Urban intelligent street lamp group intelligent operation and maintenance management method and system based on big data analysis

Abstract

The invention discloses an urban intelligent street lamp group intelligent operation and maintenance management method and system based on big data analysis. The method comprises the steps of obtaining and performing group classification on intelligent street lamps of each road based on urban function division information to obtain a plurality of street lamp groups; obtaining information of each street lamp group to construct a decision model, and producing a preliminary operation and maintenance strategy of each street lamp group; obtaining traffic flow prediction information corresponding to a road where each street lamp group is located based on historical traffic flow information; obtaining meteorological prediction information corresponding to the road where each street lamp group is located based on the historical meteorological information; inputting the traffic flow prediction information and the weather prediction information into a decision model, and optimizing the preliminary operation and maintenance strategy through a multi-target optimization algorithm of a preset value to obtain a target operation and maintenance strategy; the intelligent street lamp group is regulated and controlled according to the real-time traffic data and the weather information, more accurate and dynamic urban intelligent street lamp group management is achieved, and the method has the effect of accurate brightness regulation and control with dynamic adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of smart street lamps, and specifically to an intelligent operation and maintenance management method and system for urban smart street lamp groups based on big data analysis. Background Art

[0002] Smart street lights are modern lighting facilities that integrate advanced information technology, Internet of Things technology, sensors, artificial intelligence and other technologies. Unlike traditional street lights, smart street lights not only provide basic lighting functions, but also can perform automatic adjustment, remote monitoring, environmental perception, data collection, and integration with other urban infrastructure systems; smart street lights are an important part of smart city construction, aiming to improve urban lighting efficiency, reduce energy consumption, enhance public safety, and provide more convenience for urban management and residents.

[0003] The current smart street lights can automatically adjust their brightness to adapt to changes in the surrounding environment. For example, in areas without traffic at night, street lights can reduce their brightness to save energy; while in sections with heavy traffic, street lights will automatically increase their brightness to ensure road safety.

[0004] Although smart street lights can automatically adjust brightness, they often rely on fixed control strategies, such as triggering mechanisms based on time or ambient light, and fail to make full use of real-time traffic and meteorological data to dynamically adjust the lighting intensity and switch status of street lights;

[0005] The above management approach lacks dynamic adaptability and may lead to:

[0006] During rush hour, if street lamps cannot enhance lighting according to real-time traffic data, the sight of drivers and pedestrians will be seriously affected, especially in severe weather conditions such as rain, snow, fog and haze, when visibility is already reduced, and insufficient lighting may further aggravate traffic safety risks and increase the probability of traffic accidents. At the same time, this management method that lacks dynamic adaptability will also cause energy waste during non-peak hours. When there are few vehicles, street lamps may still remain in a high-brightness state, which not only consumes a lot of unnecessary energy, but also may cause light pollution and affect the quality of life of surrounding residents. Therefore, there is an urgent need for an intelligent operation and maintenance management method and system for urban smart street lamp groups based on big data analysis to achieve precise brightness control of smart street lamps. Summary of the invention

[0007] In order to solve the problem mentioned in the above background technology caused by the inaccurate brightness control of smart street lamps, the present invention provides an intelligent operation and maintenance management method and system for urban smart street lamp groups based on big data analysis.

[0008] The above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0009] An intelligent operation and maintenance management method for urban smart street lamp groups based on big data analysis, comprising the following steps:

[0010] Obtain urban functional zoning information, and classify the smart street lights on each road into several street light groups based on the urban functional zoning information, and obtain historical traffic flow information and historical meteorological information of the roads associated with each street light group;

[0011] Obtain status information of each street light group and public feedback information, build a decision model based on urban functional zoning information, historical traffic flow information, historical meteorological information, status information and public feedback information, and generate preliminary operation and maintenance strategies based on each street light group through the decision model;

[0012] Inputting historical traffic flow information into the trained first prediction model to obtain traffic flow prediction information corresponding to the road where each street lamp group is located output by the first prediction model;

[0013] Inputting historical meteorological information into the trained second prediction model, obtaining meteorological prediction information corresponding to the road where each street lamp group is located output by the second prediction model;

[0014] The traffic flow forecast information and weather forecast information are input into the decision-making model, and the preliminary operation and maintenance strategy is optimized through a preset multi-objective optimization algorithm to obtain the target operation and maintenance strategy.

[0015] By adopting the above scheme, the city functional zoning information is obtained, and the smart street lights on each road are grouped and classified based on the city functional zoning information to obtain several street light groups, the historical traffic flow information and historical meteorological information of the associated roads corresponding to each street light group are obtained, and the status information and public feedback information of each street light group are obtained, a decision model is constructed based on the city functional zoning information, historical traffic flow information, historical meteorological information, status information and public feedback information, and a preliminary operation and maintenance strategy based on each street light group is generated through the decision model, the historical traffic flow information is input into the trained first prediction model, and the traffic flow prediction information corresponding to the roads where each street light group is located is obtained, and the historical meteorological information is input into the trained second prediction model, and the meteorological prediction information corresponding to the roads where each street light group is located is obtained, including the visibility changes of rainy days, haze days, sunny days, etc. The vehicle flow forecast information and the weather forecast information are input into the decision-making model based on the vehicle flow forecast information and the weather forecast information, so that the decision-making model optimizes the preliminary operation and maintenance strategy with a preset multi-objective optimization algorithm to obtain the target operation and maintenance strategy; the present application generates a preliminary operation and maintenance strategy by combining urban functional zoning information, historical vehicle flow information, historical weather information, status information and public feedback information, obtains the corresponding forecast information by using the forecast model, and integrates the forecast information into the preliminary operation and maintenance strategy through a multi-objective optimization algorithm to optimize the target operation and maintenance strategy, thereby achieving more accurate and dynamic management of urban smart street light groups, and dynamically and adaptively adjusting the smart street light groups based on real-time traffic data and weather information, thereby ensuring accurate regulation of brightness and improving the energy efficiency and adaptability of smart street light groups.

