7951 results about "Rail transit" patented technology

The invention belongs to the technical field of rail transit intelligent monitoring, and particularly relates to an intelligent monitoring method and device for a rail transit air conditioning system. According to the method, the sensor array with the self-adaptive sampling frequency is used for collecting the multi-modal operation data, then the collected multi-modal operation data is preprocessed, the feature degradation track atlas is established, powerful data support is provided for subsequent fault early warning and diagnosis, and the fault diagnosis accuracy is improved. Historical abnormal events are introduced to dynamically correct the health state evaluation base line, the timeliness and accuracy of the evaluation base line are ensured, in comparative analysis of real-time operation data and the dynamic evaluation base line, a health degree scoring and dynamic threshold mechanism is adopted, quantitative evaluation of the health state of the air conditioner system is achieved, and the evaluation accuracy of the health state of the air conditioner system is improved. According to the method, a decision graph containing fault diagnosis and predictive maintenance suggestions is generated by analyzing the propagation path and time sequence relevance of abnormal parameters in the air conditioning system and combining an equipment topological relation graph, so that the troubleshooting and repairing efficiency is improved.

The present invention relates to an integrated dispatch decision-making system and method for a hub supporting multi-modal transportation coordination, and a device. The system comprises: a multi-modal hub passenger flow data fusion and analysis module, which is used for arranging and classifying collected multi-modal hub passenger flow data, performing preprocessing, performing conversion and mapping to obtain normalized passenger flow data, and finally extracting a data fusion feature, and using a normalized transport passenger flow model to perform multi-dimensional data fusion and analysis computation; a hub transportation coordination decision-making module, which is used for performing transport capacity and volume assessment on the basis of the fusion and analysis of the multi-modal passenger flow data, and using an optimal decision-making model to compute an optimal strategy result; and a hub coordination dispatch and command application module, which is used for providing an intelligent command assistance function to a rail transit dispatch and command personnel on the basis of information access of hub-line-network, and the normalized transport passenger flow model. Compared with the prior art, the present invention has the advantages of improving the connectivity and coordinated dispatch of multi-modal transportation in a complex hub scenario, etc.

The invention provides an unsupervised video monitoring anomaly detection method and system based on semantic modeling, and belongs to the technical field of computer vision, artificial intelligence and video monitoring. Comprising the following steps: carrying out key frame identification on a monitoring video by adopting an image-text joint embedding model, and carrying out target cross-frame tracking based on a depth target detection algorithm to extract behavior semantic information so as to construct a semantic behavior map; sending the key frame sequence of the graph structure information into a frame prediction model, and predicting a next frame image or a target state; and carrying out abnormal scoring on the obtained prediction result, and carrying out threshold judgment by outputting a comprehensive abnormal score value so as to determine whether the current frame is an abnormal event or not. According to the method, the intelligent level and the overall efficiency of video anomaly detection can be effectively improved on the premise of ensuring the real-time performance and the stability, and support is provided for video monitoring anomaly detection in actual scenes such as smart cities, rail transit, industrial parks and commercial security.

The invention relates to an MCP-based urban rail transit intelligent operation and maintenance service construction method. The method comprises the steps of obtaining an input question of a user; inputting the input question into the large language model for processing and extracting key information; the big language model judges data required for solving the problem according to the key information; acquiring data required for solving the problem from different MCP Clients through an MCP protocol; and outputting a final result by combining the big language model with data required for solving the problem. According to the method, the MCP protocol is introduced into the field of urban rail transit intelligent operation and maintenance for the first time, and a unified model context communication interface is constructed to realize structured interaction between a large model and multi-source operation and maintenance data, so that connection between various operation and maintenance services and intelligent agents is more efficient and standardized, and the operation and maintenance efficiency is improved. The technical problems that in a traditional system, interfaces are heterogeneous, data are closed, and a model is difficult to call external services are solved, and infrastructure support is provided for modular deployment and cross-system integration of an intelligent operation and maintenance platform.

