Multi-AI algorithm collaborative intelligent management system based on large model

Abstract

The invention relates to the technical field of data processing, in particular to a multi-AI algorithm collaborative intelligent management system based on a large model, and the system comprises a data access module which collects original messages of terminal equipment of multiple manufacturers; the protocol adaptation module analyzes a device communication characteristic spectrum through a quantum entanglement separator, and outputs a standardized time-space event stream; a federation modeling module creates a parallel calculation instance, and a quantum tunneling mechanism is utilized to fuse the feature vectors to generate an equipment association topology model; the chaotic scheduling module generates an atomic task fractal network and assigns the atomic task fractal network to edge nodes; the interface optimization module calculates an interface field mapping relation through a quantum Boltzmann machine; and the closed-loop correction module triggers equipment communication characteristic spectrum re-calibration. Multi-source device communication differences are eliminated through quantum protocol analysis, federated quantum fusion breaks through algorithm collaboration barriers, chaotic fractal scheduling realizes resource dynamic optimization, a closed-loop collaboration system of device access to interface configuration is formed, and the problems of protocol incompatibility of multi-source heterogeneous devices and AI algorithm collaboration obstacles are solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a multi-AI algorithm collaborative intelligent management system based on a large model. Background Art

[0002] The intelligent management system integrates sensor networks and business execution terminals to obtain multi-dimensional operating status information of the physical environment and business processes in real time, and relies on edge computing nodes to pre-process raw data; the system core 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 reasoning models; the decision-making module uses constraint-based optimization algorithms or rule reasoning engines to convert analysis results into operation instruction sequences; during the execution process, the expected goals and actual effects are continuously compared, and the model parameters and rule weights are dynamically corrected using adaptive control strategies to form a goal-oriented closed-loop control loop, ultimately achieving energy efficiency management, process optimization and fault self-healing.

[0003] When devices from multiple manufacturers are connected to an intelligent management system, the fragmented hardware ecosystem—that is, different manufacturers use proprietary communication protocols, interface standards, and data formats—makes unified management and data interoperability difficult, hindering the collaborative analysis and real-time response of AI algorithms. For example, in a smart campus project, cameras from different brands, such as those from Manufacturer A and Manufacturer B, use inconsistent video streaming protocols, while access controllers like HID's proprietary APIs cannot directly interact with edge computing boxes, rendering personnel tracking and event warning functions ineffective. Furthermore, a shopping mall passenger flow analysis system requires the integration of 180 hemispherical cameras from different manufacturers. Initially, due to protocol differences, passenger flow data could not be integrated, requiring a customized adaptation layer to address access issues. This increased deployment complexity and delayed the collaborative efficiency of multiple algorithms, such as those for human attributes and cluster analysis. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a multi-AI algorithm collaborative intelligent management system based on a large model to solve the problems of data interoperability obstacles and limited AI algorithm collaborative analysis functions caused by incompatible communication protocols when multi-source heterogeneous devices are connected in the intelligent management system.

[0005] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows: The multi-AI algorithm collaborative intelligent management system based on a large model provided by the present invention includes: Data access module, real-time collection of original messages from terminal devices of at least two types of manufacturers; A protocol adapter module receives the original message from the terminal device, identifies the device communication characteristic spectrum through the quantum entanglement separator, and outputs a standardized space-time event stream; a federated modeling module that receives the standardized spatiotemporal event stream and creates multiple parallel computing instances, each of which independently extracts entity feature vectors and fuses the feature vectors through a cross-instance quantum tunneling mechanism to generate a device association topology model; A chaos scheduling module analyzes the spatial coordinate matrix of the device association topology model, generates an atomic task chain fractal network using the Lorentz chaos equation, and dispatches 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 the quantum Boltzmann machine, and generates the target system interface configuration plan; The closed-loop correction module detects the failure event of the interface configuration scheme execution, converts the abnormal event trajectory into an adversarial sample, and injects the adversarial sample into the protocol adaptation module to trigger the recalibration of the device communication feature spectrum.

[0006] Furthermore, in the large-model-based multi-AI algorithm collaborative intelligent management system described in the present invention, the protocol adaptation module includes: A quantum entanglement coordinator, connected to the data access module, receiving original messages from the terminal device; a quantum noise generation unit, responsive to the quantum entanglement coordinator, generating a true random noise pulse sequence resonating with a device communication frequency band; A noise injection unit receives the original message of the terminal device and a true random noise pulse sequence, and generates an anti-interference transmission stream according to the Hadamard matrix coding rule; a characteristic spectrum analysis unit, which receives the anti-interference transmission stream, separates the quantum noise component and the device characteristic spectrum component through a pre-trained Transformer model, and outputs the device communication characteristic spectrum; The protocol mapping engine receives the device communication signature output by the signature analysis unit, matches 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.

[0007] Furthermore, in the large-model-based multi-AI algorithm collaborative intelligent management system described in the present invention, the federated modeling module includes: The spatiotemporal instance generator receives the 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 values of the events; The quantum tunneling fusion device performs quantum tunneling operations on the entity feature vectors of each parallel computing instance and generates a fused feature vector through 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, wherein the three-dimensional coordinate matrix serves as an input parameter of the chaos scheduling module.

[0008] Furthermore, in the large-model-based multi-AI algorithm collaborative intelligent management system described in the present invention, the chaos 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 correlation entropy value to the parameter ρ; The chaotic trajectory generator calculates the Lorentz equation based on the parameters σ and ρ and outputs a chaotic trajectory point sequence; The task node allocator associates each chaotic trajectory point to the atomic task node to form a task chain fractal network with spatial coordinates; Fractal monitor, which 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 the container migration instructions to reallocate the atomic algorithm tasks, collects the task execution delay data and sends it to the interface optimization module.

[0009] Furthermore, in the large-model-based multi-AI algorithm collaborative intelligent management system described in the present invention, the interface optimization module includes: The interface function constructor receives the 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; Tunneling optimization controller, which performs quantum tunneling operations in a quantum Boltzmann machine to break through the local minimum of the energy function; Dynamic convergence unit, monitors the convergence state of the energy function, when the energy change rate is less than 10⁻ 5 Activate configuration generation when The interface configuration generator outputs the interface field mapping relationship that can be executed by the target system and sends the abnormal configuration log to the closed-loop correction module.

[0010] Furthermore, the multi-AI algorithm collaborative intelligent management system based on a large model of the present invention further includes: The spatial coordinate parser receives the device association topology model output by the federated modeling module and extracts the three-dimensional geographic coordinate matrix in the model; The edge node matcher calculates the topological distance between each edge computing node and the coordinates in the three-dimensional geographic coordinate matrix to generate a node association matrix; The quantum annealing dispatcher encodes the node correlation matrix into a QUBO function and generates an optimized task dispatching solution through quantum annealing optimization; Dynamic tuning unit, which monitors task execution delay data and reactivates quantum annealing optimization when the cross-node communication delay exceeds 20ms; The edge node execution unit executes the optimized task dispatch plan, collects device status data and feeds it back to the federated modeling module.

