Intelligent street lamp escorting method and system applied to major activities

Abstract

The invention discloses an intelligent street lamp escorting method and system applied to major activities, and relates to the field of Internet of Things. The method is applied to an urban street lamp management platform and comprises the steps that real-time road condition data and traffic flow data uploaded by intelligent street lamps of a target road section are acquired, and real-time road condition information comprises multiple kinds of real-time sub-information; analyzing an influence coefficient of the real-time road condition data on the traffic flow data; based on the influence coefficient, determining an information processing proportion of the multiple real-time sub-information; according to the information processing proportion of the multiple kinds of real-time sub-information, extracting data volumes corresponding to the multiple kinds of real-time sub-information from the real-time road condition data; adjusting the traffic flow data by adopting the data volumes corresponding to the various real-time sub-information, and generating target traffic flow data; and matching the target traffic flow data with a preset guide decision table to obtain a target guide decision, and sending the target guide decision to the smart street lamp of the target road section, thereby solving the problem that the smart street lamp cannot update traffic guide information in time.

Description

Technical Field

[0001] The present application relates to the technical field of the Internet of Things, and specifically to a smart street lamp escort method and system for major events. Background Art

[0002] When a major event is held, the volume of traffic near the event venue will increase dramatically, which can easily cause traffic congestion. Therefore, during the event, the traffic near the event venue needs to be diverted in advance to ensure the smooth progress of the event.

[0003] With the development of science and technology, traffic diversion is no longer too dependent on manual work. Smart street lights play an important role in this. In addition to meeting daily lighting needs, smart street lights also have functions such as security monitoring, information services and traffic diversion. Therefore, they have become one of the important facilities for escorting major events. At present, smart street lights guide vehicles to enter or leave in an orderly manner by monitoring the traffic flow near the event site in real time. However, when monitoring traffic flow data, smart street lights will also collect real-time road condition data to provide a basis for guiding decisions. However, when the monitored road section is a congested section, it will lead to a large increase in real-time road condition data, which will extend the analysis time of guiding decisions, and make it impossible for smart street lights to update traffic guidance information in a timely manner. Summary of the invention

[0004] In response to the problem that smart street lights cannot update traffic guidance information in a timely manner, the present application provides a smart street light escort method and system for major events.

[0005] In a first aspect, the present application provides a smart street light escort method for major events, which is applied to a city street light management platform. The method includes: Obtain real-time traffic data and traffic flow data uploaded by smart street lights on the target road section, where the real-time traffic information includes multiple real-time sub-information; Analyzing the influence coefficient of the real-time road condition data on the vehicle flow data; Based on the influence coefficient, determining information processing ratios of the multiple types of real-time sub-information; extracting data volumes corresponding to the plurality of real-time sub-information respectively from the real-time traffic data according to information processing ratios of the plurality of real-time sub-information; Using the data amounts corresponding to the multiple real-time sub-information, the vehicle flow data is adjusted to generate target vehicle flow data; The target traffic flow data is matched with a preset guidance decision table to obtain a target guidance decision, and the target guidance decision is sent to the smart street lights of the target road section to guide the travel route of the vehicles on the target road section.

[0006] Optionally, calculating a first traffic flow data change rate of the target road section within a preset first time period; Based on the change rate of the first vehicle flow data, adjust the frequency with which the smart street lamp collects the real-time road condition data.

[0007] Optionally, normalizing the multiple real-time sub-information to obtain multiple standard data; In a preset second time period, calculating a change rate sequence group of target standard data and a change rate sequence group of the second vehicle flow data of the target road section, wherein the target standard data is any one of the plurality of standard data; Based on the change rate sequence group of the target specification data and the change rate sequence group of the second vehicle flow data of the target road section, calculating a plurality of correlation ratios between the change rate of the target specification data and the change rate of the second vehicle flow data; Based on the multiple correlation ratios, the influence coefficient of the target specification data on the target road section traffic flow data is calculated.