The invention relates to the technical field of rail transit, and discloses an intelligent diagnosis method and system for the health degree of urban rail equipment based on a cloud platform and a medium, the intelligent diagnosis method for the health degree of the urban rail equipment based on the cloud platform comprises the following steps: S1, multi-source data collection and edge preprocessing: collecting operation data of the urban rail equipment through a sensor, performing time sequence alignment and abnormal value filtering on the data to generate preprocessed data; s2, triggering type data uploading: based on the preprocessed data, judging whether an uploading condition is met or not through a statistical rule or a model prediction result, and uploading the data meeting the condition to a cloud platform; and S3, cloud model training and health degree prediction: constructing an equipment health state prediction model through a transfer learning algorithm by using the uploaded data. According to the invention, through deep learning modeling, edge calculation deployment and a multi-factor early warning mechanism, efficient evaluation, stable early warning and low-delay response of the equipment state are realized.

The invention relates to a rail transit full-scene intelligent construction cooperative control method and system, and the method comprises the steps: constructing a three-layer closed-loop architecture of a unified data base, a dynamic digital twin engine and a cooperative control platform, carrying out the real-time collection and fusion of multi-source heterogeneous data through the unified data base, and carrying out the synchronous processing based on a space-time alignment protocol; constructing a real-time digital twinning model by utilizing a dynamic digital twinning engine, and performing dynamic evolution prediction on a construction scene by adopting a mode of fusing a graph neural network and a physical constraint model; based on a real-time digital twinborn model, autonomous path planning, task allocation and fault prediction are performed on a plurality of construction devices through a cooperative control platform by adopting a multi-agent reinforcement learning algorithm, and cooperative operation and risk pre-control between the devices are realized. According to the invention, a data barrier can be broken, high-fidelity real-time twinning is realized, an autonomous cooperative control capability is provided, the construction efficiency is obviously improved, the safety risk is reduced, and manual intervention is reduced.

The invention belongs to the technical field of rail transit, and discloses a rail transit environment foreign matter intrusion detection method and system based on a visual model. The method comprises the following steps: firstly, extracting a suspicious region sub-graph in an original image through preprocessing, and respectively extracting a coarse-grained global feature vector and a fine-grained local feature vector by using an image encoder; meanwhile, text prompt information matched with the image content is generated, and a corresponding text feature vector is extracted; the multi-level similarity of the image feature vector and the text feature vector is calculated, and the respective weight fusion is combined to obtain a comprehensive abnormal score; and finally, dynamically modeling abnormal score distribution based on a Gaussian mixture model to realize foreign matter invasion judgment. According to the method, image and text multi-modal information is fused, the detection precision of foreign matters in a complex scene is improved through multi-scale feature association, the dynamic environment adaptability is enhanced by adopting an adaptive threshold strategy, and the problems that a traditional method is high in false detection rate and insufficient in generalization ability in a complex background of a railway are effectively solved.

The invention relates to the technical field of big data, and discloses an urban rail transit emergency processing method and system based on a visual large model, and the method comprises the steps: collecting visual data, sound data and equipment operation data at a platform and a carriage of urban rail transit, and carrying out the preprocessing and timestamp synchronization; performing dynamic frame difference processing on continuous video frames of the visual data to generate a two-dimensional motion heat map, and performing space-time aggregation; constructing a three-dimensional digital model based on engineering drawings of the platform and the carriage, and establishing a mapping relation between the two-dimensional motion heat map and the three-dimensional digital model; performing abnormal behavior detection on a mapping result, performing multi-modal fusion analysis in combination with sound data and equipment operation data, and determining the severity of an abnormal event; emergency response strategies are formulated according to severity levels of the abnormal events, a control instruction is sent to the rail transit equipment through an industrial protocol interface, and abnormal event information is pushed to a control center, inspection personnel and passenger terminals.