[0016] In a preferred example, the present application can be further configured as follows: the steps of obtaining urban functional zoning information, classifying the smart street lamps on each road into groups based on the urban functional zoning information to obtain a number of street lamp groups, and obtaining the historical traffic flow information and historical meteorological information of the roads corresponding to each street lamp group include the following steps:

[0017] Obtain the geographic location information of the smart street lights on each road, and divide the smart street lights based on K-means clustering and geographic location information to obtain several preliminary street light groups;

[0018] The preliminary street lamp groups are classified into groups based on the urban functional zoning information to obtain several street lamp groups.

[0019] By adopting the above technical solution, the geographical location information of the smart street lights on each road is obtained, and the smart street lights are divided based on K-means clustering and geographical location information to obtain several preliminary street light groups. Based on urban functional zoning information such as commercial areas, industrial areas and residential areas, the preliminary street light groups are classified and optimized to obtain several street light groups. Through preliminary division based on geographical location information and group classification optimization based on urban functional zoning information, the division of each street light group is refined to improve the accuracy and rapid responsiveness of controlling smart street lights.

[0020] In a preferred example, the present application can be further configured as follows: the steps of obtaining status information and public feedback information of each street light group, building a decision model based on urban functional zoning information, historical traffic flow information, historical meteorological information, status information and public feedback information, and generating a preliminary operation and maintenance strategy based on each street light group through the decision model include the following steps:

[0021] Obtain public feedback information, and match the corresponding street lamp group based on a preset identifier associated with the public feedback information;

[0022] Use pre-set natural language processing rules to perform sentiment analysis and semantic understanding on public feedback information, thereby extracting feedback features;

[0023] A decision-making model is constructed based on urban functional zoning information, historical traffic flow information, historical meteorological information, status information and feedback characteristics.

[0024] By adopting the above technical solution, public feedback information is obtained, and the corresponding street light group is matched through its associated preset identifier. The preset natural language processing technology is used to conduct in-depth sentiment analysis and semantic understanding of the public feedback information, so as to identify the public's specific opinions on the smart street light group, and capture the emotional tendencies in the public feedback, and then extract key feedback features. A decision model is constructed based on urban functional zoning information, historical traffic flow information, historical meteorological information, status information and feedback features, and the information association between each input information and the corresponding street light group is completed, so that the constructed decision model can accurately generate corresponding decisions for each street light group.

[0025] In a preferred example, the present application can be further configured as follows: the step of inputting the traffic flow prediction information and the weather prediction information into the decision model, and optimizing the preliminary operation and maintenance strategy through a preset multi-objective optimization algorithm to obtain the target operation and maintenance strategy includes the following steps:

[0026] Setting a first threshold and a second threshold for traffic flow prediction information in a decision model based on the city functional zoning information and the historical traffic flow information;

[0027] Using a decision model, based on a first threshold and a second threshold, each streetlight group is divided into groups according to corresponding traffic flow prediction information, and based on the group division result, a first basic control strategy corresponding to each streetlight group is generated;

[0028] The input weather forecast information is extracted as weather forecast features through the decision model, and a second basic control strategy corresponding to each street lamp group is generated based on the weather forecast features;

[0029] The first basic control strategy and the second basic control strategy are integrated into the preliminary operation and maintenance strategy through the decision model based on a preset multi-objective optimization algorithm to obtain the target operation and maintenance strategy.

[0030] By adopting the above technical scheme, a first threshold and a second threshold about the traffic flow prediction information are set in the decision model based on the urban functional zoning information and the historical traffic flow information, and each street light group is divided into groups according to the corresponding traffic flow prediction information based on the first threshold and the second threshold through the decision model, and a first basic control strategy corresponding to each street light group is generated based on the result of the group division, and the input meteorological forecast information is extracted as meteorological forecast features through the decision model, and a second basic control strategy corresponding to each street light group is generated based on the meteorological forecast features, and the first basic control strategy and the second basic control strategy are integrated into the preliminary operation and maintenance strategy based on a preset multi-objective optimization algorithm through the decision model to obtain a target operation and maintenance strategy, and the preliminary operation and maintenance strategy is integrated and optimized according to the corresponding traffic flow prediction information and meteorological forecast information associated with each street light group, so as to ensure that the formulated operation and maintenance strategy is not only in line with reality, but also can foresee and adapt to future changes in demand, thereby achieving effective optimization of the preliminary operation and maintenance strategy to make it more adaptable to lighting management needs.

[0031] In a preferred example, the present application can be further configured as follows: the first basic control strategy includes a real-time light contribution control strategy, a vehicle speed prediction control strategy, and a real-time vehicle flow information weight control strategy; the decision model is used to group the vehicle flow prediction information corresponding to each street light group based on the first threshold and the second threshold, and the step of generating the first basic control strategy corresponding to each street light group preset based on the grouping result includes the steps of:

[0032] The traffic flow prediction information greater than the first threshold is set as the real-time control group, the traffic flow prediction information less than the second threshold is set as the speed control group, and the traffic flow prediction information between the first threshold and the second threshold is set as the weight control group;

[0033] Generate a real-time light contribution control strategy for the street light group associated with the real-time control group;

[0034] Generate a vehicle speed prediction control strategy for the street light group associated with the vehicle speed control group;

[0035] A real-time traffic flow information weight control strategy is generated for the street light group associated with the weight control group.