[0008] Optionally, obtaining multiple data point information of the real-time sub-information to be extracted; Calculating reliability scores of the plurality of data point information based on preset evaluation rules; The amount of data extracted from the real-time sub-information to be extracted is determined according to the information processing ratio of the real-time sub-information to be extracted and the reliability scores of the plurality of data point information.

[0009] Optionally, standardization is performed on the plurality of data point information to obtain a plurality of standard point data, wherein the standard point data includes a stability rule standard value, a timeliness rule standard value, and an accuracy rule standard value; Setting a plurality of cluster centers of the standard point data; According to the cluster center, cluster analysis is performed on the plurality of standard point data to obtain reliability scores of the plurality of standard point data.

[0010] Optionally, counting the original data volume of multiple real-time sub-information; Based on the original data amounts of the multiple real-time sub-information, obtaining adjustment factors of the multiple real-time sub-information; Calculating the comprehensive adjustment factor based on the adjustment factors and influence coefficients corresponding to the plurality of real-time sub-information; The product of the comprehensive adjustment factor and the vehicle flow data is calculated to obtain the target vehicle flow data.

[0011] Optionally, matching the target vehicle flow data with a preset guidance decision table to obtain a target guidance decision further includes: According to the traffic composition of the target road section, guidance decisions for multiple vehicle types are generated so that when the smart street light recognizes a specific vehicle type, the guidance decision for the specific vehicle type is sent to the vehicle corresponding to the specific vehicle type.

[0012] In the second aspect, the present application provides a smart street light escort system for major events, wherein the system is an urban street light management platform, and the urban street light management platform includes an acquisition module, a processing module, and a sending module, wherein: The acquisition module is used to acquire real-time traffic data and traffic flow data uploaded by the smart street lamps on the target road section, wherein the real-time traffic information includes a variety of real-time sub-information; The processing module is used to analyze the influence coefficient of the real-time road condition data on the vehicle flow data; determine the information processing ratio of the multiple real-time sub-information based on the influence coefficient; extract the data volume corresponding to each of the multiple real-time sub-information from the real-time road condition data according to the information processing ratio of the multiple real-time sub-information; and use the data volume corresponding to each of the multiple real-time sub-information to adjust the vehicle flow data and generate target vehicle flow data; The sending module is used to match the target vehicle flow data with a preset guidance decision table to obtain a target guidance decision, and send the target guidance decision to the smart street lights on the target road section to guide the travel route of the vehicles on the target road section.

[0013] In a third aspect, the present application provides an electronic device comprising a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a method as described in any one of the first aspects.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method as described in any one of the first aspects is executed.

[0015] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application determines the degree of influence of real-time road condition data (vehicle speed, traffic density, visibility, rainfall, etc.) on traffic flow data, and divides the information processing ratio of various real-time sub-information in real-time road condition data, and then determines the information processing amount of each real-time sub-information according to the information processing capacity of the urban street light management platform; finally, extracts the corresponding proportion of each real-time sub-information from the currently detected real-time road condition data as the basis for adjusting the traffic flow data, so as to integrate the influence of real-time road condition data on traffic flow into the traffic flow data, so as to make the subsequent guidance decision making more effective. This process limits a large amount of real-time road condition data within the information processing capacity of the urban street light management platform, so that the analysis time of the guidance decision is within the normal range, ensuring that the smart street light can update the traffic guidance information in a timely manner.

[0016] 2. When judging the influence of real-time road condition data on vehicle flow data, due to the variety of data types, inconsistent units and constant changes in real-time road condition data, the judgment is difficult and inaccurate. In order to solve this problem, the present application normalizes a variety of real-time sub-information to unify data standards; then obtains the vehicle flow data of the target road section, calculates the ratio of the change rate of each standard data to the change rate of the vehicle flow data, and thus obtains the influence coefficient of each standard data on the vehicle flow data. At this time, the influence coefficient includes the changes in vehicle flow data caused by the joint influence of multiple standard data, so the influence coefficient is not accurate. Based on this, the present application calculates multiple correlation ratios (influence coefficients) of the target standard data to the vehicle flow data within the preset second time period, and then calculates the final influence coefficient of the target standard data on the vehicle flow data of the target road section based on the Spearman rank correlation coefficient calculation formula; in this calculation process, the Spearman rank correlation coefficient calculation formula only needs to understand the change law of the correlation ratio over a period of time to obtain a more accurate correlation degree between the target standard data and the vehicle flow data, thereby providing an accurate basis for the subsequent determination of the information processing ratio of various real-time sub-information. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a smart street light escort method for major events provided in an embodiment of the present application.

