A Streaming Data Analysis Method for Smart City Operation Decision-Making

Abstract

The present invention relates to the field of traffic monitoring and analysis technology, and particularly relates to a streaming data analysis method for urban operation decision-making in a smart city. First, based on the risk indicator values of various risk indicators, a preliminary anomaly confidence level indicating the anomaly of intersection nodes is initially determined; then, based on the diffusion impact of accidents in terms of time and space, and based on the risk indicator value distribution and correlation of each neighboring influence node, the moment anomaly confidence level is determined; and combined with the characteristics that environmental factors may affect the occurrence of congestion events, the instability coefficient is determined in combination with environmental anomaly situations; thereby comprehensively determining a more accurate traffic anomaly confidence level indicating the congestion impact of intersection nodes; further, based on the time and space impacts during the occurrence of congestion events, the traffic risk is quantified, so that the effect of real-time adjustment of operation decisions according to the traffic risk scoring coefficient is better, and the processing efficiency of congestion events is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic monitoring and analysis, and particularly to a method for streaming data analysis for smart city operation decision-making. Background Art

[0002] Streaming data is a continuously generated, real-time, and dynamically changing data set, which mainly comes from sensors, social media messages, financial transactions, network logs, etc. Therefore, the analysis of streaming data is not to process a fixed set of data at one time, but to continuously process the real-time generated data. In the smart city scenario, streaming data analysis can obtain the support data for smart city operation decision-making by real-time monitoring urban energy consumption data such as electricity, gas, transportation, medical care, etc., effectively improving the urban operation efficiency.

[0003] For traffic data, congestion events usually occur at intersections due to environmental impacts, traffic light failures, and traffic accidents. In order to respond to congestion events in a timely manner, the existing technology usually performs similarity analysis based on the real-time traffic flow data at intersections and the traffic flow data when congestion occurred in the historical data of each intersection, so as to judge whether a congestion event occurs at the intersection according to the calculated similarity.

[0004] However, in addition to traffic flow, the impact of congestion events will be reflected in various traffic congestion risk indicators such as real-time following distance, intersection lane occupancy rate, real-time vehicle speed, etc. Therefore, the existing method of analyzing congestion events only through traffic flow has certain limitations, resulting in a relatively low accuracy of congestion event judgment; and the existing technology does not quantify the severity of congestion events, resulting in relatively inefficient allocation of operation resources when dealing with congestion events, and unable to give appropriate treatment plans for congestion events of different severities. In summary, the existing method of analyzing congestion events by the similarity between real-time traffic flow and traffic flow in historical traffic accidents has relatively low accuracy and relatively low efficiency in dealing with congestion events. Summary of the Invention

[0005] In order to solve the technical problems that the existing method of analyzing congestion events by the similarity between real-time traffic flow and traffic flow in historical traffic accidents has relatively low accuracy and relatively low efficiency in dealing with congestion events, the purpose of this application is to provide a method for streaming data analysis for smart city operation decision-making, and the specific technical solutions adopted are as follows:

[0006] The first aspect of this application provides a method for streaming data analysis for smart city operation decision-making, including:

[0007] In the traffic data monitoring platform, collect the risk indicator values of each intersection node in the urban road network at each traffic congestion risk indicator at each moment;

[0008] Determine the corresponding preliminary anomaly confidence according to the overall magnitudes of the risk indicator values of various traffic congestion risk indicators corresponding to each intersection node at each moment; determine the neighborhood influence nodes of each intersection node at each moment according to the preliminary anomaly confidence.

[0009] Determine the spatio-temporal anomaly confidence of each intersection node at each moment according to the risk indicator value distributions and numerical correlation conditions of the corresponding neighborhood influence nodes on various traffic congestion risk indicators; determine the corresponding instability coefficient according to the environmental anomaly conditions of each intersection node at each moment.

[0010] Determine the traffic anomaly confidence of each intersection node at each moment according to the preliminary anomaly confidence, the spatio-temporal anomaly confidence, and the instability coefficient; determine the traffic risk scoring coefficient of each intersection node at each moment according to the temporal sequence change of the traffic anomaly confidence of each intersection node and the overall magnitude of the traffic anomaly confidence of the neighborhood influence nodes; perform real-time adjustment of operation decisions according to the traffic risk scoring coefficient.

[0011] Furthermore, the process of obtaining the preliminary anomaly confidence includes:

[0012] In historical data, take each moment when a congestion event occurs at each intersection node as a historical congestion moment; determine the corresponding accident risk value according to the mean value of the risk indicator values of each traffic congestion risk indicator of each intersection node at all corresponding historical accident moments.

[0013] At each moment, determine the corresponding anomaly intensity value as the difference between the risk indicator value of each traffic congestion risk indicator of each intersection node and the corresponding accident risk value; among all traffic congestion risk indicators of each intersection node at each moment, take the traffic congestion risk indicators with corresponding anomaly intensity values less than the preset anomaly threshold as the anomaly basic indicators of each intersection node.

[0014] At each moment, determine the corresponding preliminary anomaly confidence according to the number of anomaly basic indicators of each intersection node and the overall magnitude of the anomaly intensity values of each anomaly basic indicator.

[0015] Furthermore, the process of determining the corresponding preliminary anomaly confidence at each moment according to the number of anomaly basic indicators of each intersection node and the overall magnitude of the anomaly intensity values of each anomaly basic indicator includes:

[0016] At each moment, the normalized values of the abnormal intensity values of all abnormal basic indicators of each intersection node are accumulated to determine the overall abnormal value of each intersection node; the product between the number of abnormal basic indicators of each intersection node and the overall abnormal value is normalized to determine the preliminary abnormal confidence of each intersection node at each moment.

[0017] Further, the process of obtaining the neighborhood influence nodes includes:

[0018] Take the product of the preliminary abnormal confidence and the preset maximum neighborhood radius as the influence radius of each intersection node; take other intersection nodes whose chessboard distance from each intersection node is less than the influence radius as the neighborhood influence nodes of each intersection node.

