Urban building parking area intelligent planning method and system based on people flow data

Abstract

The invention discloses an urban building parking area intelligent planning method and system based on people flow data, relates to the field of building planning, solves the problem of inaccurate parking lot planning, and comprises the steps of collecting historical people flow data and historical traffic flow data of a correspondingly matched urban building of the same type as a target urban building in the last quarter; analyzing historical people flow data of the matched city building in the last quarter, and dividing the matched city building into a first type of building or a second type of building; performing category analysis on the matched city building according to the category to obtain an expected additional parking space number of the matched city building; formulating a parking lot planning scheme of the target city building according to the expected additional parking space number of the matched city building, and generating a planning completion signal or an undetermined signal; and receiving an undetermined signal or a planning completion signal corresponding to the target city building. According to the invention, accurate planning of the city building parking lot is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of building planning, and in particular to an intelligent planning method and system for parking areas of urban buildings based on pedestrian flow data. Background Art

[0002] With the acceleration of urbanization, the number of motor vehicles in cities has increased rapidly, and the parking problem has become increasingly prominent. Traditional manually managed parking lots can no longer meet the needs of users and managers for parking efficiency, safety, performance and management. The difficulty of parking not only brings inconvenience to car owners, but also aggravates urban traffic congestion and affects the normal operation of the city. The construction and management of intelligent parking lots have become an important way to solve the urban parking problem.

[0003] However, at present, when planning parking lots for urban buildings, traffic flow data is often used as the basis for planning and design. However, traffic flow data can only reflect the flow of vehicles, but cannot reflect parking demand. However, pedestrian flow data can more directly reflect people's travel needs and activity patterns. Therefore, planning and designing parking lots based on pedestrian flow can more accurately predict parking demand. To this end, the present invention proposes an intelligent planning method and system for urban building parking areas based on pedestrian flow data. Summary of the invention

[0004] The purpose of the present invention is to propose a method for automatically mapping the topology of an electric grid in an important power supply guaranteeing place, so as to solve the problems raised in the above-mentioned background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In the first aspect, a method for intelligent planning of parking areas in urban buildings based on pedestrian flow data comprises the following steps: Step S1, collecting historical pedestrian flow data and historical vehicle flow data of the same type of buildings in the target city in the previous quarter; Step S2, analyzing the historical passenger flow data of the matching city buildings in the previous quarter, and classifying the matching city buildings into first category buildings or second category buildings; Step S3, performing category analysis on the matching city buildings according to the categories to obtain the expected number of additional parking spaces for the matching city buildings; Step S4, formulating a parking lot planning scheme for the target city building according to the expected number of additional parking spaces for the matching city building, and generating a planning completion signal or a pending signal.

[0006] Furthermore, the historical passenger flow data includes the number of people retained and the number of people commuting each day, and the historical vehicle flow data includes the number of vehicles commuting and the number of vehicles retained in the parking lot.

[0007] Furthermore, the step S2 includes the following sub-steps: Step S21, obtaining historical passenger flow data of the matching city building, and obtaining the number of commuting times of the corresponding matching city building every day in the previous quarter; Step S22, adding up the number of commuting times of people every day in the previous quarter and taking the average value to obtain the average number of commuting times of the corresponding matching city building in the previous quarter; Step S23, calculating the standard deviation of commuting of people in the corresponding matching city building in the previous quarter; Step S24, taking the average number of commuters plus the standard deviation of commuters as the first demarcation value, and taking the average number of commuters minus the standard deviation of commuters as the second demarcation value; taking the first demarcation value as the right endpoint and the second demarcation value as the left endpoint to obtain a screening interval for matching the historical passenger flow data corresponding to the city building; Step S25, comparing the number of commuting times of the matching city building every day in the previous quarter with the screening interval; if the number of commuting times of the matching city building is within the screening interval, retaining the historical passenger flow data of the corresponding days, and recording the date corresponding to the retained historical passenger flow data as the retention date; If the number of commuting times is outside the screening range, the historical passenger flow data for the corresponding days will be discarded.

