Intelligent collaborative innovation incubation and campus safety fusion management platform

Abstract

The invention provides an intelligent collaborative innovation incubation and campus security fusion management platform, which relates to the technical field of fusion management and comprises a multi-modal data acquisition and fusion system, an intelligent decision and strategy optimization engine, an edge calculation and security communication architecture, a scenarized analysis and prediction module and a security assurance and model adaptation system. The multi-modal data acquisition and fusion system comprehensively collects multi-source data of a campus environment, personnel behaviors and equipment states through a multi-sensor array, realizes efficient fusion of different modal features by using a feature fusion algorithm based on tensor decomposition, provides a comprehensive and refined data basis for subsequent analysis, and improves the accuracy of data fusion. And the intelligent decision-making and strategy optimization engine learns and generates an optimal decision-making strategy for various scenes of campus safety management by virtue of a plurality of advanced algorithms such as a consensus double-Q network algorithm, an artificial fish swarm optimization algorithm based on Levy flight, a particle swarm optimization algorithm based on quantum behaviors and the like.

Description

Technical Field

[0001] The present invention relates to the field of integrated management technology, and in particular to an intelligent collaborative innovation incubation and campus safety integrated management platform. Background Art

[0002] In the current campus environment, the campus scale continues to expand, the number of students and faculty continues to increase, and the flow of personnel becomes more frequent. At the same time, innovation incubation projects, as an important way to cultivate students' innovation and practical abilities, have gradually emerged and flourished on campus. This has led to an unprecedented demand for campus safety and efficient management, which not only ensures the safety and order of daily teaching activities, but also creates a safe and stable environment for innovation incubation projects to promote their smooth development.

[0003] However, existing campus security management technologies have obvious shortcomings in many key areas:

[0004] Problem 1: In terms of data collection, traditional systems often rely on only a single type of sensor, such as deploying only surveillance cameras for video surveillance, which cannot fully perceive all kinds of information in the campus environment. This results in one-sided data collection and cannot reflect the overall status of the campus. For example, it is difficult to obtain all-round details of environmental parameters such as air quality, temperature and humidity changes, and personnel behavior on campus, which makes subsequent analysis lack sufficient data support.

[0005] The decision-making mechanism of the existing system is not intelligent enough. Most of them rely on simple rule settings and cannot make real-time and accurate decisions based on complex and ever-changing campus scenarios. For example, in access control management, it is difficult to deal with complex situations such as identity theft by relying solely on fixed identity authentication methods; in emergency response scenarios, it is impossible to quickly generate the optimal response strategy, resulting in low timeliness and accuracy of decision-making;

[0006] Problem 2: Insufficient security and privacy during data transmission. Traditional encryption communication technology is vulnerable to attacks and has the risk of data leakage. It is difficult to meet the growing demand for sensitive information protection on campus, such as students' personal privacy data and key data of innovation incubation projects.

[0007] The ability to analyze and predict different scenarios on campus is limited. In the management of large-scale crowds on campus, it is impossible to accurately predict the flow trend of the crowd, which often leads to safety hazards such as crowd congestion; in the analysis of students' mental health and emotions, there is a lack of effective multi-source data comprehensive analysis methods, making it difficult to achieve early warning; in the analysis of the correlation between campus and urban security situations, it is impossible to integrate multi-dimensional data to comprehensively predict potential security incidents;

[0008] Question 3: In terms of security guarantee and management, in the security management of innovation incubation projects, it is difficult to effectively protect project data and intellectual property rights in the existing technology. The management of campus materials is also relatively extensive. The procurement and inventory management lack intelligence and a security traceability mechanism, resulting in material waste or untimely supply, and it is impossible to ensure the clarity and traceability of the source and flow of materials.

[0009] Therefore, an intelligent collaborative innovation incubation and campus security integration management platform is needed to solve the above problems. Summary of the Invention

[0010] Technical Problems to be Solved

[0011] Aiming at the deficiencies of the existing technology, the present invention provides an intelligent collaborative innovation incubation and campus security integration management platform, which solves the problems mentioned in the above background technology.

[0012] Technical Solutions

[0013] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent collaborative innovation incubation and campus security integration management platform, including a multi-modal data acquisition and fusion system, an intelligent decision-making and strategy optimization engine, an edge computing and secure communication architecture, a scenario analysis and prediction module, and a security guarantee and model adaptation system;

[0014] The multi-modal data acquisition and fusion system collects multi-source data of the campus environment, personnel behavior, and equipment status by integrating a multi-sensor array composed of visual sensors, audio sensors, temperature and humidity sensors, and air quality sensors;

[0015] The intelligent decision-making and strategy optimization engine uses the dueling double Q-network algorithm in deep reinforcement learning, combines the artificial fish swarm optimization algorithm based on Levy flight and the particle swarm optimization algorithm based on quantum behavior, and aims at various scenarios in campus security management, including access control management and emergency response scenarios, to learn and generate optimal decision-making strategies, and at the same time dynamically adjusts the parameters of the deep reinforcement learning model to improve the accuracy and timeliness of decision-making;

[0016] The edge computing and secure communication architecture evenly deploys edge computing nodes on campus, uses a fog computing architecture based on a distributed hash table for collaborative processing, and in the process of data transmission, uses an encrypted communication technology combining quantum key distribution and homomorphic encryption to ensure the security and privacy of data;

[0017] The scenario analysis and prediction module aims at the management of large-scale activity crowds on campus, combines the ant colony-particle swarm hybrid algorithm based on dynamic weights, the simulated annealing algorithm guided by tabu search, and the neural network algorithm based on fuzzy logic to achieve accurate prediction of crowd flow and generation of dynamic guidance strategies;

[0018] In the security guarantee and model adaptation system, in the security management of innovation incubation projects, a multi-modal recognition technology that integrates three biometric features of finger vein, voiceprint, and gait is applied, combined with an access control and data traceability mechanism based on blockchain to ensure the security of project data and intellectual property rights.

[0019] Preferably, the multi-modal information integration module fuses different modal features such as visual, audio, and environmental parameters through a feature fusion algorithm based on tensor decomposition, providing a comprehensive and refined data basis for subsequent analysis. The feature fusion algorithm based on tensor decomposition uses the high-order singular value decomposition method to decompose the multi-modal data tensor, determines the decomposed low-rank tensor representation by minimizing the reconstruction error to achieve effective fusion of each modal feature. The multi-sensor array adopts a self-organizing network topology structure to realize automatic networking and data collaborative transmission between sensors, and based on the feature fusion algorithm of tensor decomposition, performs tensor decomposition through the alternating least squares method, and uses nuclear norm minimization for low-rank approximation to improve the efficiency and accuracy of feature fusion. In the self-organizing network topology structure, the sensor nodes adopt a routing algorithm based on the AODV (Ad-hoc On-Demand Distance Vector) protocol to dynamically select and maintain the data transmission path to ensure reliable data transmission. In the feature fusion algorithm based on tensor decomposition, the alternating least squares method iteratively updates the factor matrix of the tensor to gradually approximate the optimal decomposition result, and the nuclear norm minimization constrains the rank of the decomposed tensor by introducing a nuclear norm regularization term to improve the efficiency and accuracy of feature fusion.

