Intelligent urban lighting energy-saving monitoring management system based on Internet of Things technology

Abstract

The invention discloses an intelligent urban lighting energy-saving monitoring management system based on the Internet of Things technology, and relates to the technical field of urban lighting adjustment, and the system comprises a work log module, an associated lighting lamp group module, a traffic flow prediction module, a period adjustment module, and a real-time adjustment module. The associated illuminating lamp group module is used for acquiring log data of a certain illuminating lamp group, acquiring traffic flow in a certain historical adjustment record, determining a characteristic adjustment record, identifying adjacent lamp groups of the illuminating lamp group, calculating an average traffic flow change rate of a certain adjacent lamp group, and determining a characteristic lamp group; and the real-time adjusting module is used for collecting real-time data of a certain illuminating lamp group, calculating a real-time adjusting protocol score, calculating a real-time adjusting score, identifying the adjusting lamp group according to the real-time adjusting score, and calculating a real-time adjusting period of the adjusting lamp group so as to realize refined energy-saving management.

Description

Technical Field

[0001] The present invention relates to the technical field of urban lighting regulation, and in particular to an intelligent urban lighting energy-saving monitoring and management system based on the Internet of Things technology. Background Art

[0002] With the advancement of urbanization, energy consumption of urban lighting systems has become one of the main energy expenses. Traditional lighting management methods usually rely on fixed switching schedules or simple environmental sensing modes, which cannot flexibly respond to changes in the external environment and fluctuations in the flow of people. This leads to a waste of energy efficiency, especially at night or during periods with less traffic. The brightness of the lights cannot be adjusted in time, resulting in unnecessary energy consumption. In order to improve energy efficiency and enhance the intelligence level of lighting systems, intelligent lighting control systems based on Internet of Things technology have emerged. The system uses sensors, network communications and data analysis technologies to collect environmental parameters and traffic data in real time, thereby dynamically adjusting the brightness and switch status of lighting groups. Its core advantage is that it can make flexible adjustments based on actual conditions. However, the adjustment of lighting groups often requires real-time monitoring of traffic flow and adjustment of lighting groups, which requires high-frequency sensor responses, which will lead to a surge in the consumption of computing power and communication resources. If the traffic flow is collected according to a fixed period, there will often be lags, which may easily lead to safety hazards. Therefore, through multi-dimensional data fusion and predictive control, real-time adjustment and analysis cycles can be intelligently generated, and a decision can be made whether to adjust adjacent lighting groups synchronously. This not only reduces resource usage, but also enhances the environmental adaptability and safety of the lighting system, achieving more accurate and efficient energy-saving management. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent urban lighting energy-saving monitoring and management system based on Internet of Things technology to solve the problems raised in the prior art.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent urban lighting energy-saving monitoring and management system based on the Internet of Things technology, the system includes a work log module, an associated lighting group module, a traffic flow prediction module, an adjustment cycle module, and a real-time adjustment module; Work log module: responsible for collecting, storing and managing log data of intelligent lighting system; Lighting group association module: obtains log data of a lighting group, collects traffic flow in a historical adjustment record, determines characteristic adjustment records, identifies adjacent lighting groups of the lighting group, calculates the average traffic flow change rate of an adjacent lighting group, and determines characteristic lighting groups; Traffic flow prediction module: assign values ​​to environmental parameters, time information, and abnormal events, calculate the environmental score, time information score, and abnormal event score of historical adjustment records, calculate the adjustment protocol score of historical adjustment records based on the environmental score, time information score, and abnormal event score, take the adjustment protocol score of a certain lighting group and the traffic flow of the characteristic light group corresponding to the lighting group as a data group, perform function fitting, and establish a traffic flow prediction formula for the characteristic light group; Adjustment cycle module: collects the position of each street lamp in a lighting group, calculates the risk factor of the lighting group, and calculates the adjustment score of the historical adjustment record in combination with the adjustment protocol score of the historical adjustment record. The adjustment score and the adjustment cycle are used as the adjustment data group, and function fitting is performed to establish the adjustment cycle formula of the lighting group; Real-time adjustment module: collects real-time traffic flow, environmental parameters, time information, and abnormal events of a certain lighting group, calculates the real-time adjustment protocol score, inputs the real-time adjustment protocol score into the adjustment score formula, calculates the real-time adjustment score, identifies the adjustment light group based on the real-time adjustment score, and calculates the real-time adjustment cycle of the adjustment light group.

[0005] Further, the work log module includes a log data collection unit, an environmental parameter management unit, and a time information processing unit; Log data collection unit: collects log data of each lighting group through IoT devices and sensors, the log data includes adjustment records, detailed information of each street lamp in the lighting group, adjustment protocol and adjustment time, the detailed information includes the number, location and lighting parameters of the street lamp, and the adjustment protocol includes traffic flow, environmental parameters and time information; Environmental parameter management unit: responsible for collecting and storing temperature, visibility, wind speed and weather conditions; Time information processing unit: responsible for parsing and storing time features and date features, the time features include peak hours and non-peak hours, and the date features include weekdays, weekends, and holidays.

[0006] Further, the module for associating lighting groups includes a unit for determining characteristic adjustment records, a unit for identifying adjacent lighting groups, a unit for calculating average traffic flow rate change, and a unit for determining association; Determine the characteristic adjustment record unit: obtain all historical adjustment records of a certain lighting lamp group, extract the traffic flow and adjustment period of a certain flow direction in a certain historical adjustment record, calculate the average traffic flow of the flow direction, and compare the average traffic flow with a preset average traffic flow threshold. If the average traffic flow threshold is exceeded, mark the historical adjustment record as a characteristic adjustment record of the flow direction; Identify adjacent light unit groups: define the light units that can be led to a certain light unit in the direction of traffic flow as adjacent light units; The unit for calculating the average traffic flow change rate is as follows: obtaining the adjustment period of a certain characteristic adjustment record, collecting the traffic flow of a certain adjacent light group at the beginning and end of the adjustment period, calculating the traffic flow change rate of the adjacent light group in the characteristic adjustment record, summarizing all characteristic adjustment records, and calculating the average traffic flow change rate of a certain adjacent light group; Determine the correlation unit: select the adjacent light group with the largest average traffic flow change rate, then the adjacent light group is correlated with the lighting light group and is set as the characteristic light group; By determining the characteristic adjustment record unit, the system can effectively identify the moment of abnormal traffic flow change and mark the characteristic adjustment record in time, which helps managers provide accuracy for subsequent analysis; The combination of the unit for identifying adjacent light groups and the unit for calculating the average traffic flow change rate can realize real-time monitoring of traffic conditions in adjacent areas. Based on the calculation of the flow change rate, the system can automatically identify the affected light groups, realize linkage control, and further optimize the lighting effect. By adjusting the associated light groups only when necessary, the system avoids unnecessary energy consumption and further improves the energy saving effect of the lighting system.

