Smart city planning optimization system based on big data

Abstract

The invention discloses a smart city planning optimization system based on big data, and the system comprises a data collection module which is provided with sensors in a plurality of road regions, and is used for collecting traffic flow data and pedestrian flow data in the road regions; the data transmission module is used for transmitting the collected traffic flow data and people flow data to the data storage module in real time through a wireless network; the data storage module is used for receiving the traffic flow data and the people flow data and storing the traffic flow data and the people flow data according to time regions; the method relates to the technical field of smart city power energy planning, and street lamp brightness values are planned through analysis of historical traffic flow and pedestrian flow data, so that excessive illumination of street lamps in low traffic flow and pedestrian flow periods is avoided. Under the condition that the traffic flow and the pedestrian flow are extremely small, the brightness of the street lamps is reasonably reduced and even a cyclic light-off mode is adopted, so that the power consumption can be remarkably reduced and the urban electric energy expenditure can be reduced while the road illumination requirement is met.

Description

Technical Field

[0001] The present invention relates to the technical field of smart city power energy planning, and particularly to a smart city planning optimization system based on big data. Background Art

[0002] A smart city aims to achieve more scientific development, more efficient management, and a better life. Supported by information technology and communication technology, through transparent and sufficient information acquisition, extensive and secure information transmission, and effective and scientific information processing, it improves the operation efficiency of the city, enhances the level of public services, and forms a low-carbon urban ecosystem to build a new form of city. With the acceleration of the urbanization process, the urban scale is constantly expanding, the population and vehicle numbers are increasing rapidly, and urban planning and management are facing many challenges. The traditional urban construction and management models have gradually exposed many limitations in terms of resource utilization efficiency, service quality improvement, and sustainable development. The concept of smart city emerged and became an important direction for urban development.

[0003] Among urban infrastructure, the street lamp lighting system is a key component to ensure the safety of residents' night travel and the normal operation of the city. However, there are generally some problems in traditional street lamp systems. Firstly, most traditional street lamps adopt fixed brightness settings and cannot be dynamically adjusted according to the actual road usage conditions. This not only causes a large amount of energy waste, increases the urban electricity cost expenditure, but also does not meet the urgent need for energy conservation and emission reduction in modern society. Summary of the Invention

[0004] To solve the technical problems in the background art, the present invention proposes a smart city planning optimization system based on big data.

[0005] A smart city planning optimization system based on big data proposed by the present invention includes:

[0006] Data acquisition module: Sensors are set in multiple road areas to collect traffic flow data and pedestrian flow data in the road areas;

[0007] Data transmission module: Transmits the collected traffic flow data and pedestrian flow data to the data storage module in real time through a wireless network;

[0008] Data storage module: Used to receive the traffic flow data and pedestrian flow data, and store the traffic flow data and pedestrian flow data according to time regions;

[0009] Street lamp brightness planning module: Analyzes the traffic flow data and pedestrian flow data received in the data storage module in the previous historical setting period, and respectively determines the percentage brightness values of street lamps in multiple road areas in the next setting period.

[0010] Preferably, in the street lamp brightness planning module, the equation algorithm for adjusting the percentage brightness value of street lamps based on vehicle flow and pedestrian flow within a set period is as follows:

[0011] Within the set period:

[0012] Let d be the number of days variable within the set period;

[0013] Let t be the time variable within a day, with a value range of 0 hours to 24 hours, representing different moments of the day;

[0014] Let V(d, t) be the vehicle flow at time t on the d-th day, with the unit: vehicles per hour;

[0015] Let P(d, t) be the pedestrian flow at time t on the d-th day, with the unit: people per hour;

[0016] Set the vehicle flow weight coefficient as w V , w V The value range of w is 0.5 - 0.9;

[0017] The pedestrian flow weight coefficient is w P , w P The value range of w is 0.1 - 0.5;

[0018] Perform vehicle flow normalization processing:

[0019] For each day within the period, calculate the maximum vehicle flow V max (d);

[0020] V max (d) = max{V(d, t)|0 ≤ t ≤ 24};