[0008] Furthermore, the step S2 further includes the following sub-steps: Step S26, obtaining the number of personnel commuting times corresponding to the reserved days, and then obtaining the number of vehicle commuting times corresponding to the reserved days; Step S27, adding up the number of commuting times of people corresponding to the reserved days and taking the average value to obtain the average number of commuting times of people corresponding to the reserved days; adding up the number of commuting times of vehicles corresponding to the reserved days and taking the average value to obtain the average number of commuting times of vehicles corresponding to the reserved days; Step S28, calculating the correlation coefficient between human commuting and vehicle commuting; Step S29, compare the correlation coefficient with the correlation coefficient threshold. If the correlation coefficient is greater than the correlation coefficient threshold, the corresponding matching city building is recorded as a first category building; if the correlation coefficient is less than the correlation threshold, the corresponding matching city building is recorded as a second category building.

[0009] Furthermore, the step S3 includes the following sub-steps: Step S31, performing a first category analysis on a first category building; Step S32, performing a second category analysis on the second category buildings.

[0010] Furthermore, the first category analysis comprises the following steps: Step S311, obtaining the historical flow data of people corresponding to the retention days of the first category of buildings, and obtaining the number of people retained in the matching city buildings for the retention days; Step S312, drawing a personnel time distribution diagram corresponding to the first category of buildings according to the number of personnel retention corresponding to the retention days; Step S313, obtaining a maximum value point according to the personnel time distribution diagram, and recording the largest maximum value point as a peak point; Step S314, obtaining the number of vehicles retained and the number of parking spaces in the parking lot corresponding to the maximum value point; Step S315, if the number of retained vehicles is greater than or equal to the number of parking spaces in the parking lot, the corresponding maximum point is recorded as the calculation maximum point; if the number of retained vehicles is less than the number of parking spaces in the parking lot, the corresponding maximum point is recorded as the training maximum point.

[0011] Furthermore, the first category analysis further comprises the following steps: Step S316, drawing a retention correlation diagram based on the number of retained personnel and the number of retained vehicles corresponding to the training maximum value points of multiple retention days, and then performing function fitting based on the retention correlation diagram to obtain a corresponding correlation function expression; Step S317, substituting the number of personnel retention corresponding to the calculated maximum value point into the correlation function expression to calculate the expected number of vehicles retained corresponding to the calculated maximum value point; adding and averaging multiple expected vehicle retention numbers to obtain the average expected vehicle retention number; Step S318, obtaining the expected number of additional parking spaces corresponding to the first category of buildings by subtracting the number of parking spaces in the parking lot from the average expected number of vehicles to be retained.

[0012] Furthermore, the second category analysis further comprises the following steps: Step S321, obtaining historical vehicle data corresponding to the retention days of the second category building, and obtaining the number of vehicles retained at different times corresponding to the retention days; Step S322, obtaining the number of parking spaces in the parking lot corresponding to the second category building, and counting the congestion duration when the number of vehicles retained corresponding to the second category building is greater than or equal to the number of parking spaces in the parking lot; Step S323, obtaining the congestion rate of the corresponding reserved days by dividing the congestion duration by the total duration of each day; Step S324, adding up the congestion rates of all the reserved days and taking the average value to obtain the parking lot congestion rate corresponding to the second category of buildings; Step S325, comparing the parking lot congestion rate with the congestion rate threshold. If the parking lot congestion rate is greater than or equal to the first congestion rate threshold, P1 parking spaces are added to the second category building; if the parking lot congestion rate is less than the first congestion rate threshold and greater than or equal to the second congestion rate threshold, P2 parking spaces are added to the second category building; if the parking lot congestion rate is less than the second congestion rate threshold, no additional parking spaces are added; Wherein, the first congestion rate threshold is greater than the second congestion rate threshold, 0<P1<P2; Step S326, record P1 or P2 as the expected number of additional parking spaces corresponding to the second category of buildings.