[0007] Further, the traffic flow prediction module includes a calculation environment scoring unit, a calculation time information scoring unit, a calculation abnormal event unit, a calculation adjustment protocol scoring unit and a traffic flow prediction formula establishment unit; Calculating environment score unit: obtaining all environment parameters appearing in all log data, normalizing them, assigning values ​​to the normalized environment parameters, collecting environment parameters of a certain historical adjustment record, and calculating the environment score of the historical adjustment record; Calculating time information scoring unit: obtaining all time information appearing in all log data, assigning values ​​to the appearing time information, collecting time information of a certain historical adjustment record, and calculating the time information score of the historical adjustment record; Abnormal event calculation unit: obtains all abnormal events recorded in all log data, classifies and assigns values ​​to the abnormal events, collects abnormal event parameters of a certain historical adjustment record, the abnormal event parameters include the abnormal event type and severity, and calculates the abnormal event score of the historical adjustment record; A regulation protocol scoring calculation unit: collects the traffic flow, environment score, time information score and abnormal event score of a certain historical regulation record, and calculates the regulation protocol score of the historical regulation record; Establishing a traffic flow prediction formula unit: taking the adjustment protocol score of a certain lighting group and the traffic flow of the characteristic light group corresponding to the lighting group as a data group, summarizing all the adjustment records of a certain lighting group, performing function fitting, and establishing a traffic flow prediction formula for the characteristic light group; By calculating data from multiple dimensions such as environmental scores, time information scores, and abnormal event scores, the traffic flow prediction model is made more comprehensive and can more accurately reflect traffic change trends. By combining historical data for normalization and assignment, the consistency and reliability of the data are improved, thereby enhancing the accuracy of the prediction model. The computing environment scoring unit can comprehensively consider factors such as weather, temperature, wind speed, visibility, etc., so that the system can adapt to different meteorological conditions and improve the traffic flow prediction ability in bad weather. The computing time information scoring unit can distinguish different time characteristics such as weekdays, weekends, peak hours, and non-peak hours, so that the prediction results are more in line with the actual traffic conditions. The calculation abnormal event unit can score unexpected situations such as accidents, construction, and major events, so that the prediction model can be adjusted dynamically to avoid excessive prediction deviations due to abnormal events; The calculation and adjustment protocol scoring unit can comprehensively analyze traffic flow, environmental score, time information score and abnormal event score, and provide a more accurate adjustment protocol score, which can be used to optimize the adjustment strategy of the lighting system, realize on-demand dimming, improve energy efficiency, reduce energy consumption, and ensure driving safety; By establishing a traffic flow prediction formula unit, the system can perform function fitting based on historical data to form prediction models for different lighting groups, thereby improving the adaptability and generalization ability of the model.

[0008] Furthermore, the adjustment cycle module includes a risk factor calculation unit and an adjustment cycle establishment unit: Danger coefficient calculation unit: collects the position of each street lamp in a certain lighting lamp group, draws a position distribution map of the lighting lamp group, collects the bending angle and slope change values ​​of the position distribution map, and calculates the danger coefficient of the lighting lamp group; Establishing an adjustment cycle unit: collecting the adjustment protocol score of a certain historical adjustment record of a certain lighting lamp group, and calculating it with the risk factor of the lighting lamp group to obtain the adjustment score of the historical adjustment record, obtain the adjustment cycle of the historical adjustment record, take the adjustment score and the adjustment cycle as the adjustment data group, summarize all the historical adjustment records of a certain lighting lamp group, perform function fitting, and establish the adjustment cycle formula of the lighting lamp group; By calculating the hazard coefficient unit, the system can analyze the geographical characteristics of the lighting group, including factors such as bend angle and slope, and assess the potential hazard level of the road; Combining the adjustment protocol score and the risk factor, the system can establish an accurate adjustment cycle formula, so that the lighting system can be dynamically adjusted according to actual needs, rather than using fixed time intervals. This intelligent optimization method can ensure continuous and appropriate lighting on key sections while avoiding energy waste caused by excessive lighting. This method can make adaptive adjustments based on the historical adjustment records of different lighting groups, making it suitable for various types of roads, such as urban trunk roads, expressways, rural roads, etc. Through function fitting, the adjustment cycle formula can be continuously optimized to adapt to different traffic flow patterns and environmental changes; By setting the adjustment cycle reasonably, the system can reduce unnecessary lighting adjustments in low traffic flow or low-risk areas, thereby reducing energy consumption. In high-risk areas or high traffic flow periods, the system will automatically shorten the adjustment cycle to enhance the lighting effect, ensure traffic safety, and avoid excessive energy consumption; By adjusting the cycle formula, managers can optimize lighting strategies based on historical data and environmental parameters to improve the scientific nature of decision-making. Combined with the traffic flow prediction module, they can more accurately match the adjustment cycle with actual road demand to achieve truly intelligent traffic lighting management.