The invention relates to a rail transit energy efficiency balance dynamic monitoring method and system, relates to the field of rail transit energy conservation and monitoring, and solves the problems that in an energy consumption monitoring link, simple statistical analysis and a rule-based system are difficult to adapt to a complex and changeable operating environment due to fixed threshold values and rules, and misjudgment and missed judgment are easy to occur. The method comprises the following steps: according to a comparison result of actual energy consumption of equipment and a personalized energy efficiency standard threshold value, determining whether the corresponding equipment has energy consumption abnormity or not; if yes, the personalized threshold value and historical energy consumption data of the equipment are combined, the built comprehensive energy loss analysis model based on the fault tree and the Bayesian network is utilized, the fault mode of the equipment is started, the operation history, the maintenance record and the environmental factors of the equipment are combined, and Bayesian network reasoning and dynamic fault tree analysis are carried out; and the source reason of the energy loss is positioned and sent to the terminal held by the person in charge. The method has the following effect that the energy efficiency management level of rail transit is effectively improved.

The invention provides a rail transit dispatching analysis method and system based on artificial intelligence, and belongs to the technical field of rail transit dispatching.The method comprises the steps that firstly, a dynamic dispatching baseline of a rail transit system is established, the dynamic dispatching baseline comprises reference operation features, rule constraint features and scene association features, and then real-time dispatching data is collected; the method comprises the following steps: performing multi-dimensional comparative analysis on a dynamic scheduling baseline to generate a baseline deviation feature set, calling a pre-trained scheduling collaborative evaluation model to perform dynamic correlation analysis on the baseline deviation feature set to generate a scheduling collaborative evaluation result, and constructing a multi-target scheduling optimization model based on the scheduling collaborative evaluation result to perform collaborative optimization processing. According to the method, an initial scheduling adjustment scheme set is generated, finally, dynamic constraint verification processing is performed on the initial scheduling adjustment scheme set, and a final rail transit scheduling instruction is generated and used for triggering a scheduling control system to execute scheduling parameter updating operation, so that the running efficiency, safety and stability of a rail transit system can be improved.

The invention provides a BIM (Building Information Modeling)-based rail transit construction geological exploration profile generation method, which belongs to the technical field of exploration measurement and comprises the step of comprehensively improving the accuracy of rail transit construction geological exploration through an advanced data acquisition and intelligent analysis method. In a construction site, multi-dimensional data of geological drilling, geological radar, geophysical prospecting testing and the like are acquired through various precise instruments, and a three-dimensional coordinate system is firstly established; and deep feature extraction is performed on complex data by using a deep learning algorithm, and key information such as geological stratification, rock and soil parameters and underground water level is accurately obtained. Intelligent fusion of multi-source data is realized by constructing a geological stratification model, a rock and soil parameter model and an underground water level model, and a comprehensive geological information model is generated. And importing the extracted geological profile data into a BIM platform, and carrying out strict precision verification and optimization by adopting a professional geological information collaborative equation set to finally form a highly accurate and reliable rail transit construction geological exploration profile.

The invention relates to the field of rail detection, in particular to a rail transit vehicle obstacle detection method based on multi-modal data fusion, which comprises an environmental perception classification step, a credibility calculation and mode decision step, a detection data acquisition step and an obstacle detection step. A full-scene coverage detection system is constructed by integrating a visual image sensor, a millimeter-wave radar sensor and an ultrasonic radar sensor and utilizing complementary advantages of the three sensors in different environments. When a certain sensor is interfered by the environment, other sensors can still provide effective data support, a detection blind area caused by failure of a single mode is avoided, and the robustness and the detection accuracy of obstacle detection are improved.

