CN113327452A - Data processing method and device for parking lot and electronic equipment - Google Patents
Data processing method and device for parking lot and electronic equipment Download PDFInfo
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Abstract
The application provides a data processing method and device for a parking lot and electronic equipment, relates to the technical field of data processing, and solves the technical problem that analysis of the number of remaining parking spaces in the parking lot is difficult. The method comprises the following steps: acquiring a training set, wherein the training set comprises first traffic flow data of a first parking lot and first parking lot data; training a random forest model by using the first traffic flow data and the first yard data to obtain a target regression model; and determining the predicted remaining parking space ratio of the second parking lot by using the target regression model.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a data processing method and apparatus for a parking lot, and an electronic device.
Background
At present, automobile consumption demand is rapidly developed all the time, and urban parking demand increases rapidly, consequently, in order to satisfy the demand that the user parks at any time, the entry in parking area all is open under general condition, in order to confirm the remaining parking stall number in parking area at any time, then need adopt one set of complete parking stall supervisory equipment to look over the remaining parking stall quantity in current parking area.
However, a complete set of parking space monitoring equipment is often too high in purchasing cost, so that a plurality of parking lots have the problem that the number of remaining parking spaces in a parking lot is difficult to estimate, and particularly when the number of parking spaces is short in a rush hour, a vehicle is randomly placed and enters the parking lot, so that congestion in the parking lot is caused.
Disclosure of Invention
The application aims to provide a data processing method and device for a parking lot and electronic equipment, so as to solve the technical problem that analysis of the number of remaining parking spaces in the parking lot is difficult.
In a first aspect, an embodiment of the present application provides a data processing method for a parking lot, where the method includes:
acquiring a training set, wherein the training set comprises first traffic flow data of a first parking lot and first parking lot data;
training a random forest model by using the first traffic flow data and the first yard data to obtain a target regression model;
and determining the predicted remaining parking space ratio of the second parking lot by using the target regression model.
In one possible implementation, the first traffic data includes: the method comprises the steps of obtaining the number of first actual vehicles entering and exiting in a preset time period and the number of remaining actual parking spaces at the end of the preset time period, wherein the number of the actual remaining parking spaces is obtained through a preset query interface.
In one possible implementation, the step of training a random forest model by using the first traffic data and the first yard data to obtain a target regression model includes:
training a random forest model by using the first traffic data and the first yard data, and determining the optimal parameters of the random forest model in preset number by using a grid search algorithm;
and obtaining a target regression model by using the optimal parameters.
In one possible implementation, the step of determining the predicted remaining space fraction of the second parking lot using the target regression model includes:
acquiring the number of second actual vehicles entering and exiting a second parking lot in a preset time period;
determining the predicted number of remaining vehicles of the second parking lot by using the target regression model based on the second actual vehicle entering and exiting quantity;
and determining the ratio of the predicted remaining parking spaces corresponding to the predicted remaining parking spaces.
In one possible implementation, the second yard data for the second parking lot includes a first total number of vehicles; the step of determining the remaining parking space ratio corresponding to the predicted remaining parking space number comprises the following steps:
and processing the predicted remaining parking space number and the first total parking space number to obtain a predicted remaining parking space number ratio.
In one possible implementation, the method further comprises:
acquiring a test set, wherein the test set comprises third traffic flow data and third parking lot data of the first parking lot;
determining evaluation indexes of the third traffic flow data and the third yard data;
judging whether the evaluation index is out of a preset threshold range;
and if the evaluation index is out of the range of the preset threshold value, training a random forest model by using the first traffic flow data and the first yard data to obtain a target regression model until the evaluation index is in the range of the preset threshold value.
In one possible implementation, the method further comprises:
processing the actual remaining parking space number and the second total parking space number of the first parking lot to obtain the actual remaining parking space number ratio;
and comparing the predicted remaining parking space number ratio with the actual remaining parking space number ratio to determine the prediction accuracy of the target regression model.
In a second aspect, there is provided a data processing apparatus for a parking lot, the apparatus comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a training set, and the training set comprises first traffic flow data of a first parking lot and first parking lot data;
the training module is used for training a random forest model by utilizing the first traffic flow data and the first parking lot data to obtain a target regression model;
and the determining module is used for determining the predicted remaining parking space ratio of the second parking lot by utilizing the target regression model.
In a third aspect, an embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program that is executable on the processor, and the processor implements the method of the first aspect when executing the computer program.
In a fourth aspect, this embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method of the first aspect.