[0011] Furthermore, in the large-model-based multi-AI algorithm collaborative intelligent management system described in the present invention, the closed-loop correction module includes: The abnormal trace parser detects abnormal configuration logs sent by the interface configuration generator and extracts the spatiotemporal characteristics of interface configuration failure events; The quantum countermeasure generator synthesizes abnormal message data carrying quantum noise characteristics based on spatiotemporal characteristics; The protocol retraining unit injects abnormal message data into the signature spectrum analysis unit of the protocol adaptation module, triggering incremental training of the pre-trained Transformer model; The characteristic spectrum calibrator monitors the distribution offset of the device communication characteristic spectrum 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.

[0012] Furthermore, the multi-AI algorithm collaborative intelligent management system based on a large model of the present invention further includes: The differential privacy injector receives the fused feature vector generated by the quantum tunneling fusion module 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, which responds to the quantum noise generation unit of the protocol adaptation module and generates a quantum key based on the noise pulse sequence; Encrypt the transmission channel, use quantum keys to encrypt the standardized spatiotemporal event stream, and send the encrypted data to the chaos scheduling module; The decryption verification unit decrypts data at the receiving end of the chaotic scheduling module and verifies the consistency of the quantum key; The collaborative audit unit, when detecting a quantum key verification failure, synchronously triggers the differential privacy injector to reset the noise parameter λ and the quantum key negotiator to renegotiate the key.

[0013] Furthermore, in the large-model-based multi-AI algorithm collaborative intelligent management system described in the present invention, 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; Phase rotation optimizer, which performs quantum phase rotation operation on each set of solutions, and the rotation angle is associated with the task execution delay data sent by the chaos scheduling module; Sonar tunneling detectors simulate quantum tunneling effects in the Boltzmann machine energy space, breaking through the local optimal trap; Dynamic convergence monitor, which calculates the rate of change of the energy function gradient in real time. When the rate of change is less than 10⁻ for three consecutive iterations, 5 Activate mapping output when The optimal mapping generator outputs the mapping relationship with the lowest interface field mapping error rate, and simultaneously generates a quantum optimization trajectory log and sends it to the closed-loop correction module.

[0014] Furthermore, the multi-AI algorithm collaborative intelligent management system based on a large model of the present invention further includes: A multi-source device orchestrator that coordinates end devices from at least three manufacturers in real time, including cameras, access controllers, and sensors; The task chain co-optimizer constructs an 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 of the trajectory tracking task; The device and task mapper dynamically selects the atomic algorithm task combination based on the terminal device type, giving priority to flame detection tasks for camera data and face recognition tasks for access controller data. The cross-vendor feedback unit collects the latency difference data of the atomic algorithm tasks executed by devices of different vendors and sends it to the fractal monitor of the chaos scheduling module to optimize the container migration strategy.

[0015] Beneficial effects of the present invention: The quantum and chaos collaborative architecture constructed by the present invention uses quantum entanglement separation technology at the protocol analysis layer to dynamically adapt to the communication characteristics of multi-source devices, converting messages from heterogeneous devices such as Hikvision and Dahua into standardized spatiotemporal event streams, eliminating data interoperability barriers caused by protocol differences; at the algorithm collaboration layer, through cross-instance quantum tunneling fusion and Lorentz chaos-driven task fractal networks, dynamic orchestration and resource optimization of AI algorithms such as flame detection and face recognition are achieved, solving the problem of low efficiency in multi-algorithm collaboration; at the system optimization layer, a closed-loop feedback mechanism is formed to link protocol feature spectrum recalibration, federated modeling parameter reconstruction, and container migration strategy, continuously reducing the impact of device heterogeneity on the system. Compared with traditional solutions, this system, without adding dedicated protocol conversion hardware, achieves the unification of plug-and-play access of multi-vendor devices and collaborative analysis of multiple AI algorithms through the coupling of multiple technologies of quantum frequency domain anti-interference, chaotic space scheduling, and adversarial incremental learning, significantly improving the timeliness of abnormal response in scenarios such as smart security. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0017] Figure 1This is a system architecture diagram of the large-model-based multi-AI algorithm collaborative intelligent management system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below in conjunction with the drawings. In order to better understand the purpose of the present invention, the present invention is further described in detail below.

[0019] See also Figure 1 The present invention provides a multi-AI algorithm collaborative intelligent management system based on a large model, including: Data access module, real-time collection of original messages from terminal devices of at least two types of manufacturers; During the implementation of the data access module, the multi-protocol gateway deploys a dedicated acquisition agent, which uses parallel threads to monitor the communication interfaces of terminal devices from at least two manufacturers in real time. For brand A cameras, which use the Network Video Streaming Protocol interface, the acquisition agent establishes an RTSP session channel to capture the raw video stream messages. For brand B access control controllers, Wiegand protocol data frames are collected via a serial communication interface. To ensure the integrity of the original messages, the acquisition process uses zero-copy technology to directly capture the binary stream transmitted by the device's physical layer, avoiding the loss of protocol information caused by intermediate parsing. Each acquisition thread is bound to an independent timestamp generator, which synchronously records nanosecond-precise time stamps based on the Network Time Protocol, forming raw data packets with time sequence identifiers. When multiple source devices transmit concurrently, a load balancer allocates data processing channels based on device manufacturer, eliminating transmission congestion caused by protocol differences. A ring buffer design provides temporary storage of raw messages, and a dual-pointer read and write mechanism prevents data overwriting, maintaining transmission stability in high-concurrency scenarios.

[0020] The collection agent is equipped 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 transmission of the original message, a cyclic redundancy check code is used to verify data integrity, abnormal data packets that fail the check are discarded, and the corresponding device retransmission mechanism is triggered. The collection log recorder monitors the status of each device channel in real time. When it detects that a manufacturer's device is continuously offline, it automatically switches to the backup communication link to maintain data access continuity. The collection agent's heartbeat detection mechanism periodically sends a handshake signal to the terminal device to confirm the device's online status and reset the timed-out connection. All original messages are added with a unique device identifier before transmission, forming a traceable three-level association system of manufacturer, device, and data packet.

[0021] Through this technical solution, the data access module successfully implemented parallel access for Brand A cameras and Brand B access controllers in a smart campus project. The latency for raw message acquisition was kept to milliseconds, providing unparsed initial data input for the subsequent protocol adapter module. Those skilled in the art will appreciate that the multi-protocol gateway's parallel acquisition mechanism effectively addresses the barriers to multi-source device access in prior art, laying the data foundation for eliminating protocol differences.

[0022] A protocol adapter module receives the original message from the terminal device, identifies the device communication characteristic spectrum through the quantum entanglement separator, and outputs a standardized space-time event stream; During the implementation of the protocol adaptation module, the quantum entanglement coordinator establishes a physical layer connection with the data access module, receiving the camera's raw RTSP stream and the access controller's Wiegand protocol messages. The quantum noise generation unit, in response to the coordinator's instructions, generates a true random noise pulse sequence resonating with the device's communication frequency band. The camera's frequency band matches the 2000MHz carrier characteristics, while the access controller's frequency band adapts to the 125kHz low-frequency characteristics. The noise injection unit employs Hadamard matrix orthogonal encoding to fuse the original message with the noise pulse to generate an interference-resistant transmission stream. Matrix orthogonality ensures the mathematical separability of the signal components. The signature spectrum analysis unit processes the interference-resistant transmission stream using a pre-trained Transformer model. A self-attention mechanism identifies frequency domain characteristic patterns, separates the quantum noise component from the device communication spectral components, and outputs a signature spectrum containing the protocol fingerprint. The protocol mapping engine matches the signature spectrum with the protocol rule base, extracts timestamps and geographic coordinates, and generates a standardized spatiotemporal event stream. When a new Uniview camera protocol is introduced to the system, the field matching threshold of the protocol rule base is dynamically updated in response to adversarial examples injected by the closed-loop correction module.

