A parking lot revenue prediction method

Abstract

The present invention discloses a parking lot revenue prediction method, which relates to the field of digital parking operation technology. The method refines and analyzes the historical parking data of the parking lot to obtain different operating day types such as weekdays, weekends, holidays, and store anniversary events, and uses the average charging vehicle volume and average charging time of different operating day types as revenue parameters for future revenue calculation; based on a deep learning algorithm, a parking lot revenue prediction model for predicting future set time periods can be constructed; the prediction method can achieve accurate and specific time predictions for future set time periods for different parking lots, allowing parking lot managers to better plan parking resources. The prediction results can help the finance department formulate budgets more accurately, conduct cost control and financial planning, and reduce operating risks. Through revenue prediction, market trends and consumer behavior can be analyzed, and marketing strategies and pricing strategies can be adjusted in a timely manner to attract more users.

Description

Technical Field

[0001] The present invention relates to the technical field of digital parking operation, and in particular to a parking lot revenue prediction method. Background Art

[0002] The prediction of parking lot revenue is of great significance to the business decision-making of parking lots. At present, the prediction technology of parking lot revenue is mainly based on the linear statistical analysis of historical data;

[0003] Take the charging rules of a certain parking lot as an example: temporary parking fee standard: 4 yuan / hour; monthly rental fee standard: 100 yuan / month for internal employees; 150 yuan / month for merchant vehicles; 2 hours free for purchases over 50 yuan and under 100 yuan; 3 hours free for purchases over 100 yuan; 4 hours free with movie tickets.

[0004] The traditional method for predicting parking revenue is to calculate the number of parking spaces * 365 days * 4 yuan / day * parking occupancy rate. However, how many cars actually fall into the free parking period? The proportion of free parking varies significantly depending on the number of customers and the type of purchases on weekends, holidays, and weekdays. Traditional linear calculations cannot accurately predict this.

[0005] Therefore, there is an urgent need to design a method to intelligently analyze historical data and accurately predict the parking lot's revenue in a certain period of time in the future, which can provide an important reference for the commercial layout of the parking lot. Summary of the Invention

[0006] In order to solve the above-mentioned technical problem of parking lot revenue prediction, the present invention provides a parking lot revenue prediction method. The following technical solutions are adopted:

[0007] A parking lot revenue prediction method comprises the following steps:

[0008] Step 1: Collect historical parking data for a set historical time period of the parking lot. The historical parking data includes multiple operating days within the historical time period. The operating days are classified into different operating day types according to the day of the week. If the operating day is a statutory holiday, a statutory holiday type is added. If the operating day is a store anniversary event day, a store anniversary event day type is added.

[0009] Step 2: Based on the classification results, calculate the average toll traffic volume and average toll duration for each operating day category.

[0010] Step 3: Build a parking lot revenue prediction model based on a deep learning algorithm to predict the set time period. The decision tree of the parking lot revenue prediction model decomposes the time period to be predicted into multiple daily parking lot revenue forecasts in units of days. The input item of the daily parking lot revenue forecast is the corresponding operating day type. All daily parking lot revenue forecast results within the time period are accumulated to obtain the parking lot revenue forecast result within the time period.

[0011] By adopting the above technical solution, we can distinguish between the traditional method of linearly predicting parking lot revenue through historical data analysis and analyze the parking lot's historical parking data in detail to obtain different operating day types such as weekdays, weekends, holidays, and store anniversary events. The average charging vehicle volume and average charging time for each operating day type are used as revenue parameters for future revenue estimation.

[0012] When predicting parking lot revenue, you need to first determine a future time period, such as one month, half a month, or a set interval. This time period can actually be obtained from the calendar to determine the types of parking days included in it.

[0013] Based on the deep learning algorithm, a parking lot revenue prediction model can be constructed to predict the future set time period. The core decision formula of the parking lot revenue prediction model decision tree specifically considers how many different operating day types are included in the set time period. Based on the average charging vehicle volume and average charging duration of different operating day types in historical data, the corresponding average charging vehicle volume and average charging duration of different operating day types in the future set time period can be calculated, thereby achieving a more accurate prediction of the parking lot revenue on the operating day. Then, the parking lot revenue of all operating days in these future set time periods can be accumulated to obtain the parking lot revenue prediction result for the future set time period.

[0014] The prediction method can achieve accurate and specific time predictions for different parking lots within a set time period in the future. Parking lot managers can better plan parking resources, such as adjusting the allocation of parking spaces, expanding or reducing parking areas, thereby optimizing resource allocation and improving operational efficiency.

