CN111353657B - Prediction method and device for per-capita car holding capacity and terminal equipment - Google Patents
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Abstract
The application is suitable for the technical field of data processing, and provides a method, a device and a terminal device for predicting the holding capacity of a human-average car, wherein the method comprises the following steps: constructing a prediction model of the human-average car holding capacity of the cars based on an animal population growth model and a model of car quantity changing with the environment; determining a first model parameter of the historical influence factor data in the prediction model based on the historical influence factor data influencing the holding capacity of the people-average car in historical time and the prediction model; predicting the conservation quantity of the people-average car at the target time based on the prediction model, the first model parameter and prediction influence factor data influencing the conservation quantity of the people-average car at the target time; the manned car holding amount of the target time predicted by the prediction model is more accurate, and the accuracy of key factors for making future urban traffic development policies and traffic construction policies is further guaranteed.
Description
Technical Field
The application belongs to the technical field of data processing, and particularly relates to a method and a device for predicting the holding capacity of a people-average car and terminal equipment.
Background
With the improvement of living standard, the holding capacity of cars is continuously increased, and the holding capacity of cars is a key factor for making a future urban traffic development policy and a traffic construction policy, so that the prediction of the holding capacity of cars in the future has an important influence on future traffic planning.
At present, most of methods for predicting the holding capacity of cars are predicted based on the change trend of the holding capacity of cars in years in history, the considered influence factors are single, for example, only the influence of economic level on the purchasing behavior of resident cars is considered, the prediction is inaccurate, and the future traffic planning is influenced.
Disclosure of Invention
The embodiment of the application provides a prediction method and device for the conservation quantity of a human-average car and terminal equipment, and can solve the problem that the conservation quantity of the car is not accurately predicted.
In a first aspect, an embodiment of the present application provides a method for predicting a car occupancy, including:
obtaining a prediction model of the per-capita car inventory of cars, wherein the prediction model comprises an animal population growth model and a model of car quantity changing with the environment;
determining a first model parameter of the historical influence factor data in the prediction model based on the historical influence factor data influencing the holding capacity of the people-average car in historical time and the prediction model;
acquiring predicted influence factor data influencing the holding capacity of the people-average car in the target time;
and predicting the human-average car holding capacity of the target time based on the prediction model, the first model parameters and the prediction influence factor data.
In a second aspect, an embodiment of the present application provides an apparatus for predicting a car occupancy, including:
the model acquisition module is used for acquiring a prediction model of the per-capita car inventory of the cars, wherein the prediction model comprises an animal population growth model and a model of car quantity changing along with the environment;
the parameter determination module is used for determining a first model parameter of the historical influence factor data in the prediction model based on the historical influence factor data influencing the holding capacity of the people-average car in historical time and the prediction model;
the data acquisition module is used for acquiring the predicted influence factor data influencing the keeping quantity of the people-average car in the target time;
and the prediction module is used for predicting the human-average car holding capacity of the target time based on the prediction model, the first model parameters and the prediction influence factor data.
In a third aspect, an embodiment of the present application provides a terminal device, including: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for predicting the occupancy of a human-average car as described in any one of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement the method for predicting the occupancy of a human-mean-vehicle of any one of the above first aspects.