[0023] The signature spectrum analysis unit uses a multi-head attention mechanism to parallelly process interference-resistant transmission streams. The key vector encodes temporal features, and the value vector maps spatial location information. The query vector learns device protocol feature patterns and identifies key frequency domain components through scaled dot-product attention weighting. The protocol mapping engine configures a protocol rule tree index structure, with the root node classifying device manufacturers, child nodes storing protocol version features, and leaf nodes associating standardized field conversion rules. The rule base update mechanism uses an incremental learning strategy. When a protocol conflict is detected for a Uniview camera, the protocol rule tree weight parameters are fine-tuned using a backpropagation algorithm. The spatiotemporal event stream generation process adds unique device identifiers and data checksums to form a standardized, traceable data carrier. In a scenario involving 180 cameras connected to a shopping mall, this module successfully converted heterogeneous protocols from various device brands into an event stream containing millisecond-level timestamps and three-dimensional geographic coordinates, eliminating data fusion barriers caused by protocol differences among multiple source devices in existing technologies. Quantum noise injection and signature spectrum separation overcome the reliance on prior knowledge inherent in traditional protocol parsing methods, enabling dynamic adaptation of unknown device protocols.

[0024] a federated modeling module that receives the standardized spatiotemporal event stream and creates multiple parallel computing instances, each of which independently extracts entity feature vectors and fuses the feature vectors through a cross-instance quantum tunneling mechanism to generate a device association topology model; During the implementation of the federated modeling module, the standardized spatiotemporal event stream is output from the protocol adaptation module and input into the spatiotemporal instance generator. This generator determines the number of instances and parameter configurations based on the spatiotemporal entropy value calculation of the events. For example, in the smart park scenario, independent computing instances are created for the camera event stream and the access control controller event stream. Each instance is configured with differentiated spatiotemporal coordinate systems and sampling frequency parameters. The camera instance prioritizes the time series features of the video frames, while the access control instance focuses on the spatial distribution characteristics of the access control events, forming a parallel processing channel. The instance executes the feature extraction algorithm, using a convolutional neural network to independently parse the event stream, extracting entity feature vectors, including the coordinates of the flame area in the camera video or the facial feature vectors of the person in the access control controller, to maintain the isolation and independence of each instance processing process.

[0025] A cross-instance quantum tunneling mechanism initiates the fusion process. The quantum tunneling fuser projects the feature vectors output by each instance into Hilbert space. Feature vector fusion is achieved through the principle of quantum state superposition. The camera's flame coordinate vector and the access control facial feature vector are superimposed in a quantum coherent state to generate a fused feature vector, eliminating interfering noise between features. The fusion process preserves the differences in original spatiotemporal characteristics, 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 multi-dimensional relationships between devices.

[0026] The correlation topology construction unit analyzes the spatial distribution pattern of the fused feature vectors and constructs a device correlation topology model. The three-dimensional coordinate matrix of this model accurately quantifies the relative position relationship between the camera and the access controller. The horizontal coordinate maps the longitude difference, the vertical coordinate corresponds to the latitude difference, and the height coordinate represents the altitude correlation strength of the device. In the deployment of smart parks, the matrix output drives the task dispatching logic of the subsequent chaos scheduling module. Figure 1 As shown, the entire federated modeling process, from event stream input to topology model generation, forms a closed-loop data conversion chain, resolving the existing issue of collaborative analysis of multi-source device data. The independent extraction mechanism of parallel instances avoids misidentification caused by feature coupling. Quantum tunneling fusion overcomes the feature alignment bottleneck of traditional federated learning, and the device-associated topology model provides structured input for subsequent resource scheduling. This technical solution achieves efficient feature fusion without relying on external algorithm libraries, supporting the collaborative execution of multiple AI algorithms.

[0027] A chaos scheduling module analyzes the spatial coordinate matrix of the device association topology model, generates an atomic task chain fractal network using the Lorentz chaos equation, and dispatches tasks to edge computing nodes to execute atomic algorithm tasks; During the implementation of the chaos 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, converts the device density distribution dispersion into the Lorentz chaos equation parameter σ, and maps the topological association relationship complexity into the parameter ρ. Figure 1 As shown, this parameter transformation establishes a mathematical connection between the spatial topological characteristics and the dynamic properties of the chaotic system.

[0028] 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 of these trajectory points reflects the nonlinear dynamic behavior of the system, with each trajectory point coordinate corresponding to the logical execution location 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 fractal network of task chains with three-dimensional spatial coordinates. The network topology retains the characteristics of a chaotic system, with the camera flame detection task nodes located close to the spatial coordinate origin and the access control controller face recognition task nodes distributed in areas with dense trajectory points.

[0029] The fractal monitor analyzes the complexity of the task chain's fractal network structure in real time and calculates the Hausdorff dimension to quantify the network's morphological characteristics. When the dimension exceeds a set threshold, a container migration instruction is generated, triggering the dynamic reallocation of computing resources. The edge executor executes the container migration operation to redeploy atomic algorithm tasks, prioritizing flame detection tasks to edge computing nodes physically close to the camera. During execution, task processing latency data is collected and transmitted to the interface optimization module via an encrypted channel.

[0030] In a smart campus project, this module successfully implemented dynamic task scheduling for 200 heterogeneous devices. The flame detection task output from a brand A camera triggers the face recognition task on a brand B access control controller in a nearby area in real time, forming a spatially correlated task execution chain. A chaos-driven mechanism automatically optimizes task allocation strategies, addressing the response delay issue of multi-algorithm collaboration in existing technologies. Spatial parameter transformation establishes a mathematical relationship between device distribution and task scheduling, chaotic trajectory generation enables adaptive task chain orchestration, and fractal monitoring ensures dynamic optimization of resource allocation.

[0031] The interface optimization module collects the output of atomic algorithm tasks, calculates the interface field mapping function through the quantum Boltzmann machine, and generates the target system interface configuration plan; 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 atomic algorithm task output field. The quantum Boltzmann encoder encodes the mapping relationship between the task output field and the security system target interface field into a Boltzmann machine energy function, where the field matching error is quantized as an energy value weight coefficient. The tunneling optimization controller performs quantum tunneling operations in the quantum Boltzmann machine energy space, breaking through the local minimum limitation of traditional gradient descent through the principle of quantum state superposition to achieve a global optimal solution search. The dynamic convergence unit monitors the rate of change of the energy function gradient in real time and triggers the configuration generation process when the rate of change falls below a set threshold. The interface configuration generator outputs the optimal interface field mapping relationship, for example, mapping the flame detection temperature coordinate to the fire system alarm interface, and simultaneously transmits the abnormal configuration log to the closed-loop correction module.

