CN111105628A - Parking lot portrait construction method and device - Google Patents
Parking lot portrait construction method and device Download PDFInfo
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- CN111105628A CN111105628A CN201911336772.1A CN201911336772A CN111105628A CN 111105628 A CN111105628 A CN 111105628A CN 201911336772 A CN201911336772 A CN 201911336772A CN 111105628 A CN111105628 A CN 111105628A
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Abstract
The invention relates to the technical field of parking lot management, in particular to a parking lot portrait construction method and device. The method comprises the following steps: collecting parking lot operation data of a target parking lot; inputting the parking lot operation data into a preset state evaluation model so that the preset state evaluation model outputs the operation state information of the target parking lot; and acquiring an operation state label corresponding to the operation state information, and constructing a parking lot figure of the target parking lot according to the operation state label. The method comprises the steps of obtaining an operation state label of a target parking lot from massive parking lot operation data, constructing a parking lot portrait of the target parking lot based on the operation state label, displaying the operation condition of the parking lot, and providing a decision basis for operation and management of the parking lot based on the parking lot portrait, so that the management efficiency of the parking lot is improved.
Description
Technical Field
The invention relates to the technical field of parking lot management, in particular to a parking lot portrait construction method and device.
Background
The portrait can objectively and accurately describe the attribute of the target object, each concrete information of the target object is abstracted into tags, and the tags form the portrait of the target object, so that the tags are used for providing targeted service for the target object, and the parking lot portrait can provide targeted strategies for parking lot operation.
However, in the field of parking lot management, parking lots can only be managed through human experience, and there is no way to perform parking lot management by using big data analysis technology, so how to obtain operation conditions of the parking lots from massive parking data and draw a comprehensive parking lot figure is an urgent need in the field of parking lot management.
Disclosure of Invention
The invention aims to provide a parking lot portrait construction method and device, and aims to solve the technical problem of how to construct a parking lot portrait in the prior art.
The embodiment of the invention provides the following scheme:
according to a first aspect of the present invention, an embodiment of the present invention provides a parking lot figure constructing method, which is applied to an electronic device, and includes:
collecting parking lot operation data of a target parking lot;
inputting the parking lot operation data into a preset state evaluation model so that the preset state evaluation model outputs the operation state information of the target parking lot;
and acquiring an operation state label corresponding to the operation state information, and constructing a parking lot figure of the target parking lot according to the operation state label.
Preferably, before the collecting the parking lot operation data of the target parking lot, the method further includes:
collecting sample data, wherein the sample data comprises sample operation data and sample operation state information corresponding to the sample operation data;
classifying the sample data according to a preset classification rule, and acquiring a model to be trained corresponding to each type of the sample data;
and training the model to be trained corresponding to the sample data according to each type of the sample data to obtain a preset state evaluation model corresponding to each type of the sample data.
Preferably, the training the model to be trained corresponding to the sample data according to each type of the sample data to obtain the preset state evaluation model corresponding to each type of the sample data includes:
dividing each type of the sample data into a plurality of training sets and corresponding test sets;
training the model to be trained according to the training sets respectively to obtain a plurality of models to be tested;
and testing the plurality of models to be tested according to the test sets corresponding to the plurality of training sets, and selecting a preset state evaluation model from the plurality of models to be tested according to the test result.
Preferably, the testing the plurality of models to be tested according to the test sets corresponding to the plurality of training sets, and selecting a preset state evaluation model from the plurality of models to be tested according to the test result includes:
inputting the sample operation data in the test set into the model to be tested to obtain the predicted operation state information output by the model to be tested;
calculating a deviation value between the predicted running state information and the sample running state information;
and taking the model to be tested with the minimum deviation value as a preset state evaluation model.
Preferably, the dividing each type of the sample data into a plurality of training sets and corresponding test sets includes:
and dividing each type of sample data into a plurality of training sets and corresponding test sets according to a set-out method, a K-fold cross-validation method or a self-service method.
Preferably, the inputting the parking lot operation data into a preset state evaluation model to enable the preset state evaluation model to output the operation state information of the target parking lot includes:
classifying the parking lot operation data according to the preset classification rule, and acquiring a preset state evaluation model corresponding to each type of parking lot operation data;
and inputting each type of parking data into a corresponding preset state evaluation model to obtain the running state information output by the preset state evaluation model.