A thermal management method for metal solid-state hydrogen storage, a hydrogen fuel cell system thereof and a rail transit vehicle. The hydrogen fuel cell system comprises a hydrogen fuel cell module and solid-state hydrogen storage modules, which are communicated via a hydrogen pipe; a first branch is provided on the periphery of the hydrogen fuel cell module; a diverter valve is provided at a liquid outlet end of the first branch, one branch of the diverter valve being communicated with a heat dissipation system, and another branch of the diverter valve being communicated with a second branch. Using heat generated during power generation of a hydrogen fuel cell to successively start the solid-state hydrogen storage modules helps to increase the system starting speed, thus improving the efficiency of hydrogen fuel cell systems.

The invention relates to the related technical field of vibration noise control, in particular to an intelligent vibration noise control method and system using frequency spectrum characteristics, and the method comprises the steps: collecting rail transit vibration noise signals, extracting time-frequency domain mixed characteristics, carrying out multi-source separation, generating a noise source contribution degree matrix, configuring a strategy set, and monitoring changes to generate feedback. And the active damper is adjusted in real time for noise reduction. The technical problems that the noise suppression effect is limited and the noise reduction requirement of a complex time-varying scene cannot be accurately matched due to the fact that vibration noise control is difficult to adapt to a dynamically changing vibration noise environment are solved, time-frequency domain mixed features are extracted, overlapped noise sources are identified and separated more accurately, complex characteristics of vibration and noise are comprehensively considered, and the noise reduction effect is improved. And the active damper is dynamically adjusted so as to adapt to changes of vibration and noise, and the accuracy and effectiveness of dynamic adjustment are improved.

The invention relates to the field of rail transit control systems, and discloses a rail transit intelligent control system and method based on the Internet of Things, and the system comprises a sensing layer which is integrated with a visual sensor, a thermal imaging sensor, a sound sensor, a pressure sensor, a temperature and humidity sensor, a CO2 sensor and other sensors, the sensing layer collects various data of the rail transit system in real time, and the sensing layer performs joint optimization processing on sensor data by adopting deep learning and reinforcement learning algorithms through an adaptive data fusion method so as to realize high-precision real-time monitoring, data quality screening and anomaly detection; and the network layer comprises a wireless communication module and adopts an intelligent routing mechanism based on real-time network load prediction. A self-adaptive data fusion method is adopted by the sensing layer, and the sensing capability of the rail transit system is greatly improved through intelligent data processing, so that the system can realize real-time and accurate response to a complex traffic environment.

The invention discloses an intelligent operation and maintenance fault early warning method for a rail transit variable-frequency power supply, and relates to the technical field of power electronics and intelligent operation and maintenance, and the method comprises the following steps: setting a multi-point synchronous collection device to carry out the high-precision real-time collection of voltage and current signals of a variable-frequency power supply system, and carrying out the digital coding processing of data; and performing frequency domain analysis on the acquired voltage and current signals by using a preset spectrum analysis algorithm, and extracting harmonic amplitudes and phase characteristic parameters of different orders. According to the invention, through high-precision synchronous acquisition and spectrum analysis, rapid and accurate identification of harmonic abnormity is realized; a machine learning model and dynamic threshold adjustment are fused, and the adaptability and intelligent recognition capability of the system to fault modes under multiple working conditions are enhanced; and a trend prediction and graphical early warning platform is introduced, so that the fault risk is pre-judged in advance and visually responded, and the operation and maintenance efficiency and the operation safety of the rail transit power supply system are remarkably improved.

The invention provides a state monitoring and fault early warning method for a maglev vehicle system, relates to the technical field of urban rail transit data processing, and solves the problem of how to effectively combine an empirical threshold mode and a machine learning technology and give full play to a combination effect. The method comprises the steps of collecting operation data of a magnetic levitation vehicle system, performing corresponding data preprocessing operation, performing analysis and calculation in a mode of constructing a mathematical model, and evaluating the health state of the magnetic levitation vehicle system; constructing and training a fault prediction model, and inputting the evaluation data into the trained fault prediction model to obtain potential fault data of the magnetic levitation vehicle system; and generating early warning information based on the potential fault data, retrieving an expert experience library by using the early warning information, generating an intelligent decision result, and feeding back the intelligent decision result to the operation and maintenance personnel. According to the method, machine learning and expert experience are effectively combined, so that the real-time performance and the accuracy of fault early warning are improved.

