Intelligent management system for multiple ai algorithms based on large models

Abstract

The present application relates to the technical field of data processing, and more particularly to a multi-AI algorithm collaborative intelligent management system based on a large model, comprising a data access module that collects original messages of multiple manufacturer terminal devices; a protocol adaptation module that analyzes device communication characteristic spectrum through a quantum entanglement separator and outputs standardized space-time event flow; a federal modeling module that creates parallel computing instances and generates a device correlation topology model by fusing feature vectors using a quantum tunneling mechanism; a chaotic scheduling module that generates an atomic task fractal network and dispatches it to an edge node; an interface optimization module that calculates interface field mapping relationships through a quantum Boltzmann machine; and a closed-loop correction module that triggers device communication characteristic spectrum recalibration. The quantum protocol analysis eliminates differences in multi-source device communication, federal quantum fusion breaks through algorithm collaboration barriers, chaotic fractal scheduling realizes dynamic optimization of resources, and a closed-loop collaborative system is formed from device access to interface configuration, solving the problem of incompatible protocols of multi-source heterogeneous devices and AI algorithm collaboration obstacles.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a collaborative intelligent management system for multiple AI algorithms based on a large model. Background Technology

[0002] The intelligent management system integrates sensor networks and business execution terminals to acquire multi-dimensional operational status information of the physical environment and business processes in real time, and relies on edge computing nodes to preprocess raw data. The core of the system is deployed on a cloud platform, using machine learning models to jointly analyze structured and unstructured data, and establish equipment status prediction, anomaly detection, or resource demand inference models. The decision-making module uses constraint-based optimization algorithms or rule inference engines to transform the analysis results into a sequence of operation instructions. During execution, the expected goals and actual effects are continuously compared, and adaptive control strategies are used to dynamically adjust model parameters and rule weights to form a goal-oriented closed-loop control loop, ultimately achieving energy efficiency management, process optimization, and fault self-healing.

[0003] When multiple vendors' devices are integrated into an intelligent management system, the fragmentation of the hardware ecosystem—that is, different manufacturers using proprietary communication protocols, interface standards, and data formats—makes it difficult for the system to achieve unified management and data interoperability, thus hindering the collaborative analysis and real-time response of AI algorithms. For example, in a smart park project, cameras from different brands, such as those from manufacturer A and manufacturer B, use inconsistent video streaming protocols, and access controllers, such as HID, cannot directly interact with edge computing boxes using proprietary APIs, causing personnel trajectory tracking and event warning functions to fail. In addition, in a shopping mall customer flow analysis system, 180 dome cameras from different manufacturers need to be integrated. Initially, due to protocol differences, customer flow data could not be integrated, requiring a customized adaptation layer to solve the access problem, increasing deployment complexity and delaying the collaborative efficiency of multiple algorithms, such as human attribute and aggregation analysis. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-AI algorithm collaborative intelligent management system based on a large model, which solves the problems of data interoperability obstacles and limited AI algorithm collaborative analysis functions caused by incompatible communication protocols when multiple heterogeneous devices are connected in an intelligent management system.

[0005] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0006] The present invention provides a multi-AI algorithm collaborative intelligent management system based on a large model, comprising:

[0007] The data access module collects raw messages from terminal devices of at least two types of manufacturers in real time.

[0008] The protocol adaptation module receives the original message from the terminal device, identifies the device communication feature spectrum through a quantum entanglement separator, and outputs a standardized spatiotemporal event stream.

[0009] The federated modeling module receives the standardized spatiotemporal event stream, creates multiple parallel computing instances, each instance independently extracts entity feature vectors, and generates a device-related topology model by fusing the feature vectors through a cross-instance quantum tunneling mechanism.

[0010] The chaotic scheduling module parses the spatial coordinate matrix of the device-related topology model, uses the Lorentz chaotic equation to generate an atomic task chain fractal network, and assigns tasks to edge computing nodes to execute atomic algorithm tasks.

[0011] The interface optimization module collects the output of atomic algorithm tasks, calculates the interface field mapping function through a quantum Boltzmann machine, and generates the target system interface configuration scheme.

[0012] The closed-loop correction module detects interface configuration scheme execution failure events, converts abnormal event trajectories into adversarial samples, and injects the adversarial samples into the protocol adaptation module to trigger device communication feature spectrum recalibration.

[0013] Furthermore, in the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention, the protocol adaptation module includes:

[0014] A quantum entanglement coordinator, connected to the data access module, receives raw messages from the terminal device;

[0015] The quantum noise generation unit, in response to the quantum entanglement coordinator, generates a true random noise pulse sequence that resonates with the device's communication frequency band;

[0016] The noise injection unit receives the original message and the true random noise pulse sequence from the terminal device and generates an anti-interference transport stream according to the Hadamard matrix coding rules.

[0017] The feature spectrum analysis unit receives the anti-interference transmission stream, separates the quantum noise component from the device feature spectrum component through a pre-trained Transformer model, and outputs the device communication feature spectrum.

[0018] The protocol mapping engine receives the device communication feature spectrum output by the feature spectrum analysis unit, matches it with the protocol rule base to generate a standardized spatiotemporal event stream, and updates the protocol rule base in response to the adversarial examples injected by the closed-loop correction module.

[0019] Furthermore, in the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention, the federated modeling module includes:

[0020] The spatiotemporal instance generator receives a standardized spatiotemporal event stream output by the protocol adaptation module and creates at least three parallel computing instances with differentiated parameters based on the spatiotemporal entropy value of the events.

[0021] The quantum tunneling fusion device performs quantum tunneling operations on the entity feature vectors of each parallel computation instance and generates a fused feature vector through a quantum coherent superposition state.

[0022] The associated topology construction unit analyzes the spatial distribution of the fused feature vectors and constructs a device associated topology model including a three-dimensional coordinate matrix, which serves as the input parameter for the chaotic scheduling module.

[0023] Furthermore, in the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention, the chaotic scheduling module includes:

[0024] The spatial parameter converter receives the three-dimensional coordinate matrix output by the federated modeling module and maps the standard deviation of the device density distribution in the matrix to the Lorentz equation parameter σ, and the topological association entropy value to the parameter ρ.

[0025] A chaotic trajectory generator calculates the Lorentz equation based on parameters σ and ρ and outputs a sequence of chaotic trajectory points.

[0026] The task node allocator associates each chaotic trajectory point with an atomic task node, forming a task chain fractal network with spatial coordinates.

[0027] The fractal monitor calculates the Hausdorff dimension of the task chain fractal network in real time and activates the container migration instruction when the dimension value exceeds 1.26.

[0028] The edge executor executes container migration instructions to reallocate atomic algorithm tasks and collects task execution latency data to send to the interface optimization module.

[0029] Furthermore, in the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention, the interface optimization module includes:

[0030] The interface function constructor receives task execution delay data sent by the edge executor of the chaos scheduling module and extracts the output fields of the atomic algorithm task.

[0031] The quantum Boltzmann encoder encodes the mapping relationship between the task output field and the target system interface field into a Boltzmann machine energy function;

[0032] A tunneling optimization controller performs quantum tunneling operations in a quantum Boltzmann machine, breaking through the local minimum of the energy function;

[0033] Dynamic convergence unit monitors the convergence state of the energy function; when the rate of energy change is less than 10... -5 Activate configuration generation at any time;

[0034] The interface configuration generator outputs the mapping relationship of the executable interface fields of the target system and sends the abnormal configuration log to the closed-loop correction module.

[0035] Furthermore, the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention also includes:

[0036] The spatial coordinate resolver receives the device-associated topology model output by the federated modeling module and extracts the three-dimensional geographic coordinate matrix from the model.

[0037] Edge node matcher calculates the topological distance between each edge computing node and the coordinates in the 3D geographic coordinate matrix, and generates a node correlation matrix;

[0038] The quantum annealing dispatcher encodes the node correlation matrix into a QUBO function and generates an optimized task dispatch scheme through quantum annealing optimization.

[0039] The dynamic tuning unit monitors task execution latency data and reactivates quantum annealing optimization when the cross-node communication latency exceeds 20ms.

[0040] The edge node execution unit executes the optimized task assignment scheme and collects device status data to feed back to the federated modeling module.

