A smart streetlight escort method and system for major events

Abstract

A smart streetlight escort method and system for major events, relating to the field of the Internet of Things. Applied to an urban streetlight management platform, the system includes: obtaining real-time road condition and traffic flow data uploaded by smart streetlights on a target road section, where the real-time road condition information includes multiple real-time sub-information; analyzing the influence coefficient of the real-time road condition data on the traffic flow data; determining the information processing ratio of the multiple real-time sub-information based on the influence coefficient; extracting the data volume corresponding to each of the multiple real-time sub-information from the real-time road condition data based on the information processing ratio; adjusting the traffic flow data using the data volume corresponding to each of the multiple real-time sub-information to generate target traffic flow data; matching the target traffic flow data with a preset guidance decision table to obtain a target guidance decision, and sending the target guidance decision to the smart streetlights on the target road section, thereby resolving the issue of smart streetlights being unable to update traffic guidance information in a timely manner.

Description

Technical Field

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

[0002] When holding a major event, the traffic volume 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 advancement of technology, traffic diversion is no longer solely reliant on manual labor. Smart streetlights play a crucial role in this effort. In addition to meeting daily lighting needs, they also provide security monitoring, information services, and traffic diversion, making them a crucial component of major event security. Currently, smart streetlights monitor traffic flow near event locations in real time to guide vehicles in and out of venues. However, when monitoring traffic flow, smart streetlights also collect real-time traffic data to inform traffic guidance decisions. However, when monitoring congested roads, this can lead to a significant increase in real-time traffic data, prolonging the analysis time required for traffic guidance decisions and preventing smart streetlights from updating traffic guidance information in a timely manner. Summary of the Invention

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

[0005] In a first aspect, the present application provides a smart streetlight escort method for major events, which is applied to an urban streetlight management platform. The method includes:

[0006] Obtain real-time traffic data and traffic flow data uploaded by smart street lights on the target road section. The real-time traffic information includes multiple real-time sub-information;

[0007] Analyzing the influence coefficient of the real-time road condition data on the traffic flow data;

[0008] determining information processing ratios of the plurality of real-time sub-information based on the influence coefficients;

[0009] extracting data volumes corresponding to the respective multiple types of real-time sub-information from the real-time traffic data according to information processing ratios of the multiple types of real-time sub-information;

[0010] Using the data amounts corresponding to the plurality of real-time sub-information, the vehicle flow data is adjusted to generate target vehicle flow data;

[0011] 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.

[0012] Optionally, calculating a first traffic flow data change rate of the target road section within a preset first time period;

[0013] Based on the change rate of the first traffic flow data, adjust the frequency with which the smart street light collects the real-time traffic condition data.

[0014] Optionally, normalizing the plurality of real-time sub-information to obtain a plurality of normalized data;

[0015] Calculating a target standard data change rate sequence group and a second vehicle flow data change rate sequence group of the target road section within a preset second time period, wherein the target standard data is any one of the plurality of standard data;

[0016] 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 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;

[0017] Based on the multiple correlation ratios, the influence coefficient of the target specification data on the target road section traffic flow data is calculated.

[0018] Optionally, obtain multiple data point information of the real-time sub-information to be extracted;

[0019] Calculating reliability scores of the plurality of data point information based on preset evaluation rules;

[0020] 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.

[0021] 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;

[0022] Setting a plurality of cluster centers of the standard point data;

[0023] Cluster analysis is performed on the plurality of standard point data according to the cluster center to obtain reliability scores of the plurality of standard point data.

[0024] Optionally, counting the original data volume of the plurality of real-time sub-information;

[0025] Based on the original data amounts of the plurality of real-time sub-information, obtaining adjustment factors of the plurality of real-time sub-information;

[0026] Calculating the comprehensive adjustment factor based on the adjustment factors and influence coefficients corresponding to the plurality of real-time sub-information;

[0027] The product of the comprehensive adjustment factor and the vehicle flow data is calculated to obtain the target vehicle flow data.

[0028] Optionally, matching the target vehicle flow data with a preset guidance decision table to obtain a target guidance decision may further include:

[0029] According to the traffic flow 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.