[0020] Preferably, in the intelligent decision-making and policy optimization engine, the network structure of the duel double Q-network algorithm includes two branches of advantage flow and value flow, which are respectively used to evaluate action advantages and state values to improve the stability and efficiency of decision-making. The artificial fish swarm optimization algorithm based on Levy flight simulates the foraging behavior of fish swarms and uses the Levy flight strategy for global exploration and local development in the search space to optimize decision-making strategies; the particle swarm optimization algorithm based on quantum behavior represents the particle position using the superposition state of quantum bits and updates the particle state through quantum rotation gate operations to enhance the global search ability of the algorithm.

[0021] Preferably, on the edge computing nodes of the edge computing and secure communication architecture, a K-nearest neighbor algorithm integrated with an extreme learning machine, a random fern classifier, and cuckoo search optimization is used to quickly analyze and process the collected real-time data, realizing real-time monitoring and early warning of abnormal behaviors and security risks. The fog computing architecture based on a distributed hash table uses the Chord protocol to construct a distributed hash table, realizing efficient storage and lookup of data. The encryption communication technology combining quantum key distribution and homomorphic encryption uses the decoy state BB84 protocol to generate secure keys during the quantum key distribution process, and the homomorphic encryption uses a cryptosystem based on the ring learning with errors (RLWE) problem to realize additive and multiplicative homomorphic calculations on ciphertexts. Among the integrated algorithms, the extreme learning machine randomly initializes the weights and biases of the input layer and quickly calculates the weights of the output layer to achieve fast learning. The random fern classifier constructs decision ferns by randomly selecting individual features and thresholds to classify data. Among them, the cuckoo search algorithm optimizes the K value and sample weights of the K-nearest neighbor algorithm by simulating the brood parasitism behavior of cuckoos to improve the classification accuracy.

[0022] Preferably, in terms of student mental health and emotion analysis, the scenario analysis and prediction module uses a support vector machine optimized by a quantum genetic algorithm, a long short-term memory network combined with an attention mechanism, and a feature selection algorithm based on information entropy to deeply analyze the multi-source emotion data of students, realizing accurate assessment and early warning of students' mental states. In the correlation analysis of campus and urban security situations, a graph convolutional network based on a spatio-temporal attention mechanism, a gradient boosting tree based on Bayesian optimization, and a Markov chain model based on grey correlation analysis are integrated to comprehensively analyze the security data of the campus and its surrounding areas, predicting the occurrence probability and influence range of potential security events.

[0023] Preferably, in terms of campus material management, the security guarantee and model adaptation system uses blockchain-based smart contract technology and a material demand prediction model based on deep learning to realize the intelligence and secure traceability of campus material procurement and inventory management. At the same time, for campus security and innovative application scenarios, a model adaptive adjustment mechanism combining bat algorithm, grey wolf optimization algorithm, and Markov logic network algorithm is used to dynamically optimize and adapt the analysis and prediction models to ensure that the platform always operates efficiently and stably in a complex and changing campus environment.

[0024] Preferably, in the process of feature extraction of the multi-modal recognition technology that combines three biometric features in the security guarantee and model adaptation system, an end-to-end feature learning is respectively carried out by using a finger vein feature extraction model based on a convolutional neural network, a voiceprint feature extraction model based on a long short-term memory network, and a gait feature extraction model based on pose estimation and a recurrent neural network. In the feature fusion stage, a fusion network based on a multi-layer perceptron is used for feature fusion to improve the recognition accuracy.

[0025] Preferably, the access control and data traceability mechanism based on the blockchain in the security guarantee and model adaptation system uses the Ethereum blockchain platform, and smart contracts are used to manage the access rights of innovation incubation project data and trace the operation records; at the same time, the smart contract technology based on the blockchain uses the Ethereum blockchain platform, and Solidity language is used to write smart contracts to automate the order generation, approval, delivery, and receipt confirmation links in the material procurement process, and to intelligentize the inventory counting and replenishment reminder functions in inventory management, and the security traceability of material management data is realized through the distributed ledger of the blockchain.

[0026] Preferably, the material demand prediction model based on deep learning in the security guarantee and model adaptation system uses a model based on long short-term memory network (LSTM) and attention mechanism, combines historical material procurement data, campus activity arrangements, and seasonal factors to predict material demand, and finally combines a model adaptive adjustment mechanism based on bat algorithm, grey wolf optimization algorithm, and Markov logic network algorithm. Among them, the bat algorithm dynamically adjusts the pulse emission frequency and loudness according to the accuracy and recall performance indicators of the model to balance the global search and local search capabilities. In the process of group cooperation, the grey wolf optimization algorithm dynamically adjusts the roles of the leading wolves (α wolf, β wolf, δ wolf) according to the performance of the model, guiding the group to search for the optimal solution. Among them, the Markov logic network algorithm uses first-order logic rules and probabilistic graph models to reason and optimize the structure and parameters of the model.

[0027] Beneficial effects

[0028] The present invention provides an intelligent collaborative innovation incubation and campus security integrated management platform, which has the following beneficial effects:

[0029] 1. In the multi-modal data acquisition and fusion system of the present invention, multi-source data of the campus environment, personnel behavior, and equipment status are comprehensively collected through a multi-sensor array. The feature fusion algorithm based on tensor decomposition is used to achieve efficient fusion of different modal features, providing a comprehensive and refined data basis for subsequent analysis, greatly improving the integrity and usability of the data, helping to mine more valuable information. The intelligent decision-making and strategy optimization engine uses a variety of advanced algorithms such as the duel double Q-network algorithm, the artificial fish swarm optimization algorithm based on Lévy flight, and the particle swarm optimization algorithm based on quantum behavior to learn and generate optimal decision-making strategies for various scenarios of campus security management. At the same time, it dynamically adjusts the parameters of the deep reinforcement learning model, significantly improving the accuracy and timeliness of decision-making, and being able to quickly and effectively respond to various campus security situations.

[0030] 2. In the edge computing and secure communication architecture of the present invention, an encrypted communication technology combining quantum key distribution and homomorphic encryption is adopted to ensure the security and privacy of data during the data transmission process. At the same time, evenly deployed edge computing nodes and a fog computing architecture based on a distributed hash table cooperate to process data, realizing efficient data storage, search, and analysis, and improving the overall efficiency of campus data processing.

[0031] 3. In the scenario analysis and prediction module of the present invention, for the management of large-scale event crowds on campus, a combination of multiple intelligent algorithms is used to achieve accurate prediction of crowd flow and generation of dynamic diversion strategies; in aspects such as student mental health and emotional analysis, and the correlation analysis of campus and urban security situations, accurate assessment, early warning, and prediction of potential security events of relevant states are also achieved through a series of advanced algorithms, providing strong decision-making support for campus security management.