[0041] Furthermore, in the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention, the closed-loop correction module includes:

[0042] An abnormal trajectory parser detects abnormal configuration logs sent by the interface configuration generator and extracts the spatiotemporal features of interface configuration failure events.

[0043] A quantum adversarial generator synthesizes anomalous message data carrying quantum noise characteristics based on spatiotemporal features;

[0044] The protocol retraining unit injects abnormal message data into the feature spectrum analysis unit of the protocol adaptation module, triggering incremental training of the pre-trained Transformer model.

[0045] The feature spectrum calibrator monitors the distribution offset of the communication feature spectrum of the monitoring device and updates the protocol field mapping table when the offset exceeds 0.3.

[0046] The federated feedback unit sends the updated protocol field mapping table to the spatiotemporal instance generator of the federated modeling module to reconstruct the parallel computing instance parameters.

[0047] Furthermore, the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention also includes:

[0048] The differential privacy injector receives the fused feature vector generated by the quantum tunneling fusion unit of the federated modeling module, and adds Laplace noise to the feature vector transmission channel with a noise intensity parameter λ=0.3.

[0049] A quantum key negotiator, a quantum noise generation unit of the response protocol adaptation module, generates quantum keys based on noise pulse sequences;

[0050] The encrypted transmission channel uses quantum key encryption to encrypt the standardized spatiotemporal event stream and sends the ciphertext data to the chaotic scheduling module.

[0051] The decryption and verification unit decrypts the data at the receiving end of the chaotic scheduling module to verify the consistency of the quantum key.

[0052] When the collaborative auditing unit detects a failure in quantum key verification, it simultaneously triggers the differential privacy injector to reset the noise parameter λ and the quantum key negotiator to renegotiate the key.

[0053] Furthermore, in the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention, the tunneling optimization controller includes:

[0054] The quantum initialization solver receives the atomic task output fields extracted by the interface function constructor and generates 100 sets of initial interface mapping schemes.

[0055] The phase rotation optimizer performs quantum phase rotation operations on each set of schemes, with the rotation angle correlated with the task execution delay data sent by the chaotic scheduling module;

[0056] A sonar tunneling detector simulates the quantum tunneling effect in the energy space of a Boltzmann machine, breaking through the local optimum trap.

[0057] A dynamic convergence monitor calculates the rate of change of the energy function gradient in real time. If the rate of change remains below 10 for three consecutive iterations, the monitor will detect the convergence. -5 Activate the mapping output at the time;

[0058] The optimal mapping generator outputs the mapping relationship with the lowest error rate for the interface fields, and synchronously generates quantum-optimized trajectory logs which are sent to the closed-loop correction module.

[0059] Furthermore, the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention also includes:

[0060] Multi-source device coordinator, which coordinates terminal devices from at least three types of manufacturers in real time, including cameras, access controllers and sensors;

[0061] The task chain co-optimizer constructs the execution chain of atomic algorithm tasks, where the output of the flame detection task serves as the input of the face recognition task, and the face recognition result serves as the spatial constraint for the trajectory tracking task.

[0062] The device and task mapper dynamically selects atomic algorithm task combinations based on the type of terminal device, prioritizing the allocation of camera data to flame detection tasks and access control controller data to face recognition tasks.

[0063] The cross-vendor feedback unit collects latency difference data of devices from different vendors executing atomic algorithm tasks and sends it to the fractal monitor of the chaos scheduling module to optimize the container migration strategy.

[0064] Beneficial effects of this invention;

[0065] The quantum and chaotic collaborative architecture constructed in this invention employs quantum entanglement separation technology at the protocol parsing layer to dynamically adapt to the communication characteristics of multi-source devices, converting heterogeneous device messages from Hikvision, Dahua, and other devices into standardized spatiotemporal event streams, eliminating data interoperability barriers caused by protocol differences. At the algorithm collaboration layer, cross-instance quantum tunneling fusion and Lorentz chaotic-driven task fractal networks are used to achieve dynamic orchestration and resource optimization of AI algorithms such as flame detection and face recognition, solving the problem of low efficiency in multi-algorithm collaboration. At the system optimization layer, a closed-loop feedback mechanism is formed, linking protocol feature spectrum recalibration, federated modeling parameter reconstruction, and container migration strategy linkage, continuously reducing the impact of device heterogeneity on the system. Compared with traditional solutions, this system, without adding dedicated protocol conversion hardware, achieves plug-and-play access for multi-vendor devices and unified collaborative analysis of multiple AI algorithms through the coupling of multiple technologies such as quantum frequency domain anti-interference, chaotic space scheduling, and adversarial incremental learning, significantly improving the timeliness of anomaly response in scenarios such as smart security. Attached Figure Description

[0066] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0067] Figure 1 The system architecture diagram of the multi-AI algorithm collaborative intelligent management system based on a large model provided in the embodiments of the present invention is shown. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings. To better understand the objectives of this invention, it will be described in further detail below.

[0069] Please see Figure 1 The present invention provides a multi-AI algorithm collaborative intelligent management system based on a large model, comprising:

[0070] The data access module collects raw messages from terminal devices of at least two types of manufacturers in real time.

[0071] During the implementation of the data access module, a dedicated acquisition agent is deployed in the multi-protocol gateway. This agent uses parallel threads to monitor the communication interfaces of terminal devices from at least two manufacturers in real time. For brand A cameras, a network video stream transmission protocol interface is used, and the acquisition agent establishes an RTSP session channel to capture the raw video stream packets. For brand B access controllers, Wiegand protocol data frames are acquired through a serial communication interface. To ensure the integrity of the raw packets, zero-copy technology is used to directly obtain the binary stream transmitted from the device's physical layer, avoiding protocol information loss due to intermediate parsing. Each acquisition thread is bound to an independent timestamp generator, synchronously recording nanosecond-level precise time stamps based on the network time protocol, forming raw data packets with time sequence identifiers. When multiple source devices transmit concurrently, the load balancer allocates data processing channels according to device manufacturer classification, eliminating transmission congestion caused by protocol differences. A circular buffer design implements temporary storage of raw packets, and a dual-pointer read / write mechanism prevents data overwriting, maintaining transmission stability under high concurrency scenarios.

[0072] The acquisition agent is configured with an adaptive baud rate detection unit to dynamically match the communication rates of different serial devices. For network devices, link aggregation technology is enabled to bind multiple physical ports into logical channels to increase bandwidth capacity. During the raw message transmission process, cyclic redundancy check (CRC) is used to verify data integrity, discarding abnormal data packets that fail verification and triggering the corresponding device's retransmission mechanism. The acquisition log recorder monitors the status of each device channel in real time, and when a device from a certain manufacturer is detected to be continuously offline, it automatically switches to a backup communication link to maintain data access continuity. The acquisition agent's heartbeat detection mechanism periodically sends handshake signals to the terminal devices to confirm their online status and reset timed-out connections. All raw messages are appended with a unique device identifier before transmission, forming a traceable three-level association system of manufacturer, device, and data packet.

[0073] Through the above technical solution, the data access module successfully achieved parallel access for Brand A cameras and Brand B access controllers in a smart park project. The original message acquisition delay was controlled at the millisecond level, providing unparsed initial data input for the subsequent protocol adaptation module. Those skilled in the art will understand that the parallel acquisition mechanism of the multi-protocol gateway effectively solves the problem of multi-source device access barriers in the background technology, laying a data foundation for eliminating protocol differences.

[0074] The protocol adaptation module receives the original message from the terminal device, identifies the device communication feature spectrum through a quantum entanglement separator, and outputs a standardized spatiotemporal event stream.

[0075] During the implementation of the protocol adaptation module, the quantum entanglement coordinator establishes a physical layer connection with the data access module, receiving the original RTSP stream from the camera and Wiegand protocol messages from the access controller. The quantum noise generation unit responds to the coordinator's instructions, generating a true random noise pulse sequence that resonates with the device's communication frequency band. The camera's frequency band matches the 2000MHz carrier characteristics, while the access controller adapts to the 125kHz low-frequency characteristics. The noise injection unit uses the Hadamard matrix orthogonal coding rule to fuse the original message and noise pulses to generate an anti-interference transport stream. Matrix orthogonality ensures the mathematical separability of the signal components. The feature spectrum analysis unit processes the anti-interference transport stream using a pre-trained Transformer model, identifies frequency domain feature patterns using a self-attention mechanism, separates the quantum noise components from the device communication feature spectrum components, and outputs a feature spectrum containing protocol fingerprint features. The protocol mapping engine matches the feature spectrum with the protocol rule base, extracts timestamps and geographic coordinate information to generate a standardized spatiotemporal event stream. When the system integrates a new Uniview camera protocol, it responds to adversarial examples injected by the closed-loop correction module, dynamically updating the field matching thresholds in the protocol rule base.