[0009] Further, the real-time adjustment module includes a real-time adjustment protocol scoring unit, a real-time adjustment scoring unit, a real-time adjustment cycle unit, and an adjustment light group identification unit; Calculation unit for real-time regulation protocol score: collects real-time traffic flow, environmental parameters, time information, and abnormal events of a lighting group, and calculates the real-time regulation protocol score; A real-time adjustment score calculation unit: inputs the real-time adjustment agreement score into the adjustment score formula to calculate the real-time adjustment score; A real-time adjustment cycle calculation unit: inputs the real-time adjustment score into the adjustment cycle formula of the lighting lamp group to calculate the real-time adjustment cycle of the lighting lamp group; Identify and adjust the light group unit: if the real-time adjustment score is lower than the real-time adjustment score threshold, adjust the real-time adjustment period of the lighting light group; if the real-time adjustment score is equal to or exceeds the real-time adjustment score threshold, adjust the real-time adjustment period of the lighting light group and the feature light group.

[0010] Furthermore, a traffic flow prediction formula for the characteristic light group is established, including the following contents: Collect the environmental parameters of a historical adjustment record and calculate the environmental score of the historical adjustment record according to the following formula: ; Among them, A represents the environmental score, B a It is represented by the normalized value of the ath environmental parameter, C aIt is represented as the weight of the ath environmental parameter, and b is represented as the total number of environmental parameters; Collect the time information of a historical adjustment record and calculate the time information score of the historical adjustment record according to the following formula: ; Among them, D represents the time information score, E d Represented as the value of the dth time information, F d It is represented as the weight of the dth time information, and e is represented as the total number of time information; Collect the abnormal event parameters of a certain historical adjustment record, the abnormal event parameters include the abnormal event type and severity, and calculate the abnormal event score of the historical adjustment record according to the following formula: ; Among them, G represents the abnormal event score, H f Represented as the value of the fth abnormal event parameter, J f It is represented as the weight of the fth abnormal event parameter, and g is represented as the total number of abnormal event parameters; The traffic flow, environment score, time information score, and abnormal event score of a historical regulation record are collected, and the regulation agreement score of the historical regulation record is calculated according to the following formula: ; Among them, K represents the regulation agreement score, P represents the traffic flow, and L P , L A , L D , L G They are respectively expressed as the adjustment protocol weights of traffic flow, environmental score, time information score, and abnormal event score; The adjustment protocol score of a lighting group and the traffic flow of the characteristic light group corresponding to the lighting group are taken as the data group, all the adjustment records of a lighting group are summarized, and function fitting is performed to establish the traffic flow prediction formula of the characteristic light group: ; Among them, P' represents the traffic flow of the characteristic light group, α represents the weight of traffic flow prediction, and β represents the deviation value of traffic flow prediction; Through the comprehensive calculation of data from multiple dimensions including environmental score, time information score and abnormal event score, the impact of a single factor on the prediction results is avoided, the accuracy of the model is improved, and normalization processing is used to make the data of environmental parameters, time information and abnormal events consistent, thereby reducing noise and improving data reliability; The formula takes into account environmental factors such as weather, temperature, time characteristics such as peak hours, holidays, and abnormal events such as accidents, construction, and emergencies, making the prediction model applicable to different road and traffic conditions. The traffic flow prediction formula uses a data-driven function fitting method, which can be dynamically optimized based on historical data to improve the stability and generalization ability of the prediction results. Calculate the abnormal event score and incorporate it into the prediction formula, so that the system can make reasonable predictions and adjustments to traffic flow changes caused by emergencies; By adjusting the calculation of the protocol score, the system can dynamically adjust the road lighting brightness to match the real-time traffic flow, improve road visibility and safety, and combine the characteristic light group traffic flow prediction formula to predict the road traffic load in a specific time period in the future, optimize the lighting strategy in advance, and realize intelligent management.

[0011] Furthermore, a formula for adjusting the period of the lighting lamp group is established, including the following contents: Collect the bend angle and slope change values ​​of a lighting group location distribution map, and calculate the danger factor of the lighting group according to the following formula: ; Among them, M represents the risk factor, S represents the bend angle, U represents the slope change value, Q1 and Q2 represent the weights of the bend angle and slope change value respectively; The adjustment agreement score of a certain historical adjustment record of a certain lighting group is collected, and the adjustment score of the historical adjustment record is calculated according to the following formula with the risk factor of the lighting group: ; Among them, R represents the adjustment score, q1 and q2 represent the adjustment score weights of the adjustment agreement score and the risk factor, respectively; The adjustment period of the historical adjustment record is obtained, and the adjustment score and adjustment period are used as the adjustment data group. All historical adjustment records of a lighting group are summarized, and function fitting is performed to establish the adjustment period formula of the lighting group as follows: ; Wherein, T represents the adjustment period, i represents the weight of the adjustment period, and r represents the deviation value of the adjustment period; The calculation of the risk factor comprehensively considers the bend angle and slope change value, and can effectively identify high-risk areas, such as sharp bends and steep slopes. The prediction results can be used to optimize the lighting adjustment cycle, provide more reasonable lighting solutions in high-risk areas, and reduce accidents. By combining the adjustment protocol score with the risk factor, the system can accurately calculate the appropriate adjustment score to ensure that the lighting adjustment cycle matches the road safety requirements. The historical data fitting method can be used to dynamically optimize the adjustment cycle to avoid the inadaptability problem caused by a fixed cycle. In low-risk areas or low-flow periods, the system can automatically extend the adjustment cycle, reduce unnecessary lighting adjustments, and reduce energy consumption. In high-risk areas or high-flow periods, the system will automatically shorten the adjustment cycle to ensure lighting effects and balance safety and energy saving.

[0012] Furthermore, the real-time adjustment cycle of adjusting the lighting lamp group includes the following contents: If the real-time adjustment score is lower than the real-time adjustment score threshold, the traffic flow, environmental parameters, time information, and abnormal events of the lighting group are collected according to the real-time adjustment cycle of the lighting group.

