2696 results about "Network performance" patented technology

The invention provides a private domain live broadcast data storage and visitor authentication method and system based on a block chain, and relates to the technical field of data storage and access control, and the method comprises the steps: collecting multi-mode biological characteristic data of a visitor, carrying out the feature extraction and fusion through a deep neural network of a space-time attention mechanism, and carrying out the authentication of the visitor; authenticity verification is carried out in combination with an adversarial generative network, a dynamic identity token is generated, and a hash value of the dynamic identity token is written into the block chain. The method comprises the following steps: performing semantic analysis on live broadcast data flow through a self-adaptive content perception model, constructing a double-layer access control matrix according to a data sensitivity level and a credibility score, performing self-adaptive fragmentation and hierarchical encryption based on federated learning, and then storing. After an access request is subjected to identity verification, an optimal edge node is selected according to access authority and network performance parameters to construct multi-path transmission, and a transmission strategy is generated based on a service quality score and a data sensitivity level. And when the service quality is lower than a threshold value, triggering the smart contract to perform fault migration.

The invention provides a network topology intelligent generation method and system based on deep learning and topology analysis, and relates to the technical network intelligence field, and the method comprises the steps: obtaining historical topology data, carrying out the feature extraction based on time sequence division, constructing a graph neural network model, training, and evaluating the evolution trend and stability of a network topology structure. And constructing a deep reinforcement learning model to generate an optimization strategy, and carrying out iterative optimization until a network topology structure meeting requirements is generated. The network structure can be adaptively optimized, the network performance is improved, the operation and maintenance cost is reduced, and the network stability is enhanced.

The invention belongs to the technical field of image classification neural networks, and discloses a lightweight image classification neural network architecture system based on knowledge distillation. The invention provides a lightweight image classification neural network architecture system based on knowledge distillation. The lightweight image classification neural network architecture system comprises a data set preprocessing module, a teacher model preprocessing module, a subnet model construction module, a personalized training module, a real loss calculation module, a distillation loss calculation module, a total loss calculation module and a student model construction module. The invention provides a lightweight neural network architecture system based on knowledge distillation, and designs a brand new training mode by using the superiority of knowledge distillation for teacher model weight extraction: the knowledge distillation of teacher and student models is combined with a deep compression method; the system for constructing the lightweight network can be used for training various complex image classification neural networks so as to improve the network performance.

The invention discloses an intelligent operation and maintenance management and control system for an information communication network, and belongs to the technical field of communication. The system comprises the following modules: an intelligent sensing and data acquisition module which is responsible for real-time acquisition of multi-source network data, dynamic monitoring of cross-domain equipment states and conversion of heterogeneous data into a unified standard; the network intelligent analysis module integrates network performance prediction, fault risk assessment, abnormal behavior identification and security threat monitoring, and provides comprehensive network operation and maintenance insight; the intelligent resource scheduling module is used for realizing dynamic allocation of cross-domain network resources and ensuring efficient and safe utilization of the resources through multi-dimensional optimization and a real-time load balancing strategy; the intelligent decision support module is used for continuously optimizing an operation and maintenance strategy based on machine learning, analyzing a long-term operation trend and providing suggestions for management decisions; and the open integration and collaboration module provides integration of an open API interface and a third-party system, supports cross-domain collaboration management, and improves the flexibility and expansibility of the system.

The invention relates to the technical field of integration of artificial intelligence security and network security, and discloses an AI dynamic security transmission system based on an SASE framework, which realizes security access control based on AI dynamic identity verification through an SASE integration access module, acquires and predicts network performance change in real time by using a network state sensing module, and transmits the network performance change to a network server. Equipment, environment and data content are subjected to multi-dimensional analysis by means of a security risk assessment module, a quantitative risk score is generated, and a transmission protocol, parameters and encryption strength are dynamically adjusted according to a network state and the risk score by means of a dynamic transmission optimization module and a self-adaptive encryption module; the problem that safety protection and transmission efficiency are difficult to cooperate in a traditional architecture is effectively solved, low-delay and high-reliability data transmission service can be provided for AI application in a complex network environment, meanwhile, self-adaptive dynamic protection of the whole data transmission process is achieved, and data safety is comprehensively guaranteed.

