753 results about "Transmission network" patented technology

The invention relates to the technical field of atmospheric pollutant detection, and discloses a method and a system for detecting excessive emission of atmospheric pollutants. The method comprises the following steps: acquiring pollutant concentration data of multiple monitoring points in a target area to form an original data set; abnormal value detection and correction are carried out on the data set, sensor fault outliers are eliminated, and a preprocessed data set is obtained; extracting concentration change trend characteristics in a preset time window of each monitoring point, and constructing a spatial-temporal characteristic matrix; inputting the matrix into a pollutant diffusion model, calculating a transmission path and strength between monitoring points, and generating a regional transmission network; identifying a potential source region of abnormal fluctuation of pollutant concentration based on a network, and marking the potential source region as a candidate region to be checked; performing multi-scale concentration gradient analysis on the candidate area, and determining a key monitoring area; arranging mobile equipment in the key monitoring area, and collecting high-precision component data; and comparing the data with a standard emission source feature library, matching emission source types of which the similarity exceeds a threshold value, judging whether the emission exceeds the standard or not, and generating a detection report.

The invention provides an edge fusion diagnosis system and method for multi-source heterogeneous power transmission monitoring data, and belongs to the technical field of power transmission line monitoring. The multi-source heterogeneous power transmission monitoring data edge fusion diagnosis system comprises a multi-source heterogeneous data access module, a data preprocessing and standardization module, an edge computing node, an edge node collaborative diagnosis module, a data compression and feature extraction module and a cloud fusion analysis platform. The method comprises the following steps: accessing multi-source heterogeneous data, preprocessing and standardizing, locally diagnosing by an edge computing node, triggering multi-node collaborative diagnosis when the credibility is insufficient, and uploading key feature data to a cloud for global analysis and model optimization. According to the method, comprehensive integration and efficient processing of multi-source data are realized, the real-time performance, accuracy and comprehensiveness of power transmission monitoring diagnosis are improved, and the efficient monitoring requirement of a complex power transmission network can be met.

The invention provides an ecological environment dynamic monitoring method based on big data, and relates to the technical field of environmental pollution detection, and the method comprises the steps: collecting multi-source sensor data of a fixed monitoring station in an urban region, and carrying out the time alignment and space registration of the multi-source sensor data, thereby generating a standardized environmental monitoring data set. And on the basis of the data set, constructing a pollutant transmission network between the monitoring points to obtain a regional pollutant distribution characteristic graph, calculating a diffusion trend coefficient of pollutants in combination with regional wind direction data, constructing a pollutant migration path network, and generating a key supervision region map. According to the method, the pollutant concentration distribution can be dynamically partitioned, and the mobile monitoring equipment is scheduled to carry out gradient type point distribution monitoring, so that the accurate positioning of the pollution source is realized; the accuracy of pollutant diffusion trend analysis and the precision of pollution source positioning are improved, dynamic supervision of key areas is facilitated, the efficiency of environment monitoring and the fine management level are improved, and therefore the risk of environmental pollution is effectively reduced.

The invention discloses an asynchronous motor energy-saving monitoring and dynamic early warning method based on situation awareness, and relates to the technical field of asynchronous motor energy-saving monitoring. The method comprises the steps that S1, multi-source heterogeneous data are synchronously collected, power supply parameters and operation parameters are obtained in real time, and a frequency conversion working condition self-adaptive deviation value and a current stability coefficient are obtained; s2, generating four-dimensional situation factors including an overtemperature danger value, a current stability coefficient, a voltage stability coefficient and a frequency deviation value; s3, constructing a fourth-order transmission network, and generating a primary early warning signal based on the path mark combination; s4, calculating the motor efficiency in real time, triggering an efficiency abnormity mark, fusing the efficiency abnormity mark with the primary early warning signal, and reconstructing a final early warning instruction; and S5, executing hierarchical control according to the final early warning instruction. Through multi-parameter coupling and dynamic early warning transmission, the early-stage capture capability of the composite fault is improved, the early warning response delay is shortened, energy-saving optimization and fault protection of the motor are realized, and the method has a remarkable practical value.