The embodiment of the application brings the following beneficial effects:
the data processing method and device for the parking lot and the electronic equipment can acquire a training set, wherein the training set comprises first parking lot data and first parking lot data of a first parking lot; training a random forest model by using the first traffic flow data and the first yard data to obtain a target regression model; and determining the predicted remaining parking space ratio of the second parking lot by using the target regression model. In the scheme, the random forest model can be trained by utilizing the training set to obtain the target regression model, and the data in the training set comprises the first parking lot data and the first parking lot data, so that the predicted remaining parking space occupation ratio of the second parking lot can be determined by utilizing the target regression model.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flowchart of a data processing method for a parking lot according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a data processing device of a parking lot according to an embodiment of the present disclosure;
fig. 3 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "comprising" and "having," and any variations thereof, as referred to in the embodiments of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
At present, automobile consumption demand is rapidly developed all the time, and urban parking demand increases rapidly, consequently, in order to satisfy the demand that the user parks at any time, the entry in parking area all is open under general condition, in order to confirm the remaining parking stall number in parking area at any time, then need adopt one set of complete parking stall supervisory equipment to look over the remaining parking stall quantity in current parking area. However, a complete set of parking space monitoring equipment is often too high in purchasing cost, so that a plurality of parking lots have the problem that the number of remaining parking spaces in a parking lot is difficult to estimate, and particularly when the number of parking spaces is short in a rush hour, a vehicle is randomly placed and enters the parking lot, so that congestion in the parking lot is caused.
Based on this, the embodiment of the application provides a data processing method and device for a parking lot and an electronic device, and the technical problem that analysis of the number of remaining parking spaces in the parking lot is difficult can be solved through the method.
Embodiments of the present application are further described below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a data processing method for a parking lot according to an embodiment of the present application.
The method is applied to the electronic equipment. As shown in fig. 1, the method includes:
step S110, acquiring a training set;
specifically, the training set includes first traffic flow data and first yard data of a first parking lot, the electronic device may obtain the first traffic flow data and the first yard data of the first parking lot in advance, and associate the first traffic flow data and the first yard data into a table, for example, the first traffic flow data includes the number of vehicles entering and exiting in different preset time periods, and the first yard data includes the total number of parking lots, the geographic location (city, longitude and latitude, specific address, and the like), and the type (office building, house, mall, and the like); external data, such as date data, weather data, and the like, can also be acquired.
In this step, the electronic device obtains first parking lot data and first parking lot data of a first parking lot as a training set.
Step S120, training a random forest model by using first traffic flow data and first parking lot data to obtain a target regression model;
specifically, a plurality of indexes with the strongest importance can be selected according to the target regression model.
And step S130, determining the predicted remaining parking space ratio of the second parking lot by using the target regression model.
It should be noted that the second parking lot is a parking lot to be predicted, and the target regression model may be used to predict the second parking lot and determine the predicted remaining parking space ratio of the second parking lot.
In the embodiment of the application, a training set can be obtained, wherein the training set comprises first traffic data of a first parking lot and first parking lot data; training a random forest model by using the first traffic flow data and the first parking lot data to obtain a target regression model; and determining the ratio of the predicted remaining parking spaces of the second parking lot by using the target regression model. In the scheme, the random forest model can be trained by utilizing the training set to obtain the target regression model, and the data in the training set comprises the first parking lot data and the first parking lot data, so that the predicted remaining parking space occupation ratio of the second parking lot can be determined by utilizing the target regression model.
The above steps are described in detail below.
In some embodiments, based on the step S110, the first traffic data may include: and the number of the first actual vehicles entering and exiting in the preset time period, the remaining number of the actual parking spaces after the preset time period is over and the like. Based on this, the first traffic data includes: the system comprises a first actual vehicle access quantity in a preset time period and an actual remaining parking space quantity at the end of the preset time period, wherein the actual remaining parking space quantity is obtained through a preset query interface.
In some embodiments, based on step S120, the random forest model may be trained and optimized to obtain a target regression model for the electronic device. As an example, the step S120 may include the steps of:
step a), training a random forest model by utilizing first traffic flow data and first parking lot data, and determining the optimal parameters of the random forest model in preset number through a grid search algorithm;
and b), obtaining a target regression model by using the optimal parameters.
Specifically, in the step a), the random forest model is trained by using the first traffic data and the first yard data to obtain the number, the depth, the maximum node number and the like of the decision trees, and the optimal parameters of the preset number of the random forest model are determined by a manual or grid search algorithm.
And b), establishing and obtaining a target regression model by using the optimal parameters.