The invention relates to the technical field of data processing, and discloses a PTP network performance monitoring method and system based on big data analysis, and a medium. The method comprises the following steps: acquiring PTP message data, equipment state data and RTP stream data; preprocessing the data and calculating a clock error; constructing a time sequence database and an analysis model; real-time monitoring and anomaly detection are executed; performing performance scoring and fault positioning by applying machine learning; and carrying out network optimization and predictive maintenance. According to the PTP network performance monitoring method based on big data analysis, global state monitoring, intelligent anomaly detection, accurate fault positioning and predictive maintenance of the PTP network are achieved, and therefore the high-precision time synchronization requirement of a broadcast and television IP making and broadcasting system is guaranteed.

The invention relates to the technical field of communication data analysis, and discloses a communication network service health degree analysis method, system and device and a medium, and the method comprises the steps: obtaining real-time link state data, carrying out the quantitative analysis, and judging whether the link jitter exceeds a preset threshold value or not; when abnormality is detected, abnormal points can be positioned and the influence range of the abnormal points can be analyzed, so that the influence degree of nodes on the abnormal diffusion path can be identified; calculating a service quality index change value, and determining a starting point and a diffusion direction of chain degradation; by analyzing historical traffic data of the key nodes, a service traffic change trend and traffic dense area distribution characteristics are obtained. According to the method, the bandwidth allocation strategy is dynamically optimized, the bandwidth resources at the junction are reconfigured, and the regional service health degree is evaluated. And if the degradation is not effectively controlled, monitoring and detection mechanism parameters can be adaptively adjusted, and continuous analysis and control of link abnormity are realized, so that the network performance and the service quality are effectively improved.

The invention provides a public transportation network toughness evaluation method considering subway passenger evacuation and station failure propagation, and the evaluation method is used for an urban public transportation composite network, and comprises the steps: firstly obtaining urban public transportation network station map data, passenger flow OD data and vehicle operation related information; then, an urban bus composite network considering the one-to-many coupling relation between subway stations and conventional bus stations is constructed through a Space L method, and meanwhile, composite network performance indexes considering the structure and operation performance are defined in the propagation process of station cascade failure by considering the re-decision of the travel mode of blocked passengers in the urban bus composite network under sudden interruption; based on the network performance curve, the urban bus composite network toughness evaluation model is constructed to measure the toughness level of the urban bus composite network under sudden interruption; according to the method, the toughness of the urban bus composite network can be accurately evaluated, and the operation stability and safety of a bus system are improved.

The invention provides a self-adaptive network topology dynamic reconstruction method and system based on deep reinforcement learning, and relates to the technical field of deep reinforcement learning, and the method comprises the steps: obtaining the topology state information, service flow distribution information and historical reconstruction records of a current network; extracting topological correlation characteristics among nodes through graph convolution operation, and generating fusion state representation in combination with service flow information; inputting the fusion state representation into a deep reinforcement learning model to identify bottleneck nodes and redundant links, and outputting a reconstruction action candidate set; searching and evaluating the long-term cumulative income of the candidate actions through a Monte Carlo tree, and screening an optimal reconstruction action sequence; a graph coloring algorithm is utilized to allocate time slots and process resource conflicts, and a resource-feasible topology adjustment scheme is generated; and extracting a network evolution rule through tensor decomposition, and constructing a topological optimization association mapping graph. According to the method, the network bottleneck can be intelligently identified, the network topology structure is dynamically optimized, and the network performance and the resource utilization rate are effectively improved.