The invention relates to the technical field of cable fault diagnosis and positioning, in particular to a cable fault positioning method and system based on multi-source data space-time correlation, and the method comprises the steps: obtaining partial discharge, traveling wave and temperature multi-source monitoring data from a high-voltage cable-overhead line hybrid power transmission network; on the basis of space-time correlation analysis of partial discharge and temperature data, heat-electricity associated defect points are calibrated; comparing traveling waves of the cable and the overhead line, and identifying and inhibiting cross-domain propagation interference; positioning the traveling wave data which are not identified to obtain temporary coordinates, judging fault characteristics by fusing information such as sheath ring current and the like, and comparing the temporary coordinates with defect points to generate matching identification; and inputting multi-dimensional information such as fault features, defect matching identification and temperature into the fusion model, calculating the confidence coefficient of a fault mode, and outputting a cross validation positioning result. According to the invention, high-reliability positioning of mixed line faults and active early warning of insulation defects are realized by carrying out space-time correlation and fusion decision on multi-source monitoring quantities such as traveling waves, partial discharge, circulation, temperature and the like.

The invention belongs to the technical field of power supply transmission, and relates to a POE-POF power supply transmission method and system, and a storage medium. The method comprises the following steps: collecting electric quantity data of POE-POF power supply transmission network nodes in real time, and extracting a dynamic power feature set; identifying an abnormal power loss event based on the feature set and extracting an abnormal mode feature; abnormal path nodes are positioned by combining a POE-POF power supply transmission network topological structure, voltage and current distribution parameters are optimized, and an optimized power distribution scheme is generated; dynamically adjusting voltage and current values of a power supply path, evaluating an energy consumption trend and generating a trend data set; and adaptively adjusting the electric quantity acquisition parameters according to the energy consumption trend, updating the acquisition configuration and circularly executing the steps. According to the invention, intelligent detection, accurate positioning and dynamic optimization of abnormal loss of the power grid are realized, the stability and energy efficiency of the power supply network are improved, and the method is suitable for fine management and energy-saving optimization of the intelligent power grid.

The invention is applicable to the technical field of power distribution network monitoring, and provides a power grid monitoring-oriented power adaptive congestion control method, which comprises the following steps of: acquiring a network performance index and a power grid operation parameter of a power grid monitoring data transmission network, and determining a congestion state of a current network through a preset congestion evaluation model; a 5G ad hoc network node connection strength coefficient, a new energy monitoring data priority label, a west-to-east transmission channel power flow fluctuation coefficient and a regional vulnerability index are used as decision variables, and the minimum weighted transmission delay, the packet loss rate and the maximum new energy consumption responsivity are used as objective functions. Generating a control strategy corresponding to the current network congestion state according to the hybrid intelligent optimization model fusing reinforcement learning and a genetic algorithm; executing the control strategy to dynamically adjust the network resources; monitoring the execution effect of the control strategy, and feeding the result back to the model to update the parameters; and therefore, the requirements of multi-target optimization and differential service quality in a complex power grid monitoring scene can be met.

The invention relates to the technical field of telecommunication. By providing the multi-domain strategy collaborative dynamic decision optimization method, system, equipment and medium, the method comprises the following steps: extracting cross-domain common characteristics of network layer topology, equipment state and alarm information, retaining differential characteristics of a transmission network and a data network, and generating a uniform resource data set; on the basis of the unified data set, a global optimization strategy is generated through linkage decision making of a three-level collaboration module; matching the strategy into an atomic instruction set executable by a heterogeneous network manager by utilizing semantic ontology mapping and syntax tree matching; and according to the instruction execution feedback data, dynamically adjusting the strategy weight and carrying out iterative optimization, and generating an updated global strategy, so as to achieve the technical effects of improving the cross-domain resource perception precision, enhancing the hierarchical decision collaborative efficiency and reducing the multi-manufacturer instruction conversion error rate.

Sidelink positioning of user equipments (UEs) is supported by exchanging sidelink positioning messages between the UEs, where the messages may include sidelink positioning capabilities, sidelink positioning resources, sidelink Positioning Reference Signal (PRS) configurations, sidelink measurements and / or location results for one or more of the UEs, and may be used for determining the location results. Location results may include ranges, directions, locations, velocities, relative locations and relative velocities. Groups of UEs may be formed for sidelink positioning based on group criteria parameters that are used for determining UE inclusion or exclusion in a group, in a centralized or distributed group management procedure. Group management may additionally remove and add UEs and transfer UEs between groups. Network supported sidelink positioning may transfer sidelink positioning messages between a UE and a location server where a sidelink positioning message may be embedded in an LPP and / or SUPL message.