[0013] Furthermore, the step S4 includes the following sub-steps: Step S41, obtaining the number of parking spaces in the parking lot corresponding to the matching city building and the expected number of additional parking spaces, and obtaining the required number of parking spaces corresponding to the target city building by adding the number of parking spaces in the parking lot to the expected number of additional parking spaces; Step S42, obtaining the floor area of ​​the preset parking lot corresponding to the building in the target city, and obtaining the number of plannable parking spaces of the building in the target city by dividing the floor area of ​​the preset parking lot corresponding to the building in the target city by the floor area of ​​the standard parking spaces; Step S43, if the number of available parking spaces is greater than or equal to the required number of parking spaces, then parking spaces are planned according to the preset parking lot; if the number of available parking spaces is less than the required number of parking spaces, then subsequent steps are executed; Step S44, obtaining the existence of vacant areas on the road section where the target city building is located; when vacant areas exist, setting up temporary parking spaces in priority to the vacant areas; obtaining the area occupied by the vacant areas, and obtaining the number of additional parking spaces in the vacant areas by dividing the area occupied by the standard parking spaces.

[0014] Furthermore, the step S4 further includes the following sub-steps: Step S45, comparing the number of additional parking spaces in the vacant area with the expected number of additional parking spaces, if the number of additional parking spaces in the vacant area is greater than or equal to the expected number of additional parking spaces, setting up temporary parking spaces in the vacant area; If the number of additional parking spaces is less than the expected number of additional parking spaces, parking spaces will be supplemented by combining vacant areas with on-street parking spaces; Step S46, when there is no vacant area, only on-street parking spaces are considered for supplementation; Step S47, counting the total number of on-street parking spaces and vacant parking spaces, and if the total number of actually added parking spaces is greater than or equal to the expected number of additional parking spaces, generating a planning completion signal; Step S48: If the total number of parking spaces actually added is less than the expected number of parking spaces added, a pending signal is generated.

[0015] Second, the intelligent planning system for urban building parking areas based on pedestrian flow data includes: The data collection module is used to collect the historical pedestrian flow data and vehicle flow data of the same type of buildings in the target city in the previous quarter, and send them to the pedestrian flow analysis module and the building analysis module; A pedestrian flow analysis module is used to analyze the historical pedestrian flow data of the matching city buildings in the previous quarter, classify the matching city buildings into first category buildings or second category buildings and send them to the building analysis module; The building analysis module is used to analyze the matching city buildings according to their categories, obtain the expected number of additional parking spaces for the matching city buildings and send it to the parking space planning module; The parking space planning module is used to formulate a parking lot planning scheme for the target city building according to the expected number of additional parking spaces for the matching city building, generate a planning completion signal or a pending signal and send it to the user terminal; The user terminal is used to receive the pending signal or planning completion signal corresponding to the building in the target city.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention first collects the historical pedestrian flow data and the historical vehicle flow data of the matching city buildings of the same type as the target city buildings in the previous quarter; then analyzes the historical pedestrian flow data of the matching city buildings in the previous quarter, and divides the matching city buildings into first category buildings or second category buildings; then, the matching city buildings are analyzed according to the categories, and the expected number of additional parking spaces for the matching city buildings is obtained. The present invention realizes the calculation of the expected number of additional parking spaces corresponding to the matching city buildings.

[0017] 2. The present invention first formulates a parking lot planning scheme for the target city building according to the expected number of additional parking spaces of the matching city building, generates a planning completion signal or a pending signal, and receives the pending signal or planning completion signal corresponding to the target city building. If it is a pending signal, the user decides whether to adopt the corresponding parking space addition scheme. The present invention realizes intelligent planning of parking areas corresponding to urban buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0019] Figure 1 is a flow chart of the method of the present invention; Figure 2 It is the personnel time distribution diagram of the present invention; Figure 3 A retained association diagram for the present invention; Figure 4 It is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0021] Example 1: Please refer to Figure 1-Figure 3 As shown, the technical solution provided by the present invention is: an intelligent planning method for urban building parking areas based on pedestrian flow data, the method comprising the following steps: Step S1, collecting historical pedestrian flow data and historical vehicle flow data of the same type of buildings in the target city in the previous quarter; Among them, the target city buildings are the city buildings that need to be constructed, and the matching city buildings are the city buildings with the same expected construction specifications as the target city buildings; the historical passenger flow data specifically include the number of people retained and the number of people commuting every day, and the historical vehicle flow data include the number of vehicles commuting and the number of vehicles retained in the parking lot.

