the data cockpit. Figure 6 The semantic flow of multi-agent output sequences is illustrated using two-dimensional streamlines and a background vector field. In the left half of the figure, the streamlines are smooth and parallel, and the background color is cool, representing a laminar state at a low Reynolds number (i.e., logically coherent and thematically focused agents). In the right half of the figure, however, the injection of interfering instructions leads to entropy coupling and semantic Reynolds number. Exceeding the threshold, the streamlines undergo dramatic topological folding, forming distinct turbulent vortex characteristics, with localized areas exhibiting a deep red hue indicating a high alert level. This figure fully demonstrates the engineering feasibility of this invention in transforming unstructured semantic risks into a concrete physical field representation.

[0053] Furthermore, after inputting the fluid dynamics parameters into the data cockpit to render the flow field distribution view, the method further includes: displaying the flow field distribution view and the three-dimensional crystal strain view in a multi-dimensional linkage within the data cockpit; when a drilling command is received for a specific vortex characterization image in the flow field distribution view, highlighting the line defect dislocation mapping in the three-dimensional crystal strain view that has the same time series label as the specific vortex characterization image region.

[0054] In the engineering implementation of underlying data link penetration and front-end UI interaction, the key logic lies in building a strong spatiotemporal binding between unstructured semantic representations and structured application programming interface (API) call logs. Specifically, while collecting natural language output sequences, the bypass listening vehicle extracts globally unique trace identifiers (Trace IDs) and microsecond-level high-precision timestamps from the microservice mesh context, generated based on distributed tracing protocols (such as the OpenTelemetry specification). These metadata are used as association primary keys and written to the "Semantic Fluid Dynamics Parameter Table" and the "Cross-System Microservice API Call Lattice Log Table" in the backend distributed time-series database (such as InfluxDB), respectively. On the front-end interface of the data cockpit, the flow field distribution view and the running topology of the microservice architecture (i.e., the 3D crystal strain view) are rendered in parallel through a split-screen viewport. When an operations and maintenance personnel or an automated inspection script triggers a mouse click or API drill-down command on a "fluid vortex" representation image representing a severe hallucination state of the large model in the front-end flow field distribution view, the front-end controller immediately captures the time slice and Trace ID bound to the vortex object. Subsequently, the composite search criteria are transmitted to the database computing engine via the underlying layer communication bus, triggering a millisecond-level join query of the composite index. The underlying engine quickly and accurately locates the coordinates of the link node in the massive distributed log topology where the downstream parameter parsing anomaly or unauthorized call failure was directly caused by the agent generating an erroneous instruction (illusion), and sends the coordinate set back. Finally, based on the returned coordinates, the front-end rendering pipeline uses luminescent materials and particle effects to highlight and render the "code-level line defect" mapping caused by the cascading error in the adjacent 3D space (3D crystal strain view).

[0055] The aforementioned multi-dimensional physical field linkage and interaction design completely breaks down the data silos in traditional IT operations and maintenance monitoring systems, which are characterized by a disconnect between the "macro-level business dashboard (displaying only coarse-grained concurrency / success rate)" and the "micro-level code logs (massive and obscure text stack traces and error messages)." This mechanism empowers architects and automated governance scripts with a new, top-down, penetrating troubleshooting capability: it can instantly drill down from unstructured semantic risk representations (macro-level fluid vortexes) to structural collapse breakpoints at the microservice level (micro-level three-dimensional crystal line defects) with a single click. This technical effect not only reduces the root cause location time for cascading downtime caused by cognitive drift in complex distributed microservice systems from hours to milliseconds, but also significantly lowers the technical threshold for tracing and analyzing the black-box output of large models and defining responsibilities. It provides a closed-loop governance method with both a global perspective and micro-level diagnostic accuracy for the safe and controllable operation and full lifecycle management of multi-agent clusters.

[0056] Step S5: If it is determined from the flow field distribution view that the target intelligent agent has cognitive drift, then the system resistance is injected into the target intelligent agent based on the preset track decay resistance function to control the target intelligent agent to perform a decommissioning operation.