The present invention relates to an integrated train dispatching system and method for regional rail transit, and a medium. The system comprises: a network-wide operation diagram editing and display module, configured to construct and display a regional rail transit network-level operation diagram; a center / station functional module, configured to, on the basis of center / station mode configuration information, activate a corresponding functional module, and implement center dispatching or station dispatching; a line hybrid dispatching functional module, configured to, on the basis of line automation level configuration information, activate a functional module corresponding to the current automation level, and implement hybrid dispatching management of lines having different automation levels; and a full-function scenario dynamic dispatching module, configured to perform full-lifecycle dynamic dispatching of trains on the basis of different signal system conditions, the signal systems comprising CTCS2 + ATO + ATB, cross-line operation CBTC, and CBTC and CTCS2 + ATO + ATB cross-line operation. Compared with the prior art, the present invention has the characteristics of full-scenario coverage, high compatibility, a high degree of automation, and wide practicability.

The invention discloses a rail transit hub emergency regulation and control method and system based on real-time simulation, and relates to the technical field of intelligent traffic and rail transit emergency management, and the method comprises the steps: carrying out the time synchronization processing of real-time passenger flow data and train operation state data, and generating a hub state data set; constructing a multi-level simulation model, and simulating a passenger flow distribution evolution process by presetting passenger flow evacuation behaviors and train operation constraint conditions; based on an output result of the multi-level simulation model, positioning capacity limiting nodes in each evacuation path by adopting a bottleneck identification algorithm, calculating passenger flow carrying capacity, and generating an optimal path regulation and control parameter with shortest evacuation time as a target; and the optimal path regulation and control parameters are converted into a train automatic control system instruction and a passenger flow guide display instruction, the instructions are issued to each line control unit through a hub centralized dispatching system, and the train operation frequency and the passenger flow evacuation direction are adjusted. According to the invention, intelligence and collaboration of emergency regulation and control of the rail transit hub are realized.

The invention relates to the technical field of event detection and behavior specification supervision, and discloses a rail transit driver operation specification monitoring method and device based on an improved ST-GCN model, and the method comprises the steps: determining various key specification operations involved in the start and stop process of an urban rail transit train driver, key joint points corresponding to the key standard operations are calibrated in advance; collecting video data of the driver executing the various key standard operations in the internal and external environments of the vehicle, and evaluating and marking the compliance of the driver operation behaviors in the video data; constructing an ST-GCN model based on dynamic topology and a domain prior fusion layer, and training the model by using the evaluated and labeled video data; and the trained ST-GCN model based on dynamic topology and field prior fusion is utilized to monitor driver operation in real time, and a prediction result is output. According to the device and the method, the real-time performance and accuracy of supervision can be improved, and safe operation of rail transit is ensured.

The invention provides a rail transit video intelligent analysis method, medium and system, and belongs to the technical field of rail transit, and the method comprises the steps: constructing a self-adaptive resolution image hierarchical structure, and dynamically determining an optimal resolution hierarchy based on a minimum spanning tree algorithm; constructing an abnormal target movement matrix by combining an improved frame difference method with an optical flow estimation technology; applying a Hungary algorithm to realize target tracking; constructing an abnormal target change index to evaluate a behavior abnormal degree; dividing monitoring stages according to the running state of the vehicle to realize scene self-adaption; an orbit scene attention network model is introduced to identify abnormal behaviors; quantifying a risk level through an abnormal behavior risk assessment function; implementing a hierarchical processing strategy to enable a low-resolution hierarchy to be responsible for global rapid screening and a high-resolution hierarchy to be responsible for fine analysis of key areas; the risk trend is estimated in combination with the rail transit abnormal event prediction model, and the technical problems of large calculation amount and low efficiency of high-definition video processing are effectively solved.