[0032] 4. In the security guarantee and model adaptation system of the present invention, in the security management of innovation incubation projects, multi-modal recognition technology and an access control and data traceability mechanism based on blockchain are applied to ensure the security of project data and intellectual property rights; in campus material management, intelligent contract technology based on blockchain and a material demand prediction model based on deep learning are used to realize the intelligence and secure traceability of material procurement and inventory management, improving the efficiency and security of campus material management. The security guarantee and model adaptation system combines a model adaptive adjustment mechanism based on the bat algorithm, the grey wolf optimization algorithm, and the Markov logic network algorithm to dynamically optimize and adapt the analysis and prediction models, enabling the platform to always maintain efficient and stable operation in a complex and changing campus environment and adapting to the ever-changing campus security management needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a framework diagram of the fusion management platform of the present invention;

[0034] Figure 2 It is a flowchart of the fusion management platform of the present invention. Detailed implementation manners

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Specific Embodiment 1:

[0037] As Figure 1-2 shown, the intelligent collaborative innovation incubation and campus security integration management platform includes a multi-modal data acquisition and fusion system, an intelligent decision-making and strategy optimization engine, an edge computing and secure communication architecture, a scenario analysis and prediction module, and a security guarantee and model adaptation system;

[0038] The multi-modal data acquisition and fusion system collects multi-source data of the campus environment, personnel behavior, and equipment status by using a multi-sensor array composed of visual sensors, audio sensors, temperature and humidity sensors, and air quality sensors;

[0039] The intelligent decision-making and strategy optimization engine uses the dueling double Q-network algorithm in deep reinforcement learning, combines the artificial fish swarm optimization algorithm based on Levy flight and the particle swarm optimization algorithm based on quantum behavior, and aims at various scenarios in campus security management, including access control management and emergency response scenarios, to learn and generate optimal decision-making strategies, and at the same time dynamically adjusts the parameters of the deep reinforcement learning model to improve the accuracy and timeliness of decision-making;

[0040] The edge computing and secure communication architecture evenly deploys edge computing nodes on campus, uses a fog computing architecture based on a distributed hash table for collaborative processing, and uses an encrypted communication technology combining quantum key distribution and homomorphic encryption during data transmission to ensure the security and privacy of data;

[0041] The scenario analysis and prediction module aims at the management of large-scale activity crowds on campus, combines the ant colony-particle swarm hybrid algorithm based on dynamic weights, the simulated annealing algorithm guided by tabu search, and the neural network algorithm based on fuzzy logic to achieve accurate prediction of crowd flow and generation of dynamic diversion strategies;

[0042] The security guarantee and model adaptation system applies multi-modal recognition technology that integrates three biometric features of finger vein, voiceprint, and gait in the security management of innovation incubation projects, and combines the access control and data traceability mechanism based on blockchain to ensure the security of project data and intellectual property rights.

[0043] The multi-modal information integration module fuses different modal features such as vision, audio, and environmental parameters through a feature fusion algorithm based on tensor decomposition, providing a comprehensive and refined data basis for subsequent analysis. The feature fusion algorithm based on tensor decomposition uses the high-order singular value decomposition method to decompose the multi-modal data tensor, and determines the decomposed low-rank tensor representation by minimizing the reconstruction error to achieve effective fusion of each modal feature. The multi-sensor array adopts a self-organizing network topology structure to realize automatic networking and data collaborative transmission among sensors. Based on the feature fusion algorithm of tensor decomposition, tensor decomposition is carried out by the alternating least squares method, and low-rank approximation is carried out by minimizing the nuclear norm to improve the efficiency and accuracy of feature fusion. In the self-organizing network topology structure, sensor nodes use a routing algorithm based on the AODV (Ad-hoc On-Demand Distance Vector) protocol to dynamically select and maintain the data transmission path to ensure reliable data transmission. In the feature fusion algorithm based on tensor decomposition, the alternating least squares method gradually approaches the optimal decomposition result by iteratively updating the factor matrix of the tensor, and the nuclear norm minimization improves the efficiency and accuracy of feature fusion by introducing a nuclear norm regularization term to constrain the rank of the decomposed tensor.

[0044] In the intelligent decision-making and policy optimization engine, the network structure of the duel double Q-network algorithm includes two branches: the advantage stream and the value stream, which are used to evaluate the action advantage and the state value respectively to improve the stability and efficiency of decision-making. The artificial fish swarm optimization algorithm based on Levy flight simulates the foraging behavior of fish swarms and uses the Levy flight strategy for global exploration and local exploitation in the search space to optimize the decision-making strategy. The particle swarm optimization algorithm based on quantum behavior represents the particle position using the superposition state of quantum bits and updates the particle state through quantum rotation gate operations to enhance the global search ability of the algorithm.

[0045] Edge Computing and Secure Communication Architecture On edge computing nodes, by using a K-nearest neighbor algorithm integrated with an extreme learning machine, a random fern classifier, and cuckoo search optimization, real-time data collected is quickly analyzed and processed to achieve real-time monitoring and early warning of abnormal behaviors and security risks. The fog computing architecture based on a distributed hash table uses the Chord protocol to construct a distributed hash table to achieve efficient data storage and lookup. The encryption communication technology combining quantum key distribution and homomorphic encryption adopts the decoy-state BB84 protocol to generate secure keys during the quantum key distribution process, and homomorphic encryption uses a cryptosystem based on the ring learning with errors (RLWE) problem to achieve additive and multiplicative homomorphic computations on ciphertexts. Among the integrated algorithms, the extreme learning machine randomly initializes the weights and biases of the input layer and quickly calculates the weights of the output layer to achieve fast learning. The random fern classifier constructs decision ferns by randomly selecting individual features and thresholds to classify data. The cuckoo search algorithm optimizes the K value and sample weights of the K-nearest neighbor algorithm by simulating the brood parasitism behavior of cuckoos to improve classification accuracy.

[0046] Scenario Analysis and Prediction Module In terms of students' mental health and emotional analysis, by using a support vector machine optimized by a quantum genetic algorithm, a long short-term memory network combined with an attention mechanism, and a feature selection algorithm based on information entropy, multi-source emotional data of students is deeply analyzed to achieve accurate assessment and early warning of students' mental states. In the correlation analysis of campus and urban security situations, by integrating a graph convolutional network based on a spatio-temporal attention mechanism, a gradient boosting tree based on Bayesian optimization, and a Markov chain model based on grey relational analysis, security data in the campus and its surrounding areas is comprehensively analyzed to predict the occurrence probability and impact range of potential security events.

[0047] Security Assurance and Model Adaptation System In terms of campus material management, by using blockchain-based smart contract technology and a material demand prediction model based on deep learning, the intelligentization and secure traceability of campus material procurement and inventory management are achieved. At the same time, for campus security and innovative application scenarios, combined with a model adaptive adjustment mechanism based on bat algorithm, grey wolf optimization algorithm, and Markov logic network algorithm, the analysis and prediction models are dynamically optimized and adapted to ensure that the platform always maintains efficient and stable operation in a complex and changing campus environment.

[0048] Security Assurance and Model Adaptation System In the multi-modal recognition technology integrating three biometric features, during the feature extraction process, an end-to-end feature learning is respectively carried out by using a finger vein feature extraction model based on a convolutional neural network, a voiceprint feature extraction model based on a long short-term memory network, and a gait feature extraction model based on pose estimation and a recurrent neural network. In the feature fusion stage, a fusion network based on a multi-layer perceptron is used for feature fusion to improve the recognition accuracy.

[0049] In the security guarantee and model adaptation system, the access control and data traceability mechanism based on blockchain uses the Ethereum blockchain platform and utilizes smart contracts to manage the access rights to the data of innovation incubation projects and trace the operation records. At the same time, the smart contract technology based on blockchain uses the Ethereum blockchain platform and writes smart contracts in Solidity language to automate the order generation, approval, delivery, and receipt confirmation links in the material procurement process, as well as to intelligentize the inventory counting and replenishment reminder functions in inventory management, and realizes the secure traceability of material management data through the distributed ledger of blockchain.