Preferably, the acquiring the operation status label corresponding to the operation status information and constructing the parking lot figure of the target parking lot according to the operation status label includes:
acquiring an operation state label corresponding to the operation state information;
and searching the level of the operation state label in a preset mapping relation table, and constructing the parking lot portrait of the target parking lot according to the operation state label of the preset level.
Based on the same inventive concept, according to a second aspect of the present invention, an embodiment of the present invention provides a parking lot figure constructing apparatus, including:
the data acquisition module is used for acquiring the parking lot operation data of the target parking lot;
the state evaluation module is used for inputting the parking lot operation data into a preset state evaluation model so that the preset state evaluation model outputs the operation state information of the target parking lot;
and the portrait construction module is used for acquiring the running state label corresponding to the running state information and constructing the parking lot portrait of the target parking lot according to the running state label.
Based on the same inventive concept, according to a third aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the method steps of any one of the first aspects of the present invention.
Based on the same inventive concept, according to a fourth aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method steps according to any one of the first aspect of the present invention when executing the program.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the method comprises the steps of collecting parking lot operation data of a target parking lot; inputting the parking lot operation data into a preset state evaluation model so that the preset state evaluation model outputs the operation state information of the target parking lot; and acquiring an operation state label corresponding to the operation state information, and constructing a parking lot figure of the target parking lot according to the operation state label. The method comprises the steps of obtaining an operation state label of a target parking lot from massive parking lot operation data, constructing a parking lot portrait of the target parking lot based on the operation state label, displaying the operation condition of the parking lot, and providing a decision basis for operation and management of the parking lot based on the parking lot portrait, so that the management efficiency of the parking lot is improved.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present specification, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating a parking lot representation constructing method according to a first embodiment of the present invention;
FIG. 2 is a flowchart illustrating a parking lot representation constructing method according to a second embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a parking lot figure constructing apparatus according to a first embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by those skilled in the art based on the embodiments of the present invention belong to the scope of protection of the embodiments of the present invention.
First, it is stated that the term "and/or" appearing herein is merely one type of associative relationship that describes an associated object, meaning that three types of relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
Referring to fig. 1, fig. 1 is a schematic flow chart of a parking lot figure constructing method according to a first embodiment of the present invention.
In this embodiment, the parking lot figure construction method is applied to an electronic device, and the method includes:
step S10: and collecting the parking lot operation data of the target parking lot.
The parking lot operation data are peripheral scene information of the target parking lot and parking service data generated in an actual operation process, wherein the peripheral scene information comprises: application scenarios including, but not limited to, office buildings, communities, business circles, and campuses, and crowd properties including, but not limited to, employees, residents, consumers, and parents of students, and parking service data including, but not limited to, information on unit price charged, monthly rental income, temporary parking income, and temporary parking times. The parking lot operation data can influence the operation state and management decision of the parking lot, and the embodiment collects the peripheral scene information and parking service data of the target parking lot and provides basis for the management of the target parking lot.
In specific implementation, the peripheral scene information and the parking records of the target parking lot are collected, and the parking service data of the target parking lot are obtained according to the statistics of the parking records. The parking record is basic information of the vehicle in the target parking lot, including but not limited to: license plate number, vehicle entering time, vehicle leaving time, parking cost and other information. The target parking lot comprises a plurality of inlets and outlets, a camera and a control terminal are arranged at each inlet and outlet, the camera is communicated with the control terminal, and the control terminal is communicated with the electronic equipment respectively. When a vehicle enters and exits the target parking lot, the camera collects the license plate number of the vehicle, the corresponding control terminal records the vehicle entrance time and the vehicle exit time of the vehicle, the parking time and the parking cost of the vehicle are counted, the license plate number, the vehicle entrance time, the vehicle exit time, the parking cost and other information of the vehicle are collected into parking records which are sent to the electronic equipment, so that the electronic equipment counts the parking service data of the target parking lot according to the parking records, for example, the monthly parking admission is counted according to the parking time and the parking cost of the oncoming parking in one month.
Step S20: and inputting the parking lot operation data into a preset state evaluation model so that the preset state evaluation model outputs the operation state information of the target parking lot.
The preset state evaluation model comprises a corresponding relation between operation data and operation state information, wherein the operation state information is the current operation state of the parking lot, and the preset state evaluation model comprises but is not limited to: parking efficiency information, traffic status information, and profitability status information.