[0022] Step S2, analyzing the historical passenger flow data of the matching city buildings in the previous quarter, and classifying the matching city buildings into first category buildings or second category buildings; In this embodiment, step S2 includes the following sub-steps: Step S21, obtaining the historical flow data of the matching city buildings, and obtaining the number of commuting times RTQi of the corresponding matching city buildings every day in the previous quarter, where i is the number of the day, i=1, 2, ..., z, and z is the upper limit of the number; Step S22, adding up the number of commuting times of people every day in the previous quarter and taking the average value to obtain the average number of commuting times PTQ of the corresponding matching city building in the previous quarter; Step S23, calculate the standard deviation BZC of the commuting of the corresponding matching city building in the previous quarter through a formula, the specific formula is: ; Step S24, taking the average number of commuters plus the standard deviation of commuters as the first demarcation value, and taking the average number of commuters minus the standard deviation of commuters as the second demarcation value; taking the first demarcation value as the right endpoint and the second demarcation value as the left endpoint to obtain a screening interval for matching the historical passenger flow data corresponding to the city building; Step S25, comparing the number of commuting times of the matching city building every day in the previous quarter with the screening interval; if the number of commuting times of the matching city building is within the screening interval, the historical flow data of the corresponding days is retained, and the date corresponding to the retained historical flow data is recorded as the retention date b, b=1, 2, ..., x, x is a positive integer and x≤z; If the number of commuting times is outside the screening range, the historical passenger flow data of the corresponding days will be discarded; Step S26, obtaining the number of personnel commuting times RTQb corresponding to the reserved days, and obtaining the number of vehicle commuting times CTQb corresponding to the reserved days; Step S27, adding and averaging the number of commuting times of people corresponding to the reserved days to obtain the average number of commuting times of people PRT corresponding to the reserved days; adding and averaging the number of commuting times of vehicles corresponding to the reserved days to obtain the average number of commuting times of vehicles PCT corresponding to the reserved days; Step S28, calculating the correlation coefficient XG between personnel commuting and vehicle commuting by formula, the specific formula is: ; Among them, the value of XG is greater than zero and less than or equal to one; the closer XG is to one, the stronger the correlation between human commuting and vehicle commuting; Step S29, compare the correlation coefficient with the correlation coefficient threshold. If the correlation coefficient is greater than the correlation coefficient threshold, the corresponding matching city building is recorded as a first category building; if the correlation coefficient is less than the correlation threshold, the corresponding matching city building is recorded as a second category building.