[0050] In the security guarantee and model adaptation system, the material demand prediction model based on deep learning uses a model based on long short-term memory network (LSTM) and attention mechanism, combines historical material procurement data, campus activity arrangements, and seasonal factors to predict material demand, and finally combines a model adaptive adjustment mechanism based on bat algorithm, grey wolf optimization algorithm, and Markov logic network algorithm. Among them, the bat algorithm dynamically adjusts the pulse emission frequency and loudness according to the accuracy and recall performance indicators of the model to balance the global search and local search capabilities. In the process of group collaboration, the grey wolf optimization algorithm dynamically adjusts the roles of the leading wolves (α wolf, β wolf, δ wolf) according to the performance of the model to guide the group to search for the optimal solution. Among them, the Markov logic network algorithm uses first-order logic rules and probabilistic graphical models to reason and optimize the structure and parameters of the model. Specific Embodiment 2:

[0052] As Figure 1-2 shown, the following is a detailed description of the content in Embodiment 1:

[0053] In the multi-modal data acquisition and fusion system, the visual sensor selects a high-definition CMOS image sensor, and the audio sensor uses a high-sensitivity MEMS microphone array, which can cover a sound collection range with a radius of 10 meters and accurately identify audio information such as speech and abnormal sounds. The measurement accuracies of the temperature and humidity sensors are ±0.5°C and ±3%RH respectively, and can feedback the changes in the campus environment temperature and humidity in real time and accurately. The air quality sensor can detect the concentrations of 5 common pollutants including formaldehyde, PM2.5, and VOC, providing comprehensive data support for environmental assessment.

[0054] In the self-organizing network topology of the multi-sensor array, during the route discovery process of the AODV protocol, the number of retransmissions of the route request (RREQ) is set to 3 times, and the route request timeout is set to 500 milliseconds to ensure the rapid establishment of a reliable data transmission path in a complex campus environment. When the network topology changes, the sensor nodes can complete route updates within 2 seconds to ensure the continuity of data transmission.

[0055] In the feature fusion algorithm based on tensor decomposition, when performing high-order singular value decomposition, the maximum number of iterations is set to 100, and the convergence accuracy is set to 1e-6 to ensure the accuracy of the decomposition result. When the alternating least squares method iteratively updates the tensor factor matrix, the learning rate is set to 0.01 to accelerate the convergence process. The regularization parameter of nuclear norm minimization takes a value of 0.001 to avoid overfitting while ensuring the low-rank approximation effect.

[0056] For the access control management scenario, the capacity of the experience replay pool of the duel double Q-network algorithm in the intelligent decision-making and policy optimization engine is set to 10,000. Each time, 64 samples are randomly sampled from the experience replay pool for network update to improve the training efficiency. When the artificial fish swarm optimization algorithm based on Lévy flight optimizes the access control management strategy, the fish swarm size is set to 50, the Lévy flight step factor is 0.5, and the search range is dynamically adjusted according to the size of the access control area to ensure finding the optimal personnel access management strategy under complex access control rules. In the particle swarm optimization algorithm based on quantum behavior, the number of particles is 30, and the update step size of the rotation angle of the quantum rotation gate is 0.05 to enhance the global search ability for the parameters of the access control management model.

[0057] In the emergency response scenario, the duel double Q-network algorithm sets different reward mechanisms. High rewards are given for fast and effective response behaviors. For example, when a fire occurs and the fire extinguishing equipment can be activated and personnel can be evacuated within 3 minutes, the reward value is 100. While penalties are given for wrong or delayed response behaviors. For example, if the response is delayed by more than 5 minutes, the penalty value is -50. The artificial fish swarm optimization algorithm based on Lévy flight optimizes the emergency response decision-making process by simulating the fast collaborative escape behavior of fish swarms in a dangerous environment. The particle swarm optimization algorithm based on quantum behavior uses the parallelism of quantum bits to quickly search for the optimal response strategy combinations in different emergency scenarios.

[0058] In the edge computing and secure communication architecture, 20 edge computing nodes are evenly deployed on campus. Each node is equipped with 8GB of memory, a 4-core CPU, and 16GB of local storage to meet the real-time data processing and caching requirements. In the fog computing architecture based on the distributed hash table, the SHA-256 algorithm is selected for the hash function of the Chord protocol to ensure the uniform distribution of data on distributed nodes.

[0059] During the quantum key distribution process, the signal state intensity of the decoy state BB84 protocol is set to 0.5, the decoy state intensity is set to 0.1, and the secure key generation rate reaches 100 bps, meeting the security and efficiency requirements of campus data transmission. Homomorphic encryption uses a cryptosystem based on the ring learning with errors (RLWE) problem, and the ciphertext expansion rate is 2 to ensure the efficiency and security during the ciphertext calculation process.

[0060] In the fusion algorithm, the number of hidden layer nodes of the extreme learning machine is set to 100, the value range of the randomly initialized input layer weights is [-0.5, 0.5], and the value range of the bias is [-0.5, 0.5]. Each decision fern of the random fern classifier randomly selects 5 features, and 3 thresholds are randomly generated for each feature to construct an efficient classification model. In the cuckoo search algorithm, the cuckoo population size is 30, the discovery probability is 0.2, and by continuously optimizing the K value (value range: 3 - 10) and sample weights of the K-nearest neighbor algorithm, the recognition accuracy for abnormal behaviors and security hazards is improved.

[0061] For the scenario analysis and prediction module for the management of large-scale campus event crowds, in the ant colony - particle swarm hybrid algorithm based on dynamic weights, when the crowd density is greater than 50 people per square meter and the flow velocity is less than 1 meter per second, the weight of the ant colony algorithm is set to 0.7 and the weight of the particle swarm algorithm is set to 0.3; when the crowd density is less than 30 people per square meter and the flow velocity is greater than 2 meters per second, the weights are reversed. In the simulated annealing algorithm guided by tabu search, the tabu list length is set to 20, the initial temperature is 100, and the cooling rate is 0.95, effectively avoiding falling into local optima during the search process. The neural network algorithm based on fuzzy logic constructs 10 fuzzy rules. For example, when the crowd density is "high" and the flow velocity is "slow", a crowd congestion warning signal is output, and by fuzzifying the input data, the adaptability and prediction accuracy for complex crowd scenarios are improved.

[0062] In terms of students' mental health and emotional analysis, for the support vector machine optimized by the quantum genetic algorithm, the population size of the quantum genetic algorithm is 50, the number of evolutionary generations is 50, the quantum crossover probability is 0.8, and the quantum mutation probability is 0.05. By optimizing the kernel function parameters (such as the γ value of the RBF kernel function) and the penalty factor C of the support vector machine, the accuracy of emotional analysis is improved. For the long short-term memory network combined with the attention mechanism, the attention mechanism adopts an additive attention model. By calculating the dot product of the input features at each time step and the attention weight vector, attention scores are generated, focusing on key emotional features. For the feature selection algorithm based on information entropy, the information entropy of each emotional feature is calculated, and the top 10 most representative emotional features with information entropy less than 0.5 are selected to reduce the model complexity, improve the training speed, and prediction accuracy.

[0063] In the correlation analysis of campus and urban security situations, for the graph convolutional network based on spatio-temporal attention mechanism, the spatio-temporal attention module calculates the attention coefficients of different nodes at different time steps through a multi-layer perceptron. The number of hidden layer nodes of the multi-layer perceptron is 50, and the ReLU activation function is selected. For the gradient boosting tree based on Bayesian optimization, the number of iterations of the Bayesian optimization algorithm is 30. Hyperparameters such as the learning rate of the gradient boosting tree (value range: 0.01 - 0.1), the depth of the tree (value range: 3 - 10), and the subsample ratio (value range: 0.5 - 1) are optimized to improve the prediction performance of the model for the security situation of the campus and its surrounding areas. For the Markov chain model based on grey correlation analysis, the grey correlation degrees between different security factors are calculated through grey correlation analysis. When the correlation degree is greater than 0.6, it is considered that there is a strong correlation between them in the Markov chain model, and corresponding weights are given in the estimation of state transition probabilities to more accurately predict the occurrence probability and influence range of potential security events.