[0036] By adopting the above technical scheme, the traffic flow prediction information greater than the first threshold is set as the real-time control group, and the real-time light contribution control strategy is generated for the street light group associated with the real-time control group; the traffic flow prediction information less than the second threshold is set as the vehicle speed control group, and the vehicle speed prediction control strategy is generated for the street light group associated with the vehicle speed control group; the traffic flow prediction information between the first threshold and the second threshold is set as the weight control group, and the real-time traffic flow information weight control strategy is generated for the street light group associated with the weight control group; according to the difference of the traffic flow prediction information, the corresponding control strategy is customized for each related road, so as to realize the refined distinction and adaptive control of various scenarios, and ensure that the appropriate control strategy can be adopted in different situations.

[0037] In a preferred example, the present application can be further configured as follows: the step of generating a real-time light contribution control strategy for a smart street lamp group associated with a real-time control group includes the following steps:

[0038] The decision model sets the rated brightness contribution value based on urban functional zoning information, status information and public feedback information;

[0039] The photosensitive sensors arranged at intervals obtain the total brightness value of the associated roads in real time, where the total brightness value is the sum of the current vehicle light brightness contribution value and the current street light brightness contribution value;

[0040] Calculate the difference between the total brightness value and the rated brightness contribution value, the rate of change of the difference, and the cumulative value of the difference within a preset time range;

[0041] The PID control algorithm is used to adjust the current street lamp brightness contribution value according to the calculated difference, difference change rate and difference cumulative value.

[0042] By adopting the above technical solution, the decision model sets the rated brightness contribution value based on the city's functional zoning information, status information and public feedback information, and obtains the total brightness value of the associated road in real time based on the photosensitive sensors set at intervals. The total brightness value is the sum of the current vehicle light brightness contribution value and the current street light brightness contribution value. The difference between the total brightness value and the rated brightness contribution value, the rate of change of the difference and the cumulative value of the difference within a preset time range are calculated. The current street light brightness contribution value is adjusted according to the calculated difference, the rate of change of the difference and the cumulative value of the difference through the PID control algorithm. According to the real-time monitoring results of the total brightness value of the road, the current street light brightness contribution value is dynamically adjusted, thereby realizing the precise implementation of the light contribution control strategy for the street light group in the real-time control group.

[0043] In a preferred example, the present application can be further configured as follows: the decision model includes an RNN model, and the step of generating a vehicle speed prediction and control strategy for a smart street lamp group associated with a vehicle speed control group includes the steps of:

[0044] The decision model sets the basic brightness value based on the city functional zoning information, historical traffic flow information and status information;

[0045] Based on the traffic sensors arranged at intervals, the vehicle traffic information on the associated roads is obtained in real time, wherein the vehicle traffic information includes vehicle type information, real-time traffic flow, vehicle spacing and traffic speed information;

[0046] The RNN model takes vehicle type information, real-time traffic flow, vehicle spacing, and speed information as input and outputs the predicted vehicle speed.

[0047] Calculate the safe distance based on the traffic speed information, predicted speed and vehicle type information;

[0048] Dynamically adjust the base brightness value based on the safe distance.

[0049] By adopting the above technical solution, the decision model sets the basic brightness value based on the urban functional zoning information, historical traffic flow information and status information, and obtains the vehicle traffic information in the associated road in real time based on the traffic sensors set at intervals. The vehicle traffic information includes vehicle type information, real-time traffic flow, vehicle spacing and traffic speed information. The RNN model uses the vehicle type information, real-time traffic flow, vehicle spacing and traffic speed information as input, outputs the predicted vehicle speed, and calculates the safety distance based on the traffic speed information, the predicted vehicle speed and the vehicle type information. The basic brightness value is dynamically adjusted based on the calculated safety distance, the required safety distance is calculated using the vehicle traffic information, and the basic brightness value is dynamically adjusted accordingly, so as to effectively control the basic brightness value based on the speed prediction for the street light group associated with the speed control group.

[0050] In a preferred example, the present application can be further configured as follows: the step of generating a real-time vehicle flow information weight control strategy for a smart street lamp group associated with a weight control group includes the following steps:

[0051] Traffic sensors set at intervals obtain real-time traffic flow on the associated roads;

[0052] The decision-making model sets the weight ratio of real-time traffic flow information and street lamp brightness based on urban functional zoning information, historical traffic flow information, status information and public feedback information;

[0053] Match the acquired real-time traffic flow with the preset weight ratio level;

[0054] The brightness of the street lamps in the street lamp group is adjusted according to the matched weight ratio level.

[0055] By adopting the above technical solution, the real-time traffic flow in the associated roads is obtained in real time based on the traffic sensors set at intervals. The decision model sets the weight ratio level of real-time traffic flow information and street lamp brightness based on urban functional zoning information, historical traffic flow information, status information and public feedback information. The obtained real-time traffic flow is matched with the preset weight ratio level, and the brightness of the street lamps of the street lamp group is adjusted according to the matched weight ratio level. The real-time traffic flow is matched with the preset weight ratio level, and the brightness setting of the street lamp group is adjusted according to the obtained weight ratio. Dynamic weight control based on real-time traffic flow information is customized for the street lamp group under the weight control group, thereby improving the accuracy and efficiency of brightness control of the smart street lamp group.

[0056] The second object of the invention is achieved by the following technical solutions:

[0057] An intelligent operation and maintenance management system for urban smart street lamp groups based on big data analysis, comprising:

[0058] The group classification module obtains the city functional zoning information, and classifies the smart street lights on each road into several groups based on the city functional zoning information, and obtains the historical traffic flow information and historical meteorological information of the roads associated with each street light group;

[0059] The strategy generation module obtains the status information of each street light group and public feedback information, builds a decision model based on urban functional zoning information, historical traffic flow information, historical meteorological information, status information and public feedback information, and generates preliminary operation and maintenance strategies based on each street light group through the decision model;

[0060] The vehicle flow prediction module inputs the historical vehicle flow information into the trained first prediction model to obtain the vehicle flow prediction information corresponding to the road where each street lamp group is located output by the first prediction model;

[0061] The weather forecast module inputs the historical weather information into the trained second forecast model to obtain the weather forecast information corresponding to the road where each street lamp group is located output by the second forecast model;

[0062] The strategy optimization module inputs the traffic flow forecast information and weather forecast information into the decision-making model, and optimizes the preliminary operation and maintenance strategy through a preset multi-objective optimization algorithm to obtain the target operation and maintenance strategy.