[0013] Furthermore, the real-time adjustment cycle of adjusting the lighting lamp group and the characteristic lamp group includes the following contents: If the real-time adjustment score is equal to or exceeds the real-time adjustment score threshold, the traffic flow of the feature light group is predicted according to the real-time protocol score of the lighting light group, the real-time adjustment protocol score of the feature light group is calculated, and the real-time adjustment score of the feature light group is calculated, and the real-time adjustment score is input into the adjustment cycle formula of the feature light group to calculate the real-time adjustment cycle of the feature light group, and the traffic flow, environmental parameters, time information, and abnormal events of the lighting light group and the feature light group are collected according to the real-time adjustment cycles of the lighting light group and the feature light group; The real-time adjustment score can instantly reflect the actual situation of the lighting group. The system can dynamically adjust the brightness and adjustment cycle of the lighting group according to the current traffic flow, environmental parameters and abnormal events. In the case of high traffic, bad weather, accidents, etc., the system can respond quickly to increase the lighting intensity to ensure road safety. The adjustment cycle of the characteristic light group is closely related to the actual situation of the lighting group, which enhances the system's ability to respond to emergencies and reduces the risk of accidents. The adjustment score of the characteristic light group is predicted by real-time protocol scoring. Combined with historical data, the adjustment cycle of the lighting group can be accurately adjusted in real time to avoid excessive or insufficient lighting. The adjustment cycle and brightness are intelligently determined based on factors such as real-time traffic flow and environmental changes to ensure that the system can adapt to various complex and changing road conditions, thereby improving the accuracy and adaptability of lighting. This method uses real-time data collection and prediction to provide real-time decision support for traffic management departments, ensuring that intelligent traffic management systems can optimize lighting strategies more accurately.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The system can dynamically adjust the brightness and adjustment cycle of the lighting group according to real-time traffic flow, environmental changes, accidents and other abnormal events. In the event of high traffic, high risk or emergencies, the system can quickly increase the lighting brightness to ensure road safety; The system can dynamically adjust the lighting adjustment cycle according to real-time traffic flow and environmental parameter changes. In low-traffic or low-risk areas, the adjustment cycle can be extended to reduce lighting energy consumption; in high-traffic, high-risk areas, the adjustment cycle can be shortened to ensure sufficient lighting. Through intelligent control, unnecessary energy waste can be effectively avoided, achieving the goal of energy saving while maintaining sufficient and safe road lighting. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of the structure of an intelligent urban lighting energy-saving monitoring and management system based on the Internet of Things technology of the present invention; Figure 2 It is a schematic diagram of an embodiment of the intelligent urban lighting energy-saving monitoring and management system based on the Internet of Things technology of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0017] See also Figure 1 , the present invention provides a technical solution: an intelligent urban lighting energy-saving monitoring and management system based on the Internet of Things technology, the system includes a work log module, an associated lighting group module, a traffic flow prediction module, an adjustment cycle module, and a real-time adjustment module; Work log module: responsible for collecting, storing and managing log data of intelligent lighting system; Among them, the work log module includes a log data collection unit, an environmental parameter management unit, and a time information processing unit; Log data collection unit: collects log data of each lighting group through IoT devices and sensors, the log data includes adjustment records, detailed information of each street lamp in the lighting group, adjustment protocol and adjustment time, the detailed information includes the number, location and lighting parameters of the street lamp, and the adjustment protocol includes traffic flow, environmental parameters and time information; Environmental parameter management unit: responsible for collecting and storing temperature, visibility, wind speed and weather conditions; Time information processing unit: responsible for parsing and storing time features and date features, the time features include peak hours and non-peak hours, and the date features include weekdays, weekends, and holidays.

[0018] Lighting group association module: obtains log data of a lighting group, collects traffic flow in a historical adjustment record, determines characteristic adjustment records, identifies adjacent lighting groups of the lighting group, calculates the average traffic flow change rate of an adjacent lighting group, and determines characteristic lighting groups; Among them, the associated lighting lamp group module includes a unit for determining feature adjustment records, a unit for identifying adjacent lamp groups, a unit for calculating average traffic flow change rate, and a unit for determining correlation; Determine the characteristic adjustment record unit: obtain all historical adjustment records of a certain lighting lamp group, extract the traffic flow and adjustment period of a certain flow direction in a certain historical adjustment record, calculate the average traffic flow of the flow direction, and compare the average traffic flow with a preset average traffic flow threshold. If the average traffic flow threshold is exceeded, mark the historical adjustment record as a characteristic adjustment record of the flow direction; Identify adjacent light unit groups: define the light units that can be led to a certain light unit in the direction of traffic flow as adjacent light units; The unit for calculating the average traffic flow change rate is as follows: obtaining the adjustment period of a certain characteristic adjustment record, collecting the traffic flow of a certain adjacent light group at the beginning and end of the adjustment period, calculating the traffic flow change rate of the adjacent light group in the characteristic adjustment record, summarizing all characteristic adjustment records, and calculating the average traffic flow change rate of a certain adjacent light group; Determine the correlation unit: select the adjacent light group with the largest average traffic flow change rate, then the adjacent light group is correlated with the lighting light group and is set as the characteristic light group; For example, the system extracts all historical adjustment records of lamp group L_4 from the work log module. The sample data is shown in Table 1: Table 1

[0019] Assuming that the preset average traffic flow threshold is 60 vehicles / minute, the average traffic flow of the 1st and 4th historical adjustment records exceeds the threshold, and therefore are marked as characteristic adjustment records; like Figure 2 As shown, the system identifies that the adjacent light groups that can lead to the L_4 lighting group flow direction 1 are L_1, L_2, and L_3; For example, for the L_2 lamp group, the flow rate changes in the characteristic adjustment record 1 and the characteristic adjustment record 2 are shown in Table 2: Table 2

[0020] For example, the average flow rate change rate of L_1, L_2, and L_3 in characteristic adjustment record 1 and characteristic adjustment record 2 is calculated as shown in Table 3: Table 3

[0021] Then L_2 and L_4 are associated, and L_4 is set as the characteristic light group of L_2.