[0076] The feature spectrum analysis unit employs a multi-head attention mechanism to process the anti-interference transport stream in parallel. Key vectors encode temporal features, while value vectors map spatial location information. Query vectors learn device protocol feature patterns, and key frequency components are identified through scaling dot product attention weight allocation. The protocol mapping engine configures a protocol rule tree index structure, with root nodes classifying device manufacturers, child nodes storing protocol version features, and leaf nodes associating standardized field transformation rules. The rule base update mechanism uses an incremental learning strategy; when a Uniview camera protocol conflict is detected, the protocol rule tree weight parameters are fine-tuned through backpropagation. During the spatiotemporal event stream generation process, unique device identifiers and data verification codes are added, forming a traceable, standardized data carrier. In a scenario with 180 cameras connected in a shopping mall, this module successfully converted heterogeneous protocols from various brands into an event stream containing millisecond-level timestamps and 3D geographic coordinates, eliminating data fusion barriers caused by differences in multi-source device protocols in the background technology. Quantum noise injection and feature spectrum separation mechanisms overcome the dependence on prior knowledge in traditional protocol parsing methods, enabling dynamic adaptation to unknown device protocols.

[0077] The federated modeling module receives the standardized spatiotemporal event stream, creates multiple parallel computing instances, each instance independently extracts entity feature vectors, and generates a device-related topology model by fusing the feature vectors through a cross-instance quantum tunneling mechanism.

[0078] During the implementation of the federated modeling module, the standardized spatiotemporal event stream, output from the protocol adaptation module, is input into the spatiotemporal instance generator. This generator determines the number of instances and parameter configurations based on the event spatiotemporal entropy values. For example, in a smart park scenario, independent computation instances are created for camera event streams and access control event streams, respectively. Each instance is configured with a differentiated spatiotemporal coordinate system and sampling frequency parameters. Camera instances prioritize processing the time-series features of video frames, while access control instances focus on the spatial distribution characteristics of access control events, forming a parallel processing channel. Instances execute feature extraction algorithms, using convolutional neural networks to independently parse the event streams and extract entity feature vectors, including the coordinates of flame areas in camera videos or facial feature vectors of people in access control controllers, maintaining the isolation and independence of the processing processes for each instance.

[0079] A cross-instance quantum tunneling mechanism initiates the fusion process, with the quantum tunneling fusion unit projecting the feature vectors output by each instance onto the Hilbert space. Feature vector fusion is achieved through the principle of quantum superposition; the camera's flame coordinate vector and the access control's facial feature vector are superimposed in a quantum coherent state to generate a fused feature vector, eliminating interference noise between features. The fusion process preserves the original spatiotemporal differences, such as the correlation between the timestamp information of camera events and the geographic coordinates of access control events, ensuring that the fusion result accurately represents the multidimensional relationships between devices.

[0080] The associated topology building unit analyzes and fuses the spatial distribution pattern of feature vectors to construct a device association topology model. This model uses a three-dimensional coordinate matrix to accurately quantify the relative positional relationship between cameras and access controllers; the horizontal axis maps to longitude differences, the vertical axis to latitude differences, and the altitude coordinate represents the altitude association strength of the devices. In smart park deployments, the matrix output drives the task assignment logic of the subsequent chaotic scheduling module. For example... Figure 1 As shown, the entire federated modeling process, from event stream input to topology model generation, forms a closed-loop data transformation chain, solving the problem of the inability to collaboratively analyze multi-source device data in the background technology. The independent extraction mechanism of parallel instances avoids misidentification caused by feature coupling, and quantum tunneling fusion breaks through the feature alignment bottleneck of traditional federated learning. The device-related topology model provides structured input for subsequent resource scheduling. The technical solution achieves efficient feature fusion without relying on external algorithm libraries and supports the collaborative execution of multiple AI algorithms.

[0081] The chaotic scheduling module parses the spatial coordinate matrix of the device-related topology model, uses the Lorentz chaotic equation to generate an atomic task chain fractal network, and assigns tasks to edge computing nodes to execute atomic algorithm tasks.

[0082] During the implementation of the chaotic scheduling module, the spatial parameter converter receives the three-dimensional coordinate matrix of the device association topology model output by the federated modeling module. This matrix analyzes the spatial distribution characteristics of brand A cameras and brand B access controllers in the smart park, converting the device density distribution discreteness into the Lorentz chaotic equation parameter σ, and mapping the topological association complexity to the parameter ρ. For example... Figure 1 As shown, this parameter transformation establishes a mathematical relationship between spatial topological features and the dynamic characteristics of chaotic systems.

[0083] The chaotic trajectory generator iteratively solves the Lorentz equation based on parameters σ and ρ, outputting a sequence of chaotic trajectory points. The spatial distribution characteristics of the trajectory points reflect the nonlinear dynamic behavior of the system, and the coordinates of each trajectory point correspond to the logical execution position of an atomic task node. The task node allocator maps the trajectory points to atomic task nodes such as flame detection and face recognition, forming a task chain fractal network with three-dimensional spatial coordinates. The network topology preserves the characteristics of the chaotic system, with the camera flame detection task node located near the origin of the spatial coordinates, and the access control controller face recognition task node distributed in the dense area of ​​trajectory points.

[0084] The fractal monitor analyzes the fractal network structure complexity of the task chain in real time and calculates the Hausdorff dimension to quantify the network morphological characteristics. When the dimension value exceeds a set threshold, a container migration instruction is generated, triggering a dynamic reallocation of computing resources. The edge executor performs the container migration operation to redeploy the atomic algorithm tasks, prioritizing the allocation of flame detection tasks to edge computing nodes with physically nearby cameras. During execution, task processing latency data is collected and transmitted to the interface optimization module via an encrypted channel.

[0085] In a smart park project, this module successfully achieved dynamic scheduling of tasks for 200 heterogeneous devices. The flame detection task output from a Brand A camera triggers a facial recognition task from a nearby Brand B access control controller in real time, forming a spatially linked task execution chain. A chaotic driving mechanism automatically optimizes the task allocation strategy, resolving the latency issue in multi-algorithm collaborative response in the background technology. Spatial parameter transformation establishes a mathematical relationship between device distribution and task response; chaotic trajectory generation enables adaptive orchestration of the task chain; and fractal monitoring ensures dynamic optimization of resource allocation.

[0086] The interface optimization module collects the output of atomic algorithm tasks, calculates the interface field mapping function through a quantum Boltzmann machine, and generates the target system interface configuration scheme.

[0087] During the implementation of the interface optimization module, the task execution delay data transmitted by the edge executor of the chaotic scheduling module is received by the interface function constructor. This constructor parses the temperature coordinate field output by the flame detection task and the identity field of the face recognition task, extracting key data elements from the output fields of the atomic algorithm task. The quantum Boltzmann encoder encodes the mapping relationship between the task output fields and the target interface fields of the security system into a Boltzmann machine energy function, where the field matching error is quantized as an energy value weighting coefficient. The tunneling optimization controller performs quantum tunneling operations in the quantum Boltzmann machine energy space, breaking through the local minima limitation of traditional gradient descent through the principle of quantum state superposition, achieving global optimal solution search. The dynamic convergence unit monitors the gradient change rate of the energy function in real time, triggering the configuration generation process when the change rate falls below a set threshold. The interface configuration generator outputs the optimal interface field mapping relationship, such as mapping the flame detection temperature coordinates to the fire alarm interface, while simultaneously transmitting the abnormal configuration log to the closed-loop correction module.