[0063] By adopting the above technical solution, the group classification module is used to obtain urban functional zoning information, and based on the urban functional zoning information, the smart street lights on each road are grouped to obtain a number of street light groups, and the historical traffic flow information and historical meteorological information of the associated roads corresponding to each street light group are obtained; the strategy generation module is used to obtain the status information and public feedback information of each street light group, and build a decision model based on the urban functional zoning information, historical traffic flow information, historical meteorological information, status information and public feedback information, and generate preliminary operation and maintenance strategies based on each street light group through the decision model; the traffic flow prediction module is used to input the historical traffic flow information into the trained first prediction model, and obtain the traffic flow prediction information corresponding to the roads where each street light group is located output by the first prediction model; the meteorological prediction module is used to input the historical meteorological information into the trained second prediction model, and obtain the meteorological prediction information corresponding to the roads where each street light group is located output by the second prediction model; the strategy optimization module is used to input the traffic flow prediction information and the meteorological prediction information into the decision model, and optimize the preliminary operation and maintenance strategy through a preset multi-objective optimization algorithm to obtain the target operation and maintenance strategy.

[0064] In summary, the present application provides an intelligent operation and maintenance management method and system for urban smart street lamp groups based on big data analysis, which includes at least one of the following beneficial technical effects:

[0065] 1. This application obtains several street light groups by classifying the smart street lights on each road based on urban functional zoning information, constructs a decision-making model based on urban functional zoning information, historical traffic flow information, historical meteorological information, status information and public feedback information, and generates preliminary operation and maintenance strategies based on each street light group through the decision-making model. By managing the street light groups into groups, it is possible to generate customized preliminary operation and maintenance strategies for street light groups in different functional areas and with different needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flow chart of an embodiment of an intelligent operation and maintenance management method for a city smart street lamp group based on big data analysis in this application;

[0067] Figure 2 This is a flowchart for implementing step S20 in an embodiment of an intelligent operation and maintenance management method for a group of urban smart street lamps based on big data analysis in this application;

[0068] Figure 3 This is a flowchart for implementing step S50 in an embodiment of an intelligent operation and maintenance management method for a group of smart urban street lamps based on big data analysis in this application;

[0069] Figure 4This is a flowchart for implementing step S52 in an embodiment of an intelligent operation and maintenance management method for a group of smart urban street lamps based on big data analysis in this application;

[0070] Figure 5 This is a flowchart for implementing step S522 in an embodiment of an intelligent operation and maintenance management method for a city smart street lamp group based on big data analysis in the present application. DETAILED DESCRIPTION

[0071] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] In one embodiment, if Figure 1 As shown, the present application discloses an intelligent operation and maintenance management method for urban smart street lamp groups based on big data analysis, which specifically includes the following steps:

[0073] S10: Obtaining urban functional zoning information, and classifying the smart street lamps on each road into several street lamp groups based on the urban functional zoning information, and obtaining historical traffic flow information and historical meteorological information of the roads associated with each street lamp group;

[0074] In this embodiment, the smart street light is a street light system that integrates multiple technologies such as intelligent perception, intelligent control, and network communication. The urban functional zoning information is relevant information based on the division of different areas in the city, such as planned commercial areas, industrial areas, and residential areas. The group classification is the classification of smart street lights according to their corresponding geographical location information and urban functional zoning information. The historical traffic flow information is the record information of vehicle traffic conditions on related roads in the past period of time. The historical meteorological information is the record information of weather conditions on related roads in the past period of time.

[0075] Specifically, the city functional zoning information is obtained, and the smart street lights are grouped based on the geographical location information and the city functional zoning information corresponding to the smart street lights to obtain several street light groups, and the records of vehicle traffic and weather conditions on the associated roads corresponding to each street light group in the past period of time are obtained.

[0076] S20: Obtain status information of each street light group and public feedback information, build a decision model based on urban functional zoning information, historical traffic flow information, historical meteorological information, status information and public feedback information, and generate preliminary operation and maintenance strategies based on each street light group through the decision model;

[0077] In this embodiment, the status information refers to data and parameters related to the operation of the street light group, including switch status, status information and light output intensity, etc. The public feedback information refers to the public's evaluation, complaints and suggestions on a specific street light group. The decision model is a mathematical model based on big data analysis and machine learning technology, which is used to integrate and process various data sources such as urban functional zoning information, historical traffic flow information, historical meteorological information, street light group status information and public feedback information to generate a preliminary operation and maintenance strategy for the street light group. The preliminary operation and maintenance strategy is a preliminary operation and maintenance management plan for the street light group formulated by the decision model based on the input information.

[0078] Specifically, the operation-related data and parameters of each street light group are obtained, as well as the public's evaluation, complaints and suggestions on each specific street light group. Using each piece of information as input, a decision model is constructed that can systematically analyze and evaluate the possible impact of various information on the operation and maintenance strategy of the street light group, and preliminary operation and maintenance strategies for each street light group are formulated through the decision model.

[0079] S30: inputting historical traffic flow information into the trained first prediction model to obtain traffic flow prediction information corresponding to the road where each street lamp group is located output by the first prediction model;

[0080] In this embodiment, the first prediction model is a mathematical model that uses historical information to identify potential patterns and trends, and the traffic flow prediction information is the prediction of vehicle traffic on the associated roads in the future period of time.