[0022] Traffic flow prediction module: assign values ​​to environmental parameters, time information, and abnormal events, calculate the environmental score, time information score, and abnormal event score of historical adjustment records, calculate the adjustment protocol score of historical adjustment records based on the environmental score, time information score, and abnormal event score, take the adjustment protocol score of a certain lighting group and the traffic flow of the characteristic light group corresponding to the lighting group as a data group, perform function fitting, and establish a traffic flow prediction formula for the characteristic light group; Among them, the traffic flow prediction module includes a calculation environment scoring unit, a calculation time information scoring unit, a calculation abnormal event unit, a calculation adjustment protocol scoring unit and a traffic flow prediction formula establishment unit; Calculating environment score unit: obtaining all environment parameters appearing in all log data, normalizing them, assigning values ​​to the normalized environment parameters, collecting environment parameters of a certain historical adjustment record, and calculating the environment score of the historical adjustment record; Calculating time information scoring unit: obtaining all time information appearing in all log data, assigning values ​​to the appearing time information, collecting time information of a certain historical adjustment record, and calculating the time information score of the historical adjustment record; Abnormal event calculation unit: obtains all abnormal events recorded in all log data, classifies and assigns values ​​to the abnormal events, collects abnormal event parameters of a certain historical adjustment record, the abnormal event parameters include the abnormal event type and severity, and calculates the abnormal event score of the historical adjustment record; A regulation protocol scoring calculation unit: collects the traffic flow, environment score, time information score and abnormal event score of a certain historical regulation record, and calculates the regulation protocol score of the historical regulation record; Establishing a traffic flow prediction formula unit: taking the adjustment protocol score of a certain lighting group and the traffic flow of the characteristic light group corresponding to the lighting group as a data group, summarizing all the adjustment records of a certain lighting group, performing function fitting, and establishing a traffic flow prediction formula for the characteristic light group; The traffic flow prediction formula for the characteristic light group is established, including the following contents: Collect the environmental parameters of a historical adjustment record and calculate the environmental score of the historical adjustment record according to the following formula: ; Among them, A represents the environmental score, B a It is represented by the normalized value of the ath environmental parameter, C a It is represented as the weight of the ath environmental parameter, and b is represented as the total number of environmental parameters; Collect the time information of a historical adjustment record and calculate the time information score of the historical adjustment record according to the following formula: ; Among them, D represents the time information score, E d Represented as the value of the dth time information, F d It is represented as the weight of the dth time information, and e is represented as the total number of time information; Collect the abnormal event parameters of a certain historical adjustment record, the abnormal event parameters include the abnormal event type and severity, and calculate the abnormal event score of the historical adjustment record according to the following formula: ; Among them, G represents the abnormal event score, H f Represented as the value of the fth abnormal event parameter, J f It is represented as the weight of the fth abnormal event parameter, and g is represented as the total number of abnormal event parameters; The traffic flow, environment score, time information score, and abnormal event score of a historical regulation record are collected, and the regulation agreement score of the historical regulation record is calculated according to the following formula: ; Among them, K represents the regulation agreement score, P represents the traffic flow, and L P , L A , L D , L G They are respectively expressed as the adjustment protocol weights of traffic flow, environmental score, time information score, and abnormal event score; The adjustment protocol score of a lighting group and the traffic flow of the characteristic light group corresponding to the lighting group are taken as the data group, all the adjustment records of a lighting group are summarized, and function fitting is performed to establish the traffic flow prediction formula of the characteristic light group: ; Among them, P' represents the traffic flow of the characteristic light group, α represents the weight of traffic flow prediction, and β represents the deviation value of traffic flow prediction; For example, assuming the environmental parameters are as follows: the temperature is 20°C, which is normalized to 0.72, the visibility is 6 km, which is normalized to 0.6, the wind speed is 3 m / s, which is normalized to 0.5, the weather condition is cloudy, and the normalized value is set to 0.7. Assuming the weights of the environmental parameters: the temperature weight is 0.3, the visibility weight is 0.3, the wind speed weight is 0.2, and the weather condition weight is 0.2, the calculated environmental score is 0.636; Assuming the weights are 1 for peak hours, 0.5 for off-peak hours, and 1 for weekends, the weights for peak hours are 0.7, 0.2 for off-peak hours, and 0.1 for weekends, and assuming the current time is a peak hour and a weekend, the calculated time information score is 0.8; Assuming that traffic accidents are 1, equipment failures are 0.5, the weight of traffic accidents is 0.8, and the weight of equipment failures is 0.2, the calculated abnormal event score is 0.9; Assuming that the traffic flow in the historical regulation record is 400 vehicles, the traffic flow weight is 0.4, the environmental score weight is 0.3, the time information score weight is 0.2, and the abnormal event score weight is 0.1, the calculated regulation agreement score is 160.44; Assume that the adjustment agreement score of the L_2 lighting group and the traffic flow of the characteristic lighting group L_4 corresponding to the L_2 lighting group are as shown in Table 4: Table 4

[0023] Function fitting is performed and we get α is -0.4 and β is 464. Then the traffic flow prediction formula for the characteristic light group L_4 corresponding to the L_2 lighting group is P'=-0.4×K+464.

[0024] Adjustment cycle module: collects the position of each street lamp in a lighting group, calculates the risk factor of the lighting group, and calculates the adjustment score of the historical adjustment record in combination with the adjustment protocol score of the historical adjustment record. The adjustment score and the adjustment cycle are used as the adjustment data group, and function fitting is performed to establish the adjustment cycle formula of the lighting group; Among them, the adjustment cycle module includes a risk factor calculation unit and an adjustment cycle establishment unit: Danger coefficient calculation unit: collects the position of each street lamp in a certain lighting lamp group, draws a position distribution map of the lighting lamp group, collects the bending angle and slope change values ​​of the position distribution map, and calculates the danger coefficient of the lighting lamp group; Establishing an adjustment cycle unit: collecting the adjustment protocol score of a certain historical adjustment record of a certain lighting lamp group, and calculating it with the risk factor of the lighting lamp group to obtain the adjustment score of the historical adjustment record, obtain the adjustment cycle of the historical adjustment record, take the adjustment score and the adjustment cycle as the adjustment data group, summarize all the historical adjustment records of a certain lighting lamp group, perform function fitting, and establish the adjustment cycle formula of the lighting lamp group; The formula for establishing the adjustment cycle of the lighting lamp group includes the following contents: Collect the bend angle and slope change values ​​of a lighting group location distribution map, and calculate the danger factor of the lighting group according to the following formula: ; Among them, M represents the risk factor, S represents the bend angle, U represents the slope change value, Q1 and Q2 represent the weights of the bend angle and slope change value respectively; The adjustment agreement score of a certain historical adjustment record of a certain lighting group is collected, and the adjustment score of the historical adjustment record is calculated according to the following formula with the risk factor of the lighting group: ; Among them, R represents the adjustment score, q1 and q2 represent the adjustment score weights of the adjustment agreement score and the risk factor, respectively; The adjustment period of the historical adjustment record is obtained, and the adjustment score and adjustment period are used as the adjustment data group. All historical adjustment records of a lighting group are summarized, and function fitting is performed to establish the adjustment period formula of the lighting group as follows: ; Wherein, T represents the adjustment period, i represents the weight of the adjustment period, and r represents the deviation value of the adjustment period; For example, suppose there are three street lamps in the L_2 lighting group, located at A(0, 0), B(10, 0) and C(10,10), and the angle formed by the connecting line between street lamps B and C is 90°. The slope change value is set to 10%, the bend angle weight is 0.6, and the slope change weight is 0.4. The calculated risk factor is 58. Assume that the reconciliation agreement score of a historical reconciliation record is 160, the agreement score weight is 0.7, and the risk factor weight is 0.3. The calculated reconciliation score is 129.4. Assume that the adjustment score and adjustment period are shown in Table 5: Table 5