The invention discloses a large-section long-footage mechanical blasting mixed excavation method suitable for a subway station, and relates to the technical field of underground rail transit engineering, and the method comprises the following steps: obtaining a tunnel section contour, a geologic structure and blasting parameters, building a three-dimensional blasting wave propagation inversion model, and combining geology and material heterogeneity to obtain a three-dimensional blasting wave propagation inversion model; and identifying a closed space region forming reflection interference, and predicting a space point location where blasting wave energy is abnormally accumulated. Based on the three-dimensional blasting wave propagation inversion model, the abnormal wave energy accumulation area is predicted in combination with geological heterogeneity, the high-risk blasting section is recognized, parameters are reconstructed, vibration real-time monitoring and model feedback correction are assisted, a closed-loop control system is constructed, local wave crest abnormity is effectively restrained, and the safety and controllability of large-section tunnel blasting construction are improved.

The invention discloses an AI technology-based rail transit industry crew member intelligent management system, which comprises a crew member scheduling management module, and is characterized in that the crew member scheduling management module scientifically and reasonably arranges work tasks of crew members by introducing an AI-based intelligent scheduling optimization engine according to a travel diagram and operation requirements of a line; the AI-based intelligent scheduling optimization engine constructs a multi-target crew scheduling model by using traffic operation data, and generates an intelligent and dynamically adjusted scheduling scheme by using the multi-target crew scheduling model. The system has the advantages that the problems of manual shift arrangement, paper document storage, health state monitoring, attendance and attendance information management and the like in the prior art are solved. The efficiency, safety and accuracy of crew management are effectively improved, the risks of manual operation and information lag are reduced, and a more intelligent and flexible solution is provided for rail transit operation.

The invention relates to the technical field of data communication, and discloses a rail transit station-level agent implementation method and system. The method comprises the following steps: constructing a professional body base through an ARM processor, designing a LiteIP security communication bus based on an FPGA, constructing a multi-core CPU cluster to construct an intelligent body center, establishing a real-time and entity database to form a data basis, fusing federated learning and a neural network to generate a decision, and executing edge calculation to realize station-level autonomous control. According to the invention, millisecond-level data exchange and intelligent cooperative control in the station-level system are realized, and the response speed and the processing efficiency of the system in an emergency are remarkably improved.

The invention discloses an automatic fare collection method and system based on multi-modal identity recognition, and relates to the technical field of rail transit automatic fare collection systems and multi-modal biological recognition fusion, and the method comprises the following steps: 1, enabling a user to approach a recognition area, carrying out the multi-modal data collection, and carrying out the quality detection and preprocessing; 2, feature extraction and credibility preliminary calculation are carried out; 3, environment data are collected and subjected to standardization processing; step 4, adaptively predicting a weight through machine learning, and constructing weight smoothing and time domain constraints; step 5, carrying out D-S evidence step-by-step fusion, and calculating comprehensive credibility; and step 6, comparing the comprehensive credibility with a preset threshold value, if the comprehensive credibility is greater than or equal to the preset threshold value, determining that the identification is successful and the gate is opened for passing, otherwise, determining that the credibility is insufficient, and performing alternative / manual processing. The system comprises a front-end identification terminal, a read-write industrial control all-in-one machine and a cloud management platform.

The invention discloses an urban rail transit road network short-time OD passenger flow prediction method based on a space-time multi-graph neural network. The method comprises the steps of symbol definition and passenger flow data construction; a multi-relational graph structure: constructing four spatial relationships based on potential inter-station association of stations; spatial feature extraction: inputting the constructed passenger flow data, performing graph convolution operation on the constructed passenger flow data and different constructed networks, capturing global spatial features by using a Transform module, and finally performing fusion output to obtain a hidden value sequence with the spatial features and outputting the hidden value sequence; time feature extraction: using BiLSTM to respectively extract time long-distance dependence on the starting place and the destination of the OD passenger flow, fusing results, and inputting the fused results into GRU to further capture short time sequence dependence; and finally, inputting into a multi-head time convolution module, and extracting time dependence information in parallel through a plurality of convolution kernels by utilizing TCN to obtain a prediction result. The method has higher accuracy and adaptability.