[0081] Specifically, the historical vehicle flow information is input into the trained first prediction model, so as to obtain the vehicle flow prediction information corresponding to the road where each street lamp group is located output by the first prediction model.

[0082] S40: inputting historical meteorological information into the trained second prediction model to obtain meteorological prediction information corresponding to the road where each street lamp group is located output by the second prediction model;

[0083] In this embodiment, the second prediction model is a mathematical model that uses historical information to identify potential patterns and trends, and the weather forecast information is the forecast of weather changes on the associated roads in the future.

[0084] Specifically, the historical meteorological information is input into the trained second prediction model, so as to obtain the meteorological prediction information corresponding to the road where each street lamp group is located output by the second prediction model.

[0085] S50: inputting the traffic flow prediction information and the weather prediction information into the decision model, and optimizing the preliminary operation and maintenance strategy through a preset multi-objective optimization algorithm to obtain a target operation and maintenance strategy;

[0086] In this embodiment, the multi-objective optimization algorithm is an algorithm that repeatedly generates new candidate solutions in the search space, evaluates and updates the solution set according to the requirements of multiple information indicators, and finally finds a set of optimal target operation and maintenance strategies. The target operation and maintenance strategy is the control strategy corresponding to each road street lamp group obtained after the preliminary operation and maintenance strategy is optimized by the multi-objective optimization algorithm.

[0087] Specifically, the traffic flow prediction information and the weather forecast information are input into the decision-making model, so that the decision-making model optimizes the preliminary operation and maintenance strategy based on the traffic flow prediction information and the weather forecast information with a preset multi-objective optimization algorithm to obtain the target operation and maintenance strategy, thereby ensuring the precise control of the brightness and improving the energy efficiency and adaptability of the smart street lamp group.

[0088] In one embodiment, step S10 includes the steps of:

[0089] S11: Obtaining geographic location information of the smart street lights on each road, and dividing the smart street lights based on K-means clustering and geographic location information to obtain several preliminary street light groups;

[0090] S12: Classify the preliminary street lamp groups based on the urban functional zoning information to obtain a number of street lamp groups.

[0091] In this embodiment, the geographic location information is the specific geographic location data of the smart street lamp, including the longitude and latitude, street, area, etc. The K-means clustering is an algorithm for clustering each smart street lamp based on the geographic location information of the smart street lamp to obtain multiple preliminary street lamp groups.

[0092] Specifically, the geographic location information of the smart street lights on each road is obtained, and the smart street lights are divided based on K-means clustering and the corresponding longitude and latitude, streets, regions and other geographic location information to obtain several preliminary street light groups. The preliminary street light groups are grouped based on the urban functional zoning information to obtain several street light groups, thereby ensuring that the smart street lights contained in each street light group can be controlled based on the group, thereby improving the management and control efficiency of the smart street lights.

[0093] In one embodiment, if Figure 2 As shown, step S20 includes:

[0094] S21: Obtain public feedback information, and match the corresponding street lamp group based on a preset identifier associated with the public feedback information;

[0095] S22: Perform sentiment analysis and semantic understanding on public feedback information through pre-set natural language processing rules to extract feedback features;

[0096] S23: Construct a decision model based on urban functional zoning information, historical traffic flow information, historical meteorological information, status information and feedback characteristics;

[0097] In this embodiment, the preset identifier is pre-set identification information used to uniquely identify and associate the corresponding street lamp group. The natural language processing rule is a series of formalized rules, methods and techniques for extracting information from the text, analyzing grammar and semantics, and processing and understanding human language. Sentiment analysis is to identify and extract subjective information in the text and analyze the emotional tendency of the text. Semantic understanding is the computer understanding and interpretation of the meaning and meaning of words, phrases, sentences and paragraphs in natural language text. Feedback features are feature information including lighting intensity, street lamp status, geographical location of the street lamp, etc.

[0098] Specifically, public feedback information is obtained, and the corresponding street light groups are matched based on preset identifiers associated with the public feedback information. Sentiment analysis and semantic understanding are performed on the public feedback information based on preset natural language processing rules to extract feedback features. A decision model is constructed based on urban functional zoning information, historical traffic flow information, historical meteorological information, status information and feedback features. A decision model is constructed through the relevant information of each corresponding street light group, and a corresponding preliminary operation and maintenance strategy is generated for each street light group.

[0099] In one embodiment, if Figure 3 As shown, step S50 includes:

[0100] S51: setting a first threshold and a second threshold for traffic flow prediction information in a decision model based on the city functional zoning information and the historical traffic flow information;

[0101] S52: using a decision model to group each streetlight group according to corresponding traffic flow prediction information based on a first threshold and a second threshold, and generating a first basic control strategy corresponding to each streetlight group based on the grouping result;

[0102] S53: extracting the input weather forecast information as weather forecast features through the decision model, and generating a second basic control strategy corresponding to each street lamp group preset based on the weather forecast features;

[0103] S54: integrating the first basic control strategy and the second basic control strategy into the preliminary operation and maintenance strategy through the decision model based on a preset multi-objective optimization algorithm to obtain a target operation and maintenance strategy;

[0104] In this embodiment, the first threshold and the second threshold are critical values ​​set in the decision model for grouping street light groups according to traffic flow prediction information. The first basic control strategy is a preliminary street light control strategy generated based on the traffic flow prediction information and the grouping results, including but not limited to adjusting parameters such as street light brightness and switching time to adapt to lighting needs under different traffic flow conditions. The second basic control strategy is a preliminary street light control strategy generated based on meteorological forecast characteristics, including but not limited to adjusting street light brightness, switching time or enabling special lighting modes according to weather changes to adapt to lighting needs under different weather conditions.