The invention relates to a supplier behavior real-time sensing and monitoring method and device and electronic equipment. The method comprises the steps of collecting and preprocessing supplier multi-source heterogeneous data in real time, dynamically capturing a Binlog of a MySQL database through a Flink CDC framework, obtaining service behavior data in real time, collecting text log data through a Flume framework, and transmitting network behavior data in real time. Integrating the structured data, the unstructured data and the semi-structured data, and constructing a unified data transmission channel; constructing a multi-dimensional user portrait; comparing the semantic similarity between the bidding file and the bidding requirement, and triggering compliance early warning when the matching degree is lower than a threshold value; analyzing a supplier cooperation network, and marking a cluster group with abnormal transaction frequency and irrelevant business fields as a cross-bidding risk; and analyzing the access behavior sequence, and detecting a malicious access pattern to generate a malicious access alarm. According to the supplier behavior monitoring system and method, real-time performance, accuracy and multidimensional performance of supplier behavior monitoring are achieved through the architecture of multi-source heterogeneous data real-time integration, multi-dimensional portrait construction and intelligent risk identification.

A mechanism is provided efficient packing of network flits in a data processing network. Transaction messages for transmission across a communication link of a data processing network are analyzed to determine a group of transaction messages to be passed to a packing logic block for increased packing efficiency. The transaction messages are packed into slots of one or more network flits and transmitted across a communication link. The mechanism reduces the number of unused slots in a transmitted network.

The invention provides a construction safety risk grading method and system combined with fuzzy clustering, and belongs to the technical field of building construction safety risk management.The method comprises the steps that firstly, a real-time risk feature set of a construction scene is obtained, and environmental influences, equipment operation and personnel operation features of a construction area are covered; secondly, fuzzy clustering preprocessing is conducted on the real-time risk feature set, fuzzy membership degree distribution and a feature correlation degree matrix of all risk features are obtained, a risk transmission network is constructed based on the result, nodes are risk features, edges are correlation degree parameters between the features, and risk diffusion coefficients of all the nodes are calculated through the risk transmission network; and generating a risk grade division result according to a preset grading rule, and finally outputting a construction safety grading instruction containing the risk area identifier and the corresponding management and control strategy, thereby dynamically and accurately evaluating the construction safety risk.

The invention belongs to the technical field of network live broadcast, and particularly relates to a multi-live-broadcast-room matrix cooperative control system and a control method, a global unified clock reference system is established, and a public time axis covering all participating nodes is generated in a distributed calibration mode, so that audio and video streams of all live broadcast rooms are collected and played based on the same time reference, and therefore, the collection efficiency is improved. The synchronization deviation caused by clock drift is fundamentally eliminated; and a dynamic topology sensing type transmission network is further constructed based on the public time axis, each node periodically detects a link state and generates a score, an optimal transmission path set is generated in real time, and adaptive scheduling of data streams is realized. Therefore, accurate alignment of multiple paths of media streams in the time dimension is ensured, the end-to-end transmission delay is remarkably reduced, the audio and video synchronization precision and transmission stability in a cross-live-broadcasting-room interaction scene are effectively improved, and the overall audio-visual experience of a user is improved.

The invention relates to a network resource flexible scheduling method, system and device and a medium. The method is applied to a backbone transmission network and data network intelligent management and control system. The method comprises the following steps: disassembling network resources into a plurality of atomic power units in a virtualization layer, marking dynamic attribute tags for the atomic power units to obtain an atomic power library, and generating a trigger event when a real-time performance index exceeds a preset threshold value; according to the trigger event, the atomic power library and the alarm knowledge graph, carrying out combination optimization on the atomic power units to generate a resource combination scheme, and calling a three-dimensional topology model to carry out pre-verification on the resource combination scheme; and according to the execution scheme, carrying out capacity expansion and shrinkage operation on the atomic power unit through the virtualization layer, adjusting a load balancing weight based on real-time flow distribution, and generating a resource allocation result. According to the method, dynamic cooperative scheduling of network resources is realized, and the service quality and the operation efficiency of a backbone transmission network and a data network are improved.

The invention relates to the technical field of power communication network security, in particular to a power communication network risk assessment method based on risk genes and a space-time large model. According to the technical scheme, the power communication network risk assessment method based on the risk gene and the space-time large model comprises the following steps that equipment sensor data, meteorological grid data and service topology data of a power communication transmission network are obtained in real time through a multi-source data acquisition module, and space-time alignment and feature coding are executed; a risk gene map is constructed, the risk gene map comprises a three-layer map structure of an equipment vulnerability gene, an environment threat gene and a business dependence gene, and a cross-space-time risk propagation rule is defined; a multi-modal space-time large model is trained, and a framework combining Transform and a space-time diagram convolutional network is adopted. A biological gene concept is introduced into power communication network risk assessment, gene-level fault traceability is achieved, and risk factors have combinability and interpretability.