[0021] Then the normalized vehicle flow V n (d, t) is:

[0022] V n (d, t) has a value range of 0 - 1, V max (d) ≠ 0;

[0023] Perform pedestrian flow normalization processing:

[0024] For each day within the period, calculate the maximum pedestrian flow. For each day d, calculate the maximum pedestrian flow P max (d):

[0025] P max (d) = max{P(d, t)|0 ≤ t ≤ 24};

[0026] Then the normalized pedestrian flow P n(d, t) is as follows:

[0027] P n The value range of (d, t) is 0 - 1, and P max (d) ≠ 0;

[0028] Calculate the comprehensive influence factor F(d, t) at time t on the d-th day. F(d, t) is as follows:

[0029] F(d, t) = w V ×V n (d, t) + w P ×P n (d, t);

[0030] F(d, t) represents the degree of the combined demand for street lamp brightness by the traffic flow and pedestrian flow at this time on the same day;

[0031] Let L(d, t) be the percentage brightness value to which the street lamp should be adjusted at time t on the d-th day in the next cycle. The value range is 0% to 100%;

[0032] Use linear mapping to determine the street lamp brightness L(d, t):

[0033] L(d, t) = F(d, t) × 100% = (w V ×V n (d, t) + w P ×P n (d, t)) × 100%.

[0034] Preferably, if L(d, t) < 10%, then let L(d, t) = 10%.

[0035] Preferably, if L(d, t) = 0%, then let L(d, t) = 10%; and at this time, the street lamp enters the cyclic extinguishing mode;

[0036] The cyclic extinguishing mode is as follows: at time t on the d-th day, L(d, t) = 0%. In each street lamp queue in the road area, the street lamps are extinguished at intervals. If at time t + 1 on the d-th day, L(d, t) is still 0%, then the street lamps that were in the extinguished state at time t on the original d-th day are restored to the illuminated state, and the street lamps in the illuminated state are changed to the extinguished state.

[0037] Preferably, it further includes:

[0038] Street lamp real-time control module: According to the real-time collected traffic flow data and pedestrian flow data, if within a unit time, the traffic flow data or pedestrian flow data is greater than the traffic flow data or pedestrian flow data in the same time area in the previous set cycle, it enters the brightness adjustment mode, and remotely adjusts the brightness of the street lamp according to the result of the brightness adjustment mode.

[0039] Preferably, the brightness adjustment mode is as follows:

[0040] During the unit time of a time region, the vehicle flow data and pedestrian flow data collected in real time;

[0041] If the vehicle flow data is greater than the vehicle flow data in the same time region in the previous set period, then within this time region, the brightness of the street lamp rises to X times the original brightness, and X > 1;

[0042] If the pedestrian flow data is greater than the pedestrian flow data in the same time region in the previous set period, then within this time region, the brightness of the street lamp rises to Y times the original brightness, and Y > 1;

[0043] If both the vehicle flow data and pedestrian flow data are greater than the vehicle flow data and pedestrian flow data in the same time region in the previous set period, the brightness of the street lamp rises to Z times the original brightness, and Z needs to satisfy both: Z > X, Z > Y.

[0044] Preferably, in the data storage module, it is stored by time region as follows:

[0045] The 24 hours of a day are divided into one time region every 1 hour starting from 0 o'clock.

[0046] A method for optimizing the planning of a smart city based on big data includes the following steps:

[0047] S1. Arrange sensors in multiple road regions to obtain vehicle flow data and pedestrian flow data;

[0048] S2. Transmit the collected vehicle flow data and pedestrian flow data to the data storage module in real time through a wireless network;

[0049] S3. The data storage module receives the vehicle flow data and pedestrian flow data and stores them according to the time region;

[0050] S4. The street lamp brightness planning module analyzes the vehicle flow data and pedestrian flow data in the previous historical set period in the data storage module to determine the percentage brightness values of the street lamps in multiple road regions in the next set period;

[0051] S5. If the calculated percentage brightness value of the street lamp is less than 10%, then set the percentage brightness value of the street lamp to 10%; if the calculated percentage brightness value of the street lamp is 0%, then set the percentage brightness value of the street lamp to 10%, and the street lamp enters the cyclic light-off mode:

[0052] The cyclic light-off mode is as follows: within the road area in the next set period, if at time t on day d, the calculated percentage brightness value of the street lights is 0%, in each street light queue in the road area, the street lights are turned off at intervals. If at time t + 1 on day d, the calculated percentage brightness value of the street lights is still 0%, then the street lights that were off at time t on day d are restored to the illuminated state, and the street lights that were in the illuminated state are turned off;

[0053] S6. The street light real-time control module makes a judgment based on the real-time collected traffic flow data and pedestrian flow data. If the traffic flow data or pedestrian flow data is greater than the traffic flow data or pedestrian flow data in the same time area in the previous set period, it enters the brightness adjustment mode, and remotely adjusts the brightness of the street lights according to the result of the brightness adjustment mode;

[0054] The brightness adjustment mode is as follows:

[0055] Within the unit time of a time area, the real-time collected traffic flow data and pedestrian flow data;

[0056] If the traffic flow data is greater than the traffic flow data in the same time area in the previous set period, then within this time area, the brightness of the street lights rises to X times the original brightness, and X > 1;

[0057] If the pedestrian flow data is greater than the pedestrian flow data in the same time area in the previous set period, then within this time area, the brightness of the street lights rises to Y times the original brightness, and Y > 1;

[0058] If both the traffic flow data and the pedestrian flow data are greater than the traffic flow data and pedestrian flow data in the same time area in the previous set period, the brightness of the street lights rises to Z times the original brightness, and Z needs to satisfy both: Z > X, Z > Y.

[0059] In the present invention, the proposed intelligent city planning optimization system based on big data has the following beneficial technical effects:

[0060] In this way, the brightness of the street lights in the next week can be dynamically adjusted according to the traffic flow and pedestrian flow data at different times of each day within a week, and the possible difference change rules of traffic flow and pedestrian flow on different days within a one-week cycle are considered.

[0061] 1. By analyzing the historical traffic flow and pedestrian flow data to plan the percentage brightness value of the street lights, over-illumination of the street lights during low traffic flow and pedestrian flow periods is avoided. If the calculated percentage brightness value of the street lights is less than 10%, it is forcibly set to 10% to ensure the basic lighting needs. When the brightness value is 0%, the cyclic light-off mode is started, which can significantly reduce power consumption and lower the urban power energy expenditure while meeting the road lighting requirements.

[0062] 2. The street lamp real-time control module adjusts the brightness based on the real-time collected traffic flow and pedestrian flow data. When the traffic flow or pedestrian flow increases compared to the traffic flow data or pedestrian flow data in the same time period of the previous set cycle, the street lamp brightness is increased by a preset multiple to provide more sufficient lighting for pedestrians and vehicles, ensuring traffic safety and the effectiveness of road lighting. Dynamically adjusting the lighting level avoids potential safety hazards caused by insufficient lighting, and at the same time, it will not cause light pollution or energy waste due to excessive lighting, improving the overall experience of urban residents when traveling at night.

[0063] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 is a schematic block diagram of the system of the present invention;

[0065] Figure 2 is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the drawings, where the same or similar reference signs denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0067] As Figure 1 shown, a smart city planning and optimization system based on big data includes:

[0068] Data acquisition module: Sensors are set in multiple road areas to collect traffic flow data and pedestrian flow data in the road areas;

[0069] A geomagnetic sensor can be used to detect the magnetic field change caused by the passing of vehicles to determine the traffic flow, and an infrared sensor or video image analysis technology can be used to identify the pedestrian flow.

[0070] Data transmission module: Transmits the collected traffic flow data and pedestrian flow data to the data storage module in real time through a wireless network;

[0071] Data storage module: Used to receive the traffic flow data and pedestrian flow data, and store the traffic flow data and pedestrian flow data according to time periods;

[0072] In the data storage module, storing according to time periods means: Divide the 24 hours of a day, starting from 0 o'clock, into one time period every 1 hour;

[0073] This storage method facilitates the subsequent extraction and analysis of data from different time periods.