[0088] In actual deployments within smart parks, when Dahua cameras output temperature coordinate fields for flame detection, the quantum Boltzmann encoder establishes a mapping relationship between these coordinates and the building's fire protection system alarm interface. The tunneling optimization controller dynamically adjusts the mapping weights through phase rotation operations to respond to changes in system load at different times. For example... Figure 1 As shown, the entire optimization process forms a closed-loop feedback mechanism. When the interface configuration fails, the exception log triggers protocol feature spectrum recalibration. The quantum tunneling mechanism solves the problem of traditional interface mapping easily getting trapped in local optima, and dynamic convergence monitoring ensures the stability of the mapping scheme, ultimately achieving seamless integration of multiple AI algorithm outputs with the target system.

[0089] The closed-loop correction module detects interface configuration scheme execution failure events, converts abnormal event trajectories into adversarial examples, and injects the adversarial examples into the protocol adaptation module to trigger device communication feature spectrum recalibration, forming a dynamic collaborative loop of device access, model training, task scheduling, and interface configuration.

[0090] During the implementation of the closed-loop correction module, the anomaly trajectory parser continuously monitors the anomaly configuration logs output by the interface configuration generator. When an interface configuration failure event of the smart security system is detected, the log parsing algorithm extracts the spatiotemporal feature dimensions of the anomaly event, including the time sequence pattern and geographic coordinate distribution characteristics of the event. The quantum adversarial generator synthesizes anomaly message data carrying quantum noise characteristics based on the extracted spatiotemporal features. This data simulates the communication characteristics of real devices but injects protocol conflict fields. The protocol retraining unit injects the generated adversarial samples into the feature spectrum analysis unit of the protocol adaptation module, triggering the incremental training process of the pre-trained Transformer model. The training process uses the backpropagation algorithm to fine-tune the model weight parameters, improving the model's ability to identify new protocol conflicts. The feature spectrum calibrator monitors the offset of the device communication feature spectrum distribution in real time. When the offset exceeds a set threshold, it automatically updates the protocol field mapping table. The federated feedback unit transmits the updated protocol field mapping table to the spatiotemporal instance generator of the federated modeling module, driving the reconstruction of the spatiotemporal coordinate system parameters and entropy calculation weights of the parallel computing instances.

[0091] In a real-world deployment case in a smart park, when a new protocol for Uniview cameras causes interface configuration failures, the anomaly trajectory parser extracts timestamp clustering features and specific geographic coordinates. A quantum adversarial generator synthesizes and injects anomaly messages matching the 2000MHz frequency band into the system. During protocol retraining, incremental learning adjusts the feature separation layer parameters, and the feature spectrum calibrator updates the field mapping rules after detecting a shift in the device's communication feature spectrum distribution. The updated protocol rules are synchronized to the federated modeling module, and the spatiotemporal instance generator reconstructs the camera event flow processing parameters based on the new protocol features. This entire process forms a closed-loop collaborative loop from interface anomaly detection to protocol recalibration and model reconstruction, enabling new access devices to be plug-and-play without protocol changes. Spatiotemporal feature extraction technology captures the patterns of anomaly events, the quantum adversarial sample generation mechanism effectively simulates real-world protocol conflicts, and incremental training and parameter reconstruction ensure the system continuously adapts to changes in the device ecosystem.

[0092] The data access module connects to terminal devices from at least two types of manufacturers and acquires raw device messages in real time. This module collects heterogeneous data streams from devices such as cameras, access controllers, and sensors through multi-source communication interfaces, and extracts raw byte sequences using protocol-independent packet capture technology.

[0093] The protocol adaptation module receives the original device message and parses the device communication feature spectrum using a quantum entanglement separator. The quantum entanglement separator uses the principle of quantum superposition to separate protocol feature components, generating a frequency domain representation of the device communication feature spectrum. After decoding by a pre-trained Transformer model, the feature spectrum outputs a standardized spatiotemporal event stream including timestamps and geographic coordinates. This module synchronously responds to adversarial examples injected by the closed-loop correction module, dynamically updating the protocol parsing rule base.

[0094] The federated modeling module receives a standardized spatiotemporal event stream and creates multiple parallel computation instances based on the event spatiotemporal entropy values. Each instance independently runs a feature extraction algorithm, projects entity feature vectors onto a Hilbert space through a cross-instance quantum tunneling mechanism, and fuses the feature vectors using quantum coherent superposition states. After fusion, the features are analyzed for spatial distribution to construct a device association topology model including a three-dimensional coordinate matrix. The three-dimensional coordinate matrix accurately represents the spatial relationships between devices.

[0095] The chaotic scheduling module parses the spatial coordinate matrix of the device association topology model, mapping the standard deviation of the device density distribution to the Lorentz equation parameter σ, and the topology association entropy value to the parameter ρ. Based on parameters σ and ρ, the Lorentz equation is calculated to generate a sequence of chaotic trajectory points, which are then associated with atomic task nodes to form a fractal network. The Hausdorff dimension of the task chain is monitored in real time, and when the dimension exceeds a threshold, container migration is triggered, assigning flame detection and face recognition tasks to edge computing nodes for execution.

[0096] The interface optimization module collects the output of atomic algorithm tasks, extracts the task output fields, and constructs interface field mapping relationships. The mapping relationships are encoded into energy functions using a quantum Boltzmann machine, and a tunneling operation is performed in the quantum state space to break through local optima. The gradient rate of change of the energy function is monitored; when the rate of change falls below the convergence threshold, a target system interface configuration scheme is generated, and an abnormal configuration log is output synchronously.

[0097] The closed-loop correction module detects interface configuration scheme execution failure events and extracts the spatiotemporal feature trajectories of abnormal events. Based on the spatiotemporal features, adversarial examples carrying quantum noise are synthesized and injected into the protocol adaptation module to trigger device communication feature spectrum recalibration. When the feature spectrum distribution offset exceeds the tolerance threshold, the protocol field mapping table is updated, forming a dynamic optimization closed loop from device access to interface configuration.

[0098] Specifically, the protocol adaptation module of the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention includes:

[0099] A quantum entanglement coordinator, connected to the data access module, receives raw messages from the terminal device;

[0100] The quantum noise generation unit, in response to the quantum entanglement coordinator, generates a true random noise pulse sequence that resonates with the device's communication frequency band;

[0101] The noise injection unit receives the original message and the true random noise pulse sequence from the terminal device and generates an anti-interference transport stream according to the Hadamard matrix coding rules.

[0102] The feature spectrum analysis unit receives the anti-interference transmission stream, separates the quantum noise component from the device feature spectrum component through a pre-trained Transformer model, and outputs the device communication feature spectrum.

[0103] The protocol mapping engine receives the device communication feature spectrum output by the feature spectrum analysis unit, matches it with the protocol rule base to generate a standardized spatiotemporal event stream, and updates the protocol rule base in response to the adversarial examples injected by the closed-loop correction module.

[0104] The quantum entanglement coordinator is directly connected to the data access module, receiving raw messages from terminal devices from multiple manufacturers. This component actively responds to device communication requests through a quantum state correlation mechanism, establishing a physical layer synchronization connection between the device and the protocol parsing system.

[0105] The quantum noise generation unit responds to coordinator commands and generates a sequence of truly random noise pulses that resonate with the device's communication frequency band. The frequency domain characteristics of the noise pulses are dynamically adapted to the device's hardware features, improving anti-interference performance through the principle of quantum coherent superposition.

[0106] The noise injection unit receives the original device message and noise pulse sequence, and generates an anti-interference transport stream using the Hadamard matrix coding rule. Matrix orthogonality ensures the mathematical separability of the noise components and the effective signal, forming an anti-interference information carrier.

[0107] The feature spectrum analysis unit analyzes the disturbance-resistant transport stream and separates the quantum noise component from the device feature spectrum component using a pre-trained Transformer model. The Transformer's self-attention mechanism identifies frequency domain feature patterns and outputs the real and imaginary parts of the device communication feature spectrum.

[0108] The protocol mapping engine matches device communication feature spectra with the protocol rule base to generate a standardized spatiotemporal event stream including timestamps and geographic coordinates. When responding to adversarial examples injected by the closed-loop correction module, it updates the feature matching thresholds in the protocol rule base, enabling dynamic evolution of protocol parsing capabilities.

[0109] Specifically, the federated modeling module of the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention includes:

[0110] The spatiotemporal instance generator receives a standardized spatiotemporal event stream output by the protocol adaptation module and creates at least three parallel computing instances with differentiated parameters based on the spatiotemporal entropy value of the events.