In the embodiment of the application, the random forest model can be trained by utilizing the first traffic data and the first yard data, and the optimal parameters of the random forest model in the preset number are determined through a grid search algorithm; and obtaining a target regression model by using the optimal parameters. Therefore, the electronic equipment can train and optimize the random forest model and obtain a target regression model with higher accuracy.
In some embodiments, a second actual vehicle access amount of the second parking lot in a preset time period may be obtained, so that the electronic device determines the predicted remaining parking space ratio. As an example, the step S130 may include the steps of:
step c), acquiring the number of second actual vehicles entering and exiting the second parking lot in a preset time period;
step d), based on the number of the second actual vehicles entering and exiting, determining the predicted number of remaining vehicles of the second parking lot by using a target regression model;
and e), determining the ratio of the predicted remaining parking spaces corresponding to the predicted remaining parking spaces.
For the step c), specifically, the electronic device may obtain second traffic flow data of a second parking lot and second parking lot data, for example, the second traffic flow data includes a second actual vehicle in-out number in a preset time period, and the second parking lot data includes a first total number of vehicles.
And d), inputting the second actual vehicle entering and exiting quantity into the target regression model, and determining the predicted remaining vehicle position number of the second parking lot in the preset time period.
For step e), the electronic device can determine the ratio of the predicted remaining number of cars according to the predicted remaining number of cars.
In the embodiment of the application, the number of the second actual vehicles entering and exiting the second parking lot in the preset time period can be obtained; determining the predicted remaining vehicle number of the second parking lot by using a target regression model based on the second actual vehicle entering and exiting quantity; and determining the ratio of the predicted remaining parking spaces corresponding to the predicted remaining parking spaces. Therefore, the electronic device may determine, in real time, the predicted remaining parking space ratio of the second parking lot by using the target regression model based on the obtained second actual vehicle entering and exiting number in the preset time period, and further determine the remaining parking space ratio of the second parking lot.
In some embodiments, the predicted remaining parking space number may be divided by the first total parking space number, so that the electronic device obtains the predicted remaining parking space number ratio. As one example, the second parking lot data of the second parking lot includes a first total number of vehicles; the step e) may include the steps of:
and e1), processing the predicted remaining parking space number and the first total parking space number to obtain the predicted remaining parking space number ratio.
Specifically, the electronic device divides the predicted remaining parking space number by the first total parking space number to obtain the predicted remaining parking space number ratio.
In the embodiment of the application, the predicted remaining parking space number and the first total parking space number can be processed to obtain the predicted remaining parking space number ratio, so that the electronic equipment can obtain the accurate predicted remaining parking space number ratio.
In some embodiments, the traffic data and the yard data may be used as a test set and an evaluation index of the test set may be calculated, so that the electronic device verifies whether the target regression model is accurate according to the evaluation index.
As an example, the method may further include the steps of:
step f), obtaining a test set, wherein the test set comprises third traffic flow data and third parking lot data of the first parking lot;
step g), determining evaluation indexes of third traffic flow data and third yard data;
step h), judging whether the evaluation index is out of the range of the preset threshold value;
and i), if the evaluation index is out of the preset threshold range, training the random forest model by using the first traffic flow data and the first yard data to obtain a target regression model until the evaluation index is in the preset threshold range.
For the step f), the electronic device may obtain a test set, where the test set includes third traffic flow data and third yard data of the first parking lot, for example, the third traffic flow data includes the number of vehicles entering and exiting in other different preset time periods, and the third yard data includes the total number of vehicle positions, the geographic location (city, longitude and latitude, specific address, etc.), and the type (office building, residence, market, etc.).
For the above step g), it should be noted that the evaluation index is an index for evaluating the accuracy or the prediction effect of the target regression model, and for example, the evaluation index includes: mean squared error (mean _ squared _ error) or mean absolute error mean _ absolute _ error, etc.
For step h) above, it is determined, illustratively, whether the mean square error is outside a preset threshold range.
For the step i), if the mean square error is outside the preset threshold range, it indicates that the effect of the target regression model is not good, the step of training the random forest model by using the first traffic flow data and the first yard data to obtain the target regression model needs to be continuously executed until the mean square error is within the preset threshold range, and finally the target regression model which is trained offline is generated.
In the embodiment of the application, a test set can be obtained, wherein the test set comprises third traffic flow data and third parking lot data of the first parking lot; determining evaluation indexes of the third traffic data and the third yard data; judging whether the evaluation index is out of a preset threshold range; and if the evaluation index is out of the preset threshold range, training the random forest model by using the first traffic flow data and the first yard data to obtain a target regression model until the evaluation index is in the preset threshold range. Therefore, the electronic equipment can further verify the target regression model by using the evaluation index, and further obtain the target regression model with better prediction effect.