[0064] In the security guarantee and model adaptation system for the security management of innovation incubation projects, for the finger vein feature extraction model based on convolutional neural network with multi-modal recognition technology integrating three biometric features, the convolutional layer uses 5 layers of 3×3 convolutional kernels, the pooling layer uses 2×2 max pooling, and finally the finger vein feature vector is output through the fully connected layer. For the voiceprint feature extraction model based on long short-term memory network, 3 hidden layers are set, and the number of nodes in each hidden layer is 128, which can effectively capture the temporal characteristics of voiceprint signals. For the gait feature extraction model based on pose estimation and recurrent neural network, the pose estimation uses the OpenPose algorithm based on deep learning, the recurrent neural network uses GRU units, 3 hidden layers are set, and the number of nodes in each hidden layer is 128 to accurately extract gait features. In the feature fusion stage, the fusion network based on multi-layer perceptron sets 3 hidden layers, the number of nodes in each hidden layer is 256, the Sigmoid activation function is selected, and through the fusion of three biometric features, the recognition accuracy rate reaches more than 95%.

[0065] In terms of campus material management, for the intelligent contract technology based on blockchain in the material procurement process, when an order is generated, information such as the name, specification, quantity, and supplier of the purchased items is automatically filled; in the approval link, the permissions of different levels of approval personnel are set, and the approval time limit is 3 working days; in the delivery and receipt confirmation link, by docking with the logistics information system, the goods status is automatically updated. In inventory management, the inventory check is set once a week. When the inventory is lower than the safety inventory (the safety inventory quantity is dynamically adjusted according to historical data and demand prediction), the replenishment reminder function is automatically triggered.

[0066] Material demand prediction model based on deep learning. In the model based on long short-term memory network (LSTM) and attention mechanism, 3 hidden layers are set in the LSTM layer, and the number of nodes in each hidden layer is 128. The attention mechanism calculates the attention weights of different input features such as historical material procurement data, campus activity arrangements, and seasonal factors, focuses on key information, and improves the accuracy of material demand prediction. Combining with the model adaptive adjustment mechanism based on bat algorithm, grey wolf optimization algorithm and Markov logic network algorithm, the bat algorithm dynamically adjusts the pulse emission frequency (frequency range: 20 - 200Hz) and loudness (loudness range: 40 - 80dB) according to the accuracy and recall performance indicators of the model to balance the global search and local search capabilities. In the process of group cooperation of the grey wolf optimization algorithm, according to the performance of the model (such as when the accuracy is increased by more than 5%), the roles of the leading wolves (α wolf, β wolf, δ wolf) are dynamically adjusted to guide the group to search for the optimal solution. The Markov logic network algorithm uses first-order logic rules and probabilistic graphical models to reason and optimize the structure (such as increasing or decreasing the number of LSTM hidden layers) and parameters (such as adjusting the weights of the forget gate, input gate, and output gate of LSTM) of the model to ensure that the platform always operates efficiently and stably in the complex and changeable campus environment. Specific Embodiment Three:

[0068] As Figure 1-2 shown, the following conducts a detailed analysis of some core algorithms mentioned in Embodiment One, including their core mathematical formulas and explanations:

[0069] 1. Dueling Double Q-Network Algorithm

[0070] Network Structure: It contains two branches, the advantage stream and the value stream.

[0071] Advantage Function: A(s, a; θ A ), which is used to evaluate the advantage of taking action a in state s, and θ A are the parameters of the advantage stream network.

[0072] Value Function: V(s; θ V ), which is used to evaluate the value of state s, and θ V are the parameters of the value stream network.

[0073] Q-Value Calculation: The Q-value is calculated by combining the advantage stream and the value stream to improve the stability and efficiency of decision-making.

[0074] 2. Artificial Fish School Optimization Algorithm Based on Lévy Flight

[0075] Lévy Flight: Lévy flight is a random walk process, and its step size distribution follows the Lévy distribution. The probability density function of the Lévy distribution is:

[0076]

[0077] where μ is the parameter of the Lévy distribution and Γ is the gamma function. In the artificial fish swarm optimization algorithm, the Lévy flight strategy is used for global exploration and local exploitation to optimize the decision-making strategy.

[0078] Foraging behavior of the artificial fish swarm: Artificial fish determine their moving direction and step size by sensing the food concentration and distance in the surrounding environment. During the foraging process, artificial fish continuously adjust their behavior according to their current state and the information of the surrounding environment to search for more food. The artificial fish swarm optimization algorithm based on Lévy flight simulates the foraging behavior of the fish swarm and uses the Lévy flight strategy for global exploration and local exploitation in the search space to optimize the decision-making strategy.

[0079] 3. Particle Swarm Optimization Algorithm Based on Quantum Behavior

[0080] Quantum bit represents the particle position: A quantum bit can be in a superposition state, that is, it can represent multiple states simultaneously. In the particle swarm optimization algorithm based on quantum behavior, the superposition state of the quantum bit is used to represent the position of the particle, enabling the particle to explore multiple positions simultaneously and enhancing the global search ability of the algorithm.

[0081] Quantum rotation gate operation updates the particle state: The quantum rotation gate is a quantum logic gate used to perform rotation operations on quantum bits. In the particle swarm optimization algorithm based on quantum behavior, the state of the particle is updated through the quantum rotation gate operation, enabling the particle to continuously adjust its position according to its own state and the information of the surrounding environment to find the optimal solution.

[0082] The mathematical expression of the quantum rotation gate operation is:

[0083]

[0084] where Δθ i is the rotation angle of the i-th quantum bit, α i and β i are the state vectors of the i-th quantum bit, α i ′ and β i ′ are the updated state vectors. By updating the state of the particle through the quantum rotation gate operation, the particle can continuously adjust its position according to its own state and the information of the surrounding environment to find the optimal solution.

[0085] 4. Support Vector Machine Optimized by Quantum Genetic Algorithm

[0086] Quantum Genetic Algorithm:

[0087] Quantum bit encoding: Genes of chromosomes are represented by quantum bits. A quantum bit can be represented as:

[0088] |ψ> = α|0> + β|1>

[0089] where α and β are complex numbers satisfying |α| 2 + |β| 2 = 1. Through the superposition state of quantum bits, multiple states can be represented simultaneously, thus increasing the diversity of the population.

[0090] Quantum rotation gate update: The quantum rotation gate is an operation used to update chromosomes in the quantum genetic algorithm. The rotation angle Δθ of the quantum rotation gate is determined according to the fitness value of the current chromosome and the fitness value of the optimal chromosome. The calculation formula for the rotation angle is:

[0091] Δθ = s(α, β)·k·(fit i - fit g )

[0092] where s(α, β) is the sign function used to determine the rotation direction; k is a constant used to control the rotation step size; fit i is the fitness value of the current chromosome; fit g is the fitness value of the optimal chromosome. Through the operation of the quantum rotation gate, the state of the chromosome can be continuously updated, enabling the population to evolve towards the optimal solution.