[0057] It's important to note that traditional microservice governance strategies, when faced with nodes in an abnormal state, typically employ a simplistic and forceful approach: direct circuit breaking or instantly killing the container process by sending a mandatory termination signal (such as SIGKILL). While this rigid, one-size-fits-all isolation method can immediately cut off the source of the anomaly, in a high-concurrency, highly coupled multi-agent collaborative network, instantly severing all session connections of a critical agent can easily lead to a large number of unresponsive pending requests and distributed transaction rollbacks on downstream dependent nodes, ultimately triggering an avalanche effect across the entire microservice architecture. To address this issue, this invention innovatively introduces a flexible drag injection mechanism that simulates the decay of a spacecraft's orbit. By progressively implementing degradation control, it achieves the smooth disconnection and safe removal of abnormal agents from the network.

[0058] In this embodiment, determining that the target agent has cognitive drift based on the flow field distribution view includes: if the entropy-coupled semantic Reynolds number corresponding to the target agent is not greater than a preset threshold, then the semantic flow of the target agent is determined to be in a laminar state, and the target agent does not have cognitive drift; if the entropy-coupled semantic Reynolds number is greater than the threshold, then a vortex representation image is generated in the region corresponding to the target agent in the flow field distribution view, and the target agent has cognitive drift.

[0059] Specifically, the data access layer and the background monitoring process continuously poll the underlying data matrix of the front-end flow field distribution view. When the entropy coupling semantic Reynolds number within the corresponding target agent coordinate region is not greater than the preset laminar stability boundary threshold (in classic fluid dynamics mapping configurations, this critical threshold is usually set to 2300), it indicates that the natural language output sequence of the target agent maintains a high degree of logical coherence and topic focus. The underlying monitoring component will maintain the normal bypass listening mode and continuously poll and listen. However, if this indicator changes and exceeds the threshold (i.e., The front-end WebGL-based rendering engine dynamically generates "vortex" or "turbulent" images representing extreme logical chaos and divergence on the multi-dimensional twin screen based on the underlying out-of-bounds tensor parameters. At this time, the automated operation and maintenance daemon deployed on the edge computing node will combine visual rendering events with underlying time-series anomaly indicators to immediately capture and confirm this cognitive drift state. Subsequently, the control plane will call the container orchestration engine to generate a high-priority decommissioning protection work order containing a globally unique trace identifier (Trace ID) for the target agent, and asynchronously send the work order to the microservice mesh proxy where the agent resides, thereby formally activating the subsequent flexible decommissioning intervention process with resistance attenuation characteristics.

[0060] Furthermore, the process of injecting system resistance into the target intelligent agent based on a preset orbital decay resistance function to control the target intelligent agent to perform a retirement operation includes: obtaining the target intelligent agent's business activity index and dependency weights for downstream business nodes; substituting the activity index and dependency weights into the orbital decay resistance function to calculate the resistance allocation coefficient; injecting time delay into the target intelligent agent's API call chain according to the resistance allocation coefficient, and truncating the target intelligent agent's non-core read and write permissions according to the corresponding ratio; and destroying the target intelligent agent's authentication token when the number of concurrent connections of the target intelligent agent is detected to have decayed to zero.

[0061] Specifically, before implementing intervention, the underlying resource scheduler first extracts the query rate per second (QPS) of the target agent within the current sliding time window as a business activity indicator by querying the historical statistical dashboard of the service registry (such as Nacos or Consul), and simultaneously obtains the out-degree centrality of the node to the downstream business nodes in the network topology as a dependency weight, output by the graph analysis module.

[0062] To achieve a smooth and irreversible application of drag, the system substitutes the extracted indicators into a preset track attenuation drag function to calculate an exponentially increasing drag distribution coefficient. The specific formula is as follows: (7) In the formula, The drag distribution coefficient is calculated at time t, and its value is within a closed interval from 0 to 1; This is a preset initial base resistance level, used to set the initial strength of the resistance injection; The exponential evolution constant representing the drag decay determines the steepness of the exponential increase in the drag coefficient over time. To extract the business activity metrics of the target intelligent agent (such as real-time QPS values); To account for the dependency weights of downstream business nodes, this item is placed together with the business activity index in the logarithmic denominator. Its engineering and physical significance is that when the target agent's activity is extremely high and the downstream dependency is extremely heavy, the denominator becomes larger, thereby appropriately slowing down the rate of increase of the resistance coefficient, thus providing more buffer time for the high-load downstream nodes to degrade.