[0018] Figure 2 It is a structural diagram of a smart street light escort system for major events provided in an embodiment of the present application.

[0019] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0020] Explanation of the reference numerals: 1. Acquisition module; 2. Processing module; 3. Sending module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. DETAILED DESCRIPTION

[0021] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0022] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0023] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0024] In the process of urban development, in order to ensure the safety of residents' night travel, the popularity of street lights is increasing. With the development of science and technology, traditional street lights are gradually replaced by smart street lights due to their defects such as high energy consumption, poor environmental adaptability and high maintenance costs. At present, smart street lights have not only optimized and upgraded many defects of traditional street lights, but also added many convenient functions. For example, smart street lights can realize remote security monitoring, provide information services to citizens or vehicles near street lights, and assist in traffic diversion when the road is congested.

[0025] Based on the diverse functions of smart street lights, the application scenarios of smart street lights are becoming more and more extensive. In this application, smart street lights can be used in the escort scenarios of major events. The traditional escort method for major events basically relies on manual completion. After the introduction of smart street lights, with the help of the data support provided by smart street lights, the problem of low efficiency of manual escort can be greatly improved. At present, the escort work of smart street lights mainly focuses on traffic guidance. Smart street lights guide vehicles to enter or leave in an orderly manner by real-time monitoring of traffic flow near the event site. For example, when the traffic flow is large, based on the multiple entrances or exits of the event site, the optimal entrance and exit are allocated to the vehicles of each section of the road, and the guidance information is displayed on the electronic display board installed on the smart street light.

[0026] When monitoring traffic flow data, smart street lights will also collect real-time traffic data to provide a basis for guiding decisions. However, when the monitored road section is a congested section, it will lead to a large increase in real-time traffic data, thereby prolonging the analysis time of guiding decisions. At this time, smart street lights will not be able to update traffic guidance information in time, thereby aggravating road congestion.

[0027] In order to solve the above problems, the present application provides a smart street light escort method for major events, which is applied to the urban street light management platform, such as Figure 1 As shown, the method includes steps S101 to S106, which are as follows: S101. Obtain real-time traffic data and traffic flow data uploaded by smart street lamps on a target road section. The real-time traffic information includes a variety of real-time sub-information.

[0028] In the above steps, the target road section refers to the traffic road section near the event site. In actual scenarios, due to the large fluctuations in traffic volume near the event site, when the smart street lights collect real-time road conditions and traffic volume data of the target road section, if the traffic volume changes rapidly and the collection frequency is low, it is easy to fail to obtain effective information; and if the traffic volume changes slowly and the collection frequency is high, it will increase the processing burden of the urban street light management platform.

[0029] Based on this, the present application adjusts the information collection frequency of the smart street lights on the target road section in real time according to the change rate of the traffic flow data, so as to adapt to different traffic scenarios and provide effective analysis data for the urban street light management platform. Specifically, the change rate of the first traffic flow data of the target road section within the preset first time period is calculated, and then the change rate of the first traffic flow data is compared with the preset multiple traffic flow data change rate thresholds to obtain the information collection frequency corresponding to the change rate of the first traffic flow data, and then the collection frequency of the smart street lights on the target road section is adjusted according to the information collection frequency corresponding to the change rate of the first traffic flow data. It should be noted that the multiple traffic flow data change rate thresholds each correspond to an information collection frequency of a smart street light, wherein the corresponding relationship between the traffic flow data change rate threshold and the information collection frequency of the smart street light is established in advance based on historical experience.

[0030] S102: Analyze the influence coefficient of real-time road condition data on vehicle flow data.