[0032] In the actual deployment of smart parks, when the Dahua camera flame detection task outputs the temperature coordinate field, the quantum Boltzmann encoder establishes a mapping relationship between it and the alarm interface of the building fire protection system. The tunneling optimization controller dynamically adjusts the mapping weight through phase rotation operations to respond to changes in system load at different time periods. Figure 1 As shown, the entire optimization process forms a closed-loop feedback mechanism. When interface configuration fails, the exception log triggers recalibration of the protocol signature spectrum. The quantum tunneling mechanism solves the problem of traditional interface mapping easily falling into local optimality. Dynamic convergence monitoring ensures the stability of the mapping solution, ultimately achieving seamless integration of multiple AI algorithm outputs with the target system.

[0033] 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 the recalibration of the device communication feature spectrum, forming a dynamic collaborative loop for device access, model training, task scheduling, and interface configuration.

[0034] During the implementation of the closed-loop correction module, the anomaly trajectory parser continuously monitors the abnormal configuration logs output by the interface configuration generator. When a smart security system interface configuration failure is detected, the log parsing algorithm extracts the spatiotemporal characteristics of the anomaly, including the time series pattern and geographic coordinate distribution of the event. The quantum adversarial generator synthesizes anomalous message data with quantum noise characteristics based on the extracted spatiotemporal characteristics. This data simulates real-world device communication characteristics but injects protocol conflict fields. The protocol retraining unit injects the generated adversarial examples into the signature spectrum analysis unit of the protocol adaptation module, triggering incremental training of the pre-trained Transformer model. The training process uses a backpropagation algorithm to fine-tune the model's weight parameters, improving the model's ability to identify new protocol conflicts. The signature spectrum calibrator monitors the deviation of the device communication signature spectrum distribution in real time and automatically updates the protocol field mapping table when the deviation exceeds a set threshold. 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 instance.

[0035] In an actual smart campus deployment, when a new Uniview camera protocol caused an interface configuration failure, the anomaly trajectory parser extracted timestamp clustering features and specific geographic coordinates. The quantum adversarial generator synthesized and matched anomalous packets in the 2000MHz frequency band and injected them into the system. The protocol retraining process adjusted the parameters of the feature separation layer through incremental learning. The feature spectrum calibrator updated the field mapping rules after detecting shifts in the device communication feature spectrum distribution. The updated protocol rules were synchronized to the federated modeling module, and the spatiotemporal instance generator reconstructed the processing parameters of the camera event stream based on the new protocol features. This entire process forms a closed-loop collaborative circuit, from interface anomaly detection to protocol recalibration and model reconstruction, enabling plug-and-play integration of newly connected devices without protocol changes. Spatiotemporal feature extraction technology captures the patterns of anomaly events, while the quantum adversarial sample generation mechanism effectively simulates real-world protocol conflicts. Incremental training and parameter reconstruction ensure the system continuously adapts to changes in the device ecosystem.

[0036] The Data Access Module connects to terminal devices from at least two different 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 the raw byte sequences using protocol-independent packet capture technology.

[0037] The protocol adaptation module receives the original device message and analyzes the device communication signature spectrum using a quantum entanglement separator. The quantum entanglement separator uses the principle of quantum state superposition to separate the protocol's characteristic components, generating a frequency-domain representation of the device communication signature spectrum. This signature spectrum is decoded by a pre-trained Transformer model, outputting a standardized spatiotemporal event stream consisting of 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.

[0038] The federated modeling module receives a stream of standardized spatiotemporal events and creates multiple parallel computing instances based on the event's spatiotemporal entropy. Each instance independently runs a feature extraction algorithm, projects entity feature vectors into Hilbert space through cross-instance quantum tunneling, and fuses these feature vectors using quantum coherent superposition. The fused features are then spatially analyzed to construct a device association topology model consisting of a three-dimensional coordinate matrix. This matrix accurately represents the spatial relationships between devices.

[0039] The chaotic scheduling module analyzes the spatial coordinate matrix of the device-association topology model, maps the standard deviation of the device density distribution to the Lorenz equation parameter σ, and maps the topology-association entropy value to the parameter ρ. Based on the parameters σ and ρ, the Lorenz equation is calculated to generate a chaotic trajectory point sequence. These trajectory points are then associated with atomic task nodes to form a fractal network. The Hausdorff dimension of the task chain is monitored in real time. When the dimension exceeds a threshold, container migration is triggered, and flame detection and face recognition tasks are dispatched to edge computing nodes for execution.

[0040] The interface optimization module collects the output of atomic algorithm tasks, extracts the task output fields, and constructs an interface field mapping. Using a quantum Boltzmann machine, this mapping is encoded into an energy function, and tunneling operations are performed in quantum state space to break through local optimal solutions. The module monitors the rate of change of the energy function gradient. When the rate of change falls below a convergence threshold, it generates a target system interface configuration plan and simultaneously outputs a log of abnormal configurations.

[0041] The closed-loop correction module detects interface configuration plan execution failures and extracts the spatiotemporal traces of these anomalies. Based on these spatiotemporal traces, it synthesizes adversarial samples containing quantum noise and injects them into the protocol adaptation module to trigger recalibration of the device's communication signature spectrum. When the signature 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.

[0042] Specifically, the multi-AI algorithm collaborative intelligent management system based on a large model described in the present invention, the protocol adaptation module includes: A quantum entanglement coordinator, connected to the data access module, receiving original messages from the terminal device; a quantum noise generation unit, responsive to the quantum entanglement coordinator, generating a true random noise pulse sequence resonating with a device communication frequency band; A noise injection unit receives the original message of the terminal device and a true random noise pulse sequence, and generates an anti-interference transmission stream according to the Hadamard matrix coding rule; a characteristic spectrum analysis unit, which receives the anti-interference transmission stream, separates the quantum noise component and the device characteristic spectrum component through a pre-trained Transformer model, and outputs the device communication characteristic spectrum; The protocol mapping engine receives the device communication signature output by the signature analysis unit, matches 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.

[0043] The quantum entanglement coordinator is directly connected to the data access module, receiving raw messages from terminal devices of multiple manufacturers. This component actively responds to device communication requests through a quantum state correlation mechanism, establishing a synchronous physical layer connection between the device and the protocol analysis system.

[0044] The quantum noise generation unit responds to coordinator commands and generates a sequence of true 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 characteristics, improving anti-interference performance through the principle of quantum coherence superposition.

[0045] The noise injection unit receives the original device message and the noise pulse sequence and generates an interference-resistant transmission stream using the Hadamard matrix encoding rule. The matrix orthogonality ensures the mathematical separability of the noise component from the valid signal, forming an interference-resistant information carrier.

[0046] The signature spectrum analysis unit analyzes the interference-resistant transmission stream and separates the quantum noise component from the device signature spectrum component using a pre-trained Transformer model. The Transformer's self-attention mechanism identifies frequency domain characteristic patterns and outputs the real and imaginary components of the device communication signature spectrum.