In a fifth aspect, the present application provides a computer program product, when the computer program product runs on a terminal device, the terminal device is caused to execute the method for predicting the occupancy of the human-average car according to any one of the first aspect.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
Compared with the prior art, the embodiment of the application has the advantages that: the method comprises the steps of obtaining a prediction model of the conservation quantity of the human-average car, wherein the prediction model of the conservation quantity of the human-average car is composed of an animal population growth model and a model that the number of cars changes along with the environment, determining a first model parameter in the prediction model according to historical influence factor data influencing the conservation quantity of the human-average car in historical time, and finally predicting the conservation quantity of the human-average car in the target time based on the prediction model, the first model parameter and the prediction influence factor data influencing the conservation quantity of the human-average car in the target time; the model for predicting the conservation quantity of the people-average car of the car not only considers the growth rule of the car, but also considers the factor of the car changing along with the influence factor, and the model for predicting is more accurate.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic view of an application scenario of a prediction method for a human-average car holding capacity according to an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram illustrating a method for predicting the occupancy of a human-average car according to an embodiment of the present disclosure;
FIG. 3 is a flowchart illustrating a method for calculating parameters of a first model according to an embodiment of the present disclosure;
FIG. 4 is a schematic flow chart diagram illustrating a method for checking the predicted occupancy of a human-average car according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a device for predicting a retention capacity of a human-average car according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a terminal device according to an embodiment of the present application;
fig. 7 is a block diagram of a partial structure of a computer according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
At present, two methods are mainly used for predicting the holding capacity of the people-average car:
the first method is to predict according to a car change trend fitting function in historical time, and commonly used prediction methods comprise a trend extrapolation method and a time sequence method, wherein the trend extrapolation method is to extrapolate the future according to the change trend of the car holding amount in the historical time to determine the change of the car in the future; the time series method is used for predicting the development trend of a future car by using sequence data of historical time according to the fact that the development of things is malleable.
The second method is to predict the conservation quantity of the human-average car based on the economic development level (GDP), firstly determine the rule that the conservation quantity of the car changes along with the economic level by combining the economic development level and the car conservation quantity over the years, secondly predict the future economic development level, and finally predict the conservation quantity of the human-average car in the future based on the rule that the conservation quantity of the car changes along with the economic level.
The first method described above has the disadvantages that: the car holding capacity is not infinitely amplified, and the future large deviation of the car holding capacity can be predicted only according to the existing trend of the car holding capacity, so that the objective development rule of the car holding capacity cannot be accurately reflected.
The second method has the disadvantages that: the influence of the economic level on the car purchasing behavior of the consumer is not absolute, the factors influencing the car purchasing of the consumer are multivariate and complex, and the development rule of the car holding amount cannot be reflected by only considering the factors on one hand.
Therefore, the method for predicting the conservation quantity of the human-average car, which considers various influence factors, can objectively reflect the development rule of the motor vehicle and has certain reusability, is provided.
Fig. 1 is a schematic view of an application scenario of a prediction method of a human-average car holding capacity according to an embodiment of the present application, where the prediction method of the human-average car holding capacity can be used for predicting the human-average car holding capacity. The storage device 10 is used for storing data of historical time, the processor 20 is used for constructing a prediction model, obtaining the data of the historical time from the storage device 10, and predicting the holding capacity of the people-average car at the future time based on the constructed prediction model and the data of the historical time.
The method for predicting the retention of a human-average car according to the embodiment of the present application will be described in detail below with reference to fig. 1.
Fig. 2 shows a schematic flow chart of a prediction method of the occupancy of the human-average car provided by the application, and referring to fig. 2, the method is described in detail as follows:
s101, obtaining a prediction model of the per-capita car inventory of the cars, wherein the prediction model comprises an animal population growth model and a model of car quantity changing with the environment.
In this embodiment, the animal population growth model may be stored in advance, or may be obtained from an external storage device. The model of the number of cars changing with the environment can be stored in advance or obtained from an external storage device.
The animal population growth model is used for describing the growth rule of the cars, the model of the number of the cars changing along with the environment reflects the hysteresis of the car purchased by a consumer on the change of the environment, and the prediction model constructed according to the model can comprehensively and accurately reflect the change rule of the cars.