[0074] Street lamp brightness planning module: Analyze the traffic flow data and pedestrian flow data received in the data storage module within the previous set period, and respectively determine the percentage brightness values of street lamps in multiple road areas within the next set period.

[0075] In the street lamp brightness planning module, the equation algorithm for adjusting the percentage brightness value of street lamps based on traffic flow and pedestrian flow within the set period is as follows:

[0076] Within the set period, the set period can be set to one day, one week, or one month.

[0077] Let d be the number of days variable within the set period;

[0078] Let t be the time variable within a day, and its value range is from 0 hour to 24 hours, representing different moments in a day. For example, when t = 1, it is 1 am.

[0079] Let V(d, t) be the traffic flow at time t on the d-th day, with the unit: vehicles per hour;

[0080] Let P(d, t) be the pedestrian flow at time t on the d-th day, with the unit: people per hour;

[0081] Set the traffic flow weight coefficient as w V , w V The value range of w is 0.5 - 0.9;

[0082] The pedestrian flow weight coefficient is w P , w P The value range of w is 0.1 - 0.5;

[0083] In an alternative embodiment, w V = 0.6, w P = 0.4;

[0084] Perform traffic flow normalization processing:

[0085] For each day within the period, calculate the maximum traffic flow V max (d);

[0086] V max( d) = max{V(d, t)|0 ≤ t ≤ 24};

[0087] Then the normalized traffic flow V n (d, t) is:

[0088] V n (d, t) has a value range of 0 - 1, Vmax (d) ≠ 0;

[0089] Perform population normalization:

[0090] For each day in the period, calculate the maximum population flow for that day. For each day $d$, calculate the maximum population flow $P$ max (d):

[0091] P max (d) = max{P(d, t)|0 ≤ t ≤ 24};

[0092] Then the normalized population flow $P$ n (d, t) is:

[0093] P n (d, t) ranges from 0 to 1, $P$ max (d) ≠ 0;

[0094] Calculate the comprehensive influence factor $F(d, t)$ at time $t$ on the $d$-th day. $F(d, t)$ is:

[0095] $F(d, t) = w$ V × $V$ n (d, t) + w P × $P$ n (d, t);

[0096] $F(d, t)$ represents the degree of the combined demand for streetlight brightness by the traffic flow and population flow at this time on the day.

[0097] Let $L(d, t)$ be the percentage brightness value to which the streetlight should be adjusted at time $t$ on the $d$-th day in the next period. The value range is 0% to 100%;

[0098] Use linear mapping to determine the streetlight brightness $L(d, t)$:

[0099] $L(d, t) = F(d, t)×100\% = (w$ V × $V$ n (d, t) + w P × $P$ n (d, t))×100\%;

[0100] If $L(d, t) < 10\%$, then set $L(d, t) = 10\%$;

[0101] If $L(d, t) = 0\%$, then set $L(d, t) = 10\%$; and at this time the streetlight enters the cyclic light-off mode;

[0102] The cyclic light-off mode is as follows: at time t on day d, L(d, t) = 0%. In each street lamp queue within the road area, the street lamps are turned off at intervals. If at time t + 1 on day d, it is still L(d, t) = 0%, then the street lamps that were in the off state at time t on day d are restored to the illuminated state, and the street lamps in the illuminated state are turned off;

[0103] As an example, for instance, there are 10 street lamps in a street lamp queue, and the street lamp numbers are 1, 2, 3, 4, 5, 6, 7, 8, 9, 10;

[0104] At time t on day d, L(d, t) = 0%, then the street lamps with numbers 1, 3, 5, 7, 9 are turned off, and the street lamps with numbers 2, 4, 6, 8, 10 are illuminated;

[0105] At time t + 1 on day d, if it is still L(d, t) = 0%, then the street lamps with numbers 1, 3, 5, 7, 9 are illuminated, and the street lamps with numbers 2, 4, 6, 8, 10 are turned off.