[0015] Forecast results can help finance departments more accurately formulate budgets, conduct cost control and financial planning, and reduce operational risks. Revenue forecasts can also help analyze market trends and consumer behavior, allowing timely adjustments to marketing and pricing strategies to attract more users.

[0016] Optionally, in step 1, the historical parking data refers to the operating data of the parking lot in operation within 30-90 days.

[0017] By adopting the above technical solution, historical parking data is used, generally referring to the operating data of parking lots within 30-90 days. Recent historical data is more valuable for predicting future parking lot revenue.

[0018] Optionally, in step 1, the operating days are classified into Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday, statutory holidays and store anniversary event days.

[0019] By adopting the above technical solution, during the actual operation of the parking lot, the actual difference between different operating days is large. Monday to Friday is generally called the normal state. At this time point, if it is some non-industrial areas or office clusters, the parking lot traffic volume is actually poor. The corresponding normal state is actually very different every day. For example, for some specific office locations, there may be large enterprises nearby that adopt staggered closing hours. In this way, the traffic volume from Monday to Friday also varies greatly. Therefore, the normal state is also subdivided into Monday, Tuesday, Wednesday, Thursday, and Friday.

[0020] Saturdays and Sundays are generally referred to as weekends. However, for the same reason mentioned above, there may be large-scale businesses in the area that only have one day off, so they need to be treated differently. Legal holidays and store anniversary events also experience a significant increase in traffic volume, so they need to be classified separately.

[0021] This more detailed type of operating day actually does not require a large amount of data and does not burden the computer processing. However, it is of great significance for subsequent parking lot revenue forecasts and can improve the universality and accuracy of parking lot revenue forecasting methods.

[0022] Optionally, in step 2, based on the classification results of the operating days, the average toll traffic flow and average toll duration from Monday to Sunday, and the average toll traffic flow and average toll duration on statutory holidays are calculated respectively.

[0023] By adopting the above technical solution, the average charging traffic flow and the average charging time are meaningful. For the parking lot income, some uncharged parking time is meaningless to process. Here, only the charged vehicles are counted as charging traffic flow for statistical charging time.

[0024] Optionally, the core decision tree formula of the parking lot revenue prediction model is as follows:

[0025]

[0026] Where R represents the parking revenue forecast result from day a to day b, P Pv is the predicted toll traffic volume on date i, P PTis the predicted charging duration on day i, and Cs is the parking fee standard of the parking lot during the time period from day a to day b.

[0027] By adopting the above technical solution, the time period from day a to day b is the future time period to be predicted. For date i within the future time period to be predicted, the corresponding predicted toll traffic volume, predicted toll duration and parking fee standard are the input items of the prediction model. The above predicted toll traffic volume and predicted toll duration are the average toll traffic volume and average toll duration of the corresponding operating day classification of the parking lot on the corresponding date, thereby realizing rapid and intelligent prediction of parking lot revenue.

[0028] Optionally, the decision tree of the parking lot revenue prediction model adds the daily average temperature impact factor γ. The formula for determining the daily average temperature impact factor γ is as follows:

[0029]

[0030] Where β1 is the temperature coefficient in the linear regression model, which represents the average change in parking lot revenue when the average temperature changes by one unit;

[0031] T a is the average temperature of the corresponding operating day classification, R a is the average revenue of the category for the corresponding operating day.

[0032] By adopting the above technical solutions, the impact of temperature on parking revenue mainly reflects changes in parking demand and parking experience;

[0033] This reflects changes in parking demand: in hot weather, people tend to seek out indoor parking to avoid the effects of the heat, which can lead to increased parking demand for indoor parking lots and thus increase revenue. Conversely, in cold weather, outdoor parking may become more popular, affecting indoor parking revenue.

[0034] High or low temperatures can affect the customer parking experience. Providing a comfortable parking environment (such as air conditioning and sunshades) can improve customer satisfaction, increase repeat business, and ultimately boost revenue. Conversely, poor parking conditions can lead to customer churn. Therefore, for a specific parking lot, temperature fluctuations can impact revenue.

[0035] Since the environmental conditions of parking lots are basically determined, it is necessary to study the effect of temperature on parking lot revenue prediction.