In a possible implementation manner, the implementation process of step S101 may include:
the animal population growth model is as follows:
in the formula (I), the compound is shown in the specification,
is the species scale at time t, x is the time variable, γ is the species scale saturation, α is the second model parameter;
the model of the car quantity changing with the environment is as follows:
wherein the content of the first and second substances,
the predicted value of the per-capita car holding amount when the hysteresis is considered for the time t,
the per-capita car holding capacity at the time of t-1, ө is a lag coefficient,
the predicted value of the car holding amount when the hysteresis is not considered for the time t;
using animal population growth models
In models of the number of cars displaced as a function of the environment
And obtaining a prediction model of the per-capita car inventory of the car:
wherein the content of the first and second substances,
the predicted value of the per-capita car holding amount at the time t is obtained; gamma is the human mouth scale saturation; ө is a hysteresis coefficient; α is the second modelA parameter; when the first model parameters are determined using historical data,
historical influence factor data of the ith influence on the per-capita car holding capacity at the time t; when the man-by-man car holding amount of the target time is predicted,
the data of the predicted influence factors influencing the retention amount of the ith human-average car at the time t; when the historical data are used for determining the first model parameter, n is the total number of historical influence factor data influencing the per-capita car holding amount at t time; when the per-capita car holding amount at the target time is predicted, n is the total number of the predicted influence factor data influencing the per-capita car holding amount at time t,
and
are historical influence factor data
Is measured by the first model parameters of (a),
the car reserves are per capita at time t-1.
In this embodiment, since the influence factors that influence the retention amount of the all-person-vehicle can be multiple, the influence factors can be multiple
。
It should be noted that the first model parameter is determined according to the data of the historical time at a later stage, and the second model parameter may be obtained through experience or fitting of the model, for example,
the value of (2) depends on the urbanization level of the city, the super-huge city can be 0.4, and ө can be 0.05.
S102, determining a first model parameter of the historical influence factor data in the prediction model based on the historical influence factor data influencing the holding capacity of the people-average car in historical time and the prediction model.
In this embodiment, the historical influencer data includes at least: the GDP is one of a first population average GDP, a first target age group population proportion, a first household population proportion and a first family household population proportion, wherein the first target age group population proportion can be 20-60 age group population proportions, the household population proportion refers to the ratio of a local household population to a local general population, and the family household population proportion refers to the ratio of a household population to the general population. One type of historical influencer data corresponds to a set of first model parameters.
The first model parameter is determined using historical influencing factor data affecting the retention of the people-average car in the historical time, and because the first influencing factor data is known in the historical time and the retention of the people-average car in the historical time, determining the first model parameter in the predictive model using the known determined data can make the first model parameter more accurate.
By way of example, the historical time may be historical 5 years, historical 3 years, historical 4 years, or the like, and may be set as desired.
As shown in fig. 3, in a possible implementation manner, the implementation process of step S102 may include:
and S1021, acquiring historical influence factor data influencing the retention capacity of the people-average car in each prediction period in the historical time and the retention capacity of the people-average car in each prediction period.
In this embodiment, the prediction period may be one year, or may be set as needed.
And acquiring historical influence factor data of each year in historical time and the per-year per-person car holding capacity.
As an example, if the historical time is 3 years, respectively 2017, 2018 and 2019, historical influence factor data influencing the per-capita car holding capacity in 2017 and the per-capita car holding capacity in 2017 need to be acquired; historical influence factor data influencing the conservation quantity of the people-average cars in 2018, and the conservation quantity of the people-average cars in 2018; historical influence factor data influencing the stock of the people-average cars in 2019, and the stock of the people-average cars in 2019.
And S1022, fitting the prediction model based on the historical influence factor data and the per-person car holding capacity in each prediction period to obtain a first model parameter in the prediction model.
In this embodiment, when the prediction model is fitted, the historical influence factor data is an independent variable, the retention of the people-average car is a dependent variable, and since the historical influence factor data may be multiple, the first model parameter obtained by fitting may also be a multiple-group result.
S103, acquiring the predicted influence factor data influencing the keeping quantity of the people-average car in the target time.
In this embodiment, the predicting the influence factor data at least includes: the second population GDP, the second target age group population proportion, the second household population proportion and the second family household population proportion.
In a possible implementation manner, the implementation process of step S103 may include:
and determining the predicted influence factor data of the target time influencing the per-capita car holding capacity based on the historical influence factor data and/or the city planning data.
In this embodiment, some of the predicted influence factor data may be queried from the city planning data, for example, the average population GDP and the population ratio of the second target age group, etc., which have been planned in advance in the city planning data, so that the predicted influence factor data may be directly queried from the city planning data.