[0019] Further, the process of obtaining the spatio-temporal abnormal confidence includes:

[0020] In chronological order, all moments are divided into at least two monitoring time periods with the same time length; under each traffic congestion risk indicator, all risk indicator values corresponding to each neighborhood influence node in each monitoring time period are arranged in chronological order to determine the corresponding time series risk indicator sequence;

[0021] Taking the time series risk indicator sequence as a row vector, according to all the time series risk indicator sequences corresponding to all neighborhood influence nodes, determine the local spatio-temporal matrix corresponding to each monitoring time period for each traffic congestion risk indicator;

[0022] After standardizing the local spatio-temporal matrix, decompose it by the SVD decomposition algorithm to obtain the corresponding sparse matrix; determine the event abnormality degree according to the mean value of the absolute values of all non-zero elements in the sparse matrix;

[0023] Combine each row vector of the sparse matrix with any other row vector to determine the corresponding row vector binary group; traverse all row vectors of the sparse matrix to determine all row vector binary groups; take the normalized value of the Pearson correlation coefficient between the two row vectors in each row vector binary group as the corresponding reference correlation; determine the event correlation according to the mean value of the reference correlations of all row vector binary groups;

[0024] According to the normalized value of the product between the event abnormality degree and the event correlation, determine the reference confidence of the intersection node corresponding to each traffic congestion risk indicator in each monitoring time period; determine the overall confidence of each intersection node in each monitoring time period according to the mean value of the reference confidences corresponding to all traffic congestion risk indicators;

[0025] Under each intersection node, according to the overall confidence of the monitored time period at each moment, determine the corresponding spatio-temporal anomaly confidence.

[0026] Further, the process of obtaining the instability coefficient includes:

[0027] Determine the severe weather judgment coefficient according to the existence of severe weather; at each intersection node, determine the corresponding temperature standard deviation value according to the difference between the environmental temperature and the preset standard temperature at each moment; determine the corresponding humidity standard deviation value according to the difference between the environmental humidity and the preset standard humidity at each moment; determine the temperature and humidity anomaly value according to the product of the temperature standard deviation value and the humidity standard deviation value; normalize the product of the severe weather judgment coefficient and the temperature and humidity anomaly value to determine the instability coefficient of each intersection node at each moment.

[0028] Further, the process of obtaining the traffic anomaly confidence includes:

[0029] Determine the comparison confidence of each intersection node at each moment according to the mean value between the instability coefficient and the spatio-temporal anomaly confidence; normalize the product of the comparison confidence and the preliminary anomaly confidence to determine the traffic anomaly confidence of each intersection node at each moment.

[0030] Further, the process of obtaining the traffic risk scoring coefficient includes:

[0031] At each moment, determine the diffusion influence degree of each intersection node according to the mean value of the traffic anomaly confidences of all neighboring influence nodes corresponding to each intersection node;

[0032] At each path node, take the moment when the corresponding traffic anomaly confidence is less than the preset confidence threshold as the normal moment; take the closest normal moment before each moment as the suspected event anomaly moment; arrange the traffic anomaly confidences of each intersection node at all moments in chronological order and perform curve fitting to determine the anomaly confidence curve of each intersection node; determine the event evolution weight of each moment according to the traffic anomaly confidence change trend between each moment on the anomaly confidence curve and the suspected event anomaly moment;

[0033] Normalize the product of the traffic anomaly confidence, the event evolution weight, and the diffusion influence degree of each intersection node at each moment to determine the corresponding traffic risk scoring coefficient.

[0034] Further, the process of obtaining the event evolution weight includes:

[0035] On the abnormal confidence curve, when there is a maximum point between each moment and the corresponding suspected event abnormal moment, the average value of the tangent slopes of all moments between the suspected event abnormal moment and the first maximum point after it is used as the event evolution weight of each moment;

[0036] When there is no maximum point between each moment and the corresponding suspected event abnormal moment, the average value of the tangent slopes of all moments between each moment and the corresponding suspected event abnormal moment is used as the event evolution weight of each moment.

[0037] Further, the process of adjusting the operation decision according to the traffic risk scoring coefficient includes:

[0038] For each intersection node at the current moment:

[0039] When the corresponding traffic risk scoring coefficient is greater than the preset first scoring threshold and less than or equal to the preset second scoring threshold, mark the corresponding intersection node as suspected of congestion on the navigation software;

[0040] When the corresponding traffic risk scoring coefficient is greater than the preset second scoring threshold and less than or equal to the preset third scoring threshold, mark the corresponding intersection node as suspected of congestion on the navigation software and notify the traffic police to go to the corresponding intersection node for traffic command;

[0041] When the corresponding traffic risk scoring coefficient is greater than the preset third scoring threshold, mark the corresponding intersection node as suspected of congestion on the navigation software, notify the traffic police to go to the corresponding intersection node for traffic command and set up accident warning signs on the road sections adjacent to the corresponding intersection node; wherein, the preset first scoring threshold is less than the preset second scoring threshold, and the preset second scoring threshold is less than the preset third scoring threshold.

[0042] In a second aspect, the present application provides a streaming data analysis system for smart city operation decision-making, the system includes:

[0043] A data collection and preprocessing module, configured to collect the risk index values of each intersection node in the urban road network at each moment for each traffic congestion risk index in the traffic data monitoring platform;

[0044] A first determination module, configured to determine the corresponding preliminary abnormal confidence according to the overall magnitude of the risk index values of various traffic congestion risk indexes corresponding to each intersection node at each moment; determine the neighborhood influence nodes of each intersection node at each moment according to the preliminary abnormal confidence;

[0045] The second determination module is configured to determine the spatio-temporal anomaly confidence of each intersection node at each moment according to the risk index value distribution and numerical association of each corresponding neighborhood influence node on various traffic congestion risk indexes; and determine the corresponding instability coefficient according to the environmental anomaly situation of each intersection node at each moment.

[0046] The operation decision real-time adjustment module is configured to determine the traffic anomaly confidence of each intersection node at each moment according to the preliminary anomaly confidence, the spatio-temporal anomaly confidence and the instability coefficient; determine the traffic risk scoring coefficient of each intersection node at each moment according to the temporal variation of the traffic anomaly confidence of each intersection node and the overall magnitude of the traffic anomaly confidence of the neighborhood influence nodes; and perform real-time adjustment of the operation decision according to the traffic risk scoring coefficient.

[0047] In a third aspect, the present application provides a computer device, including a memory and a processor. The memory is used to store computer program code, and the processor is used to call and run the computer program code from the memory to execute the method as described in the first aspect or any embodiment of the first aspect of the present application.

[0048] In a fourth aspect, the present application provides a computer program product, where the computer program product includes computer program code, and when the computer program code is executed, it is used to execute the method as described in the first aspect or any embodiment of the first aspect of the present application.

[0049] In a fifth aspect, the present application provides a computer-readable storage medium, where the computer-readable storage medium stores computer program code, and when the computer program code is executed, it is used to execute the method as described in the first aspect or any embodiment of the first aspect of the present application.