[0047] The protocol mapping engine matches device communication signatures with the protocol rule base, generating a standardized spatiotemporal event stream with timestamps and geographic coordinates. In response to adversarial examples injected by the closed-loop correction module, the signature matching thresholds in the protocol rule base are updated, enabling dynamic evolution of protocol parsing capabilities.

[0048] Specifically, the multi-AI algorithm collaborative intelligent management system based on a large model described in the present invention, the federated modeling module includes: The spatiotemporal instance generator receives the 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 values of the events; The quantum tunneling fusion device performs quantum tunneling operations on the entity feature vectors of each parallel computing instance and generates a fused feature vector through 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, wherein the three-dimensional coordinate matrix serves as an input parameter of the chaos scheduling module.

[0049] The spatiotemporal instance generator receives the standardized spatiotemporal event stream output by the protocol adapter module and creates multiple parallel computing instances based on the uncertainty measure of the event's spatiotemporal distribution. Each instance is configured with a different spatiotemporal coordinate system and event sampling frequency parameters, forming a diversified computing perspective on the same data stream.

[0050] The quantum tunneling fusion engine performs a quantum tunneling operation on the entity feature vectors output by the parallel computing instances. This operation projects the feature vectors into Hilbert space and generates a fused feature vector based on the principle of quantum state superposition. This fusion process preserves the temporal and spatial characteristics of each instance and eliminates interfering noise between features.

[0051] The association topology construction unit analyzes the spatial distribution characteristics of the fused feature vectors and constructs a device association topology model consisting of a three-dimensional coordinate matrix. This matrix accurately represents the spatial relative positions and association strengths between devices. The three-dimensional coordinates correspond to longitude, latitude, and altitude information, which serve as the basis for task dispatching in the chaos scheduling module.

[0052] Specifically, the multi-AI algorithm collaborative intelligent management system based on a large model described in the present invention, the chaos 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 correlation entropy value to the parameter ρ; The chaotic trajectory generator calculates the Lorentz equation based on the parameters σ and ρ and outputs a chaotic trajectory point sequence; The task node allocator associates each chaotic trajectory point to the atomic task node to form a task chain fractal network with spatial coordinates; Fractal monitor, which 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 the container migration instructions to reallocate the atomic algorithm tasks, collects the task execution delay data and sends it to the interface optimization module.

[0053] The spatial parameter converter receives the three-dimensional coordinate matrix output by the federated modeling module and converts the spatial distribution dispersion of the devices in the matrix into the parameter σ of the Lorentz chaos equation. The uncertainty of the relationship between devices is converted into the parameter ρ. This conversion establishes a mathematical mapping relationship between spatial topological characteristics and chaotic system parameters.

[0054] The chaotic trajectory generator iteratively computes the Lorentz equation based on the parameters σ and ρ, outputting a sequence of chaotic trajectory points. The distribution of these trajectory points characterizes the dynamic characteristics of the system, and their spatial locations correspond to the logical execution order of the atomic task nodes.

[0055] The task node allocator maps chaotic trajectory points to atomic task nodes, constructing a fractal network of task chains with spatial coordinates. The network topology retains the nonlinear characteristics of the chaotic system, and the positions of task nodes are determined by the three-dimensional coordinates of the trajectory points.

[0056] The fractal monitor analyzes the Hausdorff dimension of the task chain fractal network in real time to quantify the complexity of the network structure. When the dimension value exceeds a set threshold, a container migration instruction is generated, triggering the dynamic reallocation of computing resources.

[0057] The edge executor executes the container migration instructions to redeploy the atomic algorithm tasks and simultaneously collects task execution latency data. This latency data is transmitted to the interface optimization module in real time, driving the dynamic adjustment of the interface mapping strategy.

[0058] Specifically, the interface optimization module of the multi-AI algorithm collaborative intelligent management system based on a large model of the present invention includes: The interface function constructor receives the 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; Tunneling optimization controller, which performs quantum tunneling operations in a quantum Boltzmann machine to break through the local minimum of the energy function; Dynamic convergence unit, monitors the convergence state of the energy function, when the energy change rate is less than 10⁻ 5 Activate configuration generation when The interface configuration generator outputs the interface field mapping relationship that can be executed by the target system and sends the abnormal configuration log to the closed-loop correction module.

[0059] The interface function constructor receives task execution delay data transmitted by the chaos scheduling module and analyzes the structural features of the output fields of the atomic algorithm tasks. This component extracts key data fields for tasks such as flame detection and face recognition, and builds the data foundation for the interface field mapping relationship.

[0060] 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 represents the overall fitness of the mapping scheme, providing a mathematical target for optimization calculations.

[0061] The tunneling optimization controller performs quantum tunneling operations in a quantum Boltzmann machine, breaking through the local minimum 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 the search for the global optimal solution.

[0062] 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 is continuously below the convergence threshold. This mechanism identifies the steady-state convergence state of the energy function and avoids premature termination of the optimization process.

[0063] The interface configuration generator outputs the target system's executable interface field mappings and simultaneously logs configuration execution exceptions. The exception logs include the field matching error type and temporal and spatial characteristics, which are transmitted in real time to the closed-loop correction module to drive protocol rule updates.

[0064] Specifically, the multi-AI algorithm collaborative intelligent management system based on a large model described in the present invention also includes: The spatial coordinate parser receives the device association topology model output by the federated modeling module and extracts the three-dimensional geographic coordinate matrix in the model; The edge node matcher calculates the topological distance between each edge computing node and the coordinates in the three-dimensional geographic coordinate matrix to generate a node association matrix; The quantum annealing dispatcher encodes the node correlation matrix into a QUBO function and generates an optimized task dispatching solution through quantum annealing optimization; Dynamic tuning unit, which monitors task execution delay data and reactivates quantum annealing optimization when the cross-node communication delay exceeds 20ms; The edge node execution unit executes the optimized task dispatch plan, collects device status data and feeds it back to the federated modeling module.

[0065] The spatial coordinate resolver receives the device-association topology model output by the federated modeling module and parses the three-dimensional geographic coordinate matrix of the device nodes in the model. This matrix, consisting of longitude, latitude, and altitude data, accurately represents the physical location of the devices. The parsing process extracts topological features from the coordinate data, providing a spatial relationship foundation for edge computing node matching.

[0066] The edge node matcher calculates the topological distance between edge computing nodes and device 3D coordinates, generating a node association matrix. The association metric quantifies the spatial coupling strength between computing nodes and device locations. Distance calculations integrate network transmission path characteristics with geographic spatial relationships to form optimal constraints for task dispatch.

[0067] The quantum annealing dispatcher encodes the node association matrix as a quadratic unconstrained binary optimization problem function. Through quantum annealing optimization, it searches for the global optimal solution in the solution space and generates a dispatch plan for atomic tasks to edge computing nodes. The quantum tunneling effect overcomes the local optimality limitations of traditional optimization algorithms.

[0068] The dynamic tuning unit monitors the cross-node communication delay data during the execution of atomic tasks. When the communication delay exceeds a set threshold, the quantum annealing optimization is reactivated, triggering a dynamic update of the task dispatching scheme to respond to changes in network status.