In the specific implementation, a state evaluation model to be trained is established, the state evaluation model to be trained is trained through a large amount of operation data and corresponding operation state information, the trained preset state evaluation model is obtained, and the identification accuracy of the preset state evaluation model is improved based on a large amount of sample training. By inputting the parking lot operation data into the preset state evaluation model, the operation state information corresponding to the parking lot operation data output by the preset state evaluation model can be obtained. For example, the parking lot operation data such as the charging unit price, the business district scene information, the temporary parking income and the like are input into the preset state evaluation model, and the preset state evaluation model outputs the corresponding annual parking income, so that the profit state of the parking lot is predicted according to the peripheral scene information and the parking business data.
Step S30: and acquiring an operation state label corresponding to the operation state information, and constructing a parking lot figure of the target parking lot according to the operation state label.
The operation state label is a keyword for describing the operation state information, and in specific implementation, a numerical range to which the operation state information belongs is determined, and the operation state label corresponding to the operation state information is determined according to the numerical range. For example, when the operation state information is a specific amount of annual parking income, determining a range to which the specific amount belongs, and determining that the corresponding operation state label is higher in annual parking income according to the range; and when the running state information is a specific numerical value of the vehicle departure efficiency, determining a range to which the specific numerical value belongs, and determining that the corresponding running state label is low in departure efficiency according to the range. The method comprises the steps of obtaining an operation state label of a target parking lot from massive parking lot operation data, constructing a parking lot portrait of the target parking lot based on the operation state label, displaying the operation condition of the parking lot, and providing decision basis for operation and management of the parking lot based on the parking lot portrait.
Further, the step S30 includes: acquiring an operation state label corresponding to the operation state information; and searching the level of the operation state label in a preset mapping relation table, and constructing the parking lot portrait of the target parking lot according to the operation state label of the preset level.
The level represents the importance of the operating state label, for example, of the two operating state labels with higher annual income and lower field efficiency, the level with higher annual income is higher than the level with lower field efficiency. The operation state labels are all keywords for describing the operation state information, the operation state labels at a preset level are main keywords for describing the operation state information, and a parking lot portrait constructed according to the operation state labels reflects all operation conditions of the target parking lot; the parking lot portrait constructed according to the operation state label of the preset level reflects the main operation condition of the target parking lot, and decision basis can be provided for the management of the parking lot.
The technical scheme provided in the embodiment of the application at least has the following technical effects or advantages:
the embodiment collects the parking lot operation data of the target parking lot; inputting the parking lot operation data into a preset state evaluation model so that the preset state evaluation model outputs the operation state information of the target parking lot; and acquiring an operation state label corresponding to the operation state information, and constructing a parking lot figure of the target parking lot according to the operation state label. The method comprises the steps of obtaining an operation state label of a target parking lot from massive parking lot operation data, constructing a parking lot portrait of the target parking lot based on the operation state label, displaying the operation condition of the parking lot, and providing a decision basis for operation and management of the parking lot based on the parking lot portrait, so that the management efficiency of the parking lot is improved.
Referring to fig. 2 and fig. 2 are schematic flow charts of a second embodiment of the parking lot figure constructing method according to the present invention, and the second embodiment of the parking lot figure constructing method according to the present invention is provided based on the first embodiment.
In this embodiment, before the step S10, the method further includes:
step S01: and collecting sample data, wherein the sample data comprises sample operation data and sample operation state information corresponding to the sample operation data.
Before the running state information of the target parking lot is evaluated through the preset state evaluation model, the preset state evaluation model is constructed, a large amount of sample data is collected when the preset state evaluation model is constructed, the sample data comprises sample running data and sample running state information corresponding to the sample running data, and when the sample data is large enough, the accuracy of the preset state evaluation model constructed according to the sample data can be guaranteed.
S02: and classifying the sample data according to a preset classification rule, and acquiring a model to be trained corresponding to each type of the sample data.
The operation state information includes categories such as parking efficiency, traffic state, and profit state, wherein the parking efficiency includes: hour utilization ratio, parking stall occupancy etc. and the influence factor of parking efficiency includes: the entrance time, the exit time, the parking quantity, the total number of parking spaces and the like. The traffic state includes: the entrance efficiency and the exit efficiency, and the like, and the influence factors of the traffic state include: the adjacent vehicle entrance time interval, the adjacent vehicle exit time interval and the like. The impact factors of the profitability status include: revenue and cost, etc. And setting the preset classification rule according to the category of the influence factor, classifying the sample data according to the preset classification rule, dividing the sample data into multiple types such as an hour utilization rate class, a parking space occupancy rate class, an entrance efficiency class, an exit efficiency class and an income class, and acquiring a model to be trained corresponding to each type of sample data.