[0050] The present application has the following beneficial effects:

[0051] The present application first preliminarily determines the preliminary anomaly confidence representing the anomaly of the intersection node based on the risk index values of various risk indexes; then, based on the diffusion influence of accidents in time and space, and based on the risk index value distribution and association of each neighborhood influence node, determines the moment anomaly confidence; and combines the characteristics that environmental factors may affect the occurrence of congestion events and combines the environmental anomaly situation to determine the instability coefficient; thereby comprehensively determining the traffic anomaly confidence that more accurately represents the congestion influence of the intersection node according to the preliminary anomaly confidence, the spatio-temporal anomaly confidence and the instability coefficient; further quantifies the traffic risk based on the time and space influence during the occurrence of the congestion event, so that the obtained traffic risk scoring coefficient can not only more accurately judge whether a congestion event occurs, but also quantify the influence degree of the congestion event, making the effect of real-time adjustment of the operation decision according to the traffic risk scoring coefficient better and the processing efficiency of the congestion event higher. Brief Description of the Drawings

[0052] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0053] Figure 1 Flowchart of a method for streaming data analysis of smart city operation decision-making provided by an embodiment of the present invention;

[0054] Figure 2 Schematic diagram of an urban road network provided by an embodiment of the present invention;

[0055] Figure 3 Structure diagram of a streaming data analysis system for smart city operation decision-making provided by an embodiment of the present invention;

[0056] Figure 4 Schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Detailed Embodiments

[0057] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will, in conjunction with the drawings and preferred embodiments, detail the specific embodiments, structures, features, and effects of a method for streaming data analysis of smart city operation decision-making proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment, and specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0059] The following will specifically describe the specific solution of a method for streaming data analysis of smart city operation decision-making provided by the present invention with reference to the drawings.

[0060] The embodiments of the present application provide a method for streaming data analysis of smart city operation decision-making. Please refer to Figure 1, which shows a flowchart of a streaming data analysis method for smart city operation decision-making provided by an embodiment of the present invention. The method includes:

[0061] Step S101: In the traffic data monitoring platform, collect the risk index values of each intersection node in the urban road network at each traffic congestion risk index at each moment.

[0062] Take all road intersections within the monitoring range in the traffic data monitoring platform as intersection nodes, and take the road between two road intersections with connecting roads as a connection line to form an urban road network; please refer to Figure 2 , which shows a schematic diagram of an urban road network provided by an embodiment of the present invention, where each point represents an intersection node, that is, the node corresponding to each road intersection, and the connection line between the nodes is the road between the road intersections.

[0063] In a specific implementation manner of the embodiment of the present invention, the sampling frequency is set to be collected once per minute, and the sampling time period is set to 30 minutes before the current moment, which can be adjusted according to the specific implementation environment.

[0064] In a specific implementation manner of the embodiment of the present invention, the traffic congestion risk indicators include the number of vehicles at intersections, the real-time following distance, the occupancy rate of intersection lanes, and the real-time vehicle speed. The traffic data monitoring platform is used to call the data values of each intersection node in the urban road network at each moment for each traffic congestion risk indicator. Cameras, radars, and geomagnetic coils are installed at each road intersection. At each moment, the real-time vehicle speed and following distance of each vehicle at the road intersection are monitored by the radar. Road intersection images are collected by the camera, and each vehicle is tracked in real time through the target detection method, and the traffic flow and the occupancy rate of intersection lanes are determined according to the number of vehicles. For the number of vehicles at intersections, the normalized value of the number of vehicles at intersections is used as the corresponding risk indicator value, that is, the greater the number of vehicles at the corresponding intersection, the more likely a congestion event will occur. For the real-time following distance, the opposite of the difference between the normalized value of the average following distance of all vehicles in the road intersection corresponding to each intersection node at each moment and the real number 1 is used as the corresponding risk indicator value. That is, the smaller the overall following distance of each vehicle in the corresponding intersection, the closer the distance between the vehicles, and the more likely a congestion event will occur. For the real-time vehicle speed, the opposite of the difference between the normalized value of the average real-time vehicle speed of all vehicles in the road intersection corresponding to each intersection node at each moment and the real number 1 is used as the corresponding risk indicator value. That is, the smaller the overall real-time vehicle speed of each vehicle in the corresponding intersection, the more likely the vehicle is affected by congestion and the slower the vehicle travels, that is, the higher the probability of a congestion event occurring. For the occupancy rate of intersection lanes, the normalized value of the occupancy rate of intersection lanes is used as the corresponding risk indicator value. That is, the greater the occupancy rate of the corresponding intersection lane, the more in line with the situation where vehicles are more concentrated on the lane during a congestion event, and the greater the probability of a congestion event occurring. It should be noted that the implementer can select other traffic congestion risk indicators according to the specific implementation environment, and when calculating the risk indicator value, it is necessary to make the risk indicator value of the corresponding traffic congestion risk indicator positively correlated with the possibility of a congestion event occurring. And in this application, through normalization processing, the influence of the dimensions of different traffic congestion risk indicators on the subsequent calculation process is reduced, making the subsequent processing process more robust.

[0065] Step S102: Determine the corresponding preliminary anomaly confidence according to the overall magnitude of the risk indicator values of various traffic congestion risk indicators corresponding to each intersection node at each moment; determine the neighborhood influence nodes of each intersection node at each moment according to the preliminary anomaly confidence.

[0066] Since the greater the risk indicator value corresponding to the traffic congestion risk indicator, the more likely the corresponding intersection node corresponds to a congestion event, therefore, the possibility of a congestion event occurring is initially characterized based on the numerical characteristics of the risk indicator value. Preferably, in some possible implementation manners of the embodiment of the present invention, the process of obtaining the preliminary anomaly confidence includes:

[0067] In historical data, each moment when a congestion event occurs at each intersection node is used as a historical congestion moment; according to the average value of the risk index values of each traffic congestion risk index at all corresponding historical accident moments of each intersection node, the corresponding accident risk value is determined; at each moment, the difference between the risk index value of each traffic congestion risk index of each intersection node and the corresponding accident risk value is used to determine the corresponding abnormal intensity value; among all traffic congestion risk indexes of each intersection node at each moment, the traffic congestion risk indexes with the corresponding abnormal intensity value less than the preset abnormal threshold are used as the abnormal basic indexes of each intersection node. In a specific implementation manner of the embodiment of the present invention, the preset abnormal threshold is set to 0 and can be adjusted according to the specific implementation environment.