[0031] In the above steps, the real-time traffic data contains a variety of real-time sub-information, including but not limited to visibility, rainfall, traffic composition and traffic density. These real-time sub-information have a certain impact on the driving state of the vehicle, thereby causing the overall traffic volume to change. However, in the same scenario, different real-time sub-information has different degrees of influence on the traffic volume. For real-time sub-information with a smaller degree of influence, when the congestion of the target road section is good, the data volume of this type of real-time sub-information is small and relatively simple. Therefore, the urban street light management platform can process the real-time traffic data uploaded by the smart street light in a timely manner in this case. However, when the congestion of the target road section gradually tends to a serious state, various real-time sub-information will gradually increase and change frequently, thereby increasing the processing difficulty of the urban street light management platform, and then causing the processing time to be extended. Based on this, the present application analyzes the influence of various real-time sub-information in the real-time traffic data, selects the real-time sub-information with lower influence, thereby reducing the data processing volume of this part of the real-time sub-information, thereby reducing the processing difficulty of the urban street light management platform, so that the urban street light management platform can respond to the sharp increase in the data volume of the real-time traffic data in a timely manner.

[0032] Specifically, first, a variety of real-time sub-information is standardized and converted into a variety of standardized data. The standardized processing includes data screening, data format unification and other standardized processing operations. Then, within the preset second time period, multiple change rate values ​​of various real-time sub-information and multiple change rate values ​​of the traffic flow data of the target road section are calculated. Specifically, the urban street light management platform divides the preset second time period into multiple sub-time periods, and then calculates the change rate values ​​of various real-time sub-information and the change rate values ​​of the traffic flow data in each sub-time period. This method converts multiple real-time sub-information in different measurement units into the change rate of the same measurement unit, thereby facilitating the subsequent influence analysis of various real-time sub-information.

[0033] Then, the change rate values ​​of various real-time sub-information in each sub-time period are stored in multiple real-time sub-information change rate sequences, and the change rate values ​​of multiple vehicle flow data are stored in the vehicle flow change rate sequence. Then, multiple correlation ratios between each real-time sub-information change rate sequence and the vehicle flow change rate sequence are calculated one by one. Taking the target specification data as an example, the change rate sequence of the target specification data is [a, b, c], and the vehicle flow change rate is [A, B, C]. Then, the multiple correlation ratios between the change rate of the target specification data and the vehicle flow change rate are a / A, b / B, c / C. The correlation ratio reflects the relative influence of the real-time sub-information on the vehicle flow data, but the change of the vehicle flow data may be affected by multiple real-time sub-information. The change of the vehicle flow data cannot be represented by a certain real-time sub-information. Therefore, the correlation ratio cannot accurately represent the influence of the real-time sub-information on the vehicle flow data. At this time, the correlation ratio needs to be verified to further determine the influence of each real-time sub-information on the vehicle flow data.

[0034] Specifically, the present application proposes to use the Spearman rank correlation coefficient to verify whether the correlation ratios between various real-time sub-information and traffic flow data are effective. For the convenience of explanation, taking the target specification data as an example, first sort the data in the change rate sequence of the target specification data to obtain the target specification data sequence Q={ , , ,···, }, and sort the data in the change rate sequence of the traffic flow data to obtain the traffic flow sequence S={ , , ,···, Then extract the rank (sort order) of each data in the target specification data sequence and the traffic flow sequence to obtain the rank sequence of the target specification data ={ , , ,···,} and the rank sequence of the traffic flow sequence ={ , , ,···, }; Then, according to the Spearman rank correlation coefficient calculation formula, the correlation of the correlation ratio between the target specification data and the traffic flow data is calculated. The specific calculation formula is: Since the Spearman rank correlation coefficient focuses on the rank of the data rather than the specific value of the original data, no matter what kind of complex numerical change relationship there is between the data, as long as they show a relative upward or downward trend as a whole, this trend can be reflected by the rank. Therefore, this calculation formula can be well applied to the uncertainty influence of the target specification data on the traffic flow data, among which, The value range of is [-1, 1]. The closer it is to 1, the stronger the positive correlation between the target specification data and the traffic flow data. The closer it is to -1, the stronger the negative correlation between the correlation ratio of the target specification data and the traffic flow data is, which can verify the validity of the correlation ratio between each real-time sub-information and the traffic flow data. Finally, the change curve of the correlation ratio between each real-time sub-information and the traffic flow data is analyzed, and the correlation curve with a linear relationship in the correlation ratio change curve is intercepted. Then, any point is selected from the intercepted correlation curve, and the point is multiplied by its corresponding Spearman rank correlation coefficient as the influence coefficient of the real-time sub-information on the traffic flow data, so as to provide an accurate basis for the subsequent determination of the information processing ratio of various real-time sub-information.