[0106] As an illustrative example:

[0107] In an optional embodiment, w V = 0.6, w P = 0.4;

[0108] If the set period is one week, then the value range is from 1 to 7, corresponding to Monday to Sunday respectively;

[0109] For example, at 18:00 on Wednesday, that is, d = 3, t = 18, the traffic flow V(3, 18) = 50 vehicles per hour, and the pedestrian flow P(3, 18) = 30 people per hour;

[0110] Assume the maximum traffic flow on Wednesday is V max (3) = 120 vehicles per hour;

[0111] The maximum pedestrian flow is P max (3) = 80 people per hour;

[0112]

[0113] Comprehensive influence factor:

[0114] F(3, 18) = w V ×V n (3, 18)+w P ×P n (3, 18) = 0.6×0.42 + 0.4×0.375 = 0.402;

[0115] The brightness of the street lamps on the next Wednesday is:

[0116] L(3, 18) = F(3, 18) × 100% = 0.402 × 100% = 40.2%

[0117] In this way, the brightness of streetlights in the next week can be dynamically adjusted according to the traffic flow and pedestrian flow data at different times of each day within a week, and the differential change rules that may exist in the traffic flow and pedestrian flow on different days within a one-week cycle are considered.

[0118] By analyzing historical traffic flow and pedestrian flow data to plan the brightness value of streetlights, over-illumination of streetlights during low traffic flow and pedestrian flow periods is avoided. For example, in the late night when the traffic flow and pedestrian flow are extremely low, reasonably reducing the brightness of streetlights or even adopting a cyclic light-off mode can significantly reduce power consumption and lower the urban energy expenditure, which conforms to the energy management concept of sustainable development.

[0119] Precisely regulating the brightness of streetlights in a data-driven manner can, on the premise of not affecting the basic function of road lighting, maximize the energy-saving goal compared with the traditional streetlight lighting system with fixed brightness, contribute to the achievement of urban energy conservation and emission reduction indicators, and have a positive significance for environmental protection.

[0120] Real-time streetlight control module: According to the real-time collected traffic flow data and pedestrian flow data, if within a unit time, the traffic flow data or pedestrian flow data is greater than the traffic flow data or pedestrian flow data in the same time area in the previous set period, enter the brightness adjustment mode, and remotely regulate the brightness of the streetlights according to the result of the brightness adjustment mode to achieve the switching of brightness.

[0121] The brightness adjustment mode is as follows:

[0122] Within a unit time in a time area, the real-time collected traffic flow data and pedestrian flow data;

[0123] If the traffic flow data is greater than the traffic flow data in the same time area in the previous set period, then within this time area, the brightness of the streetlights rises to X times the original brightness, and X > 1;

[0124] If the pedestrian flow data is greater than the pedestrian flow data in the same time area in the previous set period, then within this time area, the brightness of the streetlights rises to Y times the original brightness, and Y > 1;

[0125] If both the traffic flow data and the pedestrian flow data are greater than the traffic flow data and pedestrian flow data in the same time area in the previous set period, the brightness of the streetlights rises to Z times the original brightness, and Z needs to satisfy both: Z > X, Z > Y;

[0126] In an alternative embodiment, a time area is 1 hour and the unit time is within 0.5 hours.

[0127] The street lamp real-time control module adjusts the brightness based on the real-time collected traffic flow and pedestrian flow data. When the traffic flow or pedestrian flow suddenly increases, such as on the roads around large event venues or during peak traffic hours, the street lamps can promptly increase the brightness, providing more sufficient lighting for pedestrians and vehicles, and ensuring traffic safety and comfort.

[0128] This intelligent adjustment mode can dynamically adjust the lighting level according to the actual usage conditions of different road areas, avoiding potential safety hazards caused by insufficient lighting, and at the same time, it will not cause light pollution or energy waste due to excessive lighting, improving the overall experience of urban residents during night travel.