[0068] The accident risk value represents the numerical characteristics of the risk index values of each traffic congestion risk index when a congestion event occurs at the corresponding intersection node. If the risk index value monitored in real time is closer to the corresponding accident risk value, or even larger than the accident risk value, it indicates that the corresponding intersection node is more in line with the characteristics of the congestion event in the corresponding traffic congestion risk index, and the greater the possibility of the corresponding congestion event occurring. Correspondingly, the larger the risk index value, the larger the calculated abnormal intensity value; when the preset abnormal threshold is set to 0, it means that if the risk index value of the corresponding traffic congestion risk index is greater than the accident risk value, the corresponding traffic congestion risk index conforms to the risk index characteristics during the traffic congestion event. Therefore, it is defined as an abnormal basic index. The more the number of corresponding abnormal basic indexes, the more likely a traffic congestion event will occur; therefore, further combining the numerical characteristics of the abnormal intensity value and the number characteristics of the abnormal basic index, the preliminary abnormal confidence degree representing the possibility of the traffic congestion event is comprehensively determined; at each moment, according to the number of abnormal basic indexes of each intersection node and the overall magnitude of the abnormal intensity values of each abnormal basic index, the corresponding preliminary abnormal confidence degree is determined.

[0069] Preferably, in some possible implementation manners of the embodiment of the present invention, the process of determining the corresponding preliminary abnormal confidence degree according to the number of abnormal basic indexes of each intersection node and the overall magnitude of the abnormal intensity values of each abnormal basic index at each moment includes:

[0070] At each moment, the normalized values of the abnormal intensity values of all abnormal basic indicators of each intersection node are accumulated to determine the overall abnormal value of each intersection node; the product between the number of abnormal basic indicators and the overall abnormal value of each intersection node is normalized to determine the preliminary abnormal confidence of each intersection node at each moment. Only analyzing the abnormal intensity values of the abnormal basic indicators can enhance the influence of the traffic congestion risk indicators that conform to the congestion event, making the characterization of the possibility of the congestion event more accurate; and through the normalization process, the value of the preliminary abnormal confidence is limited within 0 to 1, avoiding the influence of dimensions.

[0071] In a specific implementation manner of the embodiment of the present invention, the process of obtaining the preliminary abnormal confidence is expressed by the formula: ; where is the preliminary abnormal confidence of the th intersection node at the th moment; is the number of abnormal basic indicators of the th intersection node at the th moment; is the risk indicator value of the th abnormal basic indicator of the th intersection node at the th moment; is the overall abnormality of the th intersection node at the th moment; is a linear normalization function. It should be noted that, unless otherwise specified, all normalization methods in the embodiments of the present invention adopt linear normalization, which can be adjusted according to the specific implementation environment.

[0072] For each intersection node, when a traffic congestion event occurs, the congestion usually affects other adjacent intersection nodes. Therefore, the present application further determines other intersection nodes that may be affected by each intersection node. And when the preliminary abnormal confidence of the intersection node is greater, it indicates that the corresponding congestion event is more likely to be real. Therefore, a larger influence range is given for more accurate analysis.

[0073] Preferably, in some possible implementation manners of the embodiment of the present invention, the process of obtaining the neighborhood influence nodes includes:

[0074] The product of the preliminary anomaly confidence level and the preset maximum neighborhood radius is used as the influence radius of each intersection node; other intersection nodes whose chessboard distance from each intersection node is less than the influence radius are used as the neighborhood influence nodes of each intersection node. In a specific implementation manner of the embodiment of the present invention, the preset maximum neighborhood radius is set to 1.5 km, which can be adjusted according to the specific implementation environment. Among them, the chessboard distance is more in line with the path planning of the city, and the implementer can also replace the chessboard distance with the Euclidean distance or the road path extension length according to the specific implementation environment, which will not be elaborated further herein.

[0075] Step S103: Determine the spatio-temporal anomaly confidence level of each intersection node at each moment according to the distribution and numerical correlation of the risk index values of the corresponding neighborhood influence nodes on various traffic congestion risk indexes; determine the corresponding instability coefficient according to the environmental anomaly situation of each intersection node at each moment.

[0076] Furthermore, by analyzing the influence of adjacent influence nodes in terms of time and space when traffic congestion occurs, while more accurately characterizing the possibility of congestion events at each intersection node, a more accurate quantitative analysis of the influence degree after the occurrence of congestion events can be carried out, so that the greater the spatio-temporal anomaly confidence level, the higher the possibility of traffic congestion at the corresponding intersection node, and the greater the influence on other roads when traffic congestion occurs, that is, the more serious the congestion.

[0077] Preferably, in some possible implementation manners of the embodiment of the present invention, the process of obtaining the spatio-temporal anomaly confidence level includes:

[0078] In the time sequence, all moments are divided into at least two monitoring time periods, where the time lengths of all monitoring time periods are the same; for each traffic congestion risk index, all the risk index values corresponding to each neighborhood influence node in each monitoring time period are arranged in time sequence to determine the corresponding time series risk index sequence; taking the time series risk index sequence as a row vector, according to all the time series risk index sequences corresponding to all neighborhood influence nodes, determine the local spatio-temporal matrix corresponding to each monitoring time period on each traffic congestion risk index. In the local spatio-temporal matrix, each column represents all the risk index values corresponding to all neighborhood influence nodes at the same moment, and each row corresponds to the time series risk index sequence. In a specific implementation manner of the embodiment of the present invention, the number of monitoring time periods is set to 5, which can be adjusted according to the specific implementation environment. By dividing the monitoring time periods, not only can the complexity of matrix analysis be reduced to improve the analysis efficiency, but also the time series influence of congestion events can be captured more sensitively by analyzing local time series characteristics.

[0079] After standardizing the local spatio-temporal matrix, it is decomposed by the SVD decomposition algorithm to obtain the corresponding sparse matrix; the degree of event anomaly is determined according to the mean value of the absolute values of all non-zero elements in the sparse matrix; when decomposing the matrix by the SVD decomposition algorithm, a low-rank matrix and a sparse matrix will be obtained; the low-rank matrix reflects the main traffic patterns in the corresponding urban road network, which is the main part of the matrix and reflects the operation rules of traffic under normal conditions; while the sparse matrix represents the sudden anomalies in traffic information, that is, low-frequency anomaly data. The larger the absolute value of its non-zero elements, the more likely the corresponding traffic information machine risk index value will mutate abnormally, and the more it conforms to the abnormal change characteristics of the risk index value of congestion events; therefore, when the degree of event anomaly representing the overall absolute value of all non-zero elements in the sparse matrix is larger, the more likely the corresponding neighborhood influence nodes will be affected by congestion events, and the higher the credibility of the corresponding intersection nodes having congestion events; therefore, when the time anomaly degree is larger, the spatio-temporal anomaly confidence degree is larger.