[0105] Specifically, a first threshold and a second threshold for traffic flow prediction information are set in the decision model based on the urban functional zoning information and the historical traffic flow information, and each street light group is divided into groups according to the corresponding traffic flow prediction information based on the first threshold and the second threshold through the decision model, and a first basic control strategy corresponding to each street light group is generated based on the group division result, and the input meteorological forecast information is extracted as meteorological forecast features through the decision model, and a second basic control strategy corresponding to each street light group is generated based on the meteorological forecast features, and the first basic control strategy and the second basic control strategy are integrated into the preliminary operation and maintenance strategy based on a preset multi-objective optimization algorithm through the decision model to obtain the target operation and maintenance strategy, thereby completing the target operation and maintenance strategy of using different optimization methods for roads with different traffic flow prediction information.

[0106] In one embodiment, the first basic control strategy includes a real-time light contribution control strategy, a vehicle speed prediction control strategy, and a real-time vehicle flow information weight control strategy, such as Figure 4 As shown, step S52 includes:

[0107] S521: setting the traffic flow prediction information greater than the first threshold as the real-time control group, setting the traffic flow prediction information less than the second threshold as the vehicle speed control group, and setting the traffic flow prediction information between the first threshold and the second threshold as the weight control group;

[0108] S522: generating a real-time light contribution control strategy for the street light group associated with the real-time control group;

[0109] S523: generating a vehicle speed prediction control strategy for the street lamp group associated with the vehicle speed control group;

[0110] S524: generating a real-time vehicle flow information weight control strategy for the street light group associated with the weight control group;

[0111] In this embodiment, the real-time light contribution control strategy is an operation and maintenance strategy for controlling the street light group according to the real-time total brightness value of the road corresponding to the street light group. The vehicle speed prediction control strategy is an operation and maintenance strategy for controlling the street light group according to the predicted vehicle speed on the road corresponding to the street light group. The real-time traffic flow information weight control strategy is an operation and maintenance strategy for controlling the street light group according to the weight ratio level corresponding to the real-time traffic flow information.

[0112] Specifically, the traffic flow prediction information greater than the first threshold is set as the real-time control group, the traffic flow prediction information less than the second threshold is set as the vehicle speed control group, and the traffic flow prediction information between the first threshold and the second threshold is set as the weighted control group. A real-time light contribution control strategy is generated for the street light group associated with the real-time control group, a vehicle speed prediction control strategy is generated for the street light group associated with the vehicle speed control group, and a real-time traffic flow information weighted control strategy is generated for the street light group associated with the weighted control group. The street light group under different traffic flow conditions is controlled through different operation and maintenance strategies, and precise control is achieved by grouping.

[0113] Furthermore, when there are abnormal features in the obtained weather forecast information, that is, when it indicates that there will be severe weather that will seriously affect vehicle driving, an emergency control strategy will be adopted: the decision model extracts features from the weather forecast information to obtain weather forecast features, and extracts abnormal features from the weather forecast features based on the preset feature extraction principle; if the extraction result is not zero, the extracted abnormal features are matched with the street light group associated with the corresponding area through the preset identifier; and the street light group is controlled based on the preset emergency operation and maintenance strategy;

[0114] Among them, the feature extraction principle is to extract abnormal features that help identify potential abnormal meteorological patterns or trends from multiple dimensions of meteorological data (such as time, space, meteorological variables, etc.). Abnormal features are meteorological elements that deviate from the normal range, including temperature anomalies, precipitation anomalies, wind speed anomalies, and air pressure anomalies. The emergency operation and maintenance strategy is a strategy for quickly and effectively responding to and solving problems when anomalies occur, that is, to guide traffic on roads associated with abnormal features based on regulating street lamp groups, specifically by dynamically adjusting the brightness or color change of street lamps to guide traffic.

[0115] In one embodiment, if Figure 5 As shown, step S522 includes:

[0116] S5221: The decision model sets the rated brightness contribution value based on urban functional zoning information, status information and public feedback information;

[0117] S5222: acquiring in real time a total brightness value of the associated road based on the photosensitive sensors arranged at intervals, where the total brightness value is the sum of the current vehicle light brightness contribution value and the current street light brightness contribution value;

[0118] S5223: Calculate the difference between the total brightness value and the rated brightness contribution value, the rate of change of the difference, and the cumulative value of the difference within a preset time range;

[0119] S5224: adjusting the current street lamp brightness contribution value according to the calculated difference, difference change rate and difference accumulation value through the PID control algorithm;

[0120] In this embodiment, the rated brightness contribution value is the brightness value that each street light group should provide, which is set by the decision model according to urban functional zoning information, status information and public feedback information. The rated brightness contribution value represents the lighting level that the smart street light group should achieve in different areas, at different times or under different conditions. The current headlight brightness contribution value is the contribution of the headlights of vehicles traveling on the corresponding road to the lighting effect and road lighting distribution. The current street light brightness contribution value is the contribution of the street light group on the corresponding road to the lighting effect and road lighting distribution. The PID control algorithm is a feedback control algorithm for adjusting the output.

[0121] Specifically, the decision model sets the rated brightness contribution value based on urban functional zoning information, status information and public feedback information, and obtains the total brightness value of the associated road in real time based on the photosensitive sensors set at intervals. The total brightness value is the sum of the current vehicle light brightness contribution value and the current street light brightness contribution value. The difference between the total brightness value and the rated brightness contribution value, the rate of change of the difference and the cumulative value of the difference within a preset time range are calculated, and the current street light brightness contribution value is adjusted according to the calculated difference, the rate of change of the difference and the cumulative value of the difference through the PID control algorithm, thereby completing the generation of a real-time lighting contribution control strategy for the smart street light group associated with the implementation control group.