[0063] After obtaining the dynamically evolving resistance distribution coefficient Subsequently, the underlying API Gateway begins executing specific engineering degradation operations. In terms of time latency injection, the gateway utilizes a dynamically adjusted token bucket algorithm to reverse the token generation rate according to the resistance allocation coefficient, and forcibly inserts a follow-up sleep function into the remote procedure call (RPC) link of the target agent, gradually increasing its original millisecond-level (e.g., 10ms) response latency to second-level (e.g., 5000ms), thereby forcing the upstream caller to trigger backoff retries or actively switch to a backup node. In terms of permission truncation, based on the underlying bitmask operation logic of the Identity and Access Management (IAM) component, the gateway... This is mapped to a privilege decay threshold. For example, the IAM controller performs a logical AND operation on the agent's 32-bit privilege mask and a dynamically generated inverse mask, prioritizing the stripping of INSERT and UPDATE write lock privileges for non-core external database tables corresponding to the lower bits of the mask, retaining only extremely basic read-only live privileges.

[0064] Figure 7 The curves showing the changes in various microservice engineering indicators during the retirement intervention process of the target intelligent agent after applying the orbital attenuation drag function proposed in this invention are presented. Figure 7 It contains three highly correlated data flow curves: the solid line that rises exponentially represents the resistance distribution coefficient. (It eventually converges smoothly to 1 over time); the dashed line with a step-like decline represents the number of valid underlying resource write lock permissions remaining after the agent performs IAM masking operations; the dotted line with a gently parabolic decay represents the query per second (QPS) statistics of the API gateway. The evolution trend of the simulation curves clearly shows that, compared to the sudden precipitous drop in QPS caused by traditional one-click circuit breakers (which easily triggers widespread downstream timeout errors), the QPS under the mechanism of this invention experiences a buffered and smooth decline period of approximately 5000 milliseconds. This curve perfectly simulates the drag decay trajectory of a spacecraft gradually entering the atmosphere, proving that this gradual intervention method can release extremely sufficient connection reset and distributed transaction rollback time for downstream nodes.

[0065] Finally, when the sidecar monitors that the number of concurrent transmission control protocol (TCP) connections of the target agent in a long-connection state has been completely released and decayed to zero by the downstream node, the security daemon will immediately physically destroy the agent's JSON Web Token (JWT) authentication token in the Redis distributed cache, completing the final hard core deregistration of the instance.

[0066] The flexible resistance injection mechanism, which simulates aerospace orbit decay, employed in this embodiment completely abandons the simplistic and brutal "one-click circuit breaker" approach in traditional microservice governance, which is prone to triggering cascading failures. By transforming a large language model agent diagnosed as experiencing cognitive drift into a decaying node with increasingly slower response times and smaller permissions, it not only elegantly avoids the continued large-scale pollution of the underlying storage by dirty data, but also provides extremely valuable timeout degradation buffers and smooth request retry time for massive downstream business nodes in high-concurrency scenarios. From the architectural foundation level, it effectively avoids unpredictable avalanche effects in multi-agent microservice clusters.

[0067] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0068] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0069] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0070] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0071] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included 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.

[0072] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. 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 full lifecycle management of an intelligent toolbox based on a data cockpit, characterized in that, include: Construct an identity and access boundary map of intelligent agents based on multi-source system interaction data; Pre-deployment strategies are generated based on the agent identity and access boundary graph and distributed to the target computing node to control multiple agents to execute automated tasks; During the execution of automated tasks by multiple agents, natural language output sequences are collected and mapped to a continuous medium field, and the fluid dynamic parameters of the continuous medium field containing entropy-coupled semantic Reynolds number are calculated. Input the fluid dynamics parameters into the data and render the flow field distribution view in the cockpit. If the flow field distribution view indicates that the target agent exhibits cognitive drift, then system resistance is injected into the target agent to control it to perform a decommissioning operation.