The invention discloses a network fault adaptive detection system based on machine learning, which relates to the technical field of network security and comprises a data acquisition module, a feature extraction and selection module, a model training and updating module, a fault detection module, a fault positioning and diagnosis module and an adaptive optimization module. According to the network fault adaptive detection system based on machine learning, by generating new features, the expression ability of a feature space is significantly enhanced, and the detection precision of a machine learning model is improved; the deep learning model based on the causal relationship is combined with the graph neural network and time sequence analysis, so that the complex causal relationship in the network is effectively processed, and the fault diagnosis is more accurate; through the combination of the multi-objective evolutionary algorithm and the fuzzy logic control algorithm, the network optimization strategy is more flexible and efficient, the network performance is improved, the weight of the optimization objective can be dynamically adjusted according to the actual demand, and the performance of the system in a complex network environment is remarkably improved.

The invention discloses an image label noise learning method based on granular ball calculation and contrast learning, and the method comprises the steps: obtaining image data, carrying out the preprocessing of the image data, inputting the preprocessed image data into a trained image label noise learning model, and obtaining an image classification result. The training process of the image label noise learning network comprises the following steps: acquiring an image training data set, and performing preprocessing and data enhancement on the image training data set; respectively extracting feature representations of the strong data enhancement sample and the weak data enhancement sample by adopting a contrast learning framework SimSiam, inputting the feature representation of the weak data enhancement sample into a granular ball calculation layer, inputting a feature matrix of the granular ball sample and a label thereof into a classifier after clustering to obtain an image classification result p, and calculating classification loss; predicting the feature representation of the strong data enhancement sample through a prediction layer, and calculating comparison loss by taking an image classification result p as an indicator function; and the network is optimized through back propagation of the comparison loss and the classification loss. According to the invention, the network performance can be obviously improved.

The invention provides a node fault intelligent diagnosis method and system for an intelligent space sensor network, and the method comprises the steps: obtaining a real-time monitoring data set of all sensor nodes in a target sensor network, carrying out the state feature extraction of the real-time monitoring data set, generating a state feature set of each sensor node, and carrying out the detection of the state feature set of each sensor node; based on a preset fault diagnosis strategy model, carrying out multi-dimensional fault matching processing on the state feature set, determining a candidate fault type set of the sensing nodes, generating a node fault processing strategy set of the sensing nodes according to a strategy mapping relation between the candidate fault type set and a preset fault processing knowledge base, and sending the node fault processing strategy set to the fault diagnosis module; and performing node optimization operation on the fault nodes in the target sensor network based on the node fault processing strategy set, and feeding back optimized node operation parameters to a control terminal of the target sensor network. According to the method, the problem that the system stability is reduced due to traditional single-point repair is effectively avoided, and continuous and reliable operation of the overall performance of the network after fault repair is ensured.

The invention discloses a knowledge distillation method, electronic equipment and a computer readable storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the grouping of an attention map matrix of a teacher network based on the number of attention heads in an initial network structure of a student network, a high-dimensional attention space of a teacher network can be divided into subspaces with the same dimension as a student network, and attention map matrixes in groups are spliced, so that each group of spliced matrixes is aligned with an attention head of an initial network structure, a relatively regular corresponding relationship between the attention maps of the teacher network and the student network is realized, and the attention map splicing efficiency is improved. Therefore, the distillation loss can be calculated, and the knowledge of the complex teacher network can be accurately transmitted to the lightweight student network. Therefore, the problem that in a multi-head attention mechanism, the number difference between the teacher network and the student network hinders the student network to fully learn the teacher network knowledge can be solved, and the technical effects of improving the knowledge distillation effect and enhancing the student network performance are achieved.

The invention provides a 5G network intelligent switching management method based on artificial intelligence, and the method comprises the following steps: collecting and processing network related data, and obtaining a network data set; constructing a switching demand prediction model used for predicting network switching time and a target and a switching effect prediction model used for predicting network performance after switching; optimizing the initial switching strategy in combination with reinforcement learning to obtain an optimal switching strategy; deploying a network switching engine model on the edge computing node, and adjusting an optimal switching strategy in real time according to the network environment change; a virtual simulation digital environment is constructed for network simulation, an optimal switching strategy is evaluated according to the simulated virtual network environment, and then a network switching engine model is optimized in real time according to an evaluation result. According to the invention, the intelligent switching decision is realized, the precision and efficiency of the switching decision can be improved, the network adaptability and diversified requirements are enhanced, and the user experience is improved.