[0036] Since the time period is set in the future, such as half a month to a month, the corresponding daily temperature can actually be obtained through the official weather forecast channel. The average temperature or the highest and lowest temperatures can be obtained by calculation. The daily average temperature is used here to assist in predicting parking lot revenue. Weather factors such as humidity and rain actually have an impact on parking lot revenue. However, the accuracy of the prediction of future time periods is generally poor after one week, and the accuracy of the average temperature prediction is generally still within a relatively accurate range. Here, when predicting parking lot revenue, adding the daily average temperature influence factor γ can help achieve more accurate parking lot revenue prediction results.

[0037] To calculate the impact factor of daily average temperature on parking revenue, we can use the concept of an impact factor, which generally refers to the relative percentage change in one variable when another variable changes by one unit.

[0038] Optionally, the daily average temperature impact factor γ is determined using the following steps:

[0039] Step a, use linear regression model to fit the data and obtain the temperature coefficient β1;

[0040] Step b, calculate the average temperature T a and average income R a ;

[0041] Step c, the obtained β1, average temperature T a and average income R a Substitute the daily average temperature impact factor γ into the determination formula to calculate the daily average temperature impact factor γ for different operating day classifications.

[0042] By adopting the above technical solution, for example, if the linear regression analysis yields β1 = 50 (meaning that for every 1°C increase in temperature, the average income increases by 50 yuan), the average temperature is 20°C, and the average income is 2,000 yuan, then the impact factor is calculated as follows:

[0043]

[0044] This means that for every one-unit change in the average daily temperature, parking revenue changes by an average of 5%. This impact factor can help us understand the importance of temperature on parking revenue. If the impact factor is close to 0, it indicates that temperature has little impact on revenue; if the impact factor is much greater than 0, it indicates that temperature is a significant factor.

[0045] Optionally, in step c, the daily average temperature impact factors γ of different operating day classifications correspond to Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday, statutory holidays and store anniversary days, and are recorded as γ1, γ2, γ3, γ4, γ5, γ6, γ7, γ8, and γ9 respectively.

[0046] By adopting the above technical solution and calculating the daily average temperature impact factor γ for different operating day classifications separately, the temperature impact factor γ can be more finely introduced into the parking lot revenue prediction model, making the parking lot revenue prediction more accurate.

[0047] Optionally, the historical daily average temperature and future daily average temperature data can be obtained by fetching the future weather forecast officially released by the China Meteorological Administration through the Internet.

[0048] Optionally, the decision tree formula of the parking revenue prediction model with the daily average temperature influencing factor γ is as follows:

[0049]

[0050] By adopting the above technical solution, the Internet can crawl the future weather forecast officially released by the China Meteorological Administration for one month. The parking revenue prediction model with the daily average temperature influencing factor γ can make the parking revenue prediction results more accurate in the short term, such as the next 30 days.

[0051] For longer-term parking revenue forecasts, the daily average temperature factor γ can be removed because the weather forecast part of the longer-term parking revenue forecast has less reference value.

[0052] In summary, the present invention includes at least one of the following beneficial technical effects:

[0053] The present invention can provide a parking lot revenue prediction method, which analyzes the parking lot's historical parking data in detail to obtain different operating day types such as weekdays, weekends, holidays, and store anniversary events, and uses the average charging vehicle volume and average charging duration of different operating day types as revenue parameters for future revenue calculation;

[0054] Based on the deep learning algorithm, a parking lot revenue prediction model can be constructed to predict the future set time period. The core decision formula of the parking lot revenue prediction model decision tree specifically considers how many different operating day types are included in the set time period. Based on the average charging vehicle volume and average charging duration of different operating day types in historical data, the corresponding average charging vehicle volume and average charging duration of different operating day types in the future set time period can be calculated, thereby achieving a more accurate prediction of the parking lot revenue on the operating day. Then, the parking lot revenue of all operating days in these future set time periods can be accumulated to obtain the parking lot revenue prediction result for the future set time period.

[0055] The prediction method can achieve accurate and specific time predictions for different parking lots within a set time period in the future. Parking lot managers can better plan parking resources, such as adjusting the allocation of parking spaces, expanding or reducing parking areas, thereby optimizing resource allocation and improving operational efficiency.

[0056] Forecast results can help finance departments more accurately formulate budgets, conduct cost control and financial planning, and reduce operational risks. Revenue forecasts can also help analyze market trends and consumer behavior, allowing timely adjustments to marketing and pricing strategies to attract more users. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of a parking lot revenue prediction method of the present invention; DETAILED DESCRIPTION

[0058] The present invention will be further described in detail below with reference to the accompanying drawings.