[0111] The quantum tunneling fusion device performs quantum tunneling operations on the entity feature vectors of each parallel computation instance and generates a fused feature vector through a quantum coherent superposition state.

[0112] The associated topology construction unit analyzes the spatial distribution of the fused feature vectors and constructs a device associated topology model including a three-dimensional coordinate matrix, which serves as the input parameter for the chaotic scheduling module.

[0113] The spatiotemporal instance generator receives a standardized spatiotemporal event stream output by the protocol adaptation module and creates multiple parallel computational instances based on the uncertainty measure of the spatiotemporal distribution of the events. Each instance is configured with a different spatiotemporal coordinate system and event sampling frequency parameters, forming a diversified computational perspective for the same data stream.

[0114] The quantum tunneling fusion processor performs a quantum tunneling operation on the entity feature vectors output by parallel computation instances. This operation projects the feature vectors onto Hilbert space, generating a fused feature vector through the principle of quantum superposition. The fusion process preserves the spatiotemporal differences between instances and eliminates interference noise between features.

[0115] The associated topology building unit analyzes and integrates the spatial distribution characteristics of feature vectors to construct a device association topology model including a three-dimensional coordinate matrix. This matrix accurately represents the spatial relative position and association strength between devices, with the three-dimensional coordinates corresponding to longitude, latitude, and altitude information, serving as the basis for task allocation by the chaotic scheduling module.

[0116] Specifically, the chaotic scheduling module of the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention includes:

[0117] The spatial parameter converter receives the three-dimensional coordinate matrix output by the federated modeling module and maps the standard deviation of the device density distribution in the matrix to the Lorentz equation parameter σ, and the topological association entropy value to the parameter ρ.

[0118] A chaotic trajectory generator calculates the Lorentz equation based on parameters σ and ρ and outputs a sequence of chaotic trajectory points.

[0119] The task node allocator associates each chaotic trajectory point with an atomic task node, forming a task chain fractal network with spatial coordinates.

[0120] The fractal monitor calculates the Hausdorff dimension of the task chain fractal network in real time and activates the container migration instruction when the dimension value exceeds 1.26.

[0121] The edge executor executes container migration instructions to reallocate atomic algorithm tasks and collects task execution latency data to send to the interface optimization module.

[0122] The spatial parameter converter receives the three-dimensional coordinate matrix output by the federated modeling module, transforms the spatial distribution discreteness of devices in the matrix into the Lorentz chaotic equation parameter σ, and the uncertainty of the correlation between devices into the parameter ρ. This transformation establishes a mathematical mapping relationship between spatial topological features and chaotic system parameters.

[0123] The chaotic trajectory generator iteratively calculates the Lorentz equation based on parameters σ and ρ, outputting a sequence of chaotic trajectory points. The distribution of trajectory points characterizes the dynamic properties of the system, and their spatial positions correspond to the logical execution order of atomic task nodes.

[0124] The task node allocator maps chaotic trajectory points to atomic task nodes, constructing a task chain fractal network with spatial coordinates. The network topology preserves the nonlinear characteristics of the chaotic system, and the position of the task node is determined by the three-dimensional coordinates of the trajectory points.

[0125] The fractal monitor analyzes the Hausdorff dimension of the task chain fractal network in real time, quantifying the network structural complexity. When the dimension value exceeds a set threshold, a container migration instruction is generated, triggering a dynamic reallocation of computing resources.

[0126] The edge executor executes container migration instructions to redeploy atomic algorithm tasks, simultaneously collecting task execution latency data. This latency data is transmitted in real-time to the interface optimization module, driving dynamic adjustments to the interface mapping strategy.

[0127] Specifically, the interface optimization module of the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention includes:

[0128] The interface function constructor receives task execution delay data sent by the edge executor of the chaos scheduling module and extracts the output fields of the atomic algorithm task.

[0129] The quantum Boltzmann encoder encodes the mapping relationship between the task output field and the target system interface field into a Boltzmann machine energy function;

[0130] A tunneling optimization controller performs quantum tunneling operations in a quantum Boltzmann machine, breaking through the local minimum of the energy function;

[0131] Dynamic convergence unit monitors the convergence state of the energy function; when the rate of energy change is less than 10... -5 Activate configuration generation at any time;

[0132] The interface configuration generator outputs the mapping relationship of the executable interface fields of the target system and sends the abnormal configuration log to the closed-loop correction module.

[0133] The interface function constructor receives task execution latency data transmitted by the chaotic scheduling module and parses the structured features of the output fields of atomic algorithm tasks. This component extracts key data fields for tasks such as flame detection and face recognition, and constructs the data foundation for the mapping relationship between interface fields.

[0134] The quantum Boltzmann encoder encodes the mapping relationship between task output fields and target system interface fields into a Boltzmann machine energy function. The encoding process quantifies the field matching error, and the energy function value characterizes the overall fit of the mapping scheme, providing a mathematical objective for optimization calculations.

[0135] The tunneling optimization controller performs quantum tunneling operations within a quantum Boltzmann machine, overcoming the local minima limitation of the energy function. By traversing the solution space through quantum superposition states, it avoids the optimization stagnation problem of traditional gradient descent methods and achieves global optimum search.

[0136] The dynamic convergence unit monitors the rate of change of the energy function gradient and activates the configuration generation process when the rate of change remains below the convergence threshold. This mechanism identifies the steady-state convergence of the energy function, preventing premature termination of the optimization process.

[0137] The interface configuration generator outputs the executable interface field mapping relationships for the target system and synchronously records configuration execution exception logs. The exception logs include field matching error types and time-space characteristics, and are transmitted in real-time to the closed-loop correction module to drive protocol rule updates.

[0138] Specifically, the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention further includes:

[0139] The spatial coordinate resolver receives the device-associated topology model output by the federated modeling module and extracts the three-dimensional geographic coordinate matrix from the model.

[0140] Edge node matcher calculates the topological distance between each edge computing node and the coordinates in the 3D geographic coordinate matrix, and generates a node correlation matrix;

[0141] The quantum annealing dispatcher encodes the node correlation matrix into a QUBO function and generates an optimized task dispatch scheme through quantum annealing optimization.

[0142] The dynamic tuning unit monitors task execution latency data and reactivates quantum annealing optimization when the cross-node communication latency exceeds 20ms.

[0143] The edge node execution unit executes the optimized task assignment scheme and collects device status data to feed back to the federated modeling module.

[0144] The spatial coordinate resolver receives the device association topology model output by the federated modeling module and resolves the 3D geographic coordinate matrix of the device nodes in the model. This matrix includes longitude, latitude, and altitude data, accurately representing the physical spatial distribution of the devices. The resolution process extracts the topological features of the coordinate data, providing a spatial relationship basis for edge computing node matching.

[0145] The edge node matcher calculates the topological distance between the edge computing nodes and the devices in three-dimensional coordinates, generating a node correlation matrix. The correlation measure quantifies the spatial coupling strength between the locations of the computing nodes and the devices, and the distance calculation integrates network transmission path characteristics and geospatial relationships to form optimal constraints for task assignment.

[0146] The quantum annealing dispatcher encodes the node affinity matrix as a quadratic unconstrained binary optimization problem function. It searches for the global optimum in the solution space through quantum annealing optimization, generating a dispatch scheme from atomic tasks to edge computing nodes. The quantum tunneling effect overcomes the local optima limitation of traditional optimization algorithms.

[0147] The dynamic tuning unit monitors cross-node communication latency data during atomic task execution. When the communication latency exceeds a set threshold, quantum annealing optimization is reactivated, triggering dynamic updates to the task assignment scheme in response to network state changes.

[0148] The edge node execution unit executes the optimized task assignment scheme and simultaneously collects device operating status data. The status data includes resource load rate and task execution trajectory, which are fed back to the federated modeling module to drive the topology model update.

[0149] Specifically, the closed-loop correction module of the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention includes:

[0150] An abnormal trajectory parser detects abnormal configuration logs sent by the interface configuration generator and extracts the spatiotemporal features of interface configuration failure events.