[0035] S103: Determine information processing ratios of various real-time sub-information based on the influence coefficients.

[0036] S104 . Extracting data volumes corresponding to the various types of real-time sub-information from the real-time traffic data according to information processing ratios of the various types of real-time sub-information.

[0037] In the above steps S103 to S104, after the smart street light uploads the real-time traffic condition data within a collection cycle to the urban street light management platform, the urban street light management platform calculates the ratio of multiple influence coefficients based on the influence coefficients of multiple real-time sub-information in the real-time traffic condition data, and then extracts various corresponding data amounts from the multiple real-time sub-information based on the ratio of the multiple influence coefficients.

[0038] In a possible implementation, in order to ensure that the data extracted from multiple real-time sub-information is valid information, the extracted data needs to be analyzed for reliability. This application sets preset evaluation rules to calculate the reliability scores of data points extracted from each real-time sub-information, and the data points with higher reliability scores are regarded as valid information, wherein the preset evaluation rules include accuracy rules, stability rules, and timeliness rules. Specifically: For the accuracy rule, first, according to the data type of the real-time sub-information, set a high-precision reference data as a comparison benchmark. The high-precision data reference source comes from manual statistical data or calibrated professional monitoring equipment data. Then, according to the current information collection frequency of the smart street lamp, set the error range s of the real-time sub-information. When the collection frequency is high, the amount of valid data is large and it is easier to capture valid data. At this time, the error range is set larger; when the collection frequency is low, the amount of valid data is small and it is difficult to capture valid data. At this time, the error range is set smaller. Then calculate the absolute value t of the difference between multiple data points in the real-time sub-information and the reference data. If the t of the target data point is less than or equal to the error range s, the accuracy rule score of the target data point is determined to be 1. If the t of the target data point is greater than the error range s, calculate the relative accuracy of the target data point under the currently set error standard, that is, s / t, and use the calculation result as the accuracy rule score.

[0039] For the stability rule, the principle is to analyze the fluctuation of multiple data points within a preset time period. If a certain data point causes a large fluctuation in the entire data sample, it means that the stability of the data point is poor. In this application, the standard deviation of the real-time sub-information within the preset time period is first calculated. The standard deviation is used to reflect the overall fluctuation of the real-time sub-information. Then the ratio of the data point to the standard deviation is calculated to obtain the overall fluctuation of the data point in the data set of the real-time sub-information. Then the ratio of the data point to the standard deviation is normalized to obtain the stability rule score of the data point.

[0040] Regarding the timeliness rule, it can be understood that the closer the collection time of the data point is to the current time, the higher the timeliness. Therefore, the present application determines the timeliness of the data point by setting a time window threshold. First, the difference between each data point and the start time point of the collection cycle of the smart street light is calculated, and then the difference is compared with the time window threshold. If the difference is less than or equal to the time window threshold, the data point is determined to be valid, and its timeliness rule score is 1. If the difference is greater than the time window threshold, the relative timeliness of the data point is calculated. For details, please refer to the above-mentioned accuracy rule calculation formula to obtain the timeliness rule score of the data.