For second impact factor data that cannot be queried from the city planning data. For example, the population proportion of the second domicile and the population proportion of the second domicile can be obtained according to the variation trend of the historical influence factor data in the historical time, a variation graph can be drawn for each historical influence factor data in the historical time, and the predicted influence factor data can be obtained according to the variation trend prediction of the variation graph.
The method for obtaining the predicted influence factor data according to the historical influence factor data in the historical time can also be used for obtaining the predicted influence factor data according to the function of the historical influence factor data in the historical time by constructing a function of the historical influence factor data and the first influence factor data, fitting the function to obtain parameters in the function, and finally determining the predicted influence factor data according to the constructed function, wherein in the function of the historical influence factor data and the time, the time is an independent variable, and the historical influence factor data is a dependent variable.
For example, if the historical influence factor data is the first family population ratio, the function y = ax + b is constructed, where y is the first family population ratio, x is time, and a and b are both parameters, values of a and b may be determined by calculation according to the first family population ratio in the historical time, and finally, the second family population ratio at the target time is determined according to y = ax + b, and the values of a and b.
And S104, predicting the holding capacity of the human-average car at the target time based on the prediction model, the first model parameters and the prediction influence factor data.
In this embodiment, the first model parameter of the predicted influence factor data and the historical influence factor data corresponding to each predicted influence factor data is input to the prediction model, and the per-capita car holding capacity at the target time can be obtained.
For example, if the predicted influence data is the second population-average GDP and the second target age group population ratio, the second population-average GDP may use the first model parameter of the first population-average GDP, and the second target age group population ratio may use the first model parameter of the first target age group population ratio.
In the embodiment of the application, based on an animal population growth model and a model that the number of cars changes along with the environment, a prediction model of the per-capita car holding capacity of the cars is constructed, the growth rule of the cars is considered, and factors that the cars change along with influence factors are considered, the prediction model of the component is more accurate, in addition, the first model parameter is obtained based on historical influence factor data influencing the per-capita car holding capacity in historical time, the accuracy of the first model parameter can be ensured, the per-capita car holding capacity at the target time predicted by the prediction model is more accurate, and the accuracy of key factors for formulating future urban traffic development policies and traffic construction policies is further ensured.
In a possible implementation manner, after step S104, the method may further include:
and S105, checking whether the predicted per capita car holding amount at the target time is matched with the actual parking space supply.
In this embodiment, it is necessary to detect the retention amount of the human-average car at the target time predicted by the prediction model, and detect the accuracy of the prediction model at the same time, if the retention amount of the human-average car at the target time predicted by the prediction model is not matched with the actual parking space supply, it indicates that the prediction model is inaccurate, the retention amount of the human-average car at the target time predicted by the prediction model cannot be used, the prediction model may be improved, and if the retention amount of the human-average car at the target time predicted by the prediction model is matched with the actual parking space supply, it indicates that the prediction model predicts accurately, and the retention amount of the human-average car at the target time predicted by the prediction model may be used.
As shown in fig. 4, in a possible implementation manner, the implementation process of step S105 may include:
and S1051, acquiring the actual parking space supply quantity and the population number of the cars at the target time.
In this embodiment, the actual parking space supply number of the car at the target time may be obtained from the city planning data, the population number may be obtained from the city planning data, and if the actual parking space supply number of the car at the target time cannot be obtained from the city planning data, the population number at the target time may be obtained according to a variation trend of the population number at the historical time.
And S1052, calculating the per-capita car saturation value at the target time based on the actual parking space supply quantity and the population total.
In this embodiment, the saturation value of the people-average car is the maximum value of the holding capacity of the people-average car, and exceeding the saturation value of the people-average car indicates that the cars are too many, and the actual parking spaces in the city cannot meet the requirements of the cars.
The per-capita car saturation value is the ratio of the actual number of the parking space supplies to the total number of the population.
And S1053, judging whether the per-capita car holding amount at the target time is matched with the actual parking space supply or not based on the per-capita car saturation value and the per-capita car holding amount at the target time.
In this embodiment, it is determined whether the retention amount of the people-by-person car at the target time matches the actual parking space supply, and it is possible to detect whether the prediction result is accurate or whether the prediction model is constructed correctly.