[0122] In one embodiment, step S523 includes:

[0123] S5231: The decision model sets a basic brightness value based on urban functional zoning information, historical traffic flow information, and status information;

[0124] S5232: acquiring vehicle traffic information on the associated road in real time based on the traffic sensors arranged at intervals, wherein the vehicle traffic information includes vehicle type information, real-time traffic flow, vehicle spacing, and traffic speed information;

[0125] S5233: using the RNN model to take vehicle type information, real-time traffic flow, vehicle spacing, and traffic speed information as input, and outputting predicted vehicle speed;

[0126] S5234: Calculate the safety distance based on the traffic speed information, the predicted vehicle speed and the vehicle type information;

[0127] S5235: Dynamically adjust the base brightness value based on the safety distance.

[0128] In this embodiment, the basic brightness value is the basic lighting brightness reference value initially set by the decision model. The traffic sensor is a device for real-time monitoring of traffic conditions on the road. It can be installed on or near the road to capture vehicle traffic information, including vehicle type, real-time traffic flow, vehicle spacing, and travel speed. Vehicle type information is information about the type of vehicles traveling on the road, such as cars, trucks, buses, motorcycles, etc. Different types of vehicles may have different requirements for road lighting. For example, large trucks may require higher lighting brightness to ensure the driver's clear vision. The vehicle spacing is the interval between two adjacent vehicles on the road. The travel speed information is the current travel speed information of the vehicle on the road. The predicted speed is the predicted travel speed of the vehicle in the future. The safety distance is the minimum distance that should be maintained between vehicles traveling on the road.

[0129] Specifically, the decision model sets a basic brightness value based on urban functional zoning information, historical traffic flow information and status information, and obtains vehicle traffic information in associated roads in real time based on traffic sensors set at intervals. The vehicle traffic information includes vehicle type information, real-time traffic flow, vehicle spacing and travel speed information. The RNN model uses vehicle type information, real-time traffic flow, vehicle spacing and travel speed information as input, outputs predicted vehicle speed, and calculates safety distance based on travel speed information, predicted speed and vehicle type information. The basic brightness value is dynamically adjusted based on the safety distance, thereby completing the control of the street light group in front of the vehicle according to the predicted vehicle speed, so that the driver can obtain sufficient road conditions and leave a safe distance to react.

[0130] In one embodiment, step S523 includes:

[0131] S5241: obtaining the real-time traffic flow in the associated road in real time based on the traffic sensors set at intervals;

[0132] S5242: The decision model sets the weight ratio level of real-time traffic flow information and street lamp brightness based on urban functional zoning information, historical traffic flow information, status information and public feedback information;

[0133] S5243: Matching the acquired real-time traffic flow with the preset weight ratio level;

[0134] S5244: Adjust the brightness of the street lamps in the street lamp group according to the weight ratio level obtained by matching.

[0135] In this embodiment, the real-time traffic flow is the number of vehicles passing through the associated roads within a specific time period, and the weight ratio level is a parameter set by the decision model based on various information (such as urban functional zoning information, historical traffic flow information, status information, and public feedback information) to determine the relationship between real-time traffic flow information and street lamp brightness.

[0136] Specifically, based on the traffic sensors set at intervals, the real-time traffic flow in the associated roads is obtained in real time. The decision-making model sets the weight ratio level of the real-time traffic flow information and the street lamp brightness based on the urban functional zoning information, historical traffic flow information, status information and public feedback information. The obtained real-time traffic flow is matched with the preset weight ratio level, and the brightness of the street lamps in the street lamp group is adjusted according to the matched weight ratio level, so as to generate the corresponding control strategy for the smart street lamp group associated with the weight control group.

[0137] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0138] In one embodiment, an intelligent operation and maintenance management system for a city smart street light group based on big data analysis is provided. The intelligent operation and maintenance management system for a city smart street light group based on big data analysis corresponds one-to-one to an intelligent operation and maintenance management method for a city smart street light group based on big data analysis in the above-mentioned embodiment.

[0139] The present invention and its embodiments are described above, which is not restrictive. What is shown in the accompanying drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in this field are inspired by it and do not deviate from the purpose of the invention, they can creatively design structural methods and embodiments similar to the technical scheme, which should all fall within the scope of protection of the present invention.

Claims

1. An intelligent operation and maintenance management method for urban smart street lamp groups based on big data analysis, characterized in that: Includes steps: Obtain urban functional zoning information, and classify the smart street lights on each road into several street light groups based on the urban functional zoning information, and obtain historical traffic flow information and historical meteorological information of the roads associated with each street light group; Obtain status information of each street light group and public feedback information, build a decision model based on urban functional zoning information, historical traffic flow information, historical meteorological information, status information and public feedback information, and generate preliminary operation and maintenance strategies based on each street light group through the decision model; Inputting historical traffic flow information into the trained first prediction model to obtain traffic flow prediction information corresponding to the road where each street lamp group is located output by the first prediction model; Inputting historical meteorological information into the trained second prediction model, obtaining meteorological prediction information corresponding to the road where each street lamp group is located output by the second prediction model; The traffic flow forecast information and weather forecast information are input into the decision-making model, and the preliminary operation and maintenance strategy is optimized through a preset multi-objective optimization algorithm to obtain the target operation and maintenance strategy.

2. According to claim 1, a method for intelligent operation and maintenance management of urban smart street lamp groups based on big data analysis is characterized in that: The step of obtaining the city functional zoning information, classifying the smart street lamps on each road into groups based on the city functional zoning information to obtain a number of street lamp groups, and obtaining the historical traffic flow information and historical meteorological information of the roads associated with each street lamp group includes the following steps: Obtain the geographic location information of the smart street lights on each road, and divide the smart street lights based on K-means clustering and geographic location information to obtain several preliminary street light groups; The preliminary street lamp groups are classified into groups based on the urban functional zoning information to obtain several street lamp groups.