[0129] Such as Figure 2 shown in a method for optimizing the planning of a smart city based on big data, including the following steps:

[0130] S1. Arrange sensors in multiple road areas to obtain traffic flow data and pedestrian flow data;

[0131] S2. Through the wireless network, transmit the collected traffic flow data and pedestrian flow data to the data storage module in real time;

[0132] S3. The data storage module receives the traffic flow data and pedestrian flow data and stores them according to time zones;

[0133] S4. The street lamp brightness planning module analyzes the traffic flow data and pedestrian flow data in the last historical setting period in the data storage module to determine the percentage brightness values of the street lamps in multiple road areas in the next setting period;

[0134] The equation algorithm for adjusting the percentage brightness value of the street lamp based on the traffic flow and pedestrian flow in the setting period is as follows:

[0135] In the setting period:

[0136] Let d be the number of days variable in the setting period;

[0137] Let t be the time variable within a day, with a value range from 0 hour to 24 hours, representing different moments in a day;

[0138] Let V(d, t) be the traffic flow at time t on the d-th day, with the unit: vehicles per hour;

[0139] Let P(d, t) be the pedestrian flow at time t on the d-th day, with the unit: people per hour;

[0140] Set the traffic flow weight coefficient as w V , w V The value range of which is 0.5 - 0.9;

[0141] The pedestrian flow weight coefficient is wP , w P The value range of w is 0.1 - 0.5;

[0142] Perform traffic volume normalization:

[0143] For each day in the period, calculate the maximum traffic volume V max (d);

[0144] V max (d) = max{V(d, t)|0 ≤ t ≤ 24};

[0145] Then the normalized traffic volume V n (d, t) is:

[0146] V n (d, t) has a value range of 0 - 1, and V max (d) ≠ 0;

[0147] Perform pedestrian flow normalization:

[0148] For each day in the period, calculate the maximum pedestrian flow. For each day d, calculate the maximum pedestrian flow P max (d):

[0149] P max (d) = max{P(d, t)|0 ≤ t ≤ 24};

[0150] Then the normalized pedestrian flow P n (d, t) is:

[0151] P n (d, t) has a value range of 0 - 1, and P max (d) ≠ 0;

[0152] Calculate the comprehensive influence factor F(d, t) at time t on the d-th day. F(d, t) is:

[0153] F(d, t) = w V ×V n (d, t) + w P ×P n (d, t);

[0154] F(d, t) represents the degree of the combined demand for street lamp brightness by the traffic volume and pedestrian flow at this time on the same day;

[0155] Let L(d, t) be the percentage brightness value to which the street lamp should be adjusted at time t on the d-th day in the next period. The value range is 0% to 100%;

[0156] Determine the street lamp brightness L(d,t) using linear mapping:

[0157] L(d,t)=F(d,t)×100%=(w V ×V n (d,t)+w P ×P n (d,t))×100%;

[0158] S5. If the calculated street lamp percentage brightness value is less than 10%, set the street lamp percentage brightness value to 10%; if the calculated street lamp percentage brightness value is 0%, set the street lamp percentage brightness value to 10%, and the street lamp enters the cyclic extinguishing mode:

[0159] The cyclic extinguishing mode is as follows: In the road area of the next set period, if at time t on the d-th day, the calculated street lamp percentage brightness value is 0%, in each street lamp queue in the road area, the street lamps are extinguished at intervals. If at time t + 1 on the d-th day, the calculated street lamp percentage brightness value is still 0%, then the street lamps that were in the extinguished state at time t on the d-th day are restored to the illuminated state, and the street lamps that were in the illuminated state are changed to the extinguished state;

[0160] S6. The street lamp real-time control module makes a judgment based on the real-time collected traffic flow data and pedestrian flow data. If the traffic flow data or pedestrian flow data is greater than the traffic flow data or pedestrian flow data in the same time area of the previous set period, it enters the brightness adjustment mode, and remotely adjusts the brightness of the street lamps according to the result of the brightness adjustment mode;

[0161] The brightness adjustment mode is as follows:

[0162] Within the unit time of a time area, the real-time collected traffic flow data and pedestrian flow data;

[0163] If the traffic flow data is greater than the traffic flow data in the same time area of the previous set period, then within this time area, the brightness of the street lamps rises to X times the original brightness, and X>1;

[0164] If the pedestrian flow data is greater than the pedestrian flow data in the same time area of the previous set period, then within this time area, the brightness of the street lamps rises to Y times the original brightness, and Y>1;

[0165] If both the traffic flow data and the pedestrian flow data are greater than the traffic flow data and pedestrian flow data in the same time area of the previous set period, the brightness of the street lamps rises to Z times the original brightness, and Z needs to satisfy both: Z>X, Z>Y.