A third-party authentication and encryption method, known as spatio-temporal encryption, is used to protect sensitive information by guaranteeing the origin and integrity of its content for recording or transmission purposes. This method is based on hidden characteristics associated with the message's location and creation date, derived from GNSS signal processing. Without requiring additional dedicated security infrastructure, the method allows for the protection and authentication of any data entered into the information system, anytime and anywhere, and enables the assessment of associated security risks at any system level. Leveraging the various levels of access protection for GNSS signal services, the solution offers different levels of robustness, with complexity and security that can be tailored to the threat level, while minimizing the impact on communication network performance.

The invention discloses a dynamic bandwidth allocation method and system based on a large model, electronic equipment and a medium, and relates to the technical field of network communication, historical flow data and multi-modal data are modeled and fused by using a pre-training architecture of ChatGLM3, so that fusion features are generated. Based on the fusion features, the system can predict future traffic demands. By defining a dynamic priority adjustment mechanism and combining a flow prediction result, the system realizes multi-service dynamic adjustment and efficient bandwidth allocation; meanwhile, a constructed flow anomaly detection mechanism can monitor flow changes in real time, and the priority of abnormal services is improved; in addition, a global resource optimization strategy ensures comprehensive scheduling of bandwidth resources in the network, and the benefit of bandwidth allocation is measured based on a utility function; the invention provides an intelligent method, so that network resources can be flexibly configured under various service requirements, and the overall service quality and network performance are improved.

The invention relates to the technical field of agricultural wireless sensor networks, and discloses an agricultural wireless sensor network topology reconstruction method based on an energy consumption balance model. The method comprises the following steps: acquiring multi-dimensional energy consumption data of each node, including node residual energy, communication load intensity and an environment interference coefficient; extracting a node energy health degree based on residual energy, generating a communication priority sequence according to communication load intensity, and analyzing an environment interference coefficient to generate an interference distribution thermodynamic diagram; inputting the result into an energy consumption balance evaluation model to generate a topology reconstruction strategy; and a self-adaptive topology adjustment map is constructed through a distributed optimization algorithm, and a node connection relation optimization scheme is output. The system is correspondingly provided with a multi-dimensional data acquisition module, an energy consumption state analysis module and the like. The problems of unbalanced energy consumption, non-uniform communication load, environmental interference and the like of the agricultural wireless sensor network can be effectively solved, the network performance is improved, the service life of the network is prolonged, and the development of precision agriculture is promoted.

The invention discloses a generative AI endogenous communication network architecture and a hierarchical collaborative scheduling method, and relates to the technical field of communication networks, the communication network architecture comprises an intention extraction layer, a mapping adaptation layer and a network execution layer, the intention extraction layer processes multi-modal data by using a generative AI model to obtain a semantic demand representation of a user, and the mapping adaptation layer carries out mapping on the semantic demand representation of the user; the mapping adaptation layer maps the semantic demand representation into a service quality index by using a generative AI model, maps a real-time network state of a heterogeneous network in the network execution layer into a network performance index, and generates a transmission scheduling strategy based on the service quality index and the network performance index, and the network execution layer works based on the transmission scheduling strategy, so that the network performance of the heterogeneous network is improved. End-to-end deterministic transmission scheduling of data packets of a service is implemented. According to the invention, efficient deterministic transmission scheduling of intelligent computing services in a 6G network can be realized.

The invention provides a QoS routing optimization method and system, a computer and a readable storage medium, and the method comprises the steps: S1, obtaining a real-time network performance data set which comprises link bandwidth, delay and packet loss rate; s2, modeling a network topology into a graph structure based on the real-time network performance data set, and generating a network state feature matrix; s3, constructing a state space and an action space according to the network state feature matrix, wherein the action space comprises a plurality of candidate paths from a source node to a target node; s4, selecting a candidate path from the action space through a main network, evaluating the performance value of the candidate path by adopting a target network, updating the main network according to the performance value, and generating an empirical data set; and S5, optimizing a path selection strategy according to the empirical data set, and updating a routing table. According to the method and the device, the optimal path meeting the QoS requirement can be found for the data flow in different service environments in the dynamic network environment.