[0023] Step S3, performing category analysis on the matching city buildings according to the categories to obtain the expected number of additional parking spaces for the matching city buildings; Among them, the parking situation of the first category of buildings is correlated with the flow of people; the parking situation of the second category of buildings is not correlated with the flow of people, or the correlation is low and can be ignored; In this embodiment, step S3 includes the following sub-steps: Step S31, performing a first category analysis on a first category building; The first category analysis process is as follows: Step S311, obtaining the historical flow data of people corresponding to the retention days of the first category of buildings, and obtaining the number of people retained in the matching city buildings for the retention days; Step S312, as Figure 2 As shown, a personnel time distribution diagram corresponding to the first category of buildings is drawn based on the number of personnel retention corresponding to the retention days; Step S313, obtaining a maximum point according to the personnel time distribution graph, and recording the largest maximum point as the peak point; wherein there may be multiple maximum points in the image, and the maximum point is the horizontal coordinate of the maximum value in a certain sub-interval in the function image, and the function image in front of the maximum point shows an upward trend, and the image behind the maximum point shows a downward trend; Step S314, obtaining the number of vehicles retained and the number of parking spaces in the parking lot corresponding to the maximum value point; Step S315: if the number of retained vehicles is greater than or equal to the number of parking spaces in the parking lot, the corresponding maximum point is recorded as a calculation maximum point; if the number of retained vehicles is less than the number of parking spaces in the parking lot, the corresponding maximum point is recorded as a training maximum point; Step S316, drawing a retention correlation diagram based on the number of retained personnel and the number of retained vehicles corresponding to the training maximum value points of multiple retention days, and then performing function fitting based on the retention correlation diagram to obtain a corresponding correlation function expression; Among them, function fitting is a mathematical method used to find a function (usually some type of mathematical model or formula) that can approximate or describe a given set of data points as closely as possible; Step S317, substituting the number of personnel retention corresponding to the calculated maximum value point into the correlation function expression to calculate the expected number of vehicles retained corresponding to the calculated maximum value point; adding and averaging multiple expected vehicle retention numbers to obtain the average expected vehicle retention number; Step S318, obtaining the expected number of additional parking spaces corresponding to the first category of buildings by subtracting the number of parking spaces in the parking lot from the average expected number of vehicles retained; Step S32, performing a second category analysis on the second category buildings; The second category analysis includes the following sub-steps: Step S321, obtaining historical vehicle data corresponding to the retention days of the second category building, and obtaining the number of vehicles retained at different times corresponding to the retention days; Step S322, obtaining the number of parking spaces in the parking lot corresponding to the second category building, and counting the congestion duration when the number of vehicles retained corresponding to the second category building is greater than or equal to the number of parking spaces in the parking lot; Step S323, obtaining the congestion rate of the corresponding reserved days by dividing the congestion duration by the total duration of each day; Step S324, adding up the congestion rates of all the reserved days and taking the average value to obtain the parking lot congestion rate corresponding to the second category of buildings; Step S325, comparing the parking lot congestion rate with the congestion rate threshold. If the parking lot congestion rate is greater than or equal to the first congestion rate threshold, P1 parking spaces are added to the second category building; if the parking lot congestion rate is less than the first congestion rate threshold and greater than or equal to the second congestion rate threshold, P2 parking spaces are added to the second category building; if the parking lot congestion rate is less than the second congestion rate threshold, no additional parking spaces are added; Wherein, the first congestion rate threshold is greater than the second congestion rate threshold, 0<P1<P2; Step S326, record P1 or P2 as the expected number of additional parking spaces corresponding to the second category of buildings.

[0024] Step S4, formulating a parking lot planning scheme for the target city building according to the expected number of additional parking spaces for the matching city building, and generating a planning completion signal or a pending signal; In this embodiment, step S4 includes the following sub-steps: Step S41, obtaining the number of parking spaces in the parking lot corresponding to the matching city building and the expected number of additional parking spaces, and obtaining the required number of parking spaces corresponding to the target city building by adding the number of parking spaces in the parking lot to the expected number of additional parking spaces; Step S42, obtaining the floor area of ​​the preset parking lot corresponding to the building in the target city, and obtaining the number of plannable parking spaces of the building in the target city by dividing the floor area of ​​the preset parking lot corresponding to the building in the target city by the floor area of ​​the standard parking spaces; Step S43, if the number of available parking spaces is greater than or equal to the required number of parking spaces, then parking spaces are planned according to the preset parking lot; if the number of available parking spaces is less than the required number of parking spaces, then subsequent steps are executed; Step S44, obtaining the existence of vacant areas on the road section where the building of the target city is located; when there are vacant areas, setting up temporary parking spaces in priority to the vacant areas; obtaining the area occupied by the vacant areas, and obtaining the number of additional parking spaces in the vacant areas by dividing the area occupied by the standard parking spaces; Step S45, comparing the number of additional parking spaces in the vacant area with the expected number of additional parking spaces, if the number of additional parking spaces in the vacant area is greater than or equal to the expected number of additional parking spaces, setting up temporary parking spaces in the vacant area; If the number of additional parking spaces is less than the expected number of additional parking spaces, parking spaces will be supplemented by combining vacant areas with on-street parking spaces; Step S46, when there is no vacant area, only on-street parking spaces are considered for supplementation; On-street parking spaces cannot be set up on the following roads: 1. Sidewalks and green belts of urban roads; 2. Road sections where the width of two-way traffic is less than eight meters or the width of one-way traffic is less than six meters; 3. Fire passages, barrier-free passages, and medical rescue passages; 4. Within 50 meters of the motor vehicle lanes on both sides of the entrances and exits of hospitals, schools, kindergartens, and nurseries, and within 5 meters of the entrances and exits of other government agencies, organizations, enterprises, institutions, and residential areas; 5. Urban expressways, urban trunk roads and other urban roads with heavy traffic; 6. Temporary parking spaces for urban roads shall not be marked on both sides of urban roads with a two-way traffic width of less than 12 meters or a one-way traffic width of less than 9 meters; 7. Other sections where parking spaces are prohibited; Step S47, counting the total number of on-street parking spaces and vacant parking spaces, and if the total number of actually added parking spaces is greater than or equal to the expected number of additional parking spaces, generating a planning completion signal; Step S48: If the total number of parking spaces actually added is less than the expected number of parking spaces added, a pending signal is generated.