[0093] 5. Graph Convolutional Network Based on Spatiotemporal Attention Mechanism

[0094] Graph Convolutional Network: For a graph where is the set of nodes, is the set of edges. Let the feature matrix of the nodes be X ∈ R N×D , where N is the number of nodes and D is the dimension of the node features. The graph convolutional network conducts convolutional operations on the graph to learn the representation of the nodes. The mathematical expression of the graph convolutional operation is:

[0095]

[0096] where H (l) is the node feature matrix of the l-th layer, H (0) = X, W (l) is the weight matrix of the l-th layer, is the adjacency matrix with self-loops, A is the original adjacency matrix, I N is the N-order identity matrix, is 's degree matrix, and its element σ(·) is an activation function. Commonly used activation functions include ReLU, Sigmoid, etc. Through multi-layer graph convolution operations, deep representations of nodes can be learned, which can be used for various graph tasks, such as node classification, link prediction, etc.

[0097] Spatio-temporal attention mechanism: The spatio-temporal attention mechanism is used to dynamically allocate attention in a graph convolutional network to capture the spatio-temporal dependencies between nodes in the graph. The spatio-temporal attention mechanism can be divided into two parts: spatial attention and temporal attention.

[0098] Spatial attention: The spatial attention mechanism is used to dynamically allocate attention in the spatial dimension to capture the spatial dependencies between nodes in the graph. The mathematical expression of the spatial attention mechanism is:

[0099]

[0100] where α i j s is the spatial attention weight of node i to node j; and are the spatial feature vectors of node i and node j respectively; f(·) is an attention scoring function. Commonly used attention scoring functions include Dot-Product Attention, Additive Attention, etc. Through the spatial attention mechanism, the spatial attention weight matrix α s ∈R N×N can be obtained. Then, multiply the spatial attention weight matrix by the spatial feature matrix of the nodes to get the weighted node spatial feature matrix, so as to capture the spatial dependencies between nodes in the graph.

[0101] 6. Bat Algorithm

[0102] Basic formula:

[0103] Position update formula:

[0104] f i = f min +(f max - f min )β

[0105] where f i is the frequency of bat i, f m in and f max are the lower and upper limits of the frequency range, and β is a random number between [0,1].

[0106] where is the velocity of bat i at time t, is the position of bat i at time t, x* is the current globally optimal position.

[0107] This formula updates the position of the bat according to the updated velocity.

[0108] Loudness and pulse emission rate update formulas:

[0109] where is the loudness of bat i at time t, and α is a constant, usually between [0, 1], used to control the attenuation of loudness.

[0110] where is the pulse emission rate of bat i at time t, is the initial pulse emission rate, and γ is a constant used to control the growth of the pulse emission rate.

[0111] 7. Grey Wolf Optimization Algorithm

[0112] Basic formula:

[0113] Calculate the social rank:

[0114] In the grey wolf population, the α wolf, β wolf, and δ wolf (the optimal, sub-optimal, and third-optimal individuals) are determined according to the fitness value, and the remaining individuals are ω wolves.

[0115] Position update formula:

[0116] where is the target position (such as the position of the α, β, or δ wolf), is the position of the current grey wolf individual, is a random vector on [0, 1].

[0117] where linearly decreases from 2 to 0 during the iteration, is a random vector on [0, 1].

[0118] This formula updates the position of the grey wolf individual.

[0119] Dynamic adjustment formula:

[0120] For the α wolf, its position is adjusted according to the model performance to guide the search of other wolves. For the β and δ wolves, their positions are updated according to their relative positions to the α wolf to guide the population to search for the optimal solution. For example, for approaching the α wolf, the position update can be expressed as For approaching the β or δ wolf, the position update is or Among them and are the corresponding distances, and are the corresponding update coefficients.

[0121] Explanation and application:

[0122] This algorithm simulates the hunting behavior of a gray wolf population, where the alpha, beta, or delta wolves lead the other wolves in the search. and randomness makes the search exploratory, while linear decrease makes the search gradually shift from global to local. In campus security management, for example, in a model for optimizing the allocation of security monitoring resources, each gray wolf individual can represent a resource allocation plan, and through continuous position updates, the resource allocation is optimized, and finally the optimal resource allocation plan is found to achieve better campus security management.

[0123] 8. Markov Logic Network Algorithm

[0124] Basic formula:

[0125] Joint probability distribution formula:

[0126]

[0127] where X is a set of variables, x is an assignment of the variables, X i is the set of the i-th conjunctive normal form (clause) set, w c is the weight of the conjunctive normal form c, f c (x) is the characteristic function of the conjunctive normal form c under the assignment x, Z is the normalization factor,

[0128]

[0129] Inference formula:

[0130] For probabilistic inference, the Markov Chain Monte Carlo (MCMC) method, such as Gibbs Sampling, can be used. Given the values of other variables, the probability of updating the variable X j is P(X j = x j | Markov blanket of X j ), where the Markov blanket contains the parent nodes, child nodes, and the parent nodes of the child nodes of X j . For discrete variables, the update probability can be expressed as:

[0131]

[0132] where x is the state considering the current variable assignment. Specific Embodiment Four:

[0134] As Figure 1-2 shown, the following is a detailed description of the algorithms in the above embodiments:

[0135] 1. Double Dueling Double Q-Network Algorithm

[0136]

[0137] Usage: In the intelligent decision-making and policy optimization engine, for various scenarios of campus security management (such as access control management and emergency response scenarios), the algorithm first inputs the current state s into the advantage stream and value stream network branches, and calculates the advantage value A(s, a; θ A ) and the value value V(s; θ V ), and then calculates the Q value through the above formula. The Q value is used to evaluate the pros and cons of taking action a in state s, so as to select the optimal action.

[0138] Practical problem solved: In the campus security management scenario, in the face of complex and changeable situations, the traditional Q-learning algorithm may have an overestimation problem when estimating the Q value, resulting in unstable decision-making. The dueling double Q-network algorithm can more accurately evaluate the value of actions by separating the advantage stream and value stream, improve the stability and efficiency of decision-making, and help the system make the optimal decision quickly in different scenarios.

[0139] Formula derivation and source: This formula is proposed by the dueling double Q-network algorithm to improve the traditional Q-learning algorithm. Its core idea is to decompose the Q value into the state value V(s) and the action advantage A(s, a). Since directly estimating A(s, a) is prone to errors, it is corrected by subtracting the average of all action advantages, making the Q value estimation more accurate. This method comes from the optimization research of the traditional Q-learning algorithm, aiming to solve the overestimation bias problem and improve the performance of the algorithm in practical applications.

[0140] 2. Artificial Fish Swarm Optimization Algorithm Based on Levy Flight

[0141]

[0142] Usage: In the intelligent decision-making and policy optimization engine, when simulating the foraging behavior of artificial fish swarms, the movement step size of artificial fish follows the Levy distribution. By generating random step sizes according to this distribution, artificial fish conduct global exploration and local exploitation in the search space. For example, the position update formula of an artificial fish at a certain moment may be where is the position of artificial fish i at time t, α is the step size control parameter, and Levy(μ) is a random number generated according to the Levy distribution.

[0143] Actual problem to be solved: In the process of optimizing the decision-making strategy for campus safety management, traditional search algorithms may easily fall into local optimal solutions. The artificial fish swarm optimization algorithm based on Levy flight utilizes the long-distance jump characteristic of Levy flight, enabling artificial fish to jump out of local optima, enhancing the global search ability, and thus finding a better decision-making strategy to improve the quality of campus safety management decisions.