[0025] Function fitting is performed and we get i=0.1397, r=-2.6482, and the adjustment period formula of the L_2 lighting group is T=0.1397×R-2.6482.

[0026] Real-time adjustment module: collects real-time traffic flow, environmental parameters, time information, and abnormal events of a lighting lamp group, calculates a real-time adjustment protocol score, inputs the real-time adjustment protocol score into the adjustment score formula, calculates a real-time adjustment score, identifies the adjustment lamp group according to the real-time adjustment score, and calculates the real-time adjustment cycle of the adjustment lamp group; Among them, the real-time adjustment module includes a real-time adjustment protocol scoring unit, a real-time adjustment scoring unit, a real-time adjustment cycle unit and a light group identification and adjustment unit; Calculation unit for real-time regulation protocol score: collects real-time traffic flow, environmental parameters, time information, and abnormal events of a lighting group, and calculates the real-time regulation protocol score; A real-time adjustment score calculation unit: inputs the real-time adjustment agreement score into the adjustment score formula to calculate the real-time adjustment score; A real-time adjustment cycle calculation unit: inputs the real-time adjustment score into the adjustment cycle formula of the lighting lamp group to calculate the real-time adjustment cycle of the lighting lamp group; Identify and adjust the light group unit: if the real-time adjustment score is lower than the real-time adjustment score threshold, adjust the real-time adjustment period of the lighting light group; if the real-time adjustment score is equal to or exceeds the real-time adjustment score threshold, adjust the real-time adjustment period of the lighting light group and the feature light group; Among them, the real-time adjustment cycle of adjusting the lighting group includes the following contents: If the real-time adjustment score is lower than the real-time adjustment score threshold, the traffic flow, environmental parameters, time information, and abnormal events of the lighting group are collected according to the real-time adjustment cycle of the lighting group; For example, the traffic flow of lighting group L_2 is 400 vehicles, and the environmental parameters are: temperature 20°C, normalized value 0.72, visibility 6km, normalized value 0.6, wind speed 3 m / s, normalized value 0.5, weather condition cloudy, normalized value 0.7; time information: peak time value 1, weekend value 1; abnormal event: traffic accident, type value 1, severity 0.8; The calculated environmental score is: 0.72×0.3+0.6×0.3+0.5×0.2+0.7×0.2=0.636; The calculation time information score is: 1×0.7+1×0.1=0.8; The abnormal event score was calculated as: 1×0.8=0.8; The calculated mediation agreement score is: 400 × 0.4 + 0.636 × 0.3 + 0.8 × 0.2 + 0.8 × 0.1 = 160.44; The real-time adjustment score is calculated as: 160.44×0.7+58×0.3=129.7; The calculated real-time adjustment period is: 0.1397×129.7−2.6482≈15 minutes; Assume that the real-time adjustment score threshold is 130, the current score is 129.7<130, and only the adjustment cycle of the lighting lamp group L_2 is adjusted to 15 minutes.

[0027] The real-time adjustment cycle of adjusting the lighting lamp group and the feature lamp group includes the following contents: If the real-time adjustment score is equal to or exceeds the real-time adjustment score threshold, the traffic flow of the feature light group is predicted according to the real-time protocol score of the lighting light group, the real-time adjustment protocol score of the feature light group is calculated, and the real-time adjustment score of the feature light group is calculated, and the real-time adjustment score is input into the adjustment cycle formula of the feature light group to calculate the real-time adjustment cycle of the feature light group, and the traffic flow, environmental parameters, time information, and abnormal events of the lighting light group and the feature light group are collected according to the real-time adjustment cycles of the lighting light group and the feature light group; Assume that the traffic flow of lighting group L_2 is 450 vehicles, environmental parameters: temperature 22°C, normalized value 0.75, visibility 5km, normalized value 0.5, wind speed 4 m / s, normalized value 0.6, weather conditions rainy, normalized value 0.4; time information: peak time value 1, holiday value 1; abnormal event: traffic accident, type value 1, severity 1.0; The calculated environmental score is: 0.75×0.3+0.5×0.3+0.6×0.2+0.4×0.2=0.635; The calculation time information score is: 1×0.7+1×0.2=0.9; The abnormal event score is calculated as: 1×0.8+1.0×0.2=1.0; The calculated mediation agreement score is: 450×0.4+0.635×0.3+0.9×0.2+1.0×0.1=180.47; The real-time adjustment score is calculated as: 180.47×0.7+58×0.3=143.729; The calculated real-time adjustment period is: 0.1397×143.729-2.6482≈17 minutes; The current score is 143.729 ≥ 130, and the adjustment cycle of the lighting group L_2 and the characteristic lighting group L_4 of the lighting group L_2 is adjusted; The traffic flow of L_4 is calculated as: -0.4×180.47+464=391.812≈392 vehicles; Assuming that the environmental parameters of L_4 are the same as those of L_2, the environmental score is 0.635. The time information of L_4: both belong to peak hours and holidays, the time score is 0.9. The abnormal events of L_4: none; The mediation agreement score of L_4 was calculated as: 392×0.4+0.635×0.3+0.9×0.2+0×0.1=157.17; Assume that the risk factor of L_4 is: bend angle 80°, slope change value 12%; The real-time regulation score of L_4 is calculated as: 157.17×0.7+(80×0.6+12×0.4)×0.3=125.859; The real-time adjustment period of L_4 is calculated as: 0.1397×125.859-2.6482≈15 minutes; The adjustment cycle of the system adjustment L_2 is 17 minutes, and the adjustment cycle of the characteristic light group L_4 is 15 minutes.