A network entity may communicate at least one first transmission with a user equipment (UE) via a first transmission-reception point (TRP) and a second TRP in a multi-TRP (mTRP). The network entity may transmit an indication to switch to a single TRP (sTRP) mode to the UE, or a condition of the UE, such as a configured specification, may provide the indication to switch to the sTRP mode. The UE may obtain the indication to switch to the sTRP mode from the network entity or from the condition. The UE may switch to communicate at least one second transmission with the network node via one of the first TRP or the second TRP in the sTRP mode based on the indication to switch to the sTRP mode.

The invention is suitable for the technical field of electric power communication, and provides an electric power communication transmission network fault self-repairing processing method, which comprises the following steps: carrying out fault analysis based on an identified alarm type, starting a self-repairing processing flow based on a generated fault detection result, extracting attribute information of an affected service to distinguish service priorities, and carrying out self-repairing processing according to the service priorities. The N-1 redundancy capability of the service is verified; if a verification result needs to start a temporary channel, executing resource verification, and migrating service flow to a temporary circuit or triggering protection channel switching after verification is passed; after the fault of the original optical path is repaired, performance acquisition and service flow test are carried out on the repaired original optical path; if the test is passed, the service flow is migrated back to the original optical path in a double-channel parallel mode, and resources occupied by the temporary channel are released; after the temporary circuit is released, generating a fault repair file, and archiving the performance data to generate an optimization suggestion; and the fault processing efficiency of the transmission network, the reliability of the network and the self-healing level are improved.

The invention relates to the technical field of digital information transmission, in particular to an intelligent force transducer data remote transmission method and system based on the Internet of Things, and the method comprises the steps: obtaining the measurement data of a plurality of force transducers, calculating a multi-dimensional rate vector, and obtaining a multi-dimensional rate vector based on the multi-dimensional rate vector in a historical time window; calculating a differential entropy to determine an instantaneous fluctuation index, determining a predictive risk gradient in combination with the historical trend of the instantaneous fluctuation index, evaluating the effective throughput of the data transmission network online, and coupling the predictive risk gradient to determine a risk-aware transmission cost factor, fusing the predictive risk gradient with the transmission cost factor, and determining the risk-aware transmission cost factor. And jointly determining a dynamic transmission strategy level, and executing hierarchical adaptive data transmission operation according to the dynamic transmission strategy level. According to the method, intelligent balance of transmission efficiency and data reliability is realized.

The invention belongs to the technical field of electric power monitoring and dispatching, and discloses a real-time electric quantity monitoring and dispatching management system, which realizes high-precision and real-time electric quantity and related parameter acquisition through a quantum sensing electric quantity acquisition unit, and ensures high-efficiency circulation of data from acquisition to processing by matching with a high-speed and stable transmission network of a global dynamic data transmission unit; the edge calculation preprocessing unit timely processes data and reduces delay, so that the system can quickly obtain the real-time state of the power grid, timely basis is provided for scheduling decision, and decision errors caused by data lag are effectively avoided; by adopting a modular design, each core unit can be flexibly combined and expanded, the quantum sensing electric quantity acquisition unit supports various communication protocols, and the global dynamic data transmission unit fuses various communication technologies, so that the requirements of different scales of power grids can be met, special interfaces and lines do not need to be customized, and the construction and maintenance cost is reduced; and the applicability of the system in a complex and changeable power environment is obviously improved.

Systems and method to minimize conditions that cause sub-synchronous oscillation (SSOs) associated with series compensated transmission grids. The implementations ensures the correct development of methods and systems that can mitigate sub-synchronous oscillation (SSOs) associated with a series compensated transmission grid and enhance the series compensation efficiency. An example implementation is a network-based SSO mitigation technology, and another example implementation is a generator-based SSO mitigation technology. The discovery and example implementations are applicable to series compensated transmission networks with synchronous generators, induction generators, wind turbines, solar photovoltaic generators, charging stations, battery storage systems, high-voltage DC transmission systems, STATCOMs, inverter-based data centers, and other inverter-based resources.