[0166] Meanwhile, the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0167] In the embodiments provided by the present invention, it should be understood that the disclosed system or method can be implemented in other ways. For example, the above-described invention embodiments are merely illustrative. For example, the division of modules is only a logical function division, and there can be other division methods in actual implementation.

[0168] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0169] In addition, the functional modules in various embodiments of the present invention can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of a combination of hardware and software functional modules.

[0170] For those skilled in the operation and maintenance field, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and can be implemented in other specific forms without departing from the basic characteristics of the present invention.

[0171] As mentioned above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the technical field, within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent replacements or changes, and should be covered by the protection scope of the present invention.

Claims

1. A smart city planning optimization system based on big data, characterized in that, Including: Data acquisition module: Sensors are set in multiple road areas to collect traffic flow data and pedestrian flow data within the road areas. Data transmission module: Transmits the collected traffic flow data and pedestrian flow data to the data storage module in real time through a wireless network. Data storage module: Used to receive the traffic flow data and pedestrian flow data, and store the traffic flow data and pedestrian flow data according to time periods. Street lamp brightness planning module: Analyzes the traffic flow data and pedestrian flow data received in the data storage module within the previous historical set period, and respectively determines the percentage brightness values of the street lamps in multiple road areas within the next set period.

2. The smart city planning optimization system based on big data according to claim 1, wherein In the street lamp brightness planning module, the equation algorithm for adjusting the percentage brightness value of the street lamp based on traffic flow and pedestrian flow within the set period is as follows: Within the set period: Let d be the number-of-days variable within the set period. Let t be the time variable within a day, with a value range of 0 hours to 24 hours, representing different moments in a day. Let V(d, t) be the traffic flow at time t on the dth day, with the unit: vehicles per hour. Let P(d, t) be the pedestrian flow at time t on the dth day, with the unit: people per hour. Set the traffic flow weight coefficient as w V , w V ranges from 0.5 to 0.9; The pedestrian flow weight coefficient is w P , w P ranges from 0.1 to 0.5; Perform traffic flow normalization processing: For each day within the period, calculate the maximum value V of the traffic flow on that day max (d); |V max (d) = max{V(d, t)|0 ≤ t ≤ 24}; Then the normalized traffic volume V on that day n (d, t) is as follows: V n (d, t) ranges from 0 to 1, V max (d) ≠ 0; Perform pedestrian flow normalization processing: For each day within the period, calculate the maximum value of the daily footfall. For each day d, calculate the maximum value P of the daily footfall max (d): P max (d) = max{P(d, t)|0 ≤ t0 ≤ 24}; Then the normalized pedestrian flow P on that day n (d, t) is as follows: P n (d, t) ranges from 0 to 1, P max (d) ≠ 0; Calculate the comprehensive influence factor F(d, t) at time t on the dth day, and F(d, t) is: F(d, t) = w V × V n (d, t) + w P × P n (d, t); F(d, t) represents the degree of comprehensive demand for street lamp brightness by traffic flow and pedestrian flow at this time on the current day. Let L(d, t) be the percentage brightness value to which the street lamp should be adjusted at time t on the dth day within the next period, with a value range of 0% to 100%. Use linear mapping to determine the street lamp brightness L(d, t): L(d, t) = F(d, t) × 100% = (wV × V n (d, t) + w P × P n (d, t)) × 100%.

3. The smart city planning optimization system based on big data according to claim 2, wherein If L(d, t) < 10%, then let L(d, t) = 10%.

4. The big data-based smart city planning optimization system according to claim 2 or 3, characterized in that If L(d, t) = 0%, then let L(d, t) = 10%; and at this time, the street lamp enters the cyclic extinguishing mode. The cyclic extinguishing mode is: At time t on the dth day, L(d, t) = 0%. Among each street lamp queue in the road area, the street lamps are extinguished at intervals. If at time t + 1 on the dth day, L(d, t) is still 0%, then the street lamps that were in the extinguished state at time t on the original dth day are restored to the illuminated state, and the street lamps in the illuminated state are changed to the extinguished state.