The invention belongs to the field of unmanned aerial vehicle communication, and discloses a flight ad hoc network distributed routing method based on multi-agent reinforcement learning, and the method comprises the steps: building four network performance models: energy consumption, packet delivery rate, throughput and end-to-end time delay; establishing a node interaction mechanism; establishing a Markov decision process, defining definitions of a state space, an action space and a space state transition probability, and designing a reward function; based on an improved multi-agent depth deterministic policy gradient algorithm DE-MADDPG, designing a flight ad hoc network distributed routing method based on multi-agent reinforcement learning; each unmanned aerial vehicle modifies a running protocol and a protocol parameter according to local network information of each node, optimizes a main network and a subnetwork of the system, and improves the overall performance of the network. The method mainly solves the problems that a traditional flight ad hoc network protocol is not high in adaptive capacity, and the network environment is not matched with the protocol. Compared with a traditional method, the method has the advantages of being low in control delay, high in control precision and good in performance.

The invention relates to the technical field of wireless communication, and discloses a wireless communication method and system for Internet of Things data transmission, and the method comprises the steps: monitoring an Internet of Things environment in real time, and collecting environment data; dynamically selecting an optimal frequency band according to the environment data, and implementing adaptive frequency hopping; dynamically allocating available channels according to the current network state and equipment requirements; the method comprises the following steps: preprocessing and fusing data from different Internet of Things terminals to form a data packet, after the data packet is generated, carrying out edge calculation according to the state of a current network, judging an optimal transmission path, and transmitting the data to target equipment; in the data transmission process, a feedback adjustment mechanism is formed by monitoring the transmission state and the environment change, and the network performance is continuously optimized. According to the method, through dynamic monitoring and self-adaptive adjustment, the efficiency and stability of data transmission are optimized in a complex and changeable environment, and the method has wide application prospects and market value.

The invention relates to the technical field of network switching, in particular to a satellite land network switching method and system for a ship, and the method comprises the following steps: carrying out the real-time tracking of the current position of the ship based on a GIS system, calculating the distance between the current position of the ship and an adjacent satellite and land network access point, and comparing the distance data with historical network performance data. According to the invention, real-time GIS tracking and BDS verification are used, the ship position is accurately monitored, and the access point distance and network performance data are dynamically analyzed, so that the prediction of the network access opportunity is more accurate and timely, the response speed of traditional network switching is optimized, the timeliness of switching is improved, and the network access time is shortened. Data transmission interruption caused by network delay or instability is avoided, real-time signal intensity monitoring and congestion information analysis are combined, the optimal network can be intelligently selected in multiple network environments, continuity of data service is ensured, experience is remarkably improved, and manual operation is reduced through the automatic network switching process.

The invention discloses a twin network performance prediction method and device based on a graph neural network and a storage medium, and belongs to the technical field of communication networks, and the method comprises the steps: carrying out the preprocessing of the obtained historical information of a physical communication network, and obtaining a dependency graph data set; initializing the state of each node in the dependency graph data set to obtain the initial hidden state of each node; the initial hidden state is used to train a pre-constructed network performance prediction model to obtain a trained network performance prediction model, and the network performance prediction model obtains iteratively updated hidden state information and adjusts model parameters through cooperative work of an attention mechanism, a message passing neural network and a gating circulation unit; and inputting the obtained real-time information of the physical communication network into the trained network performance prediction model to obtain a performance prediction result, thereby improving the stability, accuracy and efficiency of performance prediction of the digital twin communication network.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention discloses an intelligent load balancing method and system based on multipath fusion, and relates to the field of network load balancing. A distributed monitoring system is constructed to collect network performance indexes, and a Transform model is used to predict traffic; a hierarchical reinforcement learning architecture is adopted, a global strategy is generated according to a macroscopic network state, and flow distribution is optimized for a single path; kalman filtering and particle filtering are automatically switched according to the path stability; the flow is flexibly migrated based on a genetic algorithm; an index weight is calculated by using a Shapley value method and an entropy weight method, and path quality is evaluated; the distribution strategy is executed through the SDN controller, and the overall performance of the network is improved. According to the invention, network abnormity is quickly responded, the traffic migration efficiency is improved, and bandwidth waste is reduced; service differentiation scheduling is supported, the service quality is guaranteed, and the attack defense capability is enhanced; operation and maintenance efficiency is improved, fault positioning time is shortened, and efficient utilization of network resources and guarantee of service quality are realized.