[0059] An embodiment of the present invention discloses a parking lot revenue prediction method.

[0060] Reference Figure 1 , a parking lot revenue prediction method, comprising the following steps:

[0061] Step 1: Collect historical parking data for a set historical time period of the parking lot. The historical parking data includes multiple operating days within the historical time period. The operating days are classified into different operating day types according to the day of the week. If the operating day is a statutory holiday, a statutory holiday type is added. If the operating day is a store anniversary event day, a store anniversary event day type is added.

[0062] Step 2: Based on the classification results, calculate the average toll traffic volume and average toll duration for each operating day category.

[0063] Step 3: Build a parking lot revenue prediction model based on a deep learning algorithm to predict the set time period. The decision tree of the parking lot revenue prediction model decomposes the time period to be predicted into multiple daily parking lot revenue forecasts in units of days. The input item of the daily parking lot revenue forecast is the corresponding operating day type. All daily parking lot revenue forecast results within the time period are accumulated to obtain the parking lot revenue forecast result within the time period.

[0064] Different from the traditional method of linearly predicting parking lot revenue through historical data analysis, this method uses detailed analysis of parking lot historical parking data to obtain different operating day types such as weekdays, weekends, holidays, and store anniversary events. The average charging vehicle volume and average charging time for different operating day types are used as revenue parameters for future revenue calculation;

[0065] When predicting parking lot revenue, you need to first determine a future time period, such as one month, half a month, or a set interval. This time period can actually be obtained from the calendar to determine the types of parking days included in it.

[0066] Based on the deep learning algorithm, a parking lot revenue prediction model can be constructed to predict the future set time period. The core decision formula of the parking lot revenue prediction model decision tree specifically considers how many different operating day types are included in the set time period. Based on the average charging vehicle volume and average charging duration of different operating day types in historical data, the corresponding average charging vehicle volume and average charging duration of different operating day types in the future set time period can be calculated, thereby achieving a more accurate prediction of the parking lot revenue on the operating day. Then, the parking lot revenue of all operating days in these future set time periods can be accumulated to obtain the parking lot revenue prediction result for the future set time period.

[0067] The prediction method can achieve accurate and specific time predictions for different parking lots within a set time period in the future. Parking lot managers can better plan parking resources, such as adjusting the allocation of parking spaces, expanding or reducing parking areas, thereby optimizing resource allocation and improving operational efficiency.

[0068] Forecast results can help finance departments more accurately formulate budgets, conduct cost control and financial planning, and reduce operational risks. Revenue forecasts can also help analyze market trends and consumer behavior, allowing timely adjustments to marketing and pricing strategies to attract more users.

[0069] In step 1, the historical parking data refers to the operation data of the parking lot in operation within 30-90 days.

[0070] Historical parking data generally refers to the operating data of parking lots within 30-90 days. Recent historical data is more valuable for predicting future parking lot revenue.

[0071] In step 1, the operating days are classified into Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday, statutory holidays and store anniversary days.

[0072] In the actual operation of a parking lot, the actual difference between different operating days is large. Monday to Friday is generally called the normal state. At this time point, in some non-industrial areas or office clusters, the parking lot traffic volume is actually poor. The corresponding normal state is actually very different every day. For example, for some specific office locations, there may be large enterprises nearby that adopt staggered closing hours. In this case, the traffic volume from Monday to Friday also varies greatly. Therefore, the normal state is also subdivided into Monday, Tuesday, Wednesday, Thursday, and Friday.

[0073] Saturdays and Sundays are generally referred to as weekends. However, for the same reason mentioned above, there may be large-scale businesses in the area that only have one day off, so they need to be treated differently. Legal holidays and store anniversary events also experience a significant increase in traffic volume, so they need to be classified separately.

[0074] This more detailed type of operating day actually does not require a large amount of data and does not burden the computer processing. However, it is of great significance for subsequent parking lot revenue forecasts and can improve the universality and accuracy of parking lot revenue forecasting methods.

[0075] In step 2, based on the classification results of the operating days, the average toll traffic volume and average toll duration from Monday to Sunday, and the average toll traffic volume and average toll duration on statutory holidays are calculated respectively.

[0076] The average paid traffic flow and the average charging time are meaningful. For the parking lot income, some unpaid parking time is meaningless to deal with. Here, only the charged vehicles are counted as paid traffic flow for the statistical charging time.