5. The intelligent city planning optimization system based on big data according to claim 1, characterized in that It also includes: Street lamp real-time control module: According to the real-time collected traffic flow data and pedestrian flow data, if within a unit time, the traffic flow data or pedestrian flow data is greater than the traffic flow data or pedestrian flow data in the same time period within the previous set period, it enters the brightness adjustment mode, and remotely controls the brightness of the street lamp according to the result of the brightness adjustment mode.

6. The smart city planning optimization system based on big data according to claim 5, wherein, The brightness adjustment mode is: Within a unit time in a time period, the real-time collected traffic flow data and pedestrian flow data; If the traffic flow data is greater than the traffic flow data in the same time period within the previous set period, then within this time period, the brightness of the street lamp rises to X times the original brightness, and X > 1; If the pedestrian flow data is greater than the pedestrian flow data in the same time period within the previous set period, then within this time period, the brightness of the street lamp rises to Y times the original brightness, and Y > 1; When both the vehicle flow data and the pedestrian flow data are greater than the vehicle flow data and the pedestrian flow data in the same time area within the previous set period, the brightness of the street lamp rises to Z times the original brightness, and Z needs to satisfy both: Z > X, Z > Y.

7. The system for optimizing the smart city planning based on big data according to claim 1, wherein In the data storage module, the storage is based on time areas as follows: The 24 hours of a day are divided into one time area every 1 hour starting from 0 o'clock.

8. Use the big-data-based smart city planning optimization method according to any one of claims 1-7, characterized in that, It includes the following steps: S1. Arrange sensors in multiple road areas to obtain vehicle flow data and pedestrian flow data; S2. Transmit the collected vehicle flow data and pedestrian flow data to the data storage module in real time through the wireless network; S3. The data storage module receives the vehicle flow data and the pedestrian flow data and stores them according to time areas; S4. The street lamp brightness planning module analyzes the vehicle flow data and the pedestrian flow data in the previous historical set period in the data storage module to determine the percentage brightness values of the street lamps in the next set period in multiple road areas; S5. If the calculated percentage brightness value of the street lamp is less than 10%, then set the percentage brightness value of the street lamp to 10%; if the calculated percentage brightness value of the street lamp is 0%, then set the percentage brightness value of the street lamp to 10%, and the street lamp enters the cyclic light-off mode: The cyclic light-off mode is: in the road area of the next set period, if at time t on day d, the calculated percentage brightness value of the street lamp is 0%, in each street lamp queue in the road area, the street lamps are turned off at intervals. If at time t + 1 on day d, the calculated percentage brightness value of the street lamp is still 0%, then the street lamps that were turned off at time t on day d are restored to the lighting state, and the street lamps that were in the lighting state are changed to the off state; S6. The street lamp real-time control module makes a judgment based on the real-time collected vehicle flow data and pedestrian flow data. If the vehicle flow data or the pedestrian flow data is greater than the vehicle flow data or the pedestrian flow data in the same time area within the previous set period, it enters the brightness adjustment mode and remotely adjusts the brightness of the street lamp according to the result of the brightness adjustment mode: The brightness adjustment mode is: In the unit time of a time area, the real-time collected vehicle flow data and pedestrian flow data; If the vehicle flow data is greater than the vehicle flow data in the same time area within the previous set period, then within this time area, the brightness of the street lamp rises to X times the original brightness, and X > 1; If the pedestrian flow data is greater than the pedestrian flow data in the same time area within the previous set period, then within this time area, the brightness of the street lamp rises to Y times the original brightness, and Y > 1; When both the vehicle flow data and the pedestrian flow data are greater than the vehicle flow data and the pedestrian flow data in the same time area within the previous set period, the brightness of the street lamp rises to Z times the original brightness, and Z needs to satisfy both: Z > X, Z > Y.

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