[0028] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. Intelligent urban lighting energy-saving monitoring and management system based on Internet of Things technology, characterized by: The system includes a work log module, an associated lighting group module, a traffic flow prediction module, an adjustment cycle module, and a real-time adjustment module; The work log module is responsible for collecting, storing and managing the log data of the intelligent lighting system; The associated lighting group module: obtains log data of a lighting group, collects traffic flow in a historical adjustment record, determines characteristic adjustment records, identifies adjacent lighting groups of the lighting group, calculates the average traffic flow change rate of an adjacent lighting group, and determines the characteristic lighting group; The traffic flow prediction module: assigns values ​​to environmental parameters, time information, and abnormal events, calculates the environmental score, time information score, and abnormal event score of the historical adjustment record, calculates the adjustment protocol score of the historical adjustment record based on the environmental score, time information score, and abnormal event score, takes the adjustment protocol score of a certain lighting group and the traffic flow of the characteristic light group corresponding to the lighting group as a data group, performs function fitting, and establishes a traffic flow prediction formula for the characteristic light group; The adjustment cycle module collects the position of each street lamp in a lighting group, calculates the risk factor of the lighting group, and calculates the adjustment score of the historical adjustment record in combination with the adjustment protocol score of the historical adjustment record, takes the adjustment score and the adjustment cycle as the adjustment data group, performs function fitting, and establishes the adjustment cycle formula of the lighting group; The real-time adjustment module collects real-time traffic flow, environmental parameters, time information, and abnormal events of a certain lighting lamp group, calculates a real-time adjustment protocol score, inputs the real-time adjustment protocol score into an adjustment score formula, calculates a real-time adjustment score, identifies the adjustment lamp group based on the real-time adjustment score, and calculates the real-time adjustment cycle of the adjustment lamp group.

2. According to the intelligent urban lighting energy-saving monitoring and management system based on Internet of Things technology in claim 1, it is characterized in that: The work log module includes a log data collection unit, an environmental parameter management unit, and a time information processing unit; The log data collection unit collects log data of each lighting group through IoT devices and sensors, wherein the log data includes adjustment records, detailed information of each street lamp in the lighting group, adjustment protocol and adjustment time, wherein the detailed information includes the number, location and lighting parameters of the street lamp, and the adjustment protocol includes traffic flow, environmental parameters and time information; The environmental parameter management unit is responsible for collecting and storing temperature, visibility, wind speed and weather conditions; The time information processing unit is responsible for parsing and storing time features and date features. The time features include peak hours and non-peak hours. The date features include weekdays, weekends, and holidays.

3. The intelligent urban lighting energy-saving monitoring and management system based on Internet of Things technology according to claim 1 is characterized in that: The associated lighting lamp group module includes a characteristic adjustment record determination unit, an adjacent lamp group identification unit, an average traffic flow change rate calculation unit and a correlation determination unit; The characteristic adjustment record determination unit: obtains all historical adjustment records of a certain lighting lamp group, extracts the traffic flow and adjustment period of a certain flow direction in a certain historical adjustment record, calculates the average traffic flow of the flow direction, and compares the average traffic flow with a preset average traffic flow threshold, and if the average traffic flow threshold is exceeded, marks the historical adjustment record as a characteristic adjustment record of the flow direction; The adjacent light group identification unit: defines a lighting group that can be led to a certain lighting group in the direction of vehicle flow as an adjacent light group; The unit for calculating the average traffic flow change rate: obtains the adjustment period of a certain characteristic adjustment record, collects the traffic flow of a certain adjacent light group at the beginning and end of the adjustment period, calculates the traffic flow change rate of the adjacent light group in the characteristic adjustment record, summarizes all characteristic adjustment records, and calculates the average traffic flow change rate of a certain adjacent light group; The correlation determination unit selects the adjacent light group with the largest average traffic flow change rate, and the adjacent light group is correlated with the lighting light group and is set as the characteristic light group.

4. The intelligent urban lighting energy-saving monitoring and management system based on Internet of Things technology according to claim 1 is characterized in that: The traffic flow prediction module includes a calculation environment scoring unit, a calculation time information scoring unit, a calculation abnormal event unit, a calculation adjustment protocol scoring unit and a traffic flow prediction formula establishment unit; The computing environment score unit: obtains all environment parameters appearing in all log data, performs normalization processing, assigns values ​​to the normalized environment parameters, collects environment parameters of a certain historical adjustment record, and calculates the environment score of the historical adjustment record; The time information scoring calculation unit: obtains all time information appearing in all log data, assigns values ​​to the appearing time information, collects time information of a certain historical adjustment record, and calculates the time information score of the historical adjustment record; The abnormal event calculation unit: obtains all abnormal events recorded in all log data, classifies and assigns values ​​to the abnormal events, collects abnormal event parameters of a certain historical adjustment record, the abnormal event parameters include the abnormal event type and severity, and calculates the abnormal event score of the historical adjustment record; The adjustment protocol score calculation unit collects the traffic flow, environment score, time information score and abnormal event score of a certain historical adjustment record, and calculates the adjustment protocol score of the historical adjustment record; The traffic flow prediction formula establishing unit takes the adjustment protocol score of a certain lighting group and the traffic flow of the characteristic light group corresponding to the lighting group as a data group, summarizes all adjustment records of a certain lighting group, performs function fitting, and establishes the traffic flow prediction formula of the characteristic light group.