[0151] A quantum adversarial generator synthesizes anomalous message data carrying quantum noise characteristics based on spatiotemporal features;

[0152] The protocol retraining unit injects abnormal message data into the feature spectrum analysis unit of the protocol adaptation module, triggering incremental training of the pre-trained Transformer model.

[0153] The feature spectrum calibrator monitors the distribution offset of the communication feature spectrum of the monitoring device and updates the protocol field mapping table when the offset exceeds 0.3.

[0154] The federated feedback unit sends the updated protocol field mapping table to the spatiotemporal instance generator of the federated modeling module to reconstruct the parallel computing instance parameters.

[0155] The abnormal trajectory parser detects abnormal configuration logs transmitted by the interface configuration generator and extracts the timestamp sequence and geographic coordinate distribution characteristics of interface configuration failure events. Spatiotemporal features include the temporal correlation and spatial clustering patterns of events, providing a data foundation for adversarial example generation.

[0156] The quantum adversarial generator synthesizes anomalous message data carrying quantum noise features based on extracted spatiotemporal characteristics. The noise features resonate with the device's communication frequency band, and the message structure simulates the data format of a real device but injects protocol rule conflicts, generating training samples with attack capabilities.

[0157] The protocol retraining unit injects abnormal message data into the feature spectrum analysis unit of the protocol adaptation module, triggering incremental training of the pre-trained Transformer model. During training, the original model parameter knowledge is retained, and the weights of the feature separation layer are adjusted based on protocol conflict characteristics to improve the model's robustness against interference.

[0158] The feature spectrum calibrator monitors the distribution offset of the communication feature spectrum of the monitoring equipment and quantifies the matching deviation between the protocol rule base and real-time data. When the offset exceeds the tolerance threshold, it updates the protocol field mapping table and adjusts the feature matching rules to cover newly emerging protocol conflict patterns.

[0159] The federated feedback unit transmits the updated protocol rules to the spatiotemporal instance generator of the federated modeling module, driving the reconstruction of the spatiotemporal parameters of parallel computing instances. Parameter adjustments include coordinate system offset compensation and entropy calculation weight updates, enabling federated modeling to adapt to changes in protocol rules.

[0160] Specifically, the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention further includes:

[0161] The differential privacy injector receives the fused feature vector generated by the quantum tunneling fusion unit of the federated modeling module, and adds Laplace noise to the feature vector transmission channel with a noise intensity parameter λ=0.3.

[0162] A quantum key negotiator, a quantum noise generation unit of the response protocol adaptation module, generates quantum keys based on noise pulse sequences;

[0163] The encrypted transmission channel uses quantum key encryption to encrypt the standardized spatiotemporal event stream and sends the ciphertext data to the chaotic scheduling module.

[0164] The decryption and verification unit decrypts the data at the receiving end of the chaotic scheduling module to verify the consistency of the quantum key.

[0165] When the collaborative auditing unit detects a failure in quantum key verification, it simultaneously triggers the differential privacy injector to reset the noise parameter λ and the quantum key negotiator to renegotiate the key.

[0166] The differential privacy injector receives the fused feature vector transmitted by the quantum tunneling fusion unit of the federated modeling module, and injects noise perturbations that conform to a Laplace distribution during the feature vector transmission process. The noise intensity parameter controls the perturbation amplitude, balancing the strength of privacy protection with feature availability, and preventing sensitive information from being leaked by model inversion attacks.

[0167] The quantum key exchanger's response protocol adaptation module uses a pulse sequence output by its quantum noise generation unit to generate a cryptographic key based on quantum randomness. The key generation process leverages the unpredictability of noise in the device's communication frequency band to establish a physical layer security foundation.

[0168] The encrypted transmission channel employs symmetric encryption on the standardized spatiotemporal event stream output by the quantum key pair protocol adaptation module, generating a ciphertext data stream that is then transmitted to the chaotic scheduling module. The encryption process ensures the secure transmission of sensitive information such as the spatial coordinates and timestamps of the spatiotemporal events.

[0169] The decryption verification unit uses quantum key distribution to decrypt the ciphertext data at the chaotic scheduling module receiver, simultaneously verifying key consistency. The verification mechanism detects man-in-the-middle attacks and key tampering, ensuring data transmission integrity and source reliability.

[0170] The collaborative auditing unit monitors the quantum key verification results. When a verification failure event is detected, it simultaneously triggers a privacy parameter reset and key update process. The reset operation adjusts the differential privacy noise intensity, and the key update renegotiates the communication key, forming a security event-driven joint protection mechanism.

[0171] Specifically, the tunneling optimization controller in the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention includes:

[0172] The quantum initialization solver receives the atomic task output fields extracted by the interface function constructor and generates 100 sets of initial interface mapping schemes.

[0173] The phase rotation optimizer performs quantum phase rotation operations on each set of schemes, with the rotation angle correlated with the task execution delay data sent by the chaotic scheduling module;

[0174] A sonar tunneling detector simulates the quantum tunneling effect in the energy space of a Boltzmann machine, breaking through the local optimum trap.

[0175] A dynamic convergence monitor calculates the rate of change of the energy function gradient in real time. If the rate of change remains below 10 for three consecutive iterations, the monitor will detect the convergence. -5 Activate the mapping output at the time;

[0176] The optimal mapping generator outputs the mapping relationship with the lowest error rate for the interface fields, and synchronously generates quantum-optimized trajectory logs which are sent to the closed-loop correction module.

[0177] The quantum initializer receives the atomic task output fields extracted by the interface function constructor and generates multiple initial interface mapping schemes. The scheme construction covers different field matching combinations, forming the basis of the search space for the optimization algorithm and avoiding local optima traps.

[0178] The phase rotation optimizer performs a quantum phase rotation operation on each initial scheme, with the rotation angle dynamically adjusted by the task execution delay data transmitted by the chaotic scheduling module. A nonlinear correlation is established between the delay characteristics and the phase change, driving the optimization direction to adapt to the real-time state of the system.

[0179] A sonar tunneling detector simulates the quantum tunneling effect in the energy space of a Boltzmann machine, breaking through the local minimum limit of the energy function. The detector penetrates the energy barrier through a quantum state transition mechanism, enabling wide-area exploration of the global solution space.

[0180] The dynamic convergence monitor calculates the rate of change of the energy function gradient in real time to identify the convergence trend of the optimization process. When the rate of change is lower than the convergence threshold for multiple consecutive iterations, the output mechanism is activated to avoid mapping scheme defects caused by premature convergence.

[0181] The optimal mapping generator outputs the lowest error rate in the field mapping of the interface, and simultaneously generates a quantum optimized trajectory log. The log includes the phase rotation path and tunneling breakthrough point sequence, which is transmitted to the closed-loop correction module to drive the update of the protocol rule base.

[0182] Specifically, the multi-AI algorithm collaborative intelligent management system based on a large model described in this invention further includes:

[0183] Multi-source device coordinator, which coordinates terminal devices from at least three types of manufacturers in real time, including cameras, access controllers and sensors;

[0184] The task chain co-optimizer constructs the execution chain of atomic algorithm tasks, where the output of the flame detection task serves as the input of the face recognition task, and the face recognition result serves as the spatial constraint for the trajectory tracking task.

[0185] The device and task mapper dynamically selects atomic algorithm task combinations based on the type of terminal device, prioritizing the allocation of camera data to flame detection tasks and access control controller data to face recognition tasks.

[0186] The cross-vendor feedback unit collects latency difference data of devices from different vendors executing atomic algorithm tasks and sends it to the fractal monitor of the chaos scheduling module to optimize the container migration strategy.

[0187] The multi-source device coordinator coordinates the communication protocols of terminal devices from at least three types of manufacturers in real time, including cameras, access controllers, and sensors. This component establishes a unified device command distribution channel, shields the interface differences between devices from different manufacturers, and enables unified scheduling and management of heterogeneous device resources.

[0188] The task chain co-optimizer constructs a cascaded execution chain of atomic algorithm tasks, using the spatial coordinates output by the flame detection task as the regional constraint for the face recognition task, and the identity information from the face recognition results as the basis for behavioral analysis of the trajectory tracking task. The data flow between tasks forms a progressive analysis chain of space-identity-behavior.