[0025] Step S5, receiving a pending signal or a planning completion signal corresponding to a building in the target city; In practice, if it is a pending signal, the user can decide whether to adopt the corresponding parking space addition plan.

[0026] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0027] Example 2: Figure 4 As shown, based on another concept of the same invention, an intelligent planning system for urban building parking areas based on human flow data is proposed, including a data acquisition module, a human flow analysis module, a building analysis module, a parking space planning module and a user terminal; The data collection module is used to collect the historical pedestrian flow data and the historical vehicle flow data of the city buildings of the same type as the target city buildings in the previous quarter and send them to the pedestrian flow analysis module and the building analysis module; The pedestrian flow analysis module is used to analyze the historical pedestrian flow data of the matching city buildings in the previous quarter, classify the matching city buildings into first category buildings or second category buildings and send them to the building analysis module; The building analysis module is used to perform category analysis on the matching city buildings according to categories, obtain the expected number of additional parking spaces for the matching city buildings and send it to the parking space planning module; The parking space planning module is used to formulate a parking lot planning scheme for a target city building according to the expected number of additional parking spaces for the matching city building, generate a planning completion signal or a pending signal and send it to the user terminal; The user terminal is used to receive a pending signal or a planning completion signal corresponding to a building in a target city. If it is a pending signal, the user decides whether to adopt the corresponding parking space addition plan.

[0028] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent planning method for urban building parking areas based on pedestrian flow data, characterized in that: The method comprises the following steps: Step S1, collecting historical pedestrian flow data and historical vehicle flow data of the same type of buildings in the target city in the previous quarter; Step S2, analyzing the historical passenger flow data of the matching city buildings in the previous quarter, and classifying the matching city buildings into first category buildings or second category buildings; Step S3, performing category analysis on the matching city buildings according to the categories to obtain the expected number of additional parking spaces for the matching city buildings; Step S4, formulating a parking lot planning scheme for the target city building according to the expected number of additional parking spaces for the matching city building, and generating a planning completion signal or a pending signal.

2. The method for intelligent planning of urban building parking areas based on pedestrian flow data according to claim 1 is characterized in that: The historical passenger flow data includes the number of people retained and the number of people commuting each day, and the historical vehicle flow data includes the number of vehicles commuting and the number of vehicles retained in the parking lot.

3. The method for intelligent planning of parking areas of urban buildings based on passenger flow data according to claim 2 is characterized in that: The step S2 includes the following sub-steps: Step S21, obtaining historical passenger flow data of the matching city building, and obtaining the number of commuting times of the corresponding matching city building every day in the previous quarter; Step S22, adding up the number of commuting times of people every day in the previous quarter and taking the average value to obtain the average number of commuting times of the corresponding matching city building in the previous quarter; Step S23, calculating the standard deviation of commuting of people in the corresponding matching city building in the previous quarter; Step S24, taking the average number of commuters plus the standard deviation of commuting personnel as the first demarcation value, and taking the average number of commuters minus the standard deviation of commuting personnel as the second demarcation value; The first delimiting value is used as the right endpoint, and the second delimiting value is used as the left endpoint to obtain the screening interval of the historical flow data of people corresponding to the matching city building; Step S25, comparing the number of commuting times of the matching city building every day in the previous quarter with the screening interval; if the number of commuting times of the matching city building is within the screening interval, retaining the historical passenger flow data of the corresponding days, and recording the date corresponding to the retained historical passenger flow data as the retention date; If the number of commuting times is outside the screening range, the historical passenger flow data for the corresponding days will be discarded.