[0080] Combine each row vector of the sparse matrix with any other row vector to determine the corresponding row vector binary group; traverse all row vectors of the sparse matrix to determine all row vector binary groups; use the normalized value of the Pearson correlation coefficient between the two row vectors in each row vector binary group as the corresponding reference correlation; determine the event correlation according to the mean value of the reference correlations of all row vector binary groups. The non-zero element values in the sparse matrix reflect the anomalies in traffic data. Therefore, the higher the data correlation between different rows in the sparse matrix, the more similar the anomalies in the traffic data of each neighborhood influence node, the more likely they are to be affected by the same congestion event, and the greater the possibility of the corresponding intersection node having a congestion event and the more serious the congestion impact; therefore, when the event correlation is larger, the corresponding spatio-temporal anomaly confidence degree is larger.

[0081] Furthermore, combine the event correlation and the event anomaly degree under all traffic congestion risk indicators to comprehensively represent the spatio-temporal anomaly confidence degree. According to the normalized value of the product between the event anomaly degree and the event correlation, determine the reference confidence degree of the corresponding intersection node under each traffic congestion risk indicator in each monitoring time period; determine the overall confidence degree of each intersection node in each monitoring time period according to the mean value of the reference confidence degrees under all traffic congestion risk indicators; under each intersection node, determine the corresponding spatio-temporal anomaly confidence degree according to the overall confidence degree of the monitoring time period at each moment; so that the obtained moment anomaly confidence degree can represent the possibility of congestion events in terms of time and the impact degree when congestion events occur in terms of space.

[0082] In a specific implementation manner of the embodiment of the present invention, the process of obtaining the overall confidence degree is expressed by the formula: ; where is the The overall confidence level of a road intersection node in the th monitoring time period; is the total number of traffic congestion risk indicators; is the th traffic congestion risk indicator, and for the th road intersection node in the th monitoring time period, it is the mean of the reference correlations of all row vector pairs corresponding to the sparse matrix, that is, the event correlation; is the th traffic congestion risk indicator, and for the th road intersection node in the th monitoring time period, it is the mean of the absolute values of all non-zero elements corresponding to the sparse matrix, that is, the event abnormality degree; is the th traffic congestion risk indicator, and for the th road intersection node in the th monitoring time period, it is the reference confidence level.

[0083] Furthermore, analyzing each road intersection node at each moment from the environmental aspect, considering that in bad weather, such as rain, snow, heavy fog, hail, etc., the possibility of accidents increases due to slippery roads or limited visibility; abnormal temperature and humidity may affect driving safety, and abnormal temperature and humidity usually correspond to bad weather. Therefore, further according to the environmental abnormality of each road intersection node at each moment, the corresponding instability coefficient is determined, so that the greater the instability coefficient, the higher the possibility of traffic accidents, that is, congestion events, occurring at the corresponding road intersection node.

[0084] Preferably, in some possible implementation manners of the embodiments of the present invention, the process of obtaining the instability coefficient includes:

[0085] Determining a bad weather judgment coefficient according to whether there is bad weather; at each road intersection node, determining the corresponding temperature standard deviation value according to the difference between the environmental temperature at each moment and the preset standard temperature; determining the corresponding humidity standard deviation value according to the difference between the environmental humidity at each moment and the preset standard humidity; determining the temperature and humidity abnormality value according to the product of the temperature standard deviation value and the humidity standard deviation value; normalizing the product of the bad weather judgment coefficient and the temperature and humidity abnormality value to determine the instability coefficient of each road intersection node at each moment. In a specific implementation manner of the embodiments of the present invention, the preset standard temperature is set to 20 degrees Celsius, and the preset standard humidity is set to 40%, and the implementer adaptively adjusts according to the season or month in which the current moment is located. It should be noted that the humidity in the embodiments of the present invention is the standard humidity, and the environmental temperature is collected in real time through a temperature sensor installed at the road intersection, and the environmental humidity is collected in real time through a humidity sensor.

[0086] Among them, in a specific implementation manner of the embodiment of the present invention, when there is bad weather at the corresponding intersection node at the corresponding moment, the corresponding bad weather judgment coefficient is set to 1; when there is no bad weather at the corresponding intersection node at the corresponding moment, the corresponding bad weather judgment coefficient is set to 0; by setting 0 and 1, it can be ensured that the obtained instability coefficient characterizes the severity of bad weather, so as to more accurately characterize the possibility of the occurrence of congestion events. When there is bad weather, the greater the temperature standard deviation value and the greater the humidity standard deviation value, the more abnormal the environment caused by the corresponding bad weather, and the greater the instability coefficient of the corresponding environmental impact; that is, the greater the abnormal value of temperature and humidity, the higher the credibility of traffic accidents caused by bad weather interference resulting in congestion events.

[0087] In some possible implementation manners of the embodiment of the present invention, the process of obtaining the instability coefficient is expressed by the formula: ; where is the instability coefficient of the th intersection node at the th moment; is the environmental temperature of the th intersection node at the th moment; is the preset standard temperature; is the environmental humidity of the th intersection node at the th moment; is the preset standard humidity; is the absolute value symbol; is the temperature standard deviation value of the th intersection node at the th moment; is the humidity standard deviation value of the th intersection node at the th moment; is the temperature and humidity abnormal value of the th intersection node at the th moment; is the bad weather judgment coefficient of the th intersection node at the th moment.

[0088] Step S104: Determine the traffic anomaly confidence of each intersection node at each moment according to the preliminary anomaly confidence, spatio-temporal anomaly confidence, and instability coefficient; determine the traffic risk scoring coefficient of each intersection node at each moment according to the temporal variation of the traffic anomaly confidence of each intersection node and the overall magnitude of the traffic anomaly confidence of the neighboring influence nodes; perform real-time adjustment of the operation decision according to the traffic risk scoring coefficient.

[0089] So far, this application has determined the preliminary anomaly confidence representing the numerical characteristics of the risk index value of the congestion event, the spatio-temporal anomaly confidence representing the time and space influence characteristics of the congestion event, and the instability coefficient representing the influence of environmental anomalies. These three parameters jointly represent the occurrence possibility and influence of the congestion event. Therefore, further determine the traffic anomaly confidence of each intersection node at each moment according to the preliminary anomaly confidence, spatio-temporal anomaly confidence, and instability coefficient; the greater the traffic anomaly confidence, the higher the possibility of a congestion event occurring at the corresponding path node and the greater the congestion impact.