The invention provides a multi-modal fusion and semantic enhancement train positioning method and system, and belongs to the technical field of rail transit, and the method comprises the steps: carrying out the time-space alignment of data, obtaining a dense point cloud, constructing a dense semantic point cloud, and dynamically estimating the confidence coefficient weight of each type of sensors; a set residual error and a Manhattan structure constraint residual error of a plane are constructed, laser radar point cloud parameters are obtained, and visual projection constraints are constructed at the same time; constructing a comprehensive degradation scoring function to carry out degradation judgment on the current environment; when the degradation result is yes, introducing a structure and motion information independent of an external environment, maintaining trajectory estimation, and constructing a compensation constraint; introducing a prior semantic constraint and a large model semantic factor constraint; and constructing a global optimization objective function, dynamically adjusting the weight of each modal factor, obtaining an optimal estimation state, and outputting a high-precision train positioning result. According to the method, high-precision and robust track estimation in an extreme scene is realized, so that the continuity, safety and intelligence of train positioning are guaranteed.

The invention provides an urban rail transit energy management system considering new energy and energy storage access. The urban rail transit energy management system comprises a new energy power generation unit, an energy storage unit, a regenerative braking energy recovery unit and an energy management control platform, the new energy power generation unit and the energy storage unit are connected to a rail transit power supply system; the energy management control platform is connected with the power supply network and controls the units in a communication mode, energy flow in the rail transit power supply system is monitored, analyzed, optimized and dispatched so as to coordinate utilization of new energy power generation power, energy storage charging and discharging and regenerative braking energy, the energy utilization efficiency is improved, and the requirement for power grid outsourcing electric energy is lowered. The peak load of the system and the power grid electricity purchasing cost are effectively reduced, meanwhile, the overall energy utilization efficiency is improved, and energy conservation and consumption reduction are achieved while safe and stable operation of the rail transit system is ensured.

The invention discloses a bidder hypergraph association analysis method and system based on bid behavior recognition, and relates to the technical field of bid behavior detection. The method comprises the following steps: S1, analyzing a bidding document, constructing a bidding document structure tree according to key structure information, and carrying out document structure fingerprint similarity matching; s2, bidding behavior data are collected, bidding behavior similarity judgment is carried out, and corresponding measures are taken through threshold comparison; s3, constructing a high-order relation structure hypergraph, and performing hyperedge importance evaluation; s4, evaluating the bid enclosing probability of the bidding unit, and taking graded early warning measures; and S5, constructing a risk tracking and feedback curve according to a bid enclosing probability evaluation result, and generating an evidence report. The method mainly solves the problem that bidding behaviors cannot be effectively recognized due to the fact that bidding units share technical scheme templates and avoid detection through typesetting and camouflage in the urban rail transit design bidding process.

The invention relates to an urban rail transit passenger flow prediction method based on a multi-factor algorithm. The method comprises the following steps: collecting urban rail transit historical passenger flow data and related index factor sample data influencing passenger flow; constructing a factor analysis mathematical model, analyzing a plurality of index factors influencing the passenger flow, determining a common factor, and calculating a score; constructing a BP neural network model, taking the common factor score as input, and obtaining an optimal parameter set through a particle swarm optimization algorithm; generating a BP neural network prediction model for urban rail transit passenger flow prediction based on the optimal parameter set, and training through sample data to obtain an optimal BP neural network prediction model; and performing real-time passenger flow prediction based on the trained BP neural network prediction model. According to the method, the problems of insufficient prediction precision, low calculation efficiency and the like in the prior art can be solved, and the purpose of rapidly and accurately predicting the passenger flow of the station, the line and the line network is achieved.