4. The method for intelligent planning of parking areas of urban buildings based on passenger flow data according to claim 3 is characterized in that: The step S2 further comprises the following sub-steps: Step S26, obtaining the number of personnel commuting times corresponding to the reserved days, and then obtaining the number of vehicle commuting times corresponding to the reserved days; Step S27, adding up the commuting times of the personnel corresponding to the reserved days and taking the average value to obtain the average commuting times of the personnel corresponding to the reserved days; The vehicle commuting times corresponding to the reserved days are added together and averaged to obtain the average vehicle commuting times corresponding to the reserved days; Step S28, calculating the correlation coefficient between human commuting and vehicle commuting; Step S29, compare the correlation coefficient with the correlation coefficient threshold. If the correlation coefficient is greater than the correlation coefficient threshold, the corresponding matching city building is recorded as a first category building; if the correlation coefficient is less than the correlation threshold, the corresponding matching city building is recorded as a second category building.

5. The method for intelligent planning of parking areas of urban buildings based on passenger flow data according to claim 1 is characterized in that: The step S3 includes the following sub-steps: Step S31, performing a first category analysis on a first category building; Step S32, performing a second category analysis on the second category buildings.

6. The method for intelligent planning of parking areas of urban buildings based on passenger flow data according to claim 5 is characterized in that: The first category analysis includes the following steps: Step S311, obtaining the historical flow data of people corresponding to the retention days of the first category of buildings, and obtaining the number of people retained in the matching city buildings for the retention days; Step S312, drawing a personnel time distribution diagram corresponding to the first category of buildings according to the number of personnel retention corresponding to the retention days; Step S313, obtaining a maximum value point according to the personnel time distribution diagram, and recording the largest maximum value point as a peak point; Step S314, obtaining the number of vehicles retained and the number of parking spaces in the parking lot corresponding to the maximum value point; Step S315, if the number of retained vehicles is greater than or equal to the number of parking spaces in the parking lot, the corresponding maximum point is recorded as the calculation maximum point; if the number of retained vehicles is less than the number of parking spaces in the parking lot, the corresponding maximum point is recorded as the training maximum point.

7. The method for intelligent planning of parking areas of urban buildings based on passenger flow data according to claim 6 is characterized in that: The first category analysis further comprises the following steps: Step S316, drawing a retention correlation diagram based on the number of retained personnel and the number of retained vehicles corresponding to the training maximum value points of multiple retention days, and then performing function fitting based on the retention correlation diagram to obtain a corresponding correlation function expression; Step S317, substituting the number of personnel retention corresponding to the calculated maximum value point into the correlation function expression to calculate the expected number of vehicles retained corresponding to the calculated maximum value point; adding and averaging multiple expected vehicle retention numbers to obtain the average expected vehicle retention number; Step S318, obtaining the expected number of additional parking spaces corresponding to the first category of buildings by subtracting the number of parking spaces in the parking lot from the average expected number of vehicles to be retained.