[0090] Preferably, in some possible implementation manners of the embodiments of the present invention, the process of obtaining the traffic anomaly confidence includes:

[0091] Determine the comparison confidence of each intersection node at each moment according to the mean between the instability coefficient and the spatio-temporal anomaly confidence; normalize the product between the comparison confidence and the preliminary anomaly confidence to determine the traffic anomaly confidence of each intersection node at each moment. Since the instability coefficient is likely to take the value of 0, in order to avoid a large impact of the appearance of 0 on the calculation result, first calculate the mean of the instability coefficient and the spatio-temporal anomaly confidence to obtain the comparison confidence, and then weight the initially obtained preliminary anomaly confidence, so that the obtained traffic anomaly confidence is more accurate.

[0092] In a specific implementation manner of the embodiments of the present invention, the process of obtaining the traffic anomaly confidence is expressed by the formula: ; where is the traffic anomaly confidence of the th moment and the th intersection node; is the instability coefficient of the th moment and the th intersection node; is the spatio-temporal anomaly confidence of the th moment and the th intersection node; is the preliminary anomaly confidence of the th moment and the th intersection node.

[0093] Furthermore, it is necessary to analyze from the impact of congestion events. When the impact of a congestion accident is significant, this impact usually radiates to adjacent intersection nodes, making the adjacent intersection nodes also have a relatively high confidence level of traffic anomalies. And it should be noted that the occurrence of congestion events is usually relatively rapid, that is, as long as a congestion event occurs, the calculated confidence level of traffic anomalies will show a relatively rapid numerical change. Therefore, further, based on the temporal change of the confidence level of traffic anomalies at each intersection node and the overall magnitude of the confidence level of traffic anomalies at neighboring impact nodes, the impact and occurrence probability of congestion events are comprehensively characterized, such that the greater the obtained traffic risk scoring coefficient, the greater the probability of a congestion event occurring and the greater the corresponding impact when a congestion event occurs.

[0094] Preferably, in some possible implementation manners of the embodiments of the present invention, the process of obtaining the traffic risk scoring coefficient includes:

[0095] At each moment, according to the mean value of the confidence levels of traffic anomalies of all neighboring impact nodes corresponding to each intersection node, the diffusion impact degree of each intersection node is determined. A neighboring impact node is a node that may be affected by the congestion of the corresponding intersection node. The greater the mean value of the confidence levels of traffic anomalies of the corresponding neighboring impact nodes, the more likely it is that congestion events may occur at the neighboring impact nodes under the influence of the corresponding intersection node, and thus the higher the probability of a congestion event occurring at the intersection node.

[0096] At each path node, the moment when the corresponding confidence level of traffic anomalies is less than the preset confidence threshold is taken as a normal moment; the moment closest to the normal moment before each moment is taken as a suspected event abnormal moment; the confidence levels of traffic anomalies of each intersection node at all moments are arranged in chronological order and then curve fitting is performed to determine the abnormal confidence curve of each intersection node; according to the change trend of the confidence level of traffic anomalies between each moment and the suspected event abnormal moment on the abnormal confidence curve, the event evolution weight of each moment is determined. In a specific implementation manner of the embodiments of the present invention, the preset confidence threshold is set to 0.4 and can be adjusted according to the specific implementation environment.

[0097] Preferably, in some possible implementation manners of the embodiments of the present invention, the process of obtaining the event evolution weight includes:

[0098] On the abnormal confidence curve, when there is a maximum point between each moment and the corresponding suspected event abnormal moment, the mean value of the tangent slopes of all moments between the suspected event abnormal moment and the first maximum point after it is taken as the event evolution weight of each moment; when there is no maximum point between each moment and the corresponding suspected event abnormal moment, the mean value of the tangent slopes of all moments between each moment and the corresponding suspected event abnormal moment is taken as the event evolution weight of each moment.

[0099] When the congestion event starts to spread, the traffic anomaly confidence level will show a sudden increase and then remain stable. Therefore, at first, the closest normal moment before each moment is used as the suspected event anomaly moment for analysis. The sudden increase in the traffic anomaly confidence level is manifested as a relatively high slope in the upward trend of the traffic anomaly confidence level. Therefore, the greater the average value of the slope between all moments from the suspected event anomaly moment to the first maximum point after it, the more it conforms to the characteristics of the sudden increase in the traffic anomaly confidence level, and the greater the corresponding possibility of a congestion event occurring.

[0100] It should be noted that when the corresponding moment is a normal moment, the corresponding time evolution weight is set to 0; that is, when the traffic anomaly confidence level at the corresponding moment is less than the preset confidence threshold, it is considered that no congestion event occurs at the corresponding intersection node at that moment. Therefore, setting the event evolution weight to 0 is more in line with the objective facts, and the implementer can adjust the numerical value according to the specific implementation environment.

[0101] In addition, the traffic anomaly confidence level itself represents the possibility of a congestion event occurring. Therefore, further, the product of the traffic anomaly confidence level, the event evolution weight, and the diffusion influence degree at each intersection node at each moment is normalized to determine the corresponding traffic risk scoring coefficient; the greater the traffic risk scoring coefficient, the higher the possibility and the greater the impact of the corresponding intersection node having a congestion event. Finally, more accurate and efficient operation decision-making adjustments are made according to the traffic risk scoring coefficient.

[0102] In a specific implementation manner of the embodiment of the present invention, the process of obtaining the traffic risk scoring coefficient is expressed by the formula: ; where is the traffic risk scoring coefficient of the th moment at the th intersection node; is the traffic anomaly confidence level of the th moment at the th intersection node; is the average value of the traffic anomaly confidence levels of all neighboring influence nodes corresponding to the th moment at the th intersection node, that is, the corresponding diffusion influence degree; is the event evolution weight of the th moment at the th intersection node.