[0144] Formula derivation and source: The Levy distribution is a probability distribution with heavy-tailed characteristics, originally derived from the research on random processes in the fields of physics and mathematics. Introducing the Levy distribution into the optimization algorithm is to simulate the random search behavior of organisms in complex environments, making the search process have a certain degree of randomness to avoid falling into local optima and, to a certain extent, maintaining directionality to improve the search efficiency.

[0145] 3. Particle Swarm Optimization Algorithm Based on Quantum Behavior

[0146]

[0147] Actual problem to be solved: When optimizing the decision-making strategy for campus safety management, the traditional particle swarm optimization algorithm may have problems such as slow convergence speed and easy entrapment in local optima in the later stage of search. The particle swarm optimization algorithm based on quantum behavior utilizes the superposition state characteristic of quantum bits, enabling particles to have a stronger global search ability, being able to search for the optimal solution in a wider space, and improving the effect of decision-making strategy optimization.

[0148] Formula derivation and source: The quantum rotation gate is a basic operation in quantum computing, and its mathematical form is based on the rotation operation in quantum mechanics. Introducing the quantum rotation gate operation into the particle swarm optimization algorithm combines the concept of quantum computing with the traditional particle swarm optimization, and improves the search behavior of particles by simulating the evolution of quantum states. This method is derived from the research on improving the traditional particle swarm optimization algorithm.

[0149] 4. Support Vector Machine Optimized by Quantum Genetic Algorithm - Quantum Genetic Algorithm

[0150] Δθ=s(α,β)·k·(fit i -fit g )

[0151] Usage method: In the analysis of students' mental health and emotions, when using the support vector machine optimized by the quantum genetic algorithm, first encode the parameters of the support vector machine with quantum bits. For each quantum bit chromosome, according to its fitness value fit i obtained through the performance evaluation of the support vector machine on the training data) and the fitness value fit g, Calculate the rotation angle Δθ of the quantum rotation gate using the above formula, and then update the state of the chromosome through the quantum rotation gate operation, that is, update the parameters of the support vector machine. After multiple generations of iteration, find the optimal support vector machine parameters.

[0152] Practical problem solved: When the support vector machine deals with complex problems such as students' mental health and sentiment analysis, the selection of parameters has a great impact on the model performance. Traditional parameter selection methods may not be able to find the optimal parameters, resulting in insufficient generalization ability of the model. The quantum genetic algorithm uses the superposition state of quantum bits and the quantum rotation gate operation, which can search in a larger parameter space, find better support vector machine parameters, and improve the accuracy of the model in evaluating students' mental states and early warning.

[0153] Derivation and source of the formula: Adjust the rotation angle according to the difference between the fitness of the current chromosome and the optimal chromosome, so that the chromosome evolves in a better direction. This method introduces the concept of quantum computing into the genetic algorithm to improve the search ability of the traditional genetic algorithm, and it comes from the research on the optimization of the genetic algorithm.

[0154] 5. Graph Convolutional Network Based on Spatio-Temporal Attention Mechanism

[0155]

[0156] In the correlation analysis of campus and urban security situations, regard the campus and its surrounding areas as a graph structure. Nodes can represent different geographical locations, facilities, etc., and edges represent the correlation relationships between them. The initial feature matrix H of the nodes 0 is composed of various attribute data related to security. Through multiple layers of graph convolution operations, update the feature matrix H of the nodes according to the above formula each time (l+1) . For example, starting from the initial layer, go through multiple layers of graph convolution in sequence, continuously learn the deep feature representations of the nodes, and finally use them to predict the occurrence probability and impact range of potential security events.

[0157] Practical problem solved: In the security situation analysis of the campus and its surrounding areas, traditional machine learning methods are difficult to handle the complex spatial relationships between data. The graph convolutional network can effectively capture the spatial dependence relationships between nodes through convolution operations on the graph structure, learn the spatial feature representations of the nodes, and thus analyze the security situation more accurately and predict potential security events.

[0158] Formula derivation and source: This formula is derived based on the ideas of graph signal processing and convolutional neural networks. By normalizing the adjacency matrix of the graph Multiply it with the node feature matrix and the weight matrix to simulate the propagation of the convolution operation on the graph, thereby realizing the feature learning of graph data. This method is widely used in the field of graph data processing to solve the problems of feature extraction and analysis of graph-structured data.

[0159] 6. Bat Algorithm

[0160] Explanation and Application:

[0161] In the bat algorithm, bat individuals move in the search space by adjusting their own frequencies, speeds, and positions. The update of the frequency f is based on the range of f and f and a random number β, simulating the change in the search frequency of bats. The update of the speed takes into account the differences between the current speed, the current position, and the global optimal position, guiding the bat towards a better solution. The position is then updated according to the updated speed. The loudness decreases with the number of iterations t, while the pulse emission rate gradually increases. These two parameters jointly control the search behavior of the bats. i of min and max range and a random number β, simulating the change in the search frequency of bats. The speed is updated considering the differences between the current speed, the current position, and the global optimal position, guiding the bat towards a better solution. The position is then updated according to the updated speed. The loudness decreases with the number of iterations t, while the pulse emission rate gradually increases. These two parameters jointly control the search behavior of the bats.

[0162] In the campus safety and innovation application scenario, when using the bat algorithm to dynamically optimize and adapt the analysis and prediction model, for example, optimizing the parameters of a campus safety risk assessment model, each bat can represent a set of model parameters. By continuously updating the position (i.e., parameter values) and determining the search behavior according to the loudness and pulse emission rate, the optimal combination of model parameters can be finally found, making the model perform optimally in the complex and changing campus environment.

[0163] 7. Markov Logic Network Algorithm

[0164] Explanation and Application:

[0165] The Markov logic network combines first-order logic and Markov networks, integrating domain knowledge into the probability model through logical rules and weights. In the campus safety and innovation application scenario, it can be used to reason and optimize the structure and parameters of the analysis and prediction model. For example, according to the logical rules of campus safety (such as "if it is night and there is no monitoring, the safety risk is high", etc.) and weights, the occurrence probability of campus safety events can be reasoned. By adjusting the weights and rules, the network structure and parameters can be optimized according to the actual data and model performance to better adapt to the complex campus environment.

[0166] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a reference structure" does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0167] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Intelligent collaborative innovation incubation and campus safety integration management platform, characterized by: Including multimodal data collection and fusion system, intelligent decision-making and strategy optimization engine, edge computing and secure communication architecture, scenario analysis and prediction module, and security assurance and model adaptation system; The multimodal data acquisition and fusion system collects multi-source data on campus environment, personnel behavior and equipment status using a multi-sensor array consisting of visual sensors, audio sensors, temperature and humidity sensors and air quality sensors; The intelligent decision-making and strategy optimization engine uses the duel dual Q network algorithm in deep reinforcement learning, combined with the artificial fish school optimization algorithm based on Levy flight and the particle swarm optimization algorithm based on quantum behavior, to learn and generate optimal decision-making strategies for various scenarios in campus security management, including access control management and emergency response scenarios, and dynamically adjust the parameters of the deep reinforcement learning model to improve the accuracy and timeliness of decision-making; The edge computing and secure communication architecture deploys edge computing nodes evenly on campus, adopts a fog computing architecture based on a distributed hash table for collaborative processing, and uses encryption communication technology based on a combination of quantum key distribution and homomorphic encryption during data transmission to ensure data security and privacy. The scenario analysis and prediction module is aimed at the management of large-scale campus crowds. It combines the ant colony-particle swarm hybrid algorithm based on dynamic weights, the simulated annealing algorithm guided by taboo search, and the neural network algorithm based on fuzzy logic to achieve accurate prediction of crowd flow and generation of dynamic diversion strategies. In the security management of innovative incubation projects, the security assurance and model adaptation system applies multimodal recognition technology that integrates three biometric features: finger vein, voiceprint and gait, combined with blockchain-based access control and data traceability mechanisms to ensure the security of project data and intellectual property rights.