[0077] The core formula of the decision tree for the parking lot revenue prediction model is as follows:

[0078]

[0079] Where R represents the parking revenue forecast result from day a to day b, P Pv is the predicted toll traffic volume on date i, P PT is the predicted charging duration on day i, and Cs is the parking fee standard of the parking lot during the time period from day a to day b.

[0080] The time period from day a to day b is the future time period to be predicted. For date i within the future time period to be predicted, the corresponding predicted toll traffic volume, predicted toll duration and parking fee standard are the input items of the prediction model. The above-mentioned predicted toll traffic volume and predicted toll duration are the average toll traffic volume and average toll duration of the corresponding operating day classification of the parking lot on the corresponding date, realizing rapid and intelligent prediction of parking lot revenue.

[0081] The decision tree of the parking lot revenue prediction model adds the daily average temperature impact factor γ. The formula for determining the daily average temperature impact factor γ is as follows:

[0082]

[0083] Where β1 is the temperature coefficient in the linear regression model, which represents the average change in parking lot revenue when the average temperature changes by one unit;

[0084] T a is the average temperature of the corresponding operating day classification, R a is the average revenue of the category for the corresponding operating day.

[0085] The impact of temperature on parking revenue mainly reflects changes in parking demand and parking experience;

[0086] This reflects changes in parking demand: in hot weather, people tend to seek out indoor parking to avoid the effects of the heat, which can lead to increased parking demand for indoor parking lots and thus increase revenue. Conversely, in cold weather, outdoor parking may become more popular, affecting indoor parking revenue.

[0087] High or low temperatures can affect the customer parking experience. Providing a comfortable parking environment (such as air conditioning and sunshades) can improve customer satisfaction, increase repeat business, and ultimately boost revenue. Conversely, poor parking conditions can lead to customer churn. Therefore, for a specific parking lot, temperature fluctuations can impact revenue.

[0088] Since the environmental conditions of parking lots are basically determined, it is necessary to study the effect of temperature on parking lot revenue prediction.

[0089] Since the time period is set in the future, such as half a month to a month, the corresponding daily temperature can actually be obtained through the official weather forecast channel. The average temperature or the highest and lowest temperatures can be obtained by calculation. The daily average temperature is used here to assist in predicting parking lot revenue. Weather factors such as humidity and rain actually have an impact on parking lot revenue. However, the accuracy of the prediction of future time periods is generally poor after one week, and the accuracy of the average temperature prediction is generally still within a relatively accurate range. Here, when predicting parking lot revenue, adding the daily average temperature influence factor γ can help achieve more accurate parking lot revenue prediction results.

[0090] To calculate the impact factor of daily average temperature on parking revenue, we can use the concept of an impact factor, which generally refers to the relative percentage change in one variable when another variable changes by one unit.

[0091] The daily average temperature impact factor γ is determined using the following steps:

[0092] Step a, use linear regression model to fit the data and obtain the temperature coefficient β1;

[0093] Step b, calculate the average temperature T a and average income R a ;

[0094] Step c, the obtained β1, average temperature T a and average income R a Substitute the daily average temperature impact factor γ into the determination formula to calculate the daily average temperature impact factor γ for different operating day classifications.

[0095] For example, if the linear regression analysis yields β1 = 50 (meaning that for every 1°C increase in temperature, income increases by an average of 50 yuan), the average temperature is 20°C, and the average income is 2,000 yuan, then the impact factor is calculated as follows:

[0096]

[0097] This means that for every one-unit change in the average daily temperature, parking revenue changes by an average of 5%. This impact factor can help us understand the importance of temperature on parking revenue. If the impact factor is close to 0, it indicates that temperature has little impact on revenue; if the impact factor is much greater than 0, it indicates that temperature is a significant factor.

[0098] In step c, the daily average temperature impact factors γ of different operating day classifications are recorded as γ1, γ2, γ3, γ4, γ5, γ6, γ7, γ8, and γ9, corresponding to Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday, statutory holidays, and store anniversary activities.

[0099] By calculating the daily average temperature impact factor γ for different operating day classifications separately, the temperature impact factor γ can be more finely introduced into the parking lot revenue prediction model, making the parking lot revenue prediction more accurate.

[0100] The historical and future daily average temperature data are obtained by crawling the future weather forecast officially released by the China Meteorological Administration through the Internet.

[0101] The decision tree formula of the parking lot revenue prediction model with the daily average temperature influencing factor γ is as follows:

[0102]

[0103] The official weather forecast released by the China Meteorological Administration can be obtained from the Internet for one month. The parking revenue prediction model with the daily average temperature factor γ is more accurate for the short term, such as the next 30 days.