[0030] In a second aspect, the present application provides a smart streetlight escort system for major events. The system is an urban streetlight management platform, which includes an acquisition module, a processing module, and a sending module, wherein:

[0031] The acquisition module is used to obtain real-time traffic data and traffic flow data uploaded by the smart street lights on the target road section, and the real-time traffic information includes multiple real-time sub-information;

[0032] The processing module is configured to analyze an influence coefficient of the real-time traffic condition data on the vehicle flow data; determine information processing ratios of the multiple types of real-time sub-information based on the influence coefficients; extract data volumes corresponding to the multiple types of real-time sub-information from the real-time traffic condition data according to the information processing ratios of the multiple types of real-time sub-information; and adjust the vehicle flow data using the data volumes corresponding to the multiple types of real-time sub-information to generate target vehicle flow data;

[0033] 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 vehicle's route on the target road section.

[0034] 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.

[0035] 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.

[0036] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0037] 1. This application determines the degree to which real-time traffic data (such as speed, traffic density, visibility, and rainfall) affects traffic flow data, thereby dividing the information processing ratios of various real-time sub-information within the real-time traffic data. The information processing capacity of each real-time sub-information is then determined based on the information processing capacity of the urban streetlight management platform. Finally, the corresponding data of each real-time sub-information ratio is extracted from the currently detected real-time traffic data, which serves as the basis for adjusting traffic flow data. This integrates the impact of real-time traffic data on traffic flow into the traffic flow data, thereby making subsequent guidance decisions more effective. This process limits the large amount of real-time traffic data to the information processing capacity of the urban streetlight management platform, thereby keeping the analysis time for guidance decisions within a normal range and ensuring that smart streetlights can update traffic guidance information in a timely manner.

[0038] 2. When determining the degree of influence of real-time road condition data on traffic flow data, due to the diverse data types, inconsistent units, and constant change in real-time road condition data, the determination is difficult and inaccurate. To address this issue, the present application normalizes multiple real-time sub-information to unify the data standards. Traffic flow data for the target road section is then obtained, and the ratio of the change rate of each standard data to the change rate of the traffic flow data is calculated to obtain the influence coefficient of each standard data on the traffic flow data. However, the influence coefficient at this time includes the changes in traffic flow data caused by the combined influence of multiple standard data, and is therefore inaccurate. Based on this, the present application calculates multiple correlation ratios (influence coefficients) of the target standard data to the traffic flow data within a preset second time period. Then, based on the Spearman rank correlation coefficient calculation formula, the final influence coefficient of the target standard data on the traffic flow data of the target road section is calculated. In this calculation process, the Spearman rank correlation coefficient calculation formula only needs to understand the changing pattern of the correlation ratios over a period of time to obtain a relatively accurate degree of correlation between the target standard data and the traffic flow data, thereby providing an accurate basis for subsequently determining the information processing ratios of various real-time sub-information. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a smart streetlight escort method for major events provided in an embodiment of the present application.

[0040] Figure 2This is a structural diagram of the smart street light escort system for major events provided in an embodiment of the present application.

[0041] Figure 3 This is a structural diagram of an electronic device provided in an embodiment of the present application.

[0042] 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

[0043] In order to enable those skilled in the art 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.

[0044] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0045] In the description of the embodiments of the present application, the term "multiple" means 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 are not to 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 "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0046] As cities develop, streetlights are becoming increasingly common to ensure the safety of residents traveling at night. However, with the advancement of technology, traditional streetlights are being replaced by smart streetlights due to their high energy consumption, poor environmental adaptability, and high maintenance costs. Currently, smart streetlights not only address these shortcomings but also add numerous convenient features. For example, smart streetlights can enable remote security monitoring, provide information services to residents and vehicles near the streetlights, and assist in traffic flow control during congestion.

[0047] 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 monitoring the traffic flow near the event site in real time. 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 vehicles on each road section, and the guidance information is displayed on the electronic display board installed on the smart street light.

[0048] 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 congested, it will lead to a large increase in real-time road condition 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 a timely manner, thereby aggravating road congestion.