In a possible implementation manner, the implementation procedure of step S1053 may include:
and if the saturation value of the people-average car is larger than or equal to the retention amount of the people-average car at the target time, matching the retention amount of the people-average car at the target time with the supply of the actual parking space.
And if the saturation value of the people-average car is smaller than the holding capacity of the people-average car at the target time, the holding capacity of the people-average car at the target time is not matched with the actual parking space supply.
In this embodiment, if the saturation value of the people-average car is greater than or equal to the retention capacity of the people-average car at the target time, it is indicated that the predicted retention capacity of the people-average car at the target time is accurate, and the actual parking space supply quantity can meet the requirements of the cars; if the saturation value of the people-average car is smaller than the quantity of the people-average car reserved at the target time, the predicted quantity of the people-average car reserved at the target time is inaccurate, and the actual parking space supply quantity cannot meet the requirement of the car.
In the embodiment of the application, the conservation quantity of the people-oriented cars at the target time is detected to be matched with the supply of the actual parking spaces, whether the conservation quantity of the people-oriented cars at the predicted target time meets the actual condition can be detected, and whether the predicted data is available is judged, so that a reliable basis is provided for city planning.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 5 shows a block diagram of a prediction apparatus for a per-capita car inventory according to an embodiment of the present application, and for convenience of explanation, only the parts related to the embodiment of the present application are shown.
Referring to fig. 5, the apparatus 200 may include: a model acquisition module 210, a parameter determination module 220, a data acquisition module 230, and a prediction module 240.
The model obtaining module 210 is configured to obtain a prediction model of the human-average car inventory of the cars, where the prediction model includes an animal population growth model and a model of car quantity changing with the environment;
the parameter determining module 220 is configured to determine a first model parameter of the historical influence factor data in the prediction model based on the historical influence factor data influencing the retention amount of the people-average car in historical time and the prediction model;
the data acquisition module 230 is used for acquiring the predicted influence factor data influencing the holding capacity of the people-average car in the target time;
and the prediction module 240 is used for predicting the human-average car holding capacity at the target time based on the prediction model, the first model parameters and the prediction influence factor data.
In one possible implementation form of the method,
the model of the car quantity changing with the environment is as follows:
wherein the content of the first and second substances,
the predicted value of the per-capita car holding amount when the hysteresis is considered for the time t,
the per-capita car holding capacity at the time of t-1, ө is a lag coefficient,
the predicted value of the car holding amount when the hysteresis is not considered for the time t;
wherein the content of the first and second substances,
adopting an animal population growth model, wherein the animal population growth model comprises the following steps:
in the formula (I), the compound is shown in the specification,
is the species scale at time t, x is the time variable, γ is the species scale saturation, and α is the second model parameter.
In one possible implementation, the historical impact factor data includes at least: the GDP is one of the first population average GDP, the population proportion of a first target age group, the population proportion of a first household and the population proportion of a first family household;
the parameter determination module 220 may be specifically configured to:
acquiring historical influence factor data influencing the retention capacity of the people-average car in each prediction period in historical time and the retention capacity of the people-average car in each prediction period;
and fitting the prediction model based on the historical influence factor data and the per-person car holding capacity in each prediction period to obtain a first model parameter in the prediction model.
In a possible implementation manner, the data obtaining module 230 may specifically be configured to:
and determining the predicted influence factor data of the target time influencing the per-capita car holding capacity based on the historical influence factor data and/or the city planning data.
In one possible implementation, the apparatus 200 further includes:
and the inspection module is used for inspecting whether the predicted per capita car holding amount at the target time is matched with the actual parking space supply.
In one possible implementation, the verification module may include:
the data acquisition unit is used for acquiring the actual parking space supply quantity and the population number of the car at the target time;
the calculating unit is used for calculating the per-capita car saturation value at the target time based on the actual parking space supply quantity and the population total;
and the comparison unit is used for judging whether the per-capita car holding amount at the target time is matched with the actual parking space supply or not based on the per-capita car saturation value and the per-capita car holding amount at the target time.