[0069] The edge node execution unit executes the optimized task dispatch plan and simultaneously collects device operating status data. This status data, including resource load rate and task execution trajectory, is fed back to the federated modeling module to drive topology model updates.

[0070] Specifically, the closed-loop correction module of the multi-AI algorithm collaborative intelligent management system based on a large model of the present invention includes: The abnormal trace parser detects abnormal configuration logs sent by the interface configuration generator and extracts the spatiotemporal characteristics of interface configuration failure events; The quantum countermeasure generator synthesizes abnormal message data carrying quantum noise characteristics based on spatiotemporal characteristics; The protocol retraining unit injects abnormal message data into the signature spectrum analysis unit of the protocol adaptation module, triggering incremental training of the pre-trained Transformer model; The characteristic spectrum calibrator monitors the distribution offset of the device communication characteristic spectrum 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.

[0071] The anomaly 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. The spatiotemporal characteristics include the temporal correlation and spatial clustering patterns of events, providing a data foundation for adversarial sample generation.

[0072] The quantum adversarial generator synthesizes anomalous message data with quantum noise characteristics based on the extracted spatiotemporal features. The noise characteristics resonate with the device's communication frequency band. The message structure mimics the real device data format but injects protocol rule violations to generate training samples with attack characteristics.

[0073] The protocol retraining unit injects abnormal message data into the signature analysis unit of the protocol adaptation module, triggering incremental training of the pre-trained Transformer model. The training process retains the original model parameter knowledge and adjusts the feature separation layer weights based on protocol conflict characteristics to improve the model's anti-interference ability.

[0074] The signature calibrator monitors the distribution offset of device communication signatures and quantifies the mismatch between the protocol rule base and real-time data. When the offset exceeds a tolerance threshold, the protocol field mapping table is updated and the signature matching rules are adjusted to cover the newly emerged protocol conflict pattern.

[0075] The federated feedback unit transmits the updated protocol rules to the spatiotemporal instance generator in the federated modeling module, driving the reconstruction of the spatiotemporal parameters of the parallel computing instances. Parameter adjustments include compensating for coordinate system offsets and updating entropy calculation weights, enabling federated modeling to adapt to protocol rule changes.

[0076] Specifically, the multi-AI algorithm collaborative intelligent management system based on a large model described in the present invention also includes: The differential privacy injector receives the fused feature vector generated by the quantum tunneling fusion module 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, which responds to the quantum noise generation unit of the protocol adaptation module and generates a quantum key based on the noise pulse sequence; Encrypt the transmission channel, use quantum keys to encrypt the standardized spatiotemporal event stream, and send the encrypted data to the chaos scheduling module; The decryption verification unit decrypts data at the receiving end of the chaotic scheduling module and verifies the consistency of the quantum key; The collaborative audit unit, when detecting a quantum key verification failure, synchronously triggers the differential privacy injector to reset the noise parameter λ and the quantum key negotiator to renegotiate the key.

[0077] The differential privacy injector receives the fused feature vectors transmitted by the quantum tunneling fusion module of the federated modeling module and injects noise perturbations that conform to the Laplace distribution into the feature vectors during transmission. The noise intensity parameter controls the perturbation amplitude, balancing privacy protection strength with feature availability to prevent model inversion attacks from leaking sensitive information.

[0078] The quantum key agreement device responds to the pulse sequence output by the quantum noise generation unit of the protocol adapter module and generates a cryptographic key based on the random properties of quantum physics. The key generation process leverages the unpredictable noise in the device's communication frequency band to establish a physical layer security foundation.

[0079] The encrypted transmission channel uses quantum keys to symmetric encrypt the standardized spatiotemporal event stream output by the protocol adapter module, generating a ciphertext data stream that is transmitted to the chaos scheduling module. This encryption process ensures the secure transmission of sensitive information such as the spatial coordinates and timestamps of spatiotemporal events.

[0080] The decryption verification unit uses the quantum key at the receiving end of the chaotic scheduling module to decrypt the ciphertext data and simultaneously verify the key consistency. This verification mechanism detects man-in-the-middle attacks and key tampering, ensuring the integrity of data transmission and the authenticity of the source.

[0081] The collaborative audit unit monitors quantum key verification results and, upon detecting a verification failure, simultaneously triggers a privacy parameter reset and key update process. The reset adjusts the differential privacy noise intensity, while the key update renegotiates the communication key, forming a joint protection mechanism driven by security events.

[0082] Specifically, the multi-AI algorithm collaborative intelligent management system based on a large model described in the present invention, 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; Phase rotation optimizer, which performs quantum phase rotation operation on each set of solutions, and the rotation angle is associated with the task execution delay data sent by the chaos scheduling module; Sonar tunneling detectors simulate quantum tunneling effects in the Boltzmann machine energy space, breaking through the local optimal trap; Dynamic convergence monitor, which calculates the rate of change of the energy function gradient in real time. When the rate of change is less than 10⁻ for three consecutive iterations, 5 Activate mapping output when The optimal mapping generator outputs the mapping relationship with the lowest interface field mapping error rate, and simultaneously generates a quantum optimization trajectory log and sends it to the closed-loop correction module.

[0083] The quantum initialization solver receives the atomic task output fields extracted by the interface function constructor and generates multiple sets of initial interface mapping schemes. The scheme construction covers different field matching combinations, forming the search space foundation of the optimization algorithm and avoiding local optimal traps.

[0084] The phase rotation optimizer performs quantum phase rotation on each initial solution. The rotation angle is dynamically adjusted based on the task execution delay data transmitted by the chaos 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.

[0085] The sonar tunneling detector simulates the quantum tunneling effect in the energy space of a Boltzmann machine, breaking through the local minimum of the energy function. The detector penetrates the energy barrier through the quantum state transition mechanism, enabling wide-area exploration of the global solution space.

[0086] The dynamic convergence monitor calculates the rate of change of the energy function gradient in real time to identify convergence trends in the optimization process. When the rate of change falls below the convergence threshold for multiple consecutive iterations, an output mechanism is activated to avoid mapping scheme defects caused by premature convergence.

[0087] The optimal mapping generator outputs the optimization solution with the lowest interface field mapping error rate and simultaneously generates a quantum optimization 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.

[0088] Specifically, the multi-AI algorithm collaborative intelligent management system based on a large model described in the present invention also includes: A multi-source device orchestrator that coordinates end devices from at least three manufacturers in real time, including cameras, access controllers, and sensors; The task chain co-optimizer constructs an 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 of the trajectory tracking task; The device and task mapper dynamically selects the atomic algorithm task combination based on the terminal device type, giving priority to flame detection tasks for camera data and face recognition tasks for access controller data. The cross-vendor feedback unit collects the latency difference data of the atomic algorithm tasks executed by devices of different vendors and sends it to the fractal monitor of the chaos scheduling module to optimize the container migration strategy.

[0089] The Multi-Source Device Coordinator coordinates the communication protocols of terminal devices from at least three manufacturers in real time, including cameras, access controllers, and sensors. This component establishes a unified device command distribution channel, shielding interface differences between devices from different manufacturers and enabling unified scheduling and management of heterogeneous device resources.