[0049] In order to solve the above problems, this 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:

[0050] S101. Obtain real-time traffic condition data and traffic flow data uploaded by smart street lights on a target road section. The real-time traffic condition information includes a variety of real-time sub-information.

[0051] 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 condition data 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 cause the failure to obtain effective information; 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.

[0052] 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, thereby adapting to different traffic scenarios and providing 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 correspondence 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.

[0053] S102: Analyze the influence coefficient of real-time road condition data on traffic flow data.

[0054] In the above steps, the real-time traffic data includes multiple real-time sub-information, including but not limited to visibility, rainfall, traffic flow composition, and traffic flow density. This real-time sub-information has a certain impact on the driving status of vehicles, thereby causing changes in overall traffic flow. However, in the same scenario, different real-time sub-information has different degrees of impact on traffic flow. For real-time sub-information with a smaller impact, when congestion on 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 promptly process the real-time traffic data uploaded by the smart street lights in this situation. However, when congestion on the target road section gradually tends to be severe, the various real-time sub-information will gradually increase and change frequently, increasing the processing difficulty of the urban street light management platform and further increasing processing time. Based on this, the present application performs an influence analysis on the various real-time sub-information in the real-time traffic data, selects the real-time sub-information with lower influence, and thus reduces the data processing volume of this part of the real-time sub-information. This reduces the processing difficulty of the urban street light management platform and enables the urban street light management platform to promptly respond to the sharp increase in the volume of real-time traffic data.

[0055] Specifically, the various real-time sub-information is first normalized, converting them into standardized data. This normalization includes standardization operations such as data screening and data format unification. Then, within a preset second time period, multiple change rate values ​​for the various real-time sub-information and multiple change rate values ​​for the target road section's traffic flow data are calculated. Specifically, the urban streetlight management platform divides the preset second time period into multiple sub-time periods and then calculates the change rate values ​​for the various real-time sub-information and traffic flow data within each sub-time period. This method converts the real-time sub-information in different measurement units into a single rate of change, facilitating subsequent analysis of the impact of the various real-time sub-information.

[0056] The change rates of various real-time sub-information within each sub-time period are then stored in multiple real-time sub-information change rate sequences, and the change rate values ​​of multiple traffic flow data are stored in a traffic flow change rate sequence. Multiple correlation ratios are then calculated between each real-time sub-information change rate sequence and the traffic flow change rate sequence. For example, if the target standard data has a change rate sequence of [a, b, c] and a traffic flow change rate of [A, B, C], then the correlation ratios between the target standard data change rate and the traffic flow change rate are a / A, b / B, and c / C. These correlation ratios reflect the relative impact of the real-time sub-information on the traffic flow data. However, changes in traffic flow data may be influenced by multiple real-time sub-information elements. Changes in traffic flow data cannot be attributed to a single real-time sub-information element. Therefore, these correlation ratios cannot accurately represent the impact of the real-time sub-information on the traffic flow data. Correlation verification of the correlation ratios is also necessary to further determine the impact of each real-time sub-information element on the traffic flow data.

[0057] Specifically, this application proposes to use the Spearman rank correlation coefficient to verify whether the correlation ratio between various real-time sub-information and traffic flow data is valid. For the convenience of explanation, taking the target specification data as an example, first sort the data in the target specification data change rate sequence to obtain the target specification data sequence Q={ , , ,···, }, and sort the data in the change rate sequence of 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 traffic flow sequence ={ , , ,···, }; Then, the correlation of the correlation ratio between the target specification data and the traffic flow data is calculated according to the Spearman rank correlation coefficient calculation formula. The calculation formula is as follows:

[0058]

[0059] 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 is well applicable to the uncertainty influence of target specification data on 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 the correlation is to -1, the stronger the negative correlation between the target specification data and the traffic flow data. This verifies the validity of the correlation ratio between each real-time sub-information and traffic flow data. Finally, we analyze the correlation ratio curves between each real-time sub-information and traffic flow data, intercept the correlation curves that show a linear relationship within the correlation ratio curves, and then select any point from the intercepted correlation curves. Multiplying this point by its corresponding Spearman rank correlation coefficient determines the influence coefficient of the real-time sub-information on the traffic flow data. This provides an accurate basis for determining the information processing ratios for each real-time sub-information.