In a possible implementation manner, the comparing unit may specifically be configured to:
and if the saturation value of the people-average car is larger than or equal to the retention amount of the people-average car at the target time, matching the retention amount of the people-average car at the target time with the supply of the actual parking space.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
An embodiment of the present application further provides a terminal device, and referring to fig. 6, the terminal device 400 may include: at least one processor 410, a memory 420, and a computer program stored in the memory 420 and executable on the at least one processor 410, wherein the processor 410 when executing the computer program implements the steps of any of the method embodiments described above, such as the steps S101 to S104 in the embodiment shown in fig. 2. Alternatively, the processor 410, when executing the computer program, implements the functions of the modules/units in the above-described device embodiments, such as the functions of the modules 110 to 140 shown in fig. 5.
Illustratively, a computer program may be partitioned into one or more modules/units, which are stored in the memory 420 and executed by the processor 410 to accomplish the present application. The one or more modules/units may be a series of computer program segments capable of performing specific functions, which are used to describe the execution of the computer program in the terminal device 400.
Those skilled in the art will appreciate that fig. 6 is merely an example of a terminal device and is not limiting and may include more or fewer components than shown, or some components may be combined, or different components such as input output devices, network access devices, buses, etc.
The Processor 410 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 420 may be an internal storage unit of the terminal device, or may be an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. The memory 420 is used for storing the computer programs and other programs and data required by the terminal device. The memory 420 may also be used to temporarily store data that has been output or is to be output.
The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.
The prediction method for the occupancy of the car in the per capita provided by the embodiment of the application can be applied to terminal equipment such as a computer, a tablet computer, a notebook computer, a netbook, a Personal Digital Assistant (PDA) and the like, and the embodiment of the application does not limit the specific type of the terminal equipment at all.
Take the terminal device as a computer as an example. Fig. 7 is a block diagram illustrating a partial structure of a computer provided in an embodiment of the present application. Referring to fig. 7, the computer includes: a communication circuit 510, a memory 520, an input unit 530, a display unit 540, an audio circuit 550, a wireless fidelity (WiFi) module 560, a processor 570, and a power supply 580.
The following describes each component of the computer in detail with reference to fig. 7:
the communication circuit 510 may be used for receiving and transmitting signals during information transmission and reception or during a call, and in particular, receives an image sample transmitted by the image capturing device and then processes the image sample to the processor 570; in addition, the image acquisition instruction is sent to the image acquisition device. Typically, the communication circuit includes, but is not limited to, an antenna, at least one Amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, the communication circuit 510 may also communicate with networks and other devices via wireless communication. The wireless communication may use any communication standard or protocol, including but not limited to Global System for Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE)), e-mail, Short Messaging Service (SMS), and the like.
The memory 520 may be used to store software programs and modules, and the processor 570 performs various functional applications of the computer and data processing by operating the software programs and modules stored in the memory 520. The memory 520 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the computer, etc. Further, the memory 520 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The input unit 530 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the computer. Specifically, the input unit 530 may include a touch panel 531 and other input devices 532. The touch panel 531, also called a touch screen, can collect touch operations of a user on or near the touch panel 531 (for example, operations of the user on or near the touch panel 531 by using any suitable object or accessory such as a finger or a stylus pen), and drive the corresponding connection device according to a preset program. Alternatively, the touch panel 531 may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, and sends the touch point coordinates to the processor 570, and can receive and execute commands sent by the processor 570. In addition, the touch panel 531 may be implemented by various types such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. The input unit 530 may include other input devices 532 in addition to the touch panel 531. In particular, other input devices 532 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.
The display unit 540 may be used to display information input by a user or information provided to the user and various menus of the computer. The Display unit 540 may include a Display panel 541, and optionally, the Display panel 541 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch panel 531 may cover the display panel 541, and when the touch panel 531 detects a touch operation on or near the touch panel 531, the touch panel is transmitted to the processor 570 to determine the type of the touch event, and then the processor 570 provides a corresponding visual output on the display panel 541 according to the type of the touch event. Although in fig. 7, the touch panel 531 and the display panel 541 are two independent components to implement the input and output functions of the computer, in some embodiments, the touch panel 531 and the display panel 541 may be integrated to implement the input and output functions of the computer.