[0103] Preferably, in some possible implementation manners of the embodiment of the present invention, the process of making operation decision adjustments according to the traffic risk scoring coefficient includes:

[0104] For each intersection node at the current moment:

[0105] When the corresponding traffic risk score coefficient is greater than the preset first score threshold and less than or equal to the preset second score threshold, mark the corresponding intersection node as suspected of congestion on the navigation software; when the corresponding traffic risk score coefficient is greater than the preset second score threshold and less than or equal to the preset third score threshold, mark the corresponding intersection node as suspected of congestion on the navigation software and notify the traffic police to go to the corresponding intersection node for traffic command; when the corresponding traffic risk score coefficient is greater than the preset third score threshold, mark the corresponding intersection node as suspected of congestion on the navigation software, notify the traffic police to go to the corresponding intersection node for traffic command and set up accident warning signs on the road sections adjacent to the corresponding intersection node. In some possible implementation manners of the embodiments of the present invention, the preset first score threshold is set to 0.5, the preset second score threshold is set to 0.7, and the preset third score threshold is set to 0.9. As the traffic risk score coefficient gradually increases, the possibility of a congestion event occurring becomes higher and higher. When the traffic risk score coefficient is greater than the preset first score threshold, it is considered that a congestion event has occurred; during the continuous increase process, the greater the value of the traffic risk score coefficient, the greater the impact. It should be noted that specific operation decisions can be adjusted according to the specific implementation environment and will not be elaborated further here.

[0106] In summary, a method for streaming data analysis of smart city operation decisions first initially determines a preliminary anomaly confidence level representing the anomaly of intersection nodes based on the risk indicator values of various risk indicators; then, based on the diffusion impact of accidents in time and space, determines the moment anomaly confidence level based on the risk indicator value distribution and association of each neighborhood impact node; and combines the characteristics of environmental factors that may affect the occurrence of congestion events and combines the environmental anomaly situation to determine the instability coefficient; thereby comprehensively determining a traffic anomaly confidence level that more accurately represents the congestion impact of intersection nodes according to the preliminary anomaly confidence level, spatio-temporal anomaly confidence level, and instability coefficient; further quantifies the traffic risk based on the time and space impacts during the occurrence of congestion events, so that the obtained traffic risk score coefficient can not only more accurately judge whether a congestion event occurs, but also quantify the impact degree of the congestion event, making the effect of real-time adjustment of operation decisions according to the traffic risk score coefficient better and the processing efficiency of congestion events higher.

[0107] This application also provides a streaming data analysis system for smart city operation decisions. Please refer to Figure 3 which shows the structure diagram of a streaming data analysis system for smart city operation decisions provided by an embodiment of the present invention. The system includes: a data acquisition and preprocessing module 301, a first determination module 302, a second determination module 303, and an operation decision real-time adjustment module 304.

[0108] A data acquisition and preprocessing module 301 is configured to collect, in a traffic data monitoring platform, the risk index values of each intersection node in the urban road network at each moment for each traffic congestion risk index.

[0109] A first determination module 302 is configured to determine the corresponding preliminary anomaly confidence level according to the overall magnitude of the risk index values of various traffic congestion risk indexes corresponding to each intersection node at each moment; and determine the neighborhood influence nodes of each intersection node at each moment according to the preliminary anomaly confidence level.

[0110] A second determination module 303 is configured to determine the spatio-temporal anomaly confidence level of each intersection node at each moment according to the risk index value distribution and numerical association of the corresponding neighborhood influence nodes for various traffic congestion risk indexes; and determine the corresponding instability coefficient according to the environmental anomaly situation of each intersection node at each moment.

[0111] An operation decision real-time adjustment module 304 is configured to determine the traffic anomaly confidence level of each intersection node at each moment according to the preliminary anomaly confidence level, spatio-temporal anomaly confidence level, and instability coefficient; determine the traffic risk scoring coefficient of each intersection node at each moment according to the temporal sequence change of the traffic anomaly confidence level of each intersection node and the overall magnitude of the traffic anomaly confidence level of the neighborhood influence nodes; and perform real-time adjustment of operation decisions according to the traffic risk scoring coefficient.

[0112] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, a streaming data analysis system for smart city operation decision-making and a method embodiment of a streaming data analysis method for smart city operation decision-making provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be elaborated here.

[0113] An embodiment of the present application further provides a computer device. Please refer to Figure 4 which shows a schematic structural diagram of a computer device provided in an embodiment of the present invention. The computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. When the processor 402 executes the computer program 403, the computer device can execute any one of the above-described streaming data analysis methods for smart city operation decision-making.

[0114] The embodiments of the present application also provide a computer program product. When the computer program product runs on a computer device, it enables the computer device to execute any of the above-introduced streaming data analysis methods for smart city operation decision-making.

[0115] The embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium stores computer program code. When the computer program code runs on a computer device, it enables the computer device to execute any of the above-introduced streaming data analysis methods for smart city operation decision-making.

[0116] In the embodiments provided by the present application, it should be understood that the provided computer device, computer program product, and computer-readable storage medium are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can refer to the beneficial effects in the methods provided above, which will not be elaborated here.

[0117] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0118] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for streaming data analysis of smart city operation decision-making, characterized in that, The method includes: In a traffic data monitoring platform, collecting the risk index values of each intersection node in the urban road network at each moment for each traffic congestion risk index; According to the overall magnitude of the risk index values of various traffic congestion risk indexes corresponding to each intersection node at each moment, determining the corresponding preliminary anomaly confidence level; determining the neighborhood influence nodes of each intersection node at each moment according to the preliminary anomaly confidence level; According to the risk index value distribution and numerical association of each corresponding neighborhood influence node for various traffic congestion risk indexes, determining the spatio-temporal anomaly confidence level of each intersection node at each moment; determining the corresponding instability coefficient according to the environmental anomaly situation of each intersection node at each moment; According to the preliminary anomaly confidence level, the spatio-temporal anomaly confidence level, and the instability coefficient, determining the traffic anomaly confidence level of each intersection node at each moment; according to the temporal sequence change of the traffic anomaly confidence level of each intersection node and the overall magnitude of the traffic anomaly confidence levels of the neighborhood influence nodes, determining the traffic risk scoring coefficient of each intersection node at each moment; making real-time adjustments to the operation decision according to the traffic risk scoring coefficient.

2. The streaming data analysis method for smart city operation decision-making according to claim 1, characterized in that The process of obtaining the preliminary anomaly confidence level includes: In historical data, taking each moment when a congestion event occurs at each intersection node as a historical congestion moment; determining the corresponding accident risk value according to the mean value of the risk index values of each traffic congestion risk index of each intersection node at all corresponding historical accident moments; At each moment, determining the corresponding anomaly intensity value as the difference between the risk index value of each traffic congestion risk index of each intersection node and the corresponding accident risk value; among all the traffic congestion risk indexes of each intersection node at each moment, taking the traffic congestion risk indexes with the corresponding anomaly intensity value less than the preset anomaly threshold as the anomaly basic indexes of each intersection node; At each moment, determining the corresponding preliminary anomaly confidence level according to the number of anomaly basic indexes of each intersection node and the overall magnitude of the anomaly intensity values of each anomaly basic index.