[0104] For longer-term parking revenue forecasts, the daily average temperature factor γ can be removed because the weather forecast part of the longer-term parking revenue forecast has less reference value.

[0105] The following uses a specific embodiment to illustrate the implementation principle of a parking lot revenue prediction method of the present invention:

[0106] For a parking lot, its historical data is analyzed. Based on the parking lot historical data of the previous 60 days, the data of different operating days are obtained as shown in Table 1:

[0107] Table 1

[0108] Operating day type Average toll traffic Average charging time Monday 354 1.83 Tuesday 325 1.74 Wednesday 362 1.75 Thursday 291 1.72 Friday 350 1.71 Saturday 619 1.82 Sunday 389 1.87 statutory holidays 556 2.12

[0109] First, we need to make a revenue forecast for the next seven days, from September 26, 2024, to October 2, 2024. We also need to forecast parking revenue for the two days before and during National Day to provide reference data for operations management before National Day. The comparison between the predicted parking revenue and the actual revenue is shown in Table 2:

[0110] Table 2

[0111]

[0112] It can be seen that the gap between the predicted income of 17,854 yuan and the actual income of 17,676 yuan is small, and the prediction result is accurate.

[0113] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A parking lot revenue prediction method, characterized in that: The following steps are involved: Step 1: Collect historical parking data for a set historical time period of the parking lot. The historical parking data includes multiple operating days within the historical time period. The operating days are classified into different operating day types according to the day of the week. If the operating day is a statutory holiday, a statutory holiday type is added. If the operating day is a store anniversary event day, a store anniversary event day type is added. Step 2: Based on the classification results, calculate the average toll traffic volume and average toll duration for each operating day category. Step 3: Build a parking revenue prediction model for a set time period based on a deep learning algorithm. The decision tree of the parking revenue prediction model decomposes the time period to be predicted into multiple daily parking revenue predictions on a daily basis. The input item for the daily parking revenue prediction is the corresponding operating day type. All daily parking revenue prediction results within the time period are accumulated to obtain the parking revenue prediction result for the time period. The core formula of the decision tree for the parking lot revenue prediction model is as follows: Where R represents the parking revenue forecast result from day a to day b, P Pv is the predicted toll traffic volume on date i, P PT is the predicted charging duration on day i, and Cs is the parking fee standard of the parking lot during the time period from day a to day b; The decision tree of the parking lot revenue prediction model adds the daily average temperature impact factor γ. The formula for determining the daily average temperature impact factor γ is as follows: Where β1 is the temperature coefficient in the linear regression model, which represents the average change in parking lot revenue when the average temperature changes by one unit; T a is the average temperature of the corresponding operating day classification, R a is the average revenue of the category corresponding to the operating day; The decision tree formula of the parking lot revenue prediction model with the daily average temperature influencing factor γ is as follows:

2. A parking lot revenue prediction method according to claim 1, characterized in that: In step 1, the historical parking data refers to the operation data of the parking lot in operation within 30-90 days.

3. A parking lot revenue prediction method according to claim 2, characterized in that: In step 1, the operating days are classified into Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday, statutory holidays and store anniversary days.

4. A parking lot revenue prediction method according to claim 3, characterized in that: In step 2, based on the classification results of the operating days, the average toll traffic volume and average toll duration from Monday to Sunday, and the average toll traffic volume and average toll duration on statutory holidays are calculated respectively.

5. A parking lot revenue prediction method according to claim 4, characterized in that: The daily average temperature impact factor γ is determined using the following steps: Step a, use linear regression model to fit the data and obtain the temperature coefficient β1; Step b, calculate the average temperature T a and average income R a ; Step c, the obtained β1, average temperature T a and average income R a Substitute the daily average temperature impact factor γ into the determination formula to calculate the daily average temperature impact factor γ for different operating day classifications.

6. A parking lot revenue prediction method according to claim 5, characterized in that: In step c, the daily average temperature impact factors γ of different operating day classifications are recorded as γ1, γ2, γ3, γ4, γ5, γ6, γ7, γ8, and γ9, corresponding to Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday, statutory holidays, and store anniversary activities.

7. A parking lot revenue prediction method according to claim 6, characterized in that: The historical and future daily average temperature data are obtained by crawling the future weather forecast officially released by the China Meteorological Administration through the Internet.

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