In some embodiments, the predicted remaining space fraction may be compared to the actual remaining space fraction to enable the electronic device to determine a prediction accuracy of the target regression model. As an example, the above method may further include the steps of:
step j), processing the actual remaining parking space number and the second total parking space number of the first parking lot to obtain the actual remaining parking space number ratio;
and k), comparing the predicted remaining vehicle number ratio with the actual remaining vehicle number ratio, and determining the prediction accuracy of the target regression model.
For the step j), specifically, the first parking lot data of the first parking lot includes the second total parking space number, and the actual remaining parking space ratio may be obtained by dividing the actual remaining parking space number by the second total parking space number.
For the step k), the electronic device compares the ratio of the predicted remaining parking space number with the ratio of the actual remaining parking space number, and the prediction accuracy of the target regression model can be determined.
In the embodiment of the application, the actual remaining parking space number and the second total parking space number of the first parking lot can be processed to obtain the actual remaining parking space number ratio; and comparing the predicted remaining vehicle number ratio with the actual remaining vehicle number ratio to determine the prediction accuracy of the target regression model. Therefore, the electronic device can determine the prediction accuracy of the target regression model by predicting the remaining parking space ratio and the actual remaining parking space ratio.
In some embodiments, as an example, after the training set and the test set are obtained in the above steps, the traffic data and the yard data in the training set and the test set may be further processed, which specifically includes the following steps:
data preprocessing: and filling missing values of the traffic flow data and the yard data, and processing abnormal values.
Characteristic engineering: 1) normalization treatment: dimension differences of different parking lots caused by the scale of the total parking spaces are eliminated; 2) discrete variable conversion: the classification variables are encoded to become numerical variables.
For example, the classification variable refers to that when the types of an office building, a house, a market and the like are subjected to discrete conversion, the office building is converted into 1, the house is converted into 2, the market is converted into 3 and the like.
Fig. 2 provides a schematic structural diagram of a data processing device of a parking lot. As shown in fig. 2, the data processing apparatus 200 of the parking lot includes:
an obtaining module 201, configured to obtain a training set, where the training set includes first traffic data of a first parking lot and first parking lot data;
the training module 202 is used for training the random forest model by using the first traffic flow data and the first yard data to obtain a target regression model;
and the determining module 203 is used for determining the predicted remaining parking space ratio of the second parking lot by using the target regression model.
In some embodiments, the first traffic data comprises: the system comprises a first actual vehicle access quantity in a preset time period and an actual remaining parking space quantity at the end of the preset time period, wherein the actual remaining parking space quantity is obtained through a preset query interface.
In some embodiments, the training module is to:
training a random forest model by using first traffic flow data and first parking lot data, and determining the optimal parameters of the random forest model in a preset number through a grid search algorithm;
and obtaining a target regression model by using the optimal parameters.
In some embodiments, the determining module comprises:
the second acquisition module is used for acquiring the number of second actual vehicles entering and exiting the second parking lot in a preset time period;
the second determining module is used for determining the predicted number of remaining vehicles in the second parking lot by using the target regression model based on the number of the second actual vehicles entering and exiting;
and the third determining module is used for determining the ratio of the predicted remaining parking spaces corresponding to the predicted remaining parking spaces.
In some embodiments, the second yard data for the second parking lot includes a first total number of vehicles; the third determining module is to:
and processing the predicted remaining parking space number and the first total parking space number to obtain the predicted remaining parking space number ratio.
In some embodiments, the data processing apparatus of the parking lot is further configured to:
acquiring a test set, wherein the test set comprises third traffic flow data and third parking lot data of the first parking lot;
determining evaluation indexes of the third traffic data and the third yard data;
judging whether the evaluation index is out of a preset threshold range;
and if the evaluation index is out of the preset threshold range, training the random forest model by using the first traffic flow data and the first yard data to obtain a target regression model until the evaluation index is in the preset threshold range.
In some embodiments, the data processing apparatus of the parking lot is further configured to:
processing the actual remaining parking space number and the second total parking space number of the first parking lot to obtain the actual remaining parking space number ratio;
and comparing the predicted remaining vehicle number ratio with the actual remaining vehicle number ratio to determine the prediction accuracy of the target regression model.
The data processing device for the parking lot provided by the embodiment of the application has the same technical characteristics as the data processing method for the parking lot provided by the embodiment of the application, so that the same technical problems can be solved, and the same technical effects are achieved.
As shown in fig. 3, an electronic device 300 provided in an embodiment of the present application includes a memory 301 and a processor 302, where the memory stores a computer program that is executable on the processor, and the processor implements the steps of the method provided in the foregoing embodiment when executing the computer program.