2. The method according to claim 1, characterized in that, The calculations include the hydrodynamic parameters of the continuous medium field with entropy-coupled semantic Reynolds number, including: The output sequence is converted into a semantic vector; Based on the temporal rate of change and distribution of the semantic vector, the semantic flow velocity and semantic fluid density are calculated respectively. Based on the logarithmic probability distribution of the output sequence, the semantic information entropy increment is calculated; The decoding temperature parameters that generate the output sequence are mapped to model temperature compensation coefficients; The entropy-coupled semantic Reynolds number is calculated based on the semantic flow velocity, semantic fluid density, semantic information entropy increment, model temperature compensation coefficient, and semantic viscosity coefficient.

3. The method according to claim 2, characterized in that, The formula for calculating the entropy-coupled semantic Reynolds number is as follows: in, For entropy coupling semantic Reynolds number, For semantic fluid density, For semantic flow speed, The length of the feature sequence of the natural language output sequence. The preset semantic stickiness coefficient, For semantic information entropy increment, This is the model temperature compensation coefficient.

4. The method according to claim 1, characterized in that, The determination of cognitive drift of the target agent based on the flow field distribution view includes: If the entropy coupling semantic Reynolds number corresponding to the target agent is not greater than the preset threshold, then the semantic flow of the target agent is determined to be in a laminar state, and the target agent is determined not to have cognitive drift. If the entropy coupling semantic Reynolds number is greater than the threshold, a vortex representation image is generated in the region corresponding to the target agent in the flow field distribution view, and it is determined that the target agent has cognitive drift.

5. The method according to claim 1, characterized in that, The process of injecting system resistance into the target agent to control the target agent to perform a decommissioning operation includes: Obtain the target intelligent agent's business activity metrics and its dependency weights on downstream business nodes; Substituting the activity index and dependency weight into the orbital attenuation drag function, the drag distribution coefficient is calculated. According to the resistance allocation coefficient, a time delay is injected into the API call chain of the target intelligent agent, and the non-core read and write permissions of the target intelligent agent are truncated in a corresponding proportion. When the number of concurrent connections of the target intelligent agent is detected to have decayed to zero, the authentication token of the target intelligent agent is destroyed.

6. The method according to claim 1, characterized in that, The process of multiple agents performing the automated task also includes: Capture the full-link execution logs of multiple agents when performing automated tasks; Map the automated operation flow in the end-to-end execution log to a lattice growth process in three-dimensional space; If abnormal execution results are detected in the end-to-end execution log, a corresponding crystallographic defect map is generated during the lattice growth process. A three-dimensional crystal strain view containing the crystallographic defect map is simultaneously rendered in the data cockpit.

7. The method according to claim 6, characterized in that, The generation of corresponding crystallographic defect maps during lattice growth includes: If the abnormal execution result is a local logic error of a single computing node, a point defect mapping is generated at the corresponding location in three-dimensional space. If the abnormal execution result is a cascading error caused by cross-system data pollution, then a line defect dislocation mapping is generated in three-dimensional space, and the strain tensor distortion degree corresponding to the line defect dislocation mapping is calculated.

8. The method according to claim 7, characterized in that, The method further includes: When a drill-down command is received for a specific vortex characterization image in the flow field distribution view, the line defect dislocation mapping with the same time label as the specific vortex characterization image region is highlighted in the 3D crystal strain view.

9. The method according to claim 1, characterized in that, Before generating the pre-deployment strategy based on the agent identity and access boundary graph, the following is also included: Calculate the basic infection number of each node in the identity and access boundary graph when it encounters malicious command injection; Based on each basic reproduction number, a permission infection heatmap is rendered in the data cockpit to predict the probability of cascading spread of security risks.

10. The method according to claim 1, characterized in that, The pre-deployment strategy based on agent identity and access boundary graph generation includes: Obtain the API call frequency and resource consumption periods of the agent to be deployed in order to construct multi-dimensional niche characteristics; Based on the preset niche overlap index model, the overlap between multidimensional niche features and the features of the running agent is calculated. If the feature overlap is greater than a preset overlap threshold, a niche competition phase diagram is generated in the data cockpit, and the generation of a pre-deployment strategy containing agents to be deployed is rejected.

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