The invention relates to the technical field of network communication, and discloses an intelligent control method and system of a switch. The method comprises the following steps: receiving a heterogeneous data stream in a target network domain; performing data processing by using a preset flow feature aggregation model, and generating a flow dynamic pressure field distribution diagram and a protocol priority mapping matrix; constructing an adaptive scheduling threshold model, and generating a multi-target flow control instruction set; a cascade effect is simulated based on a network congestion chained conduction model, and collaborative routing parameters are optimized; and iteratively optimizing through a mixed integer programming framework, and outputting a network scheduling action sequence to a switch control engine. The system correspondingly comprises a data receiving module, a data processing module, an instruction generation module, a simulation optimization module and an instruction output module. According to the method, heterogeneous data can be effectively processed, congestion can be accurately predicted, flow control can be optimized, the overall performance of the network can be improved, and communication requirements in a complex network environment can be met.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention belongs to the technical field of air-space-ground integrated networks, and discloses an unmanned aerial vehicle track optimization and task offloading method for an air-space-ground integrated network. According to the method, the task unloading efficiency in the air-space-ground integrated network is optimized, optimization of energy consumption and time delay is focused on, various conditions of the air-space-ground integrated network are comprehensively considered, a decoupling communication mode is considered, and the influence of service interruption is reduced. And constructing an optimization model to minimize the weighted sum of time delay and energy consumption. In order to improve the collaboration of heterogeneous agents in a space-air-ground integrated network, based on a hierarchical reinforcement learning framework, a hierarchical learning-based two-stage Qmix algorithm is proposed, and the collaboration of a low-orbit satellite and an unmanned aerial vehicle is reinforced. According to Pycharm platform simulation and multi-scene verification, the method has remarkable advantages in average response time delay, energy consumption and QoS indexes, the network performance is effectively improved, and user requirements are met.

The invention discloses a cross-domain collaborative interface dynamic configuration method and device and a computer program product. According to the cross-domain collaborative interface dynamic configuration method, service flow priorities are determined by analyzing service layer data, and an initial interface configuration scheme is generated in combination with network state information. And when the initial scheme does not meet the transmission requirements, optimizing the interface parameters by adopting a dynamic programming algorithm, generating a new configuration scheme, and issuing the new configuration scheme to the target network management system. In the service switching process, network performance is monitored in real time, interface parameters are dynamically optimized and updated to a network management system, and intelligent management of service flow and efficient utilization of network resources are achieved. The method can adapt to different manufacturer devices and network levels, guarantees the service quality of cross-domain services, and improves the operation efficiency and reliability of an electric power communication network.

The invention discloses an application method for parking project calculation, and relates to the technical field of parking lot control. The application method of parking project measurement and calculation comprises the following steps: S1, data acquisition; s2, evaluating a network state; s3, acquiring an environment compensation coefficient; and S4, evaluating the operation state of the camera. According to the invention, the network abnormity evaluation value is obtained through the obtained network performance data, whether network optimization is carried out or not is judged based on the network abnormity evaluation value and the preset network abnormity threshold value, and then the operation state data of the main camera and the environment data obtained after optimization are analyzed to obtain the environment compensation coefficient. And according to the operation state data of the main camera, the environment compensation coefficient and the network abnormity evaluation value, obtaining an operation abnormity evaluation value, and based on the operation abnormity evaluation value and a preset operation abnormity threshold value, determining whether to adjust the main camera, thereby improving parking space guiding accuracy. The problem of low parking space guiding accuracy in parking space guiding application of parking project measurement and calculation in the prior art is solved.