[0041] A single scoring method cannot indicate whether a data point is valid. Therefore, this application performs cluster analysis on multiple rule scores of a data point to obtain a comprehensive score as the reliability score of the data point. Specifically: first, standardize the information of multiple data points to obtain multiple standard point data. The standardization process in this application is normalization processing, and each rule score of a data point is converted into a numerical value in the [0,1] interval. Then, the binary search method is used to set the cluster center. Taking the three rule scores in this application as an example, the cluster centers are (0,0,0) and (1,1,1), where (0,0,0) represents a low reliability center and (1,1,1) represents a high reliability center. Then, the Euclidean distance of each data point is calculated to obtain the low reliability center distance d1 of each data point and the high reliability center distance d2 of each data point. Finally, the reliability score of the data point is determined based on d1 and d2. If d1 is less than d2, it means that the data point is closer to the low reliability center, and the reliability score is: R=(d1 / dmax) / 2. When d1 is 0, it means that the data point is at the low reliability center, then R=0. When d1 is dmax, it means that the data point is farthest from the low reliability center, R=0.5. If d1 is greater than or equal to d2, it means that the data point is closer to the high reliability center, then R=(1+(1-d2 / dmax)) / 2. When d2 is 0, it means that the data point is at the high reliability center, then R=1. When d1 is dmax, it means that the data point is farthest from the low reliability center, R=0.5, where dmax is the maximum distance from all data points to the low reliability center. Cluster analysis can well reflect the relative position of data points between two cluster centers with high and low reliability, thereby achieving quantitative evaluation of the reliability of data points.

[0042] S105. Use the data amounts corresponding to the various real-time sub-information to adjust the vehicle flow data and generate target vehicle flow data.

[0043] In the above steps, the current traffic flow data can only reflect the road conditions at the current moment, while the actual road conditions will change a lot due to the influence of real-time road condition data, which will cause the guidance decisions made based on the current traffic flow data to be unable to adapt to subsequent road changes. Therefore, the present application adjusts the current traffic flow data to predict the stable value of traffic flow data in the future. At this time, the guidance decisions generated by the urban street light management platform based on the predicted stable value of traffic flow data can well adapt to the various situations that are about to occur in the current target section, making vehicle guidance more efficient. The specific adjustment process is: first, the original data volume of multiple real-time sub-information is counted, and the original data volume refers to the amount of data that has not been extracted by the smart street light within a collection cycle, and then the multiple real-time sub-information is normalized to determine the adjustment factors of the multiple real-time sub-information, wherein the normalization process is to unify the data volume of each real-time sub-information to the range of [0,1] by calculating the ratio of the data volume of each real-time sub-information to the total data volume of multiple real-time sub-information, so as to obtain the adjustment factor of each real-time sub-information. Then, according to the influence coefficients corresponding to various real-time sub-information, the sum of the products of the adjustment factors and the influence coefficients of each real-time sub-information is calculated to obtain the comprehensive adjustment factor, and finally the product between the comprehensive adjustment factor and the traffic flow data is calculated to obtain the target traffic flow data. This scheme reasonably allocates the weight of each factor according to the predetermined influence coefficient, so that the final comprehensive adjustment factor can accurately reflect the comprehensive impact of various real-time road conditions on traffic flow, so that the guidance decision can adapt to the various traffic conditions that are about to change.

[0044] S106 matches the target traffic flow data with the preset guidance decision table to obtain a target guidance decision, and sends the target guidance decision to the smart street lights on the target road section to guide the route of the vehicles on the target road section.

[0045] In the above steps, when the smart street light guides the vehicle's route, it can only guide the vehicle through an electronic display board, which causes different types of vehicles in the traffic flow to be unable to enter the correct lane, thereby aggravating the traffic jam. Based on this, this application adds guidance decisions for multiple vehicle types to the target guidance decision according to the traffic composition of the target road section, so that when the smart street light identifies a specific vehicle type, it can send its corresponding guidance information to the on-board system of the specific vehicle to remind the driver to drive into the correct turning lane in advance, so as to facilitate the specific vehicle to exit the current congested section, thereby improving the traffic efficiency of the congested section and reducing the degree of congestion.