8. The method for intelligent planning of parking areas of urban buildings based on passenger flow data according to claim 5 is characterized in that: The second category analysis further comprises the following steps: Step S321, obtaining historical vehicle data corresponding to the retention days of the second category building, and obtaining the number of vehicles retained at different times corresponding to the retention days; Step S322, obtaining the number of parking spaces in the parking lot corresponding to the second category building, and counting the congestion duration when the number of vehicles retained corresponding to the second category building is greater than or equal to the number of parking spaces in the parking lot; Step S323, obtaining the congestion rate of the corresponding reserved days by dividing the congestion duration by the total duration of each day; Step S324, adding up the congestion rates of all the reserved days and taking the average value to obtain the parking lot congestion rate corresponding to the second category of buildings; Step S325, comparing the parking lot congestion rate with the congestion rate threshold. If the parking lot congestion rate is greater than or equal to the first congestion rate threshold, P1 parking spaces are added to the second category building; if the parking lot congestion rate is less than the first congestion rate threshold and greater than or equal to the second congestion rate threshold, P2 parking spaces are added to the second category building; if the parking lot congestion rate is less than the second congestion rate threshold, no additional parking spaces are added; Wherein, the first congestion rate threshold is greater than the second congestion rate threshold, 0<P1<P2; Step S326, record P1 or P2 as the expected number of additional parking spaces corresponding to the second category of buildings.

9. The method for intelligent planning of parking areas of urban buildings based on passenger flow data according to claim 8 is characterized in that: The step S4 includes the following sub-steps: Step S41, obtaining the number of parking spaces in the parking lot corresponding to the matching city building and the expected number of additional parking spaces, and obtaining the required number of parking spaces corresponding to the target city building by adding the number of parking spaces in the parking lot to the expected number of additional parking spaces; Step S42, obtaining the floor area of ​​the preset parking lot corresponding to the building in the target city, and obtaining the number of plannable parking spaces of the building in the target city by dividing the floor area of ​​the preset parking lot corresponding to the building in the target city by the floor area of ​​the standard parking spaces; Step S43, if the number of available parking spaces is greater than or equal to the required number of parking spaces, then parking spaces are planned according to the preset parking lot; if the number of available parking spaces is less than the required number of parking spaces, then subsequent steps are executed; Step S44, obtaining the existence of vacant areas on the road section where the target city building is located; when vacant areas exist, setting up temporary parking spaces in priority to the vacant areas; obtaining the area occupied by the vacant areas, and obtaining the number of additional parking spaces in the vacant areas by dividing the area occupied by the standard parking spaces.

10. The method for intelligent planning of parking areas of urban buildings based on passenger flow data according to claim 9, characterized in that: The step S4 also includes the following sub-steps: Step S45, comparing the number of additional parking spaces in the vacant area with the expected number of additional parking spaces, if the number of additional parking spaces in the vacant area is greater than or equal to the expected number of additional parking spaces, setting up temporary parking spaces in the vacant area; If the number of additional parking spaces is less than the expected number of additional parking spaces, parking spaces will be supplemented by combining vacant areas with on-street parking spaces; Step S46, when there is no vacant area, only on-street parking spaces are considered for supplementation; Step S47, counting the total number of on-street parking spaces and vacant parking spaces, and if the total number of actually added parking spaces is greater than or equal to the expected number of additional parking spaces, generating a planning completion signal; Step S48: If the total number of parking spaces actually added is less than the expected number of parking spaces added, a pending signal is generated.

11. An intelligent planning system for urban building parking areas based on pedestrian flow data, characterized in that: In combination with the method for intelligent planning of parking areas of urban buildings based on pedestrian flow data according to any one of claims 1 to 10, the system comprises: The data collection module is used to collect the historical pedestrian flow data and vehicle flow data of the same type of buildings in the target city in the previous quarter, and send them to the pedestrian flow analysis module and the building analysis module; A pedestrian flow analysis module is used to analyze the historical pedestrian flow data of the matching city buildings in the previous quarter, classify the matching city buildings into first category buildings or second category buildings and send them to the building analysis module; The building analysis module is used to analyze the matching city buildings according to their categories, obtain the expected number of additional parking spaces for the matching city buildings and send it to the parking space planning module; The parking space planning module is used to formulate a parking lot planning scheme for the target city building according to the expected number of additional parking spaces for the matching city building, generate a planning completion signal or a pending signal and send it to the user terminal; The user terminal is used to receive the pending signal or planning completion signal corresponding to the building in the target city. If it is a pending signal, the user decides whether to adopt the corresponding parking space addition plan.

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