The invention discloses a patient transfer intelligent monitoring platform based on the Internet of Things and the intelligent wearing technology, and relates to the technical field of transfer intelligent monitoring, and the platform comprises an intelligent wearing equipment module which is used for carrying out the time sequence collection of physical sign data in a transfer process, and obtaining a physical sign monitoring data sequence; the medical equipment interconnection module is used for carrying out simultaneous multi-terminal data acquisition; the data transmission network module is used for determining an infusion pump infusion speed analysis result, a physical sign abnormal mode analysis result, a monitoring terminal abnormal mode analysis result and an oxygen consumption reserve analysis result; and the alarm and display module is used for graded alarm and shared display of the electronic handover bill. The technical problems that in the prior art, continuous dynamic monitoring cannot be achieved in the transferring process of the invasive mechanical ventilation patient, the early warning capability is lacked, and handover communication is not smooth are solved, and the technical effects that physical sign data and medical equipment parameters in the transferring process of the patient are collected in real time, and transferring monitoring continuity and handover efficiency are improved are achieved.

A computer-implemented method performed by a computing device is provided to correct anomalies in a packet synchronized system associated with a telecommunications network. The method includes identifying (1810) an anomaly in synchronization in a transport network based on an evaluation of performance of the packet synchronized system with a graph-based ML model. The anomaly includes a time misalignment between a plurality of radio access nodes in the telecommunications network. The method further includes initiating (1812) a remedial action to correct the time misalignment.

The invention relates to an AIoT-based fan tower inclination on-line monitoring and early warning method and system, and belongs to the technical field of intelligent operation and maintenance of wind power equipment, and the system comprises an end side sensing layer which is used for collecting inclination, settlement, vibration and environment data in real time through multi-point arrangement of inclination angle, vibration and wind speed sensor groups; the edge calculation layer is used for executing space-time consistency processing, dynamic weight fusion and vibration data compression sensing on the multi-source heterogeneous data; the cloud analysis layer is used for establishing a mapping relation among wind speed, vibration and inclination amount based on an LSTM neural network, inputting a time serialization wind speed mean value, vibration spectrum energy and an inclination angle difference value, outputting an inclination trend prediction value, adaptively adjusting a dynamic threshold according to the environment wind speed intensity, and generating an early warning instruction when the threshold is exceeded; and the IoT transmission network is used for converging edge data through ZigBee-Mesh and uploading the edge data to the cloud through 4G / 5G. Through an end-edge-cloud cooperative intelligent architecture, core technical support is provided for safe and efficient operation of the wind power plant.

The invention provides a forward error correction capability diffusion method and a related device. The method comprises the steps that the forward error correction capacity of a first node is determined, the forward error correction capacity comprises a coding algorithm set, the maximum grouping duration and the maximum protection level, and the forward error correction capacity of the first node comprises a first coding algorithm set, the first maximum grouping duration and the first maximum protection level; packaging the first coding algorithm set, the first maximum grouping duration and the first maximum protection level into a first capability message; and if the first node has the directly connected downlink node, sending a first capability message to the downlink node, so that the downlink node determines the forward error correction capability of the first node based on the first capability message. The forward error correction capability of the node in the transmission network is diffused downstream through the message, so that the receiving end node can determine the forward error correction coding mode of the sending end node based on the message to execute corresponding decoding, and the bandwidth utilization rate and the computing resource utilization rate are improved.

The invention discloses a distributed traveling wave distance measurement method, equipment and a storage medium. The system is composed of monitoring terminal equipment arranged along a power transmission line, an APN data transmission network and a central station system. The monitoring terminal acquires a traveling wave signal through a Rogowski coil and calibrates time through a GPS synchronous clock, and data is encrypted and then transmitted to the central station; the central station adopts a distributed cluster architecture, analyzes multi-manufacturer data through a unified equipment protocol, calculates a fault position by using a double-end positioning method in combination with a wave velocity self-learning technology, and identifies a fault type based on wave tail time characteristics. The method comprises the steps of monitoring point arrangement, data encryption transmission, waveform feature extraction, fault positioning, graded early warning and the like. According to the system and the method, the problems of poor compatibility, low positioning precision and low operation and maintenance efficiency of equipment of multiple manufacturers are solved, and high-precision positioning and intelligent diagnosis of power transmission line faults are realized.