S03: and training the model to be trained corresponding to the sample data according to each type of the sample data to obtain a preset state evaluation model corresponding to each type of the sample data.
The model to be trained corresponding to the sample data is trained according to the sample data of each type, so that the preset state evaluation model corresponding to the sample data of each type is obtained.
Further, the step S03 includes:
dividing each type of the sample data into a plurality of training sets and corresponding test sets;
training the model to be trained according to the training sets respectively to obtain a plurality of models to be tested;
and testing the plurality of models to be tested according to the test sets corresponding to the plurality of training sets, and selecting a preset state evaluation model from the plurality of models to be tested according to the test result.
In the training process of machine learning at present, a sample is divided into a training set and a test set, and in this embodiment, each type of sample data is divided into a plurality of training sets and corresponding test sets, each training set can be trained to obtain a model to be tested, the obtained model to be tested is tested according to the test set corresponding to the training set, the accuracy of each model to be tested is determined, the model to be tested with the highest accuracy is selected from the plurality of models to be tested as a preset state evaluation model corresponding to the type of sample data, and the accuracy of the preset state evaluation model can be improved.
Further, the testing the plurality of models to be tested according to the test sets corresponding to the plurality of training sets, and selecting a preset state evaluation model from the plurality of models to be tested according to the test result includes: inputting the sample operation data in the test set into the model to be tested to obtain the predicted operation state information output by the model to be tested; calculating a deviation value between the predicted running state information and the sample running state information; and taking the model to be tested with the minimum deviation value as a preset state evaluation model.
Further, the dividing each type of the sample data into a plurality of training sets and corresponding test sets includes: and dividing each type of sample data into a plurality of training sets and corresponding test sets according to a set-out method, a K-fold cross-validation method or a self-service method. Firstly, dividing the sample data into a plurality of data sets, and then dividing each data set into a training set and a testing set by a leave-out method, a K-fold cross-validation method or a self-service method. The set-out method directly divides the data set into two mutually exclusive sets, wherein one set is used as a training set, and the other set is used as a testing set. The bootstrap method has a uniform sampling put back from a given training set, that is, whenever a sample is selected, it is likely to be reselected and added again to the training set, and samples that do not enter the training set eventually form a test set. The K-fold cross-validation method includes the steps that all data are divided into K sub-samples, one of the sub-samples is selected to serve as a test set in a non-repeated mode, the other K-1 samples are used for training, K times are repeated, results of the K times are averaged or other indexes are used, a single estimation is finally obtained, each sub-sample can be guaranteed to participate in training and is tested, and the generalization error is reduced.
Further, the inputting the parking lot operation data into a preset state evaluation model to enable the preset state evaluation model to output the operation state information of the target parking lot includes: classifying the parking lot operation data according to a preset classification rule, and acquiring a preset state evaluation model corresponding to each type of parking lot operation data; and inputting each type of parking data into a corresponding preset state evaluation model to obtain the running state information output by the preset state evaluation model.
The technical scheme provided in the embodiment of the application at least has the following technical effects or advantages:
in this embodiment, sample data is acquired, where the sample data includes sample operation data and sample operation state information corresponding to the sample operation data; classifying the sample data according to a preset classification rule, and acquiring a model to be trained corresponding to each type of the sample data; training the model to be trained corresponding to the sample data according to each type of the sample data to obtain a preset state evaluation model corresponding to each type of the sample data, and improving the prediction accuracy of the preset state evaluation model based on a large amount of sample training.
Based on the same inventive concept, the embodiment of the present invention further provides a parking lot figure constructing device, specifically, referring to fig. 3, the parking lot figure constructing device includes:
the data acquisition module 10 is used for acquiring parking lot operation data of a target parking lot;
the state evaluation module 20 is configured to input the parking lot operation data into a preset state evaluation model, so that the preset state evaluation model outputs the operation state information of the target parking lot;
and the figure constructing module 30 is used for acquiring the operation state label corresponding to the operation state information and constructing the parking lot figure of the target parking lot according to the operation state label.