[0060] S103: Determine information processing ratios of the various real-time sub-information based on the influence coefficients.

[0061] S104 : extracting data volumes corresponding to the various real-time sub-information from the real-time traffic data according to information processing ratios of the various real-time sub-information.

[0062] 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 quantities from the multiple real-time sub-information based on the ratio of the multiple influence coefficients.

[0063] In one possible implementation, in order to ensure that the data extracted from multiple real-time sub-information is valid information, it is also necessary to perform a reliability analysis on the extracted data. This application sets preset evaluation rules to calculate the reliability score of the 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:

[0064] For the accuracy rule, first, based on the data type of the real-time sub-information, a high-precision reference data is set as a comparison benchmark. The high-precision data reference source comes from manual statistical data or calibrated professional monitoring equipment data. Then, the error range s of the real-time sub-information is set based on the current information collection frequency of the smart street light. When the collection frequency is high, the amount of valid data is large and it is easier to capture valid data. In this case, 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. In this case, the error range is set smaller. The absolute value t of the difference between multiple data points in the real-time sub-information and the reference data is then calculated. 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, the relative accuracy of the target data point under the currently set error standard is calculated, that is, s / t. The calculation result is used as the accuracy rule score.

[0065] The stability rule analyzes the fluctuations of multiple data points over a preset time period. If a particular data point causes significant fluctuations in the entire data sample, this indicates that the data point has poor stability. Specifically, this application first calculates the standard deviation of the real-time sub-information over the preset time period. The standard deviation reflects the overall fluctuations of the real-time sub-information. The ratio of the data point to the standard deviation is then calculated to determine the overall fluctuation of the data point within the real-time sub-information dataset. This ratio is then normalized to obtain the stability rule score for the data point.

[0066] 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, this application judges 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.

[0067] 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, the information of multiple data points is standardized 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 dichotomy method is used to set the cluster center. Taking the three rule scores of 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, then 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, the data point is closer to the high-reliability center, and R = (1 + (1-d2 / dmax)) / 2. When d2 is 0, the data point is at the high-reliability center, and R = 1. When d1 is dmax, the data point is farthest from the low-reliability center, and R = 0.5, where dmax is the maximum distance from all data points to the low-reliability center. Cluster analysis can effectively reflect the relative position of a data point between two cluster centers of high and low reliability, thereby enabling quantitative assessment of the reliability of a data point.

[0068] S105 : Using the data amounts corresponding to the various real-time sub-information, adjust the traffic flow data to generate target traffic flow data.

[0069] 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 will occur in the current target road section, making vehicle guidance more efficient. The specific adjustment process is: first, the original data volume of multiple real-time sub-information is counted. The original data volume refers to the amount of data that has not been extracted by the smart street light within a collection cycle. Then, the multiple real-time sub-information is normalized to determine the adjustment factors of the multiple real-time sub-information. Among them, 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, based on the influence coefficients corresponding to various real-time sub-information items, the sum of the product of the adjustment factors and the influence coefficients for each real-time sub-information item is calculated to obtain a comprehensive adjustment factor. Finally, the comprehensive adjustment factor is multiplied by the traffic flow data to obtain the target traffic flow data. This solution rationally assigns weight to each factor based on the predetermined influence coefficients, ensuring that the resulting comprehensive adjustment factor accurately reflects the combined impact of various real-time road conditions on traffic flow, thereby enabling guidance decisions to adapt to various impending traffic changes.

[0070] 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 vehicle's route on the target road section.

[0071] In the above steps, when the smart street light guides the vehicle's route, it can only guide the vehicle through one electronic display board, which results in different types of vehicles in the traffic flow being unable to enter the correct lane, thereby aggravating the traffic jam. Based on this, the present application adds guidance decisions for multiple vehicle types to the target guidance decision according to the traffic composition of the target road section. 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, making it easier for the specific vehicle to exit the current congested road section, thereby improving the traffic efficiency of the congested road section and reducing the degree of congestion.