The invention discloses a traditional Chinese medicine teaching and clinical simulation method and system based on a six-channel transmission theory, and particularly relates to the technical field of teaching simulation. The method comprises the following steps: acquiring disease course record data and symptom time sequence data, reconstructing a symptom evolution chain of each case and a corresponding six-channel identification label, and extracting six-channel disease course node time sequence characteristic data; the method comprises the following steps: constructing a single-path six-path transmission candidate network and a six-path transmission candidate topological network to obtain standard single-path six-path transmission network data and multi-branch six-path transmission topological structure data, and performing graph fusion and path weight re-calibration to generate six-path transmission topological network model data; and judging whether the current disease course node meets a trans-meridian bifurcation simulation triggering condition or not in combination with the symptom combination of the patient, and generating corresponding teaching feedback data and contrast learning data, so that a six-meridian transmission multi-branch nonlinear evolution process is presented in informatization teaching and clinical simulation scenes.

The invention relates to a joint beam and track design method for multi-unmanned aerial vehicle auxiliary sensing energy supply, which is expected to obtain better performance while considering multi-equipment position uncertainty and unmanned aerial vehicle energy consumption. According to the scheme, firstly, a system model and a two-stage sensing auxiliary energy transfer scheme are constructed, then the optimization problem of beam forming and associated scheduling in the sensing stage and the optimization problem of beam forming and trajectory planning in the WPT stage are established respectively, and the two optimization problems are solved by using an optimization algorithm, so that the sensing performance and the WPT efficiency of the system are maximized respectively. By establishing a simulation model for simulation and comparison, the advantages of the proposed algorithm are verified. The multi-unmanned-aerial-vehicle auxiliary sensing, communication and energy transmission network can well serve multiple communication users and multiple energy receiving devices, sensing auxiliary wireless power transmission is achieved, meanwhile, the communication performance requirement is met, and the multi-unmanned-aerial-vehicle auxiliary sensing, communication and energy transmission network has the great advantages in the aspects of sensing precision and wireless power transmission efficiency.

The invention discloses an emergency broadcasting method and system integrating a Beidou short message and a LoRa technology, and relates to the technical field of satellite communication and emergency broadcasting, in particular to the emergency broadcasting method and system integrating the Beidou short message and the LoRa technology. Comprising an emergency broadcast platform, a Beidou short message transmission network, a LoRa Internet of Things distribution network and a multimode emergency broadcast terminal. Cross-regional backbone transmission without dependence of a ground network is realized through a Beidou satellite link, and localized and low-cost data distribution and terminal access are carried out by using a LoRa network. The multi-mode terminal supports multi-channel receiving, broadcasting and state returning and has multi-energy power supply guarantee. According to the invention, an emergency information transmission system with integrated space and ground, longitudinal penetration and transverse cooperation is formed, and high reliability, wide coverage and rapid transmission of emergency information in an extreme environment are ensured.

The invention relates to the technical field of fire safety. The building fire safety intelligent monitoring method and system are provided, and the method comprises the steps: carrying out registration authentication and communication resource configuration on multi-mode fire fighting equipment, and generating an equipment registration center database; dynamically allocating communication resources by using a two-dimensional priority arbitration mechanism, and generating a hierarchical transmission queue; carrying out anti-interruption transmission on the monitoring data through a triple modular redundancy transmission network, and generating a multi-path transmission state report; acquiring the multi-source data transmitted by the hierarchical transmission queue, performing space-time alignment and knowledge graph association processing on the multi-source data transmitted to the edge gateway, and outputting a structured security situation report; the structured security situation report is converted into a control instruction, so that the data loss risk of a high-risk area is reduced, the supporting capacity of multi-source data for fire-fighting command decision is improved, and the problems of linkage control resource mismatching and insufficient response are reduced.

The invention discloses an operation risk management method and system, and the method comprises the steps: collecting store operation data according to a preset period, and carrying out the preprocessing of the data, and obtaining the structured risk information; performing quantitative evaluation based on a dynamic quantile reference algorithm, calculating a deviation degree of a business index relative to a space-time dynamic reference value, determining a core index anomaly degree, and outputting a risk level; generating a store risk label according to the risk level and the core index anomaly, constructing a risk conduction network, and generating a targeted treatment scheme; and finally, outputting a risk management report through closed-loop verification. According to the method, dynamic quantitative evaluation and accurate targeted treatment of the operation risk are realized, the accuracy and treatment efficiency of risk identification are remarkably improved through intelligent adjustment of the space-time dynamic reference value and construction of the risk conduction network, and meanwhile, the manual intervention cost is reduced.