[0046] Reference Figure 2The present application also provides a smart street light escort system for major events. The system is an urban street light management platform. The urban street light management platform includes an acquisition module 1, a processing module 2, and a sending module 3, wherein: Acquisition module 1 is used to obtain real-time traffic data and traffic flow data uploaded by smart street lamps on the target road section. The real-time traffic information includes multiple real-time sub-information; Processing module 2 is used to analyze the influence coefficient of real-time road condition data on vehicle flow data; determine the information processing ratio of multiple real-time sub-information based on the influence coefficient; extract the data volume corresponding to each of the multiple real-time sub-information from the real-time road condition data according to the information processing ratio of the multiple real-time sub-information; use the data volume corresponding to each of the multiple real-time sub-information to adjust the vehicle flow data and generate target vehicle flow data; The sending module 3 is used to match the target traffic flow data with the preset guidance decision table to obtain the target guidance decision, and send the target guidance decision to the smart street lights on the target section to guide the route of the vehicles on the target section.

[0047] In a possible implementation, a first vehicle flow data change rate of a target road section within a preset first time period is calculated; Based on the change rate of the first vehicle flow data, adjust the frequency with which the smart street lamp collects real-time traffic data.

[0048] In a possible implementation, a plurality of real-time sub-information is normalized to obtain a plurality of normalized data; In a preset second time period, a change rate sequence group of target standard data and a change rate sequence group of second vehicle flow data of a target road section are calculated, wherein the target standard data is any one of a plurality of standard data; Based on the change rate sequence group of the target specification data and the change rate sequence group of the second vehicle flow data of the target road section, a plurality of correlation ratios between the change rate of the target specification data and the change rate of the second vehicle flow data are calculated; Based on multiple correlation ratios, the influence coefficient of the target specification data on the traffic flow data of the target road section is calculated.

[0049] In a possible implementation, multiple data point information of the real-time sub-information to be extracted is obtained; Calculate the reliability scores of multiple data point information based on preset evaluation rules; The amount of data extracted from the real-time sub-information to be extracted is determined according to the information processing ratio of the real-time sub-information to be extracted and the reliability scores of the multiple data point information.

[0050] In a possible implementation, multiple data point information is standardized to obtain multiple standard point data, where the standard point data includes a stability rule standard value, a timeliness rule standard value, and an accuracy rule standard value; Set the cluster centers of multiple standard point data; According to the cluster center, cluster analysis is performed on multiple standard point data to obtain the reliability scores of the multiple standard point data.

[0051] In a possible implementation, raw data volumes of multiple real-time sub-information are counted; Based on the original data amount of the multiple real-time sub-information, an adjustment factor of the multiple real-time sub-information is obtained; Calculate a comprehensive adjustment factor based on the adjustment factors and influence coefficients corresponding to the various real-time sub-information; The product of the comprehensive adjustment factor and the vehicle flow data is calculated to obtain the target vehicle flow data.

[0052] In a possible implementation, the target vehicle flow data is matched with a preset guidance decision table to obtain a target guidance decision, which specifically includes: According to the traffic composition of the target road section, guidance decisions for multiple vehicle types are generated so that when the smart street light recognizes a specific vehicle type, the guidance decision for the specific vehicle type is sent to the vehicle corresponding to the specific vehicle type.

[0053] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0054] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0055] The communication bus 302 is used to realize the connection and communication between these components.

[0056] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0057] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0058] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0059] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , as a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface module, and an application program for a smart street light escort method for major events.

[0060] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the memory 305 to store an application program for a smart street light escort method applied to major events. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0061] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0062] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0063] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0064] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0065] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0066] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0067] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A smart street light escort method for major events, characterized in that: Applied to the urban street light management platform, the method includes: Obtain real-time traffic data and traffic flow data uploaded by smart street lights on the target road section, where the real-time traffic information includes multiple real-time sub-information; Analyzing the influence coefficient of the real-time road condition data on the vehicle flow data; Based on the influence coefficient, determining information processing ratios of the multiple types of real-time sub-information; extracting data volumes corresponding to the plurality of real-time sub-information respectively from the real-time traffic data according to information processing ratios of the plurality of real-time sub-information; Using the data amounts corresponding to the multiple real-time sub-information, the vehicle flow data is adjusted to generate target vehicle flow data; The target traffic flow data is matched with a preset guidance decision table to obtain a target guidance decision, and the target guidance decision is sent to the smart street lights of the target road section to guide the travel route of the vehicles on the target road section.