[0072] Reference Figure 2This application also provides a smart streetlight escort system for major events. The system is an urban streetlight management platform. The urban streetlight management platform includes an acquisition module 1, a processing module 2, and a sending module 3, wherein:

[0073] Acquisition module 1 is used to obtain real-time traffic data and traffic flow data uploaded by smart street lights on the target road section. The real-time traffic information includes multiple real-time sub-information;

[0074] Processing module 2 is configured to analyze the influence coefficient of the real-time traffic condition data on the traffic 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 traffic condition data according to the information processing ratio; and adjust the traffic flow data using the data volume corresponding to each of the multiple real-time sub-information to generate target traffic flow data;

[0075] 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 vehicle's route on the target section.

[0076] In one possible implementation, a first traffic flow data change rate of a target road section within a preset first time period is calculated;

[0077] Based on the change rate of the first vehicle flow data, adjust the frequency of the smart street light's collection of real-time traffic data.

[0078] In one possible implementation, a plurality of real-time sub-information is normalized to obtain a plurality of normalized data;

[0079] Calculating a target standard data change rate sequence group and a second vehicle flow data change rate sequence group of a target road section within a preset second time period, wherein the target standard data is any one of a plurality of standard data;

[0080] 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 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;

[0081] 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.

[0082] In a possible implementation, multiple data points of the real-time sub-information to be extracted are obtained;

[0083] Calculate the reliability scores of multiple data points based on preset evaluation rules;

[0084] The amount of data extracted from the real-time sub-information to be extracted is determined based on the information processing ratio of the real-time sub-information to be extracted and the reliability scores of the multiple data point information.

[0085] 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;

[0086] Set the cluster centers of multiple standard point data;

[0087] 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.

[0088] In a possible implementation, the raw data volume of the plurality of real-time sub-information is counted;

[0089] 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;

[0090] Calculate a comprehensive adjustment factor based on the adjustment factors and impact coefficients corresponding to the various real-time sub-information;

[0091] The product between the comprehensive adjustment factor and the traffic flow data is calculated to obtain the target traffic flow data.

[0092] In a possible implementation, matching the target traffic flow data with a preset guidance decision table to obtain a target guidance decision may further include:

[0093] 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.

[0094] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be 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 embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0095] This 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 .

[0096] The communication bus 302 is used to implement the connection and communication between these components.

[0097] 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.

[0098] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0099] The processor 301 may include one or more processing cores. Using various interfaces and circuits, the processor 301 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, as well as accesses data stored in the memory 305, to perform various server functions and process data. Optionally, the processor 301 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 301 but implemented as a separate chip.

[0100] Among them, the memory 305 may include a random access memory (RAM) or 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 also optionally 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 streetlight escort method for major events.

[0101] 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 application program stored in the memory 305 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 simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited to the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0102] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0103] 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 merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, 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 interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0104] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0105] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0106] If the integrated unit is implemented as 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 this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing 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 method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0107] The foregoing is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. In other words, any equivalent variations and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure and the practical implications thereof.

[0108] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.

Claims

1. A smart streetlight escort method for major events, characterized in that: Applied to the urban streetlight management platform, the method includes: Obtain real-time traffic data and traffic flow data uploaded by smart street lights on the target road section. The real-time traffic data contains multiple real-time sub-information; Analyze the influence coefficient of the real-time traffic data on the traffic flow data, specifically: Normalizing the plurality of real-time sub-information to obtain a plurality of standard data; Calculating a target standard data change rate sequence group and a second vehicle flow data change rate sequence group of the target road section within a preset second time period, wherein the target standard data is any one of the plurality of standard data; 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 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; Based on the plurality of correlation ratios, an influence coefficient of the target specification data on the target road section traffic flow data is calculated; The calculating of the influence coefficient of the target specification data on the target road section traffic flow data based on the plurality of correlation ratios specifically includes: Sorting the data in the target standard data change rate sequence group to obtain a target standard data sequence; sorting the data in the change rate sequence group of the vehicle flow data to obtain a vehicle flow sequence; Extract the rank of each data in the target standard data sequence and the traffic flow sequence to obtain the rank sequence of the target standard data ={ , , ,···, } and the rank sequence of traffic flow sequence ={ , , ,···, }; According to the rank sequence of the target specification data sequence and the rank sequence of the vehicle flow sequence, the Spearman rank correlation coefficient calculation formula is used to calculate the correlation of the correlation ratio between the target specification data and the vehicle flow data, wherein the Spearman rank correlation coefficient calculation formula is specifically as follows: ； Analyze the change curve of the correlation ratio between each of the real-time sub-information and the traffic flow data, and intercept the correlation curve that shows a linear relationship in the correlation ratio change curve; Selecting any point from the intercepted correlation curve and multiplying the point by its corresponding Spearman rank correlation coefficient as the influence coefficient of the real-time sub-information on the traffic flow data; determining information processing ratios of the plurality of real-time sub-information based on the influence coefficients; extracting data volumes corresponding to the respective multiple types of real-time sub-information from the real-time traffic data according to information processing ratios of the multiple types of real-time sub-information; Using the data amounts corresponding to the plurality of 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 acquisition of real-time traffic data and traffic flow data uploaded by the smart street lights 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 traffic flow data, adjust the frequency with which the smart street light collects the real-time traffic condition data.