3. According to claim 1, the intelligent operation and maintenance management method of urban smart street lamp groups based on big data analysis is characterized by: The steps of obtaining status information of each street light group and public feedback information, building a decision model based on urban functional zoning information, historical traffic flow information, historical meteorological information, status information and public feedback information, and generating a preliminary operation and maintenance strategy based on each street light group through the decision model include the following steps: Obtain public feedback information, and match the corresponding street lamp group based on a preset identifier associated with the public feedback information; Use pre-set natural language processing rules to perform sentiment analysis and semantic understanding on public feedback information, thereby extracting feedback features; A decision-making model is constructed based on urban functional zoning information, historical traffic flow information, historical meteorological information, status information and feedback characteristics.

4. According to claim 1, the intelligent operation and maintenance management method of urban smart street lamp groups based on big data analysis is characterized by: The step of inputting the traffic flow prediction information and the weather prediction information into the decision model, and optimizing the preliminary operation and maintenance strategy through a preset multi-objective optimization algorithm to obtain the target operation and maintenance strategy includes the following steps: Setting a first threshold and a second threshold for traffic flow prediction information in a decision model based on the city functional zoning information and the historical traffic flow information; Using the decision model, the streetlight groups are divided into groups according to the corresponding traffic flow prediction information based on the first threshold and the second threshold, and the first basic control strategy corresponding to the preset streetlight groups is generated based on the group division result; The input weather forecast information is extracted as weather forecast features through the decision model, and a second basic control strategy corresponding to each street lamp group is generated based on the weather forecast features; The first basic control strategy and the second basic control strategy are integrated into the preliminary operation and maintenance strategy through the decision model based on a preset multi-objective optimization algorithm to obtain the target operation and maintenance strategy.

5. According to claim 4, a method for intelligent operation and maintenance management of urban smart street lamp groups based on big data analysis is characterized in that: The first basic control strategy includes a real-time light contribution control strategy, a vehicle speed prediction control strategy, and a real-time vehicle flow information weight control strategy. The step of grouping the vehicle flow prediction information corresponding to each street light group based on the first threshold and the second threshold by using the decision model, and generating the first basic control strategy corresponding to each street light group preset based on the grouping result, includes the steps of: The traffic flow prediction information greater than the first threshold is set as the real-time control group, the traffic flow prediction information less than the second threshold is set as the speed control group, and the traffic flow prediction information between the first threshold and the second threshold is set as the weight control group; Generate a real-time light contribution control strategy for the street light group associated with the real-time control group; Generate a vehicle speed prediction control strategy for the street light group associated with the vehicle speed control group; Generate a real-time traffic flow information weight control strategy for the street light group associated with the weight control group.

6. The intelligent operation and maintenance management method of urban smart street lamp groups based on big data analysis according to claim 5 is characterized by: The step of generating a real-time light contribution control strategy for a smart street lamp group associated with a real-time control group comprises the following steps: The decision model sets the rated brightness contribution value based on urban functional zoning information, status information and public feedback information; The photosensitive sensors set at intervals obtain the total brightness value of the associated roads in real time, where the total brightness value is the sum of the current vehicle light brightness contribution value and the current street light brightness contribution value; Calculate the difference between the total brightness value and the rated brightness contribution value, the rate of change of the difference, and the cumulative value of the difference within a preset time range; The PID control algorithm is used to adjust the current street lamp brightness contribution value according to the calculated difference, difference change rate and difference cumulative value.

7. According to claim 5, a method for intelligent operation and maintenance management of urban smart street lamp groups based on big data analysis is characterized in that: The decision model includes an RNN model, and the step of generating a vehicle speed prediction and control strategy for a smart street lamp group associated with a vehicle speed control group includes the following steps: The decision model sets the basic brightness value based on the city functional zoning information, historical traffic flow information and status information; Based on the traffic sensors arranged at intervals, the vehicle traffic information on the associated roads is obtained in real time, wherein the vehicle traffic information includes vehicle type information, real-time traffic flow, vehicle spacing and traffic speed information; The RNN model takes vehicle type information, real-time traffic flow, vehicle spacing, and speed information as input and outputs the predicted vehicle speed. Calculate the safe distance based on the traffic speed information, predicted speed and vehicle type information; Dynamically adjust the base brightness value based on the safe distance.

8. The intelligent operation and maintenance management method of urban smart street lamp groups based on big data analysis according to claim 5 is characterized by: The step of generating a real-time vehicle flow information weight control strategy for the smart street lamp group associated with the weight control group includes the following steps: Traffic sensors set at intervals obtain real-time traffic flow on the associated roads; The decision-making model sets the weight ratio level of real-time traffic flow information and street lamp brightness based on urban functional zoning information, historical traffic flow information, status information and public feedback information; Match the acquired real-time traffic flow with the preset weight ratio level; The brightness of the street lamps in the street lamp group is adjusted according to the matched weight ratio level.

9. An intelligent operation and maintenance management system for urban smart street lamp groups based on big data analysis, characterized in that: include: The group classification module obtains the city functional zoning information, and classifies the smart street lights on each road into several groups based on the city functional zoning information, and obtains the historical traffic flow information and historical meteorological information of the roads associated with each street light group; The strategy generation module obtains the status information of each street light group and public feedback information, builds a decision model based on urban functional zoning information, historical traffic flow information, historical meteorological information, status information and public feedback information, and generates preliminary operation and maintenance strategies based on each street light group through the decision model; The vehicle flow prediction module inputs the historical vehicle flow information into the trained first prediction model to obtain the vehicle flow prediction information corresponding to the road where each street lamp group is located output by the first prediction model; The weather forecast module inputs the historical weather information into the trained second forecast model to obtain the weather forecast information corresponding to the road where each street lamp group is located output by the second forecast model; The strategy optimization module inputs the traffic flow forecast information and weather forecast information into the decision-making model, and optimizes the preliminary operation and maintenance strategy through a preset multi-objective optimization algorithm to obtain the target operation and maintenance strategy.

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