Based on the same inventive concept, embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements any of the method steps described above.
Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method steps described in any of the foregoing are implemented.
Compared with the prior art, the embodiment of the invention has the following advantages and beneficial effects:
the embodiment collects the parking lot operation data of the target parking lot; inputting the parking lot operation data into a preset state evaluation model so that the preset state evaluation model outputs the operation state information of the target parking lot; and acquiring an operation state label corresponding to the operation state information, and constructing a parking lot figure of the target parking lot according to the operation state label. The method comprises the steps of obtaining an operation state label of a target parking lot from massive parking lot operation data, constructing a parking lot portrait of the target parking lot based on the operation state label, displaying the operation condition of the parking lot, and providing a decision basis for operation and management of the parking lot based on the parking lot portrait, so that the management efficiency of the parking lot is improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (modules, systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A parking lot portrait construction method is applied to electronic equipment and is characterized by comprising the following steps:
collecting parking lot operation data of a target parking lot;
inputting the parking lot operation data into a preset state evaluation model so that the preset state evaluation model outputs the operation state information of the target parking lot;
and acquiring an operation state label corresponding to the operation state information, and constructing a parking lot figure of the target parking lot according to the operation state label.
2. The parking lot representation construction method of claim 1, wherein before collecting parking lot operation data of a target parking lot, the method further comprises:
collecting sample data, wherein the sample data comprises sample operation data and sample operation state information corresponding to the sample operation data;
classifying the sample data according to a preset classification rule, and acquiring a model to be trained corresponding to each type of the sample data;
and training the model to be trained corresponding to the sample data according to each type of the sample data to obtain a preset state evaluation model corresponding to each type of the sample data.
3. The parking lot figure construction method according to claim 2, wherein the training of the model to be trained corresponding to the sample data according to each type of the sample data to obtain the preset state evaluation model corresponding to each type of the sample data comprises:
dividing each type of the sample data into a plurality of training sets and corresponding test sets;
training the model to be trained according to the training sets respectively to obtain a plurality of models to be tested;
and testing the plurality of models to be tested according to the test sets corresponding to the plurality of training sets, and selecting a preset state evaluation model from the plurality of models to be tested according to the test result.
4. The parking lot figure construction method according to claim 3, wherein the step of testing the plurality of models to be tested according to the test sets corresponding to the plurality of training sets and selecting a preset state evaluation model from the plurality of models to be tested according to the test result comprises the steps of:
inputting the sample operation data in the test set into the model to be tested to obtain the predicted operation state information output by the model to be tested;
calculating a deviation value between the predicted running state information and the sample running state information;
and taking the model to be tested with the minimum deviation value as a preset state evaluation model.
5. The method for constructing a parking lot figure according to claim 3, wherein said dividing each type of said sample data into a plurality of training sets and corresponding testing sets comprises:
and dividing each type of sample data into a plurality of training sets and corresponding test sets according to a set-out method, a K-fold cross-validation method or a self-service method.
6. The parking lot figure construction method according to claim 2, wherein the inputting of the parking lot operation data into a preset state evaluation model to enable the preset state evaluation model to output the operation state information of the target parking lot comprises:
classifying the parking lot operation data according to the preset classification rule, and acquiring a preset state evaluation model corresponding to each type of parking lot operation data;
and inputting each type of parking data into a corresponding preset state evaluation model to obtain the running state information output by the preset state evaluation model.
7. The parking lot representation construction method according to any one of claims 1 to 6, wherein the obtaining of the operation state label corresponding to the operation state information and the construction of the parking lot representation of the target parking lot according to the operation state label comprise:
acquiring an operation state label corresponding to the operation state information;
and searching the level of the operation state label in a preset mapping relation table, and constructing the parking lot portrait of the target parking lot according to the operation state label of the preset level.
8. A parking lot figure constructing apparatus, comprising:
the data acquisition module is used for acquiring the parking lot operation data of the target parking lot;
the state evaluation module is used for inputting the parking lot operation data into a preset state evaluation model so that the preset state evaluation model outputs the operation state information of the target parking lot;
and the portrait construction module is used for acquiring the running state label corresponding to the running state information and constructing the parking lot portrait of the target parking lot according to the running state label.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method steps of any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method steps of any of claims 1-7 when executing the program.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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