[0189] The device and task mapper dynamically allocates atomic algorithm task combinations based on the type of terminal device. Video stream data collected by cameras is preferentially assigned to flame detection tasks, event-triggered data from access control controllers is directed to face recognition tasks, and sensor time-series data is associated with trajectory tracking tasks, achieving optimal matching between device capabilities and algorithm requirements.

[0190] The cross-vendor feedback unit collects latency difference data for atomic algorithm tasks executed on devices from different vendors, quantifying the execution efficiency deviations caused by protocol differences. Latency characteristic data is sent in real-time to the fractal monitor of the chaos scheduling module, driving the optimization of weight parameters in the container migration strategy and eliminating vendor compatibility differences.

[0191] At the protocol parsing layer, quantum entanglement separation technology identifies the communication feature spectrum of devices by simulating the superposition characteristics of quantum states; essentially, it extracts the frequency domain fingerprint features of multi-source device messages. The pre-trained Transformer model employs a self-attention mechanism to separate effective signal and noise components, and its training process learns from the protocol pattern libraries of devices from manufacturers such as Hikvision and Dahua. The protocol mapping engine dynamically matches protocol rules according to the feature spectrum, converting heterogeneous messages into standardized event streams containing timestamps and geographic coordinates, eliminating communication protocol differences between cameras and access controllers. When the system detects a protocol conflict from a newly accessed device, the closed-loop correction module generates adversarial examples carrying quantum noise, triggering a feature spectrum recalibration mechanism to update the protocol rule base.

[0192] At the algorithm collaboration layer, the federated modeling module creates multiple parallel computing instances to achieve feature decoupling. Each instance uses differentiated parameters to process the spatiotemporal event stream, and the quantum tunneling mechanism fuses feature vectors through Hilbert spatial projection, essentially breaking through the feature alignment bottleneck of traditional federated learning. The generated device-related topology model 3D coordinate matrix accurately represents the spatial positional relationship between cameras and sensors. The chaotic scheduling module transforms the device distribution features in the matrix into Lorentz equation parameters and uses the inherent randomness of the chaotic system to generate a task fractal network. The task node allocator dynamically arranges atomic tasks such as flame detection and face recognition based on chaotic trajectory points. When the fractal monitor detects that the network complexity exceeds the limit, it triggers container migration to achieve adaptive optimization of edge computing resources.

[0193] At the interface optimization layer, the quantum Boltzmann machine encodes the mapping relationship between the atomic task output fields and the target system interface as an energy function. The tunneling optimization controller simulates the quantum tunneling effect to break through local optima, and its phase rotation operation is associated with the execution delay data of edge tasks, enabling the interface mapping strategy to respond to system load changes in real time. If the mapping scheme output by the interface configuration generator fails to execute, the abnormal trajectory parser extracts spatiotemporal features to generate adversarial examples, forming a closed-loop feedback from interface anomalies to protocol recalibration.

[0194] In the security architecture, a differential privacy injector adds noise perturbation to the feature vector based on a Laplace distribution to prevent model inversion attacks from leaking device location information. A quantum key exchanger utilizes the unpredictability of device communication noise to generate cryptographic keys, and an encrypted transmission channel ensures the secure transmission of spatiotemporal event streams. A collaborative auditing unit, upon key verification failure, resets noise parameters and renegotiation of the key, establishing a collaborative protection system between the physical and application layers.

[0195] Through the above-mentioned technological coupling, quantum protocol parsing eliminates access barriers for multi-source devices, federated quantum fusion achieves cross-algorithm feature collaboration, and chaotic fractal scheduling optimizes edge resource allocation. Ultimately, in scenarios such as smart parks, a dynamic closed loop of device access, algorithm collaboration, and interface configuration is formed, solving the problem of algorithm collaboration failure caused by multi-source heterogeneous devices in the background technology.

[0196] In a smart park scenario involving multiple vendors' devices, the data access module collects raw RTSP streams from brand A cameras and proprietary protocol messages from brand B access controllers in real time via multi-source communication interfaces. After the quantum entanglement coordinator of the protocol adaptation module establishes a physical layer connection, the quantum noise generation unit generates noise pulses that resonate with the camera's 2000MHz frequency band. The noise injection unit generates an anti-interference transport stream according to the Hadamard matrix encoding rules, and the device communication feature spectrum is separated by a pre-trained Transformer model. This feature spectrum contains protocol fingerprint features, and the protocol mapping engine outputs a standardized spatiotemporal event stream after matching it with the rule base, eliminating the problem of interrupted personnel trajectory tracking caused by differences in the protocols between Hikvision and Dahua devices.

[0197] The spatiotemporal instance generator in the federated modeling module creates three parallel computational instances based on the event spatiotemporal entropy values, each using a different spatiotemporal coordinate system to analyze the camera and access control event streams. The quantum tunneling fusion unit projects the feature vectors output from the instances onto a Hilbert space, generating a fused feature vector through quantum coherent superposition. The associated topology construction unit then constructs a device association topology model based on this model, whose three-dimensional coordinate matrix accurately represents the spatial positional relationship between the camera and the access control controller.

[0198] The spatial parameter converter in the chaotic scheduling module maps the standard deviation of device density in the coordinate matrix to the Lorentz equation parameter σ, and the topological correlation entropy to the parameter ρ. After the chaotic trajectory generator outputs the trajectory point sequence, the task node allocator forms the execution chain for the flame detection and face recognition tasks. When the fractal monitor detects that the Hausdorff dimension of the task chain exceeds the threshold, the edge actuator triggers container migration, dynamically allocating the flame detection task to the edge node closest to the camera.

[0199] The interface optimization module's interface function constructor extracts the temperature coordinate field from the flame detection task output, and the quantum Boltzmann encoder encodes its mapping relationship with the security system interface fields as an energy function. After the tunneling optimization controller breaks through the local minimum through quantum tunneling, the interface configuration generator outputs the optimal mapping scheme. If the mall customer flow analysis system experiences interface configuration failure, the closed-loop correction module's quantum adversarial generator synthesizes an abnormal message carrying quantum noise and injects it into the protocol adaptation module, triggering feature spectrum recalibration. When the feature spectrum calibrator detects that the distribution offset exceeds the limit, it updates the protocol field mapping table, and the federated feedback unit synchronously drives the spatiotemporal instance generator to reconstruct the calculation parameters.

[0200] In terms of security mechanisms, the differential privacy injector adds Laplace noise during the transmission of fused feature vectors. After the quantum key negotiator generates a key based on the noise impulses, the encrypted transmission channel encrypts the spatiotemporal event stream. When the decryption verification unit detects a failure in quantum key verification, the collaborative auditing unit synchronously triggers a noise parameter reset and key renegotiation to ensure the information security of cross-vendor camera data during transmission.

[0201] Through the aforementioned technological coupling, the system enables real-time driving of facial recognition tasks by access controllers based on camera flame detection results in smart park projects, reducing latency during multi-algorithm collaborative response. The closed-loop correction module cumulatively triggers multiple protocol feature spectrum recalibrations, allowing newly connected cameras to achieve plug-and-play functionality without protocol changes, effectively resolving algorithm collaboration barriers caused by multi-source heterogeneous devices in the background technology.

[0202] This invention addresses the incompatibility issue of communication protocols among multi-source devices through a quantum protocol parsing layer. The protocol adaptation module utilizes a quantum entanglement separator to identify the characteristic spectrum of device communication, converting raw messages from different manufacturers into a unified, standardized spatiotemporal event stream. A quantum noise injection mechanism dynamically adapts to the device's communication frequency band, and a pre-trained Transformer model separates effective signals from interference components, constructing device-independent protocol parsing capabilities and eliminating data interoperability barriers caused by protocol differences.

[0203] The federated modeling module overcomes the barriers to algorithmic collaboration through quantum-level feature fusion. A cross-instance quantum tunneling mechanism projects the feature vectors of parallel computing instances onto a Hilbert space, generating a 3D topological model that fuses the relationships between devices. The spatial coordinate matrix of this model drives the chaotic scheduling module to generate an atomic task fractal network. The Lorentz chaotic equation transforms the device distribution characteristics into task scheduling parameters, enabling dynamic allocation and resource optimization of atomic tasks such as flame detection and face recognition.