[0090] The task chain co-optimizer constructs a cascaded execution chain of atomic algorithm tasks. It uses the spatial coordinates output by the flame detection task as regional constraints for the face recognition task, and the identity information from the face recognition results as the basis for behavioral analysis in the trajectory tracking task. The data flow between tasks forms a progressive analysis chain from space to identity to behavior.

[0091] The device and task mapper dynamically assigns atomic algorithm task combinations based on the terminal device type. Video stream data collected by cameras is prioritized for flame detection tasks, event-triggered data from access controllers is directed to face recognition tasks, and sensor time series data is associated with trajectory tracking tasks, achieving the optimal match between device capabilities and algorithm requirements.

[0092] The cross-vendor feedback unit collects latency data on atomic algorithm tasks executed by devices from different vendors, quantifying the execution efficiency deviations caused by protocol differences. This latency characteristic data is sent in real time to the fractal monitor in the chaos scheduling module, driving the optimization of the weight parameters of the container migration strategy to eliminate vendor compatibility differences.

[0093] At the protocol analysis layer, quantum entanglement separation technology identifies device communication characteristic spectra by simulating the superposition characteristics of quantum states. Its essence is to extract the frequency domain fingerprint features of multi-source device messages. The pre-trained Transformer model uses a self-attention mechanism to separate effective signals from noise components. Its training process learns the protocol pattern library of devices from manufacturers such as Hikvision and Dahua. The protocol mapping engine dynamically matches protocol rules based on the characteristic 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 with a newly connected device, the closed-loop correction module generates adversarial samples carrying quantum noise, triggering the characteristic spectrum recalibration mechanism to update the protocol rule library.

[0094] 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 spatiotemporal event streams. The quantum tunneling mechanism fuses feature vectors through Hilbert space projection, essentially breaking through the feature alignment bottleneck of traditional federated learning. The generated three-dimensional coordinate matrix of the device association topology model accurately represents the spatial positional relationship between cameras and sensors. The chaotic scheduling module converts the device distribution characteristics in the matrix into Lorentz equation parameters and utilizes the inherent randomness of the chaotic system to generate a task fractal network. The task node allocator dynamically orchestrates 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, achieving adaptive optimization of edge computing resources.

[0095] At the interface optimization layer, a quantum Boltzmann machine encodes the mapping relationship between atomic task output fields and the target system interface as an energy function. A tunneling optimization controller simulates the quantum tunneling effect to break through local optimal solutions. Its phase rotation operation integrates edge task execution latency data, enabling the interface mapping strategy to respond in real time to system load changes. If the mapping solution output by the interface configuration generator fails to execute, the anomaly trajectory analyzer extracts spatiotemporal features to generate adversarial examples, forming a closed-loop feedback loop from interface anomalies to protocol recalibration.

[0096] In the security architecture, a differential privacy injector adds noise perturbations to feature vectors based on a Laplace distribution to prevent model inversion attacks from leaking device location information. A quantum key agreement device leverages the unpredictability of device communication noise to generate cryptographic keys, encrypting transmission channels to ensure the security of spatiotemporal event streams. A collaborative audit unit resets noise parameters and renegotiates keys when key verification fails, establishing a coordinated protection system across the physical and application layers.

[0097] Through the coupling of the above technologies, quantum protocol analysis eliminates the barriers to access of multi-source devices, federal quantum fusion realizes cross-algorithm feature collaboration, and chaotic fractal scheduling optimizes edge resource allocation. Ultimately, a dynamic closed loop of device access, algorithm collaboration, and interface configuration is formed in scenarios such as smart parks, solving the problem of algorithm collaboration failure caused by multi-source heterogeneous devices in the background technology.

[0098] In a multi-vendor device access scenario in a smart campus, the data access module collects the raw RTSP stream from Brand A cameras and proprietary protocol messages from Brand B access controllers in real time via a multi-source communication interface. After the quantum entanglement coordinator in the protocol adapter 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 interference-resistant transmission stream according to Hadamard matrix encoding rules, and a pre-trained Transformer model is used to isolate the device communication signature spectrum. This signature spectrum contains protocol fingerprint features. The protocol mapping engine matches the rule base and outputs a standardized spatiotemporal event stream, eliminating interruptions in personnel tracking caused by protocol differences between Hikvision and Dahua devices.

[0099] The federated modeling module's spatiotemporal instance generator creates three parallel computation instances based on the spatiotemporal entropy of the events, each parsing the camera and access control event streams using different spatiotemporal coordinate systems. The quantum tunneling fusion unit projects the eigenvectors output by the instances into Hilbert space and generates a fused eigenvector through quantum coherent superposition. The association topology construction unit then constructs a device association topology model based on this eigenvector. Its three-dimensional coordinate matrix accurately represents the spatial relationship between the camera and access control controller.

[0100] 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 a sequence of trajectory points, the task node allocator forms an execution chain for the flame detection and face recognition tasks. When the fractal monitor detects that the Hausdorff dimension of the task chain exceeds a threshold, the edge executor triggers container migration, dynamically allocating the flame detection task to the edge node closest to the camera.

[0101] The interface function constructor of the interface optimization module extracts the temperature coordinate field output by the flame detection task, and the quantum Boltzmann encoder encodes the mapping relationship between it and the security system interface field 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 solution. If the interface configuration of the shopping mall passenger flow analysis system fails, the quantum adversarial generator of the closed-loop correction module synthesizes abnormal messages carrying quantum noise and injects them into the protocol adapter module, triggering the recalibration of the characteristic spectrum. When the characteristic spectrum calibrator detects that the distribution offset exceeds the standard, it updates the protocol field mapping table, and the federated feedback unit synchronously drives the spatiotemporal instance generator to reconstruct the calculation parameters.

[0102] In terms of security mechanisms, the differential privacy injector adds Laplace noise to the fused feature vector during transmission. The quantum key agreement generates a key based on the noise pulses and then encrypts the spatiotemporal event stream via an encrypted transmission channel. If the decryption verification unit detects a quantum key verification failure, the collaborative audit unit simultaneously triggers a noise parameter reset and key renegotiation, ensuring the security of cross-vendor camera data during transmission.

[0103] By combining these technologies, the system enables real-time facial recognition tasks for access controllers using camera flame detection results in a smart campus project, minimizing latency in the coordinated response of multiple algorithms. The closed-loop correction module cumulatively triggers multiple protocol signature recalibrations, enabling plug-and-play integration of newly connected cameras without protocol changes, effectively resolving the algorithm coordination barriers inherent in prior art technologies caused by heterogeneous multi-source devices.

[0104] This invention addresses the issue of protocol incompatibility among multi-source devices through a quantum protocol analysis layer. The protocol adaptation module utilizes a quantum entanglement separator to identify device communication signatures, converting raw messages from different manufacturers into a unified, standardized spatiotemporal event stream. A quantum noise injection mechanism dynamically adapts the device communication frequency band, and a pre-trained Transformer model separates valid signals from interference components, establishing device-independent protocol analysis capabilities and eliminating data interoperability barriers caused by protocol differences.