The audio circuit 550 may provide an audio interface between a user and a computer. The audio circuit 550 may transmit the received electrical signal converted from the audio data to a speaker, and convert the electrical signal into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 550 and converted into audio data, which is then processed by the audio data output processor 570, and then transmitted to, for example, another computer via the communication circuit 510, or the audio data is output to the memory 520 for further processing.
WiFi belongs to a short-distance wireless transmission technology, and a computer can help a user send and receive e-mails, browse webpages, access streaming media and the like through the WiFi module 560, which provides wireless broadband internet access for the user. Although fig. 7 shows the WiFi module 560, it is understood that it does not belong to the essential constitution of the computer, and may be omitted entirely as needed within the scope not changing the essence of the invention.
The processor 570 is a control center of the computer, connects various parts of the entire computer using various interfaces and lines, performs various functions of the computer and processes data by operating or executing software programs and/or modules stored in the memory 520 and calling data stored in the memory 520, thereby monitoring the entire computer. Optionally, processor 570 may include one or more processing units; preferably, the processor 570 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 570.
The computer also includes a power supply 580 (e.g., a battery) for powering the various components, and preferably, the power supply 580 is logically coupled to the processor 570 via a power management system that provides management of charging, discharging, and power consumption.
The embodiment of the application also provides a computer readable storage medium, wherein a computer program is stored in the computer readable storage medium, and when the computer program is executed by a processor, the steps in each embodiment of the prediction method for the conservation quantity of the people-average car can be realized.
The embodiment of the application provides a computer program product, and when the computer program product runs on a mobile terminal, the steps in each embodiment of the prediction method for the retention capacity of the people-average car can be realized when the mobile terminal is executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (6)
1. A method for predicting the holding capacity of a human-average car is characterized by comprising the following steps:
obtaining a prediction model of the per-capita car inventory of cars, wherein the prediction model comprises an animal population growth model and a car quantity-as-environment change model, and the car quantity-as-environment change model comprises:
wherein the content of the first and second substances,
the predicted value of the per-capita car holding amount when the hysteresis is considered for the time t,
the per-capita car holding capacity at the time of t-1, ө is a lag coefficient,
the predicted value of the car holding amount when the hysteresis is not considered for the time t;
wherein the content of the first and second substances,
adopting an animal population growth model, wherein the animal population growth model comprises the following steps:
in the formula (I), the compound is shown in the specification,
is the species scale at time t, x is the time variable, γ is the species scale saturation, α is the second model parameter;
acquiring historical influence factor data influencing the retention capacity of the people-average car in each prediction period in historical time and the retention capacity of the people-average car in each prediction period;
fitting the prediction model based on the historical influence factor data and the per-person car holding capacity in each prediction period to obtain a first model parameter in the prediction model, wherein the historical influence factor data at least comprises the following data: when the prediction model is fitted, historical influence factor data are independent variables, the population proportion of the first household nationality and the population proportion of the first family household population, the historical influence factor data are multiple, the first model parameters obtained by fitting are also multiple groups of results, and if the first model parameters are multiple groups, one group with the highest fitting degree and significance is required to be searched as the first model parameters;
based on the historical influence factor data and/or the city planning data, determining prediction influence factor data of the target time influencing the holding capacity of the people-average car, wherein when the prediction influence factor data cannot be inquired from the city planning data, a change map is drawn based on the historical influence factor data, and the change map is obtained based on the change trend prediction of the change map; or a function of time and historical influence factor data is built, the function is fitted, parameters in the function are determined, and prediction influence factor data are determined according to the built function;
predicting the human-average car holding capacity of the target time based on the prediction model, the first model parameters and the prediction influence factor data;
and checking whether the predicted reserved quantity of the human-average car at the target time is matched with the actual parking space supply, determining whether the prediction model is accurate, if the predicted reserved quantity of the human-average car at the target time is not matched with the actual parking space supply, the prediction model is not accurate in prediction, improving the prediction model, and if the predicted reserved quantity of the human-average car at the target time is matched with the actual parking space supply, the prediction model is accurate.