2. The method according to claim 1, characterized in that The step of obtaining the real-time traffic data and traffic flow data uploaded by the smart street lamps on the target road section specifically includes: Calculating a first traffic flow data change rate of the target road section within a preset first time period; Based on the change rate of the first vehicle flow data, adjust the frequency with which the smart street lamp collects the real-time road condition data.

3. The method according to claim 1, characterized in that The analysis of the influence coefficient of the real-time road condition data on the vehicle flow data is specifically: Performing normalization processing on the multiple real-time sub-information to obtain multiple normalized data; In a preset second time period, calculating a change rate sequence group of target standard data and a change rate sequence group of the second vehicle flow data of the target road section, wherein the target standard data is any one of the plurality of standard data; Based on the change rate sequence group of the target specification data and the change rate sequence group of the second vehicle flow data of the target road section, calculating a plurality of correlation ratios between the change rate of the target specification data and the change rate of the second vehicle flow data; Based on the multiple correlation ratios, the influence coefficient of the target specification data on the target road section traffic flow data is calculated.

4. The method according to claim 1, characterized in that The extracting, from the real-time traffic information, data volumes corresponding to the various types of real-time sub-information respectively according to the information processing ratios of the various types of real-time sub-information is specifically: Acquire multiple data point information of the real-time sub-information to be extracted; Calculating reliability scores of the plurality of data point information based on preset evaluation rules; The amount of data extracted from the real-time sub-information to be extracted is determined according to the information processing ratio of the real-time sub-information to be extracted and the reliability scores of the plurality of data point information.

5. The method according to claim 4, characterized in that The reliability scores of the plurality of data point information are calculated based on the preset evaluation rules, specifically: Performing standardization processing on the plurality of data point information to obtain a plurality of standard point data, wherein the standard point data includes a stability rule standard value, a timeliness rule standard value, and an accuracy rule standard value; Setting a plurality of cluster centers of the standard point data; According to the cluster center, cluster analysis is performed on the plurality of standard point data to obtain reliability scores of the plurality of standard point data.

6. The method according to claim 1, characterized in that The vehicle flow data is adjusted by using the data amounts corresponding to the various real-time sub-information to generate the target vehicle flow data, specifically: Counting the original data volume of the plurality of real-time sub-information; Based on the original data amounts of the multiple real-time sub-information, obtaining adjustment factors of the multiple real-time sub-information; Calculating the comprehensive adjustment factor based on the adjustment factors and influence coefficients corresponding to the plurality of real-time sub-information; The product of the comprehensive adjustment factor and the vehicle flow data is calculated to obtain the target vehicle flow data.

7. The method according to claim 1, characterized in that The step of matching the target vehicle flow data with a preset guidance decision table to obtain a target guidance decision specifically includes: According to the traffic composition of the target road section, guidance decisions for multiple vehicle types are generated so that when the smart street light recognizes a specific vehicle type, the guidance decision for the specific vehicle type is sent to the vehicle corresponding to the specific vehicle type.

8. A smart street light escort system used in major events, characterized in that: The system is a city street light management platform, which comprises an acquisition module (1), a processing module (2) and a sending module (3), wherein: The acquisition module (1) is used to acquire real-time traffic data and vehicle flow data uploaded by the smart street lamps on the target road section, wherein the real-time traffic information includes a plurality of real-time sub-information; The processing module (2) is used to analyze the influence coefficient of the real-time road condition data on the vehicle flow data; determine the information processing ratio of the multiple types of real-time sub-information based on the influence coefficient; extract the data volume corresponding to each of the multiple types of real-time sub-information from the real-time road condition data according to the information processing ratio of the multiple types of real-time sub-information; and use the data volume corresponding to each of the multiple types of real-time sub-information to adjust the vehicle flow data and generate target vehicle flow data; The sending module (3) is used to match the target vehicle flow data with a preset guidance decision table to obtain a target guidance decision, and send the target guidance decision to the smart street lights on the target road section to guide the travel route of the vehicles on the target road section.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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