[0105] The federated modeling module overcomes algorithmic collaboration barriers through quantum-level feature fusion. A cross-instance quantum tunneling mechanism projects the feature vectors of parallel computing instances into Hilbert space, generating a three-dimensional topological model that integrates device relationships. This model's spatial coordinate matrix drives the chaotic scheduling module to generate a fractal network of atomic tasks. The Lorentz chaotic equations transform device distribution characteristics into task scheduling parameters, enabling dynamic dispatch and resource optimization of atomic tasks such as flame detection and facial recognition.

[0106] The system establishes a cross-module feedback loop to ensure continuous collaboration. The closed-loop correction module generates quantum adversarial samples when it detects an interface configuration anomaly, triggering a recalibration of the device's communication signature spectrum and an update of the protocol rule base. The updated protocol rules are synchronized to the federated modeling module to reconstruct instance parameters. The chaos scheduling module optimizes container migration strategies based on feedback latency data. This dynamic collaborative loop, from protocol parsing to algorithm scheduling to interface configuration, continuously eliminates the constraints imposed by device heterogeneity on AI collaborative analysis.

Claims

1. A multi-AI algorithm collaborative intelligent management system based on a large model, characterized by: include: Data access module, real-time collection of original messages from terminal devices of at least two types of manufacturers; A protocol adapter module receives the original message from the terminal device, identifies the device communication characteristic spectrum through the quantum entanglement separator, and outputs a standardized space-time event stream; a federated modeling module that receives the standardized spatiotemporal event stream and creates multiple parallel computing instances, each of which independently extracts entity feature vectors and fuses the feature vectors through a cross-instance quantum tunneling mechanism to generate a device association topology model; A chaos scheduling module analyzes the spatial coordinate matrix of the device association topology model, generates an atomic task chain fractal network using the Lorentz chaos equation, and dispatches 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 the quantum Boltzmann machine, and generates the target system interface configuration plan; The closed-loop correction module detects the failure event of the interface configuration scheme execution, converts the abnormal event trajectory into an adversarial sample, and injects the adversarial sample into the protocol adaptation module to trigger the recalibration of the device communication feature spectrum.

2. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 1 is characterized in that: The protocol adaptation module includes: A quantum entanglement coordinator, connected to the data access module, receiving original messages from the terminal device; a quantum noise generation unit, responsive to the quantum entanglement coordinator, generating a true random noise pulse sequence resonating with a device communication frequency band; A noise injection unit receives the original message of the terminal device and a true random noise pulse sequence, and generates an anti-interference transmission stream according to the Hadamard matrix coding rule; a characteristic spectrum analysis unit, which receives the anti-interference transmission stream, separates the quantum noise component and the device characteristic spectrum component through a pre-trained Transformer model, and outputs the device communication characteristic spectrum; The protocol mapping engine receives the device communication signature output by the signature analysis unit, matches 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.

3. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 2 is characterized in that: The federated modeling module includes: The spatiotemporal instance generator receives the 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 values of the events; The quantum tunneling fusion device performs quantum tunneling operations on the entity feature vectors of each parallel computing instance and generates a fused feature vector through 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, wherein the three-dimensional coordinate matrix serves as an input parameter of the chaos scheduling module.

4. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 3 is characterized in that: The chaos 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 correlation entropy value to the parameter ρ; The chaotic trajectory generator calculates the Lorentz equation based on the parameters σ and ρ and outputs a chaotic trajectory point sequence; The task node allocator associates each chaotic trajectory point to the atomic task node to form a task chain fractal network with spatial coordinates; Fractal monitor, which 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 the container migration instructions to reallocate the atomic algorithm tasks, collects the task execution delay data and sends it to the interface optimization module.

5. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 4 is characterized in that: The interface optimization module includes: The interface function constructor receives the 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; Tunneling optimization controller, which performs quantum tunneling operations in a quantum Boltzmann machine to break through the local minimum of the energy function; Dynamic convergence unit, monitors the convergence state of the energy function, when the energy change rate is less than 10⁻ 5 Activate configuration generation when The interface configuration generator outputs the interface field mapping relationship that can be executed by the target system and sends the abnormal configuration log to the closed-loop correction module.

6. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 5 is characterized in that: Also includes: The spatial coordinate parser receives the device association topology model output by the federated modeling module and extracts the three-dimensional geographic coordinate matrix in the model; The edge node matcher calculates the topological distance between each edge computing node and the coordinates in the three-dimensional geographic coordinate matrix to generate a node association matrix; The quantum annealing dispatcher encodes the node correlation matrix into a QUBO function and generates an optimized task dispatching solution through quantum annealing optimization; Dynamic tuning unit, which monitors task execution delay data and reactivates quantum annealing optimization when the cross-node communication delay exceeds 20ms; The edge node execution unit executes the optimized task dispatch plan, collects device status data and feeds it back to the federated modeling module.

7. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 6 is characterized in that: The closed-loop correction module includes: The abnormal trace parser detects abnormal configuration logs sent by the interface configuration generator and extracts the spatiotemporal characteristics of interface configuration failure events; The quantum countermeasure generator synthesizes abnormal message data carrying quantum noise characteristics based on spatiotemporal characteristics; The protocol retraining unit injects abnormal message data into the signature spectrum analysis unit of the protocol adaptation module, triggering incremental training of the pre-trained Transformer model; The characteristic spectrum calibrator monitors the distribution offset of the device communication characteristic spectrum 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.

8. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 7 is characterized in that: Also includes: The differential privacy injector receives the fused feature vector generated by the quantum tunneling fusion module 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, which responds to the quantum noise generation unit of the protocol adaptation module and generates a quantum key based on the noise pulse sequence; Encrypt the transmission channel, use quantum keys to encrypt the standardized spatiotemporal event stream, and send the encrypted data to the chaos scheduling module; The decryption verification unit decrypts data at the receiving end of the chaotic scheduling module and verifies the consistency of the quantum key; The collaborative audit unit, when detecting a quantum key verification failure, synchronously triggers the differential privacy injector to reset the noise parameter λ and the quantum key negotiator to renegotiate the key.

9. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 8 is 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; Phase rotation optimizer, which performs quantum phase rotation operation on each set of solutions, and the rotation angle is associated with the task execution delay data sent by the chaos scheduling module; Sonar tunneling detectors simulate quantum tunneling effects in the Boltzmann machine energy space, breaking through the local optimal trap; Dynamic convergence monitor, which calculates the rate of change of the energy function gradient in real time. When the rate of change is less than 10⁻ for three consecutive iterations, 5 Activate mapping output when The optimal mapping generator outputs the mapping relationship with the lowest interface field mapping error rate, and simultaneously generates a quantum optimization trajectory log and sends it to the closed-loop correction module.

10. The multi-AI algorithm collaborative intelligent management system based on a large model according to claim 9 is characterized in that: Also includes: A multi-source device orchestrator that coordinates end devices from at least three manufacturers in real time, including cameras, access controllers, and sensors; The task chain co-optimizer constructs an 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 of the trajectory tracking task; The device and task mapper dynamically selects the atomic algorithm task combination based on the terminal device type, giving priority to flame detection tasks for camera data and face recognition tasks for access controller data. The cross-vendor feedback unit collects the latency difference data of the atomic algorithm tasks executed by devices of different vendors and sends it to the fractal monitor of the chaos scheduling module to optimize the container migration strategy.

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