2. The method of predicting the occupancy of a human-average car as set forth in claim 1, wherein said checking whether the predicted occupancy of a human-average car at the target time matches an actual parking space supply comprises:
acquiring the actual parking space supply quantity and the population number of the cars at the target time;
calculating the per-capita car saturation value at the target time based on the actual parking space supply quantity and the population total;
and judging whether the per-capita car holding capacity at the target time is matched with the actual parking space supply or not based on the per-capita car saturation value and the per-capita car holding capacity at the target time.
3. The method for predicting the retention capacity of the human-average car according to claim 2, wherein the step of determining whether the retention capacity of the human-average car at the target time matches the actual parking space supply based on the saturation value of the human-average car and the retention capacity of the human-average car at the target time comprises:
and if the saturation value of the people-average car is larger than or equal to the retention amount of the people-average car at the target time, matching the retention amount of the people-average car at the target time with the supply of the actual parking space.
4. An apparatus for predicting a retention amount of a human-average car, comprising:
the model acquisition module is used for acquiring a prediction model of the per-capita car inventory of the cars, wherein the prediction model comprises an animal population growth model and a car quantity variation with the environment model, and the car quantity variation with the environment model comprises:
wherein the content of the first and second substances,
the predicted value of the per-capita car holding amount when the hysteresis is considered for the time t,
the per-capita car holding capacity at the time of t-1, ө is a lag coefficient,
the predicted value of the car holding amount when the hysteresis is not considered for the time t;
wherein the content of the first and second substances,
adopting an animal population growth model, wherein the animal population growth model comprises the following steps:
in the formula (I), the compound is shown in the specification,
is the species scale at time t, x is the time variable, γ is the species scale saturation, α is the second model parameter;
the parameter determination module is used for determining a first model parameter of the historical influence factor data in the prediction model based on the historical influence factor data influencing the holding capacity of the people-average car in historical time and the prediction model;
the data acquisition module is used for acquiring the predicted influence factor data influencing the keeping quantity of the people-average car in the target time;
the prediction module is used for predicting the human-average car holding capacity of the target time based on the prediction model, the first model parameters and the prediction influence factor data;
the inspection module is used for inspecting whether the predicted reserved quantity of the human-average car at the target time is matched with the actual parking space supply or not, determining whether the prediction model is accurate or not, if the predicted reserved quantity of the human-average car at the target time is not matched with the actual parking space supply, the prediction model is inaccurate in prediction, improving the prediction model, and if the predicted reserved quantity of the human-average car at the target time is matched with the actual parking space supply, the prediction model is accurate;
the parameter determination module may be specifically configured to:
acquiring historical influence factor data influencing the retention capacity of the people-average car in each prediction period in historical time and the retention capacity of the people-average car in each prediction period;
fitting the prediction model based on the historical influence factor data and the per-person car holding capacity in each prediction period to obtain a first model parameter in the prediction model, wherein the historical influence factor data at least comprises the following data: when the prediction model is fitted, historical influence factor data are independent variables, the population proportion of the first household nationality and the population proportion of the first family household population, the historical influence factor data are multiple, the first model parameters obtained by fitting are also multiple groups of results, and if the first model parameters are multiple groups, one group with the highest fitting degree and significance is required to be searched as the first model parameters;
the data acquisition module may be specifically configured to:
based on the historical influence factor data and/or the city planning data, determining the predicted influence factor data of the target time influencing the holding capacity of the people-average car, wherein when the predicted influence factor data cannot be inquired from the city planning data, a change map is drawn based on the historical influence factor data, and the change trend is predicted based on the change map; or constructing a function of time and historical influence factor data, fitting the function, determining parameters in the function, and determining predicted influence factor data according to the function of the component.
5. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method of predicting the human-average car occupancy according to any one of claims 1 to 3 when executing the computer program.
6. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements the method for predicting the human-average car holding capacity according to any one of claims 1 to 3.
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