3. The method according to claim 1, characterized in that The extracting of the data amounts corresponding to the respective multiple types of real-time sub-information from the real-time traffic data according to the information processing ratios of the multiple types of real-time sub-information is specifically: 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.

4. The method according to claim 3, characterized in that The reliability scores of the plurality of data point information are calculated based on the preset evaluation rules, specifically: Standardizing 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; Cluster analysis is performed on the plurality of standard point data according to the cluster center to obtain reliability scores of the plurality of standard point data.

5. The method according to claim 1, wherein The vehicle flow data is adjusted by using the data amounts corresponding to the various real-time sub-information to generate 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 plurality of real-time sub-information, obtaining adjustment factors of the plurality of real-time sub-information; Calculating a comprehensive adjustment factor based on the adjustment factors and impact 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.

6. 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 flow 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.

7. A smart streetlight escort system used in major events, characterized by: The system is a city streetlight management platform, which includes 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 traffic flow data uploaded by smart street lights on a target road section, wherein the real-time traffic data includes a variety 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, specifically: Normalizing the plurality of real-time sub-information to obtain a plurality of standard data; Calculating a target standard data change rate sequence group and a second vehicle flow data change rate sequence group of the target road section within a preset second time period, wherein the target standard data is any one of the plurality of standard data; 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 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; Based on the plurality of correlation ratios, an influence coefficient of the target specification data on the target road section traffic flow data is calculated; The calculating of the influence coefficient of the target specification data on the target road section traffic flow data based on the plurality of correlation ratios specifically includes: Sorting the data in the target standard data change rate sequence group to obtain a target standard data sequence; sorting the data in the change rate sequence group of the vehicle flow data to obtain a vehicle flow sequence; Extract the rank of each data in the target standard data sequence and the traffic flow sequence to obtain the rank sequence of the target standard data ={ , , ,···, } and the rank sequence of traffic flow sequence ={ , , ,···, }; According to the rank sequence of the target specification data sequence and the rank sequence of the vehicle flow sequence, the Spearman rank correlation coefficient calculation formula is used to calculate the correlation of the correlation ratio between the target specification data and the vehicle flow data, wherein the Spearman rank correlation coefficient calculation formula is specifically as follows: ； Analyze the change curve of the correlation ratio between each of the real-time sub-information and the traffic flow data, and intercept the correlation curve that shows a linear relationship in the correlation ratio change curve; Selecting any point from the intercepted correlation curve and multiplying the point by its corresponding Spearman rank correlation coefficient as the influence coefficient of the real-time sub-information on the traffic flow data; determining information processing ratios of the plurality of real-time sub-information based on the influence coefficients; extracting data volumes corresponding to the respective multiple types of real-time sub-information from the real-time traffic data according to information processing ratios of the multiple types of real-time sub-information; Using the data amounts corresponding to the plurality of real-time sub-information, the vehicle flow data is adjusted to 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 of the target road section to guide the travel route of the vehicles on the target road section.

8. 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 6.

9. 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 6 is performed.

Images