Referring to fig. 3, the electronic device further includes: a bus 303 and a communication interface 304, wherein the processor 302, the communication interface 304 and the memory 301 are connected through the bus 303; the processor 302 is adapted to execute executable modules, such as computer programs, stored in the memory 301.
The Memory 301 may include a Random Access Memory (RAM), and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 304 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
Bus 303 may be an ISA bus, PCI bus, 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, only one double-headed arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus.
The memory 301 is used for storing a program, and the processor 302 executes the program after receiving an execution instruction, and the method performed by the apparatus defined by the process disclosed in any of the foregoing embodiments of the present application may be applied to the processor 302, or implemented by the processor 302.
The processor 302 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 302. The Processor 302 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 301, and the processor 302 reads the information in the memory 301 and completes the steps of the method in combination with the hardware thereof.
Corresponding to the data processing method for the parking lot, the embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer executable instructions, and when the computer executable instructions are called and executed by a processor, the computer executable instructions cause the processor to execute the steps of the data processing method for the parking lot.
The data processing device of the parking lot provided by the embodiment of the application can be specific hardware on the equipment or software or firmware installed on the equipment. The device provided by the embodiment of the present application has the same implementation principle and technical effect as the foregoing method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiments where no part of the device embodiments is mentioned. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the foregoing systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
For another example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
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.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the data processing method for a parking lot according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the scope of the embodiments of the present application. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A data processing method for a parking lot, the method comprising:
acquiring a training set, wherein the training set comprises first traffic flow data of a first parking lot and first parking lot data;
training a random forest model by using the first traffic flow data and the first yard data to obtain a target regression model;
and determining the predicted remaining parking space ratio of the second parking lot by using the target regression model.
2. The data processing method for a parking lot according to claim 1, wherein the first traffic data includes: the method comprises the steps of obtaining the number of first actual vehicles entering and exiting in a preset time period and the number of remaining actual parking spaces at the end of the preset time period, wherein the number of the actual remaining parking spaces is obtained through a preset query interface.
3. The data processing method for the parking lot as claimed in claim 1, wherein the step of training a random forest model by using the first traffic data and the first yard data to obtain a target regression model comprises:
training a random forest model by using the first traffic data and the first yard data, and determining the optimal parameters of the random forest model in preset number by using a grid search algorithm;
and obtaining a target regression model by using the optimal parameters.
4. The data processing method for the parking lot as claimed in claim 1, wherein the step of determining the predicted remaining space ratio of the second parking lot by using the target regression model comprises:
acquiring the number of second actual vehicles entering and exiting a second parking lot in a preset time period;
determining the predicted number of remaining vehicles of the second parking lot by using the target regression model based on the second actual vehicle entering and exiting quantity;
and determining the ratio of the predicted remaining parking spaces corresponding to the predicted remaining parking spaces.
5. The data processing method for a parking lot according to claim 4, wherein the second lot data of the second parking lot includes a first total number of cars; the step of determining the remaining parking space ratio corresponding to the predicted remaining parking space number comprises the following steps:
and processing the predicted remaining parking space number and the first total parking space number to obtain a predicted remaining parking space number ratio.
6. The data processing method for a parking lot according to claim 1, characterized by further comprising:
acquiring a test set, wherein the test set comprises third traffic flow data and third parking lot data of the first parking lot;
determining evaluation indexes of the third traffic flow data and the third yard data;
judging whether the evaluation index is out of a preset threshold range;
and if the evaluation index is out of the range of the preset threshold value, training a random forest model by using the first traffic flow data and the first yard data to obtain a target regression model until the evaluation index is in the range of the preset threshold value.
7. The data processing method for a parking lot according to claim 5, characterized by further comprising:
processing the actual remaining parking space number and the second total parking space number of the first parking lot to obtain the actual remaining parking space number ratio;
and comparing the predicted remaining parking space number ratio with the actual remaining parking space number ratio to determine the prediction accuracy of the target regression model.
8. A data processing apparatus for a parking lot, the apparatus comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a training set, and the training set comprises first traffic flow data of a first parking lot and first parking lot data;
the training module is used for training a random forest model by utilizing the first traffic flow data and the first parking lot data to obtain a target regression model;
and the determining module is used for determining the predicted remaining parking space ratio of the second parking lot by utilizing the target regression model.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program operable on the processor, and wherein the processor implements the steps of the method of any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium having stored thereon computer executable instructions which, when invoked and executed by a processor, cause the processor to execute the method of any of claims 1 to 7.
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