5. The intelligent urban lighting energy-saving monitoring and management system based on Internet of Things technology according to claim 1 is characterized in that: The adjustment cycle module includes a risk coefficient calculation unit and an adjustment cycle establishment unit: The unit for calculating the risk factor: collects the position of each street lamp in a lighting lamp group, draws a position distribution map of the lighting lamp group, collects the bending angle and slope change values ​​of the position distribution map, and calculates the risk factor of the lighting lamp group; The unit for establishing an adjustment cycle collects an adjustment protocol score of a historical adjustment record of a certain lighting group, and calculates it with the risk factor of the lighting group to obtain the adjustment score of the historical adjustment record, obtains the adjustment cycle of the historical adjustment record, uses the adjustment score and the adjustment cycle as an adjustment data group, summarizes all historical adjustment records of a certain lighting group, performs function fitting, and establishes an adjustment cycle formula for the lighting group.

6. The intelligent urban lighting energy-saving monitoring and management system based on Internet of Things technology according to claim 1 is characterized in that: The real-time adjustment module includes a real-time adjustment protocol scoring unit, a real-time adjustment scoring unit, a real-time adjustment cycle unit, and a light group identification and adjustment unit; The real-time adjustment protocol scoring calculation unit collects the real-time traffic flow, environmental parameters, time information, and abnormal events of a lighting group and calculates the real-time adjustment protocol score; The real-time adjustment score calculation unit: inputs the real-time adjustment protocol score into the adjustment score formula to calculate the real-time adjustment score; The real-time adjustment cycle calculation unit: inputs the real-time adjustment score into the adjustment cycle formula of the lighting lamp group to calculate the real-time adjustment cycle of the lighting lamp group; The unit for identifying and adjusting the light group: if the real-time adjustment score is lower than the real-time adjustment score threshold, the real-time adjustment period of the lighting light group is adjusted; if the real-time adjustment score is equal to or exceeds the real-time adjustment score threshold, the real-time adjustment period of the lighting light group and the feature light group is adjusted.

7. The intelligent urban lighting energy-saving monitoring and management system based on Internet of Things technology according to claim 4 is characterized in that: The traffic flow prediction formula of the characteristic light group is established, including the following contents: Collect the environmental parameters of a historical adjustment record and calculate the environmental score of the historical adjustment record according to the following formula: ； Among them, A represents the environmental score, B a It is represented by the normalized value of the ath environmental parameter, C a It is represented as the weight of the ath environmental parameter, and b is represented as the total number of environmental parameters; Collect the time information of a historical adjustment record and calculate the time information score of the historical adjustment record according to the following formula: ； Among them, D represents the time information score, E d Represented as the value of the dth time information, F d It is represented as the weight of the dth time information, and e is represented as the total number of time information; Collect the abnormal event parameters of a certain historical adjustment record, the abnormal event parameters include the abnormal event type and severity, and calculate the abnormal event score of the historical adjustment record according to the following formula: ； Among them, G represents the abnormal event score, H f Represented as the value of the fth abnormal event parameter, J f It is represented as the weight of the fth abnormal event parameter, and g is represented as the total number of abnormal event parameters; The traffic flow, environment score, time information score, and abnormal event score of a historical regulation record are collected, and the regulation agreement score of the historical regulation record is calculated according to the following formula: ； Among them, K represents the regulation agreement score, P represents the traffic flow, and L P , L A , L D , L G They are respectively expressed as the adjustment protocol weights of traffic flow, environmental score, time information score, and abnormal event score; The adjustment protocol score of a lighting group and the traffic flow of the characteristic light group corresponding to the lighting group are taken as the data group, all the adjustment records of a lighting group are summarized, and function fitting is performed to establish the traffic flow prediction formula of the characteristic light group: ； Among them, P' represents the traffic flow of the characteristic light group, α represents the weight of traffic flow prediction, and β represents the deviation value of traffic flow prediction.

8. The intelligent urban lighting energy-saving monitoring and management system based on Internet of Things technology according to claim 5 is characterized in that: The adjustment cycle formula of the lighting lamp group is established, including the following contents: Collect the bend angle and slope change values ​​of a lighting group location distribution map, and calculate the danger factor of the lighting group according to the following formula: ； Among them, M represents the risk factor, S represents the bend angle, U represents the slope change value, Q1 and Q2 represent the weights of the bend angle and slope change value respectively; The adjustment agreement score of a certain historical adjustment record of a certain lighting group is collected, and the adjustment score of the historical adjustment record is calculated according to the following formula with the risk factor of the lighting group: ； Among them, R represents the adjustment score, q1 and q2 represent the adjustment score weights of the adjustment agreement score and the risk factor, respectively; The adjustment period of the historical adjustment record is obtained, and the adjustment score and adjustment period are used as the adjustment data group. All historical adjustment records of a lighting group are summarized, and function fitting is performed to establish the adjustment period formula of the lighting group as follows: ； Wherein, T represents the adjustment period, i represents the weight of the adjustment period, and r represents the deviation value of the adjustment period.

9. The intelligent urban lighting energy-saving monitoring and management system based on Internet of Things technology according to claim 6 is characterized in that: Adjust the real-time adjustment cycle of the lighting group, including the following: If the real-time adjustment score is lower than the real-time adjustment score threshold, the traffic flow, environmental parameters, time information, and abnormal events of the lighting group are collected according to the real-time adjustment cycle of the lighting group.

10. The intelligent urban lighting energy-saving monitoring and management system based on Internet of Things technology according to claim 6 is characterized in that: Adjust the real-time adjustment cycle of the lighting group and the characteristic light group, including the following: If the real-time adjustment score is equal to or exceeds the real-time adjustment score threshold, the traffic flow of the feature light group is predicted according to the real-time protocol score of the lighting light group, the real-time adjustment protocol score of the feature light group is calculated, and the real-time adjustment score of the feature light group is calculated, and the real-time adjustment score is input into the adjustment cycle formula of the feature light group to obtain the real-time adjustment cycle of the feature light group, and according to the real-time adjustment cycles of the lighting light group and the feature light group, the traffic flow, environmental parameters, time information, and abnormal events of the lighting light group and the feature light group are collected.

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