2. The intelligent collaborative innovation incubation and campus safety integration management platform according to claim 1 is characterized by: The multimodal information integration module fuses different modal features such as vision, audio and environmental parameters through a feature fusion algorithm based on tensor decomposition, providing a comprehensive and refined data basis for subsequent analysis. The feature fusion algorithm based on tensor decomposition uses a high-order singular value decomposition method to decompose the multimodal data tensor, and determines the low-rank tensor representation after decomposition by minimizing the reconstruction error to achieve effective fusion of each modal feature. The multi-sensor array adopts a self-organizing network topology structure to achieve automatic networking and data collaborative transmission between sensors, and the feature fusion algorithm based on tensor decomposition uses the alternating least squares method to perform tensor Decomposition, low-rank approximation is performed using nuclear norm minimization to improve the efficiency and accuracy of feature fusion. In the self-organizing network topology, the sensor nodes use a routing algorithm based on the AODV (Ad-hoc On-Demand Distance Vector) protocol to dynamically select and maintain data transmission paths to ensure reliable data transmission. In the feature fusion algorithm based on tensor decomposition, the alternating least squares method iteratively updates the factor matrix of the tensor and gradually approaches the optimal decomposition result. Nuclear norm minimization introduces a nuclear norm regularization term to constrain the rank of the decomposed tensor, thereby improving the efficiency and accuracy of feature fusion.

3. The intelligent collaborative innovation incubation and campus safety integration management platform according to claim 1 is characterized by: In the intelligent decision-making and strategy optimization engine, the network structure of the duel dual Q network algorithm includes two branches, the advantage flow and the value flow, which are used to evaluate the action advantage and the state value respectively to improve the stability and efficiency of decision-making. The artificial fish swarm optimization algorithm based on Levy flight simulates the foraging behavior of fish swarms and uses the Levy flight strategy to perform global exploration and local development in the search space to optimize the decision-making strategy; the particle swarm optimization algorithm based on quantum behavior uses the superposition state of quantum bits to represent the particle position, updates the particle state through quantum revolving gate operations, and enhances the global search capability of the algorithm.

4. The intelligent collaborative innovation incubation and campus safety integration management platform according to claim 1 is characterized by: The edge computing and secure communication architecture uses a K-nearest neighbor algorithm that integrates an extreme learning machine, a random fern classifier, and cuckoo search optimization on the edge computing node to quickly analyze and process the collected real-time data, thereby realizing real-time monitoring and early warning of abnormal behaviors and safety hazards. The fog computing architecture based on a distributed hash table uses the Chord protocol to construct a distributed hash table to achieve efficient storage and search of data. The encryption communication technology based on the combination of quantum key distribution and homomorphic encryption uses a decoyed state BB84 protocol to generate a security key during the quantum key distribution process. Homomorphic encryption uses a cryptographic system based on the ring learning error problem to achieve homomorphic calculation of addition and multiplication on ciphertext. In the fused algorithm, the extreme learning machine randomly initializes the input layer weights and biases, quickly calculates the output layer weights, and realizes rapid learning. The random fern classifier constructs a decision fern by randomly selecting a single feature and threshold to classify the data. The cuckoo search algorithm optimizes the K value and sample weight of the K-nearest neighbor algorithm by simulating the nest parasitic behavior of the cuckoo, thereby improving the classification accuracy.

5. The intelligent collaborative innovation incubation and campus safety integration management platform according to claim 1 is characterized by: In terms of students' mental health and emotional analysis, the scenario-based analysis and prediction module uses a support vector machine optimized based on a quantum genetic algorithm, a long short-term memory network combined with an attention mechanism, and a feature selection algorithm based on information entropy to conduct in-depth analysis of students' multi-source emotional data, thereby achieving accurate assessment and early warning of students' mental states. In the correlation analysis of campus and urban security situations, a graph convolutional network based on a spatiotemporal attention mechanism, a gradient boosting tree based on Bayesian optimization, and a Markov chain model based on grey correlation analysis are integrated to conduct a comprehensive analysis of security data on the campus and surrounding areas, and predict the probability of occurrence and impact range of potential security incidents.

6. The intelligent collaborative innovation incubation and campus safety integration management platform according to claim 1 is characterized by: In terms of campus material management, the security assurance and model adaptation system uses blockchain-based smart contract technology and deep learning-based material demand prediction models to achieve intelligent and safe traceability of campus material procurement and inventory management. At the same time, in view of campus safety and innovative application scenarios, the model adaptive adjustment mechanism based on the bat algorithm, gray wolf optimization algorithm and Markov logic network algorithm is combined to dynamically optimize and adapt the analysis and prediction models to ensure that the platform always maintains efficient and stable operation in a complex and changeable campus environment.

7. The intelligent collaborative innovation incubation and campus safety integration management platform according to claim 6 is characterized by: The security assurance and model adaptation system integrates the multimodal recognition technology of three biometric features. In the feature extraction process, a finger vein feature extraction model based on a convolutional neural network, a voiceprint feature extraction model based on a long short-term memory network, and a gait feature extraction model based on posture estimation and a recurrent neural network are respectively used for end-to-end feature learning. In the feature fusion stage, a fusion network based on a multi-layer perceptron is used for feature fusion to improve the recognition accuracy.

8. The intelligent collaborative innovation incubation and campus safety integration management platform according to claim 7 is characterized by: The blockchain-based access control and data traceability mechanism in the security assurance and model adaptation system adopts the Ethereum blockchain platform, and uses smart contracts to achieve access rights management and operation record tracing for innovation incubation project data; at the same time, the blockchain-based smart contract technology adopts the Ethereum blockchain platform, and uses the Solidity language to write smart contracts to achieve automation of order generation, approval, delivery and receipt confirmation in the material procurement process, as well as the intelligentization of inventory counting and replenishment reminder functions in inventory management, and achieve secure traceability of material management data through the distributed ledger of the blockchain.

9. The intelligent collaborative innovation incubation and campus safety integration management platform according to claim 8 is characterized by: The material demand prediction model based on deep learning in the security assurance and model adaptation system adopts a model based on long short-term memory network (LSTM) and attention mechanism, combines historical material procurement data, campus activity arrangements and seasonal factors to predict material demand, and finally combines the model adaptive adjustment mechanism based on bat algorithm, gray wolf optimization algorithm and Markov logic network algorithm. The bat algorithm dynamically adjusts the pulse emission frequency and loudness according to the accuracy and recall performance indicators of the model to balance the global search and local search capabilities. The gray wolf optimization algorithm dynamically adjusts the role of the leading wolf according to the performance of the model during group collaboration to guide the group to search for the optimal solution. The Markov logic network algorithm uses first-order logic rules and probabilistic graph models to infer and optimize the structure and parameters of the model.

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