3. The method for streaming data analysis of smart city operation decision-making according to claim 2, wherein, The process of determining the corresponding preliminary anomaly confidence level according to the number of anomaly basic indexes of each intersection node and the overall magnitude of the anomaly intensity values of each anomaly basic index at each moment includes: At each moment, performing an accumulation operation on the normalized values of the anomaly intensity values of all the anomaly basic indexes of each intersection node to determine the overall anomaly value of each intersection node; normalizing the product of the number of anomaly basic indexes of each intersection node and the overall anomaly value to determine the preliminary anomaly confidence level of each intersection node at each moment.

4. A method for streaming data analysis of smart city operation decision-making according to claim 1, characterized in that, The process of obtaining the neighborhood influence nodes includes: Taking the product of the preliminary anomaly confidence level and the preset maximum neighborhood radius as the influence radius of each intersection node; taking other intersection nodes with the chessboard distance less than the influence radius from each intersection node as the neighborhood influence nodes of each intersection node.

5. A method for streaming data analysis of smart city operation decision-making according to claim 1, characterized in that The process of obtaining the spatio-temporal anomaly confidence level includes: In chronological order, all moments are divided into at least two monitoring time periods, where the time lengths of all monitoring time periods are the same; under each traffic congestion risk indicator, for each neighborhood influence node, all the risk indicator values corresponding to each monitoring time period are arranged in chronological order to determine the corresponding time-series risk indicator sequence. Taking the time-series risk indicator sequence as a row vector, according to all the time-series risk indicator sequences corresponding to all neighborhood influence nodes, determine the local spatio-temporal matrix corresponding to each monitoring time period under each traffic congestion risk indicator. After normalizing the local spatio-temporal matrix, decompose it using the SVD decomposition algorithm to obtain the corresponding sparse matrix; according to the mean value of the absolute values of all non-zero elements in the sparse matrix, determine the event abnormality degree. Combine each row vector of the sparse matrix with any other row vector to determine the corresponding row vector pair; traverse all row vectors of the sparse matrix to determine all row vector pairs; take the normalized value of the Pearson correlation coefficient between the two row vectors in each row vector pair as the corresponding reference correlation; according to the mean value of the reference correlations of all row vector pairs, determine the event correlation. According to the normalized value of the product between the event abnormality degree and the event correlation, determine the reference confidence of the intersection node corresponding to each monitoring time period under each traffic congestion risk indicator; according to the mean value of the reference confidences corresponding to all traffic congestion risk indicators, determine the overall confidence of each intersection node in each monitoring time period. Under each intersection node, according to the overall confidence of the monitoring time period in which each moment is located, determine the corresponding spatio-temporal abnormality confidence.

6. The streaming data analysis method for smart city operation decision-making according to claim 1, characterized in that The process of obtaining the instability coefficient includes: Determine the bad weather judgment coefficient according to the existence of bad weather; at each intersection node, according to the difference between the environmental temperature at each moment and the preset standard temperature, determine the corresponding temperature standard deviation value; according to the difference between the environmental humidity at each moment and the preset standard humidity, determine the corresponding humidity standard deviation value; according to the product between the temperature standard deviation value and the humidity standard deviation value, determine the temperature and humidity abnormality value; normalize the product between the bad weather judgment coefficient and the temperature and humidity abnormality value to determine the instability coefficient of each intersection node at each moment.

7. A method for streaming data analysis of smart city operation decision-making according to claim 1, characterized in that The process of obtaining the traffic abnormality confidence includes: According to the mean value between the instability coefficient and the spatio-temporal abnormality confidence, determine the comparison confidence of each intersection node at each moment; normalize the product between the comparison confidence and the preliminary abnormality confidence to determine the traffic abnormality confidence of each intersection node at each moment.

8. A method for streaming data analysis of smart city operation decision-making according to claim 1, characterized in that The process of obtaining the traffic risk scoring coefficient includes: At each moment, according to the mean value of the traffic abnormality confidences of all neighborhood influence nodes corresponding to each intersection node, determine the diffusion influence degree of each intersection node. Under each path node, the moments when the corresponding traffic anomaly confidence is less than the preset confidence threshold are regarded as normal moments; the moment closest to the normal moment before each moment is regarded as the suspected event anomaly moment; the traffic anomaly confidences of each intersection node at all moments are arranged in chronological order and then curve-fitted to determine the anomaly confidence curve of each intersection node; according to the traffic anomaly confidence change trend between each moment on the anomaly confidence curve and the suspected event anomaly moment, the event evolution weight of each moment is determined. Normalize the product of the traffic anomaly confidence, the event evolution weight, and the diffusion influence degree of each intersection node at each moment to determine the corresponding traffic risk scoring coefficient.

9. A method for streaming data analysis of smart city operation decision-making according to claim 8, characterized in that, The process of obtaining the event evolution weight includes: On the anomaly confidence curve, when there is a maximum point between each moment and the corresponding suspected event anomaly moment, the average of the tangent slopes of all moments between the suspected event anomaly moment and the first maximum point after it is used as the event evolution weight of each moment. When there is no maximum point between each moment and the corresponding suspected event anomaly moment, the average of the tangent slopes of all moments between each moment and the corresponding suspected event anomaly moment is used as the event evolution weight of each moment.

10. A method for streaming data analysis of smart city operation decision-making according to claim 1, characterized in that The process of adjusting the operation decision according to the traffic risk scoring coefficient includes: For each intersection node at the current moment: When the corresponding traffic risk scoring coefficient is greater than the preset first scoring threshold and less than or equal to the preset second scoring threshold, mark the corresponding intersection node as suspected of congestion on the navigation software. When the corresponding traffic risk scoring coefficient is greater than the preset second scoring threshold and less than or equal to the preset third scoring threshold, mark the corresponding intersection node as suspected of congestion on the navigation software and notify the traffic police to go to the corresponding intersection node for traffic command. When the corresponding traffic risk scoring coefficient is greater than the preset third scoring threshold, mark the corresponding intersection node as suspected of congestion on the navigation software, notify the traffic police to go to the corresponding intersection node for traffic command, and set up an accident warning sign on the road adjacent to the corresponding intersection node; where the preset first scoring threshold is less than the preset second scoring threshold, and the preset second scoring threshold is less than the preset third scoring threshold.

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