[0204] The system constructs a cross-module feedback loop to ensure continuous collaboration. When the closed-loop correction module detects interface configuration anomalies, it generates quantum adversarial examples, triggering device communication feature spectrum recalibration and protocol rule base updates. The updated protocol rules are synchronized to the federated modeling module to reconstruct instance parameters, and the chaotic scheduling module optimizes container migration strategies based on the feedback latency data. From protocol parsing and algorithm scheduling to interface configuration, a dynamic collaborative loop is formed, continuously eliminating the constraints of device heterogeneity on AI collaborative analysis.

Claims

1. A multi-AI algorithm collaborative intelligent management system based on a large model, characterized in that: include: The data access module collects raw messages from terminal devices of at least two types of manufacturers in real time. The protocol adaptation module receives the original message from the terminal device, identifies the device communication feature spectrum through a quantum entanglement separator, and outputs a standardized spatiotemporal event stream. The federated modeling module receives the standardized spatiotemporal event stream, creates multiple parallel computing instances, each instance independently extracts entity feature vectors, and generates a device-related topology model by fusing the feature vectors through a cross-instance quantum tunneling mechanism. The chaotic scheduling module parses the spatial coordinate matrix of the device-related topology model, uses the Lorentz chaotic equation to generate an atomic task chain fractal network, and assigns tasks to edge computing nodes to execute atomic algorithm tasks. The interface optimization module collects the output of atomic algorithm tasks, calculates the interface field mapping function through a quantum Boltzmann machine, and generates the target system interface configuration scheme. The closed-loop correction module detects interface configuration scheme execution failure events, converts abnormal event trajectories into adversarial samples, and injects the adversarial samples into the protocol adaptation module to trigger device communication feature spectrum recalibration. The protocol adaptation module includes: A quantum entanglement coordinator, connected to the data access module, receives raw messages from the terminal device; The quantum noise generation unit, in response to the quantum entanglement coordinator, generates a true random noise pulse sequence that resonates with the device's communication frequency band; The noise injection unit receives the original message and the true random noise pulse sequence from the terminal device and generates an anti-interference transport stream according to the Hadamard matrix coding rules. The feature spectrum analysis unit receives the anti-interference transmission stream, separates the quantum noise component from the device feature spectrum component through a pre-trained Transformer model, and outputs the device communication feature spectrum. The protocol mapping engine receives the device communication feature spectrum output by the feature spectrum analysis unit, matches it with the protocol rule base to generate a standardized spatiotemporal event stream, and updates the protocol rule base in response to the adversarial samples injected by the closed-loop correction module. The federated modeling module includes: The spatiotemporal instance generator receives a standardized spatiotemporal event stream output by the protocol adaptation module and creates at least three parallel computing instances with differentiated parameters based on the spatiotemporal entropy value of the events. The quantum tunneling fusion device performs quantum tunneling operations on the entity feature vectors of each parallel computation instance and generates a fused feature vector through a quantum coherent superposition state. The associated topology construction unit analyzes the spatial distribution of the fused feature vectors and constructs a device associated topology model including a three-dimensional coordinate matrix, which serves as the input parameter for the chaotic scheduling module.

2. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 1, characterized in that, The chaotic scheduling module includes: The spatial parameter converter receives the three-dimensional coordinate matrix output by the federated modeling module and maps the standard deviation of the device density distribution in the matrix to the Lorentz equation parameter σ, and the topological association entropy value to the parameter ρ. A chaotic trajectory generator calculates the Lorentz equation based on parameters σ and ρ and outputs a sequence of chaotic trajectory points. The task node allocator associates each chaotic trajectory point with an atomic task node, forming a task chain fractal network with spatial coordinates. The fractal monitor calculates the Hausdorff dimension of the task chain fractal network in real time and activates the container migration instruction when the dimension value exceeds 1.

26. The edge executor executes container migration instructions to reallocate atomic algorithm tasks and collects task execution latency data to send to the interface optimization module.

3. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 2, characterized in that, The interface optimization module includes: The interface function constructor receives task execution delay data sent by the edge executor of the chaos scheduling module and extracts the output fields of the atomic algorithm task. The quantum Boltzmann encoder encodes the mapping relationship between the task output field and the target system interface field into a Boltzmann machine energy function; A tunneling optimization controller performs quantum tunneling operations in a quantum Boltzmann machine, breaking through the local minimum of the energy function; Dynamic convergence unit monitors the convergence state of the energy function; when the rate of energy change is lower than... Activate configuration generation at any time; The interface configuration generator outputs the mapping relationship of the executable interface fields of the target system and sends the abnormal configuration log to the closed-loop correction module.

4. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 3, characterized in that, Also includes: The spatial coordinate resolver receives the device-associated topology model output by the federated modeling module and extracts the three-dimensional geographic coordinate matrix from the model. Edge node matcher calculates the topological distance between each edge computing node and the coordinates in the 3D geographic coordinate matrix, and generates a node correlation matrix; The quantum annealing dispatcher encodes the node correlation matrix into a QUBO function and generates an optimized task dispatch scheme through quantum annealing optimization. The dynamic tuning unit monitors task execution latency data and reactivates quantum annealing optimization when the cross-node communication latency exceeds 20ms. The edge node execution unit executes the optimized task assignment scheme and collects device status data to feed back to the federated modeling module.

5. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 4, characterized in that, The closed-loop correction module includes: An abnormal trajectory parser detects abnormal configuration logs sent by the interface configuration generator and extracts the spatiotemporal features of interface configuration failure events. A quantum adversarial generator synthesizes anomalous message data carrying quantum noise characteristics based on spatiotemporal features; The protocol retraining unit injects abnormal message data into the feature spectrum analysis unit of the protocol adaptation module, triggering incremental training of the pre-trained Transformer model. The feature spectrum calibrator monitors the distribution offset of the communication feature spectrum of the monitoring device and updates the protocol field mapping table when the offset exceeds 0.

3. The federated feedback unit sends the updated protocol field mapping table to the spatiotemporal instance generator of the federated modeling module to reconstruct the parallel computing instance parameters.

6. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 5, characterized in that, Also includes: The differential privacy injector receives the fused feature vector generated by the quantum tunneling fusion unit of the federated modeling module, and adds Laplace noise to the feature vector transmission channel with a noise intensity parameter λ=0.

3. A quantum key negotiator, a quantum noise generation unit of the response protocol adaptation module, generates quantum keys based on noise pulse sequences; The encrypted transmission channel uses quantum key encryption to encrypt the standardized spatiotemporal event stream and sends the ciphertext data to the chaotic scheduling module. The decryption and verification unit decrypts the data at the receiving end of the chaotic scheduling module to verify the consistency of the quantum key. When the collaborative auditing unit detects a failure in quantum key verification, it simultaneously triggers the differential privacy injector to reset the noise parameter λ and the quantum key negotiator to renegotiate the key.

7. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 6, characterized in that, The tunneling optimization controller includes: The quantum initialization solver receives the atomic task output fields extracted by the interface function constructor and generates 100 sets of initial interface mapping schemes. The phase rotation optimizer performs quantum phase rotation operations on each set of schemes, with the rotation angle correlated with the task execution delay data sent by the chaotic scheduling module; A sonar tunneling detector simulates the quantum tunneling effect in the energy space of a Boltzmann machine, breaking through the local optimum trap. A dynamic convergence monitor calculates the rate of change of the energy function gradient in real time. If the rate of change remains below a certain threshold for three consecutive iterations... Activate the mapping output at the time; The optimal mapping generator outputs the mapping relationship with the lowest error rate for the interface fields, and synchronously generates quantum-optimized trajectory logs which are sent to the closed-loop correction module.

8. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 7, characterized in that, Also includes: Multi-source device coordinator, which coordinates terminal devices from at least three types of manufacturers in real time, including cameras, access controllers and sensors; The task chain co-optimizer constructs the execution chain of atomic algorithm tasks, where the output of the flame detection task serves as the input of the face recognition task, and the face recognition result serves as the spatial constraint for the trajectory tracking task. The device and task mapper dynamically selects atomic algorithm task combinations based on the type of terminal device, prioritizing the allocation of camera data to flame detection tasks and access control controller data to face recognition tasks. The cross-vendor feedback unit collects latency difference data of devices from different vendors executing atomic algorithm tasks and sends it to the fractal monitor of the chaos scheduling module to optimize the container migration strategy.

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