CN113344373A - Data processing method and device based on parking lot and electronic equipment - Google Patents
Data processing method and device based on parking lot and electronic equipment Download PDFInfo
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Abstract
The application provides a data processing method and device based on a parking lot and electronic equipment, relates to the technical field of data processing, and solves the technical problem that analysis of operation conditions of the parking lot is difficult. The method comprises the following steps: acquiring indexes of the parking lot and index data corresponding to the indexes; clustering and factor analyzing the index data to obtain a preset number of target indexes and target index data corresponding to the target indexes; establishing an index system according to the target index and the target index data; and determining the health degree index of the parking lot through the index system.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a data processing method and apparatus based on a parking lot, and an electronic device.
Background
At present, with the improvement of living standard and the acceleration of urbanization process of people, the number of traveling vehicles is greatly increased, so that the urban parking demand is also rapidly increased, and the number of parking lots is also rapidly increased along with the urban parking demand. Specifically, the types of parking lot include: the system comprises a public parking lot, a special parking lot or a road parking lot and the like, wherein the special parking lot refers to a parking lot constructed by an investor outside a road and is a place special for parking vehicles in a unit and a residential district; the public parking lot is an open-air or indoor charging business parking lot which is built according to city planning and is matched with public buildings and specially used for parking social vehicles, is called a public parking lot, and is divided into an on-road parking lot and an off-road parking lot.
However, the supply of the parking lot is closely linked with the parking demand, if the operation of the parking lot is not standardized, the operation condition of the parking lot is deteriorated, the urban traffic problem is directly aggravated, and the difficulty of comprehensively analyzing the operation condition of the parking lot by a manager in the prior art is high.
Disclosure of Invention
The application aims to provide a data processing method and device based on a parking lot and electronic equipment, so as to solve the technical problem that analysis of operation conditions of the parking lot is difficult.
In a first aspect, an embodiment of the present application provides a data processing method based on a parking lot, where the method includes:
acquiring indexes of the parking lot and index data corresponding to the indexes;
clustering and factor analyzing the index data to obtain a preset number of target indexes and target index data corresponding to the target indexes;
establishing an index system according to the target index and the target index data;
and determining the health degree index of the parking lot through the index system.
In one possible implementation, the step of performing clustering processing and factor analysis processing on the index data to obtain a preset number of target indexes and target index data corresponding to the target indexes includes:
clustering the index data to obtain multiple types of first indexes, wherein each type of first indexes corresponds to multiple first index data;
and performing factor analysis processing on each type of first indexes to obtain a preset number of target indexes and target index data corresponding to the target indexes.
In a possible implementation, the clustering process is performed on the index data to obtain multiple types of first indexes, and the method includes:
and clustering the index data by using a dispersion square sum method to obtain a plurality of types of first indexes.
In a possible implementation, after the step of clustering the index data to obtain multiple classes of first indexes, where each class of the first indexes corresponds to multiple first index data, the method further includes:
carrying out nonparametric K-W inspection on the first index and first index data corresponding to the first index;
and when the probability value of first index data corresponding to the first index is larger than a preset threshold value, determining that the first index obtained by clustering and target index data corresponding to the first index are reasonable.
In one possible implementation, the step of performing factor analysis processing on each type of the first indexes to obtain a preset number of target indexes and target index data corresponding to the target indexes includes:
determining a correlation coefficient matrix of the first index data, and determining a plurality of eigenvalues of the correlation coefficient matrix;
determining the cumulative variance contribution rate corresponding to the characteristic value;
arranging a plurality of characteristic values according to a preset sequence, determining a preset number of target characteristic values according to the value range of the cumulative variance contribution rate, and establishing a factor analysis model according to common factors corresponding to the target characteristic values;
and determining a target index corresponding to the target characteristic value and target index data corresponding to the target index in each type of first index.
In one possible implementation, the step of establishing an index system according to the target index and the target index data includes:
classifying the target indexes to generate target indexes corresponding to preset data types;
and establishing an index system according to the target index and target index data corresponding to the target index.
In one possible implementation, the step of determining the health index of the parking lot through the index system includes:
calculating the index system, and determining a target index corresponding to the preset data type;
and carrying out weighted average processing on the target index to generate and determine the health degree index of the parking lot.
In a second aspect, a parking lot-based data processing apparatus is provided, comprising:
the acquisition module is used for acquiring indexes of the parking lot and index data corresponding to the indexes;
the processing module is used for carrying out clustering processing and factor analysis processing on the index data to obtain a preset number of target indexes and target index data corresponding to the target indexes;
the establishing module is used for establishing an index system according to the target index and the target index data;
and the determining module is used for determining the health degree index of the parking lot through the index system.
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:
according to the data processing method and device based on the parking lot and the electronic equipment, the index of the parking lot and the index data corresponding to the index can be acquired; clustering and factor analyzing the index data to obtain a preset number of target indexes and target index data corresponding to the target indexes; establishing an index system according to the target index and the target index data; and determining the health degree index of the parking lot through the index system. According to the scheme, the target indexes and the target index data can be obtained by utilizing a clustering method and a factor analysis method, an index system is established according to the target indexes and the target index data, and the health degree index of the parking lot is calculated through the index system.
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 parking lot-based data processing method according to an embodiment of the present application;
fig. 2 is another schematic flow chart of a parking lot-based data processing method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a parking lot-based data processing apparatus according to an embodiment of the present application;
fig. 4 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, with the improvement of living standard and the acceleration of urbanization process of people, the number of traveling vehicles is greatly increased, so that the urban parking demand is also rapidly increased, and the number of parking lots is also rapidly increased along with the urban parking demand. Specifically, the types of parking lot include: the system comprises a public parking lot, a special parking lot or a road parking lot and the like, wherein the special parking lot refers to a parking lot constructed by an investor outside a road and is a place special for parking vehicles in a unit and a residential district; the public parking lot is an open-air or indoor charging business parking lot which is built according to city planning and is matched with public buildings and specially used for parking social vehicles, is called a public parking lot, and is divided into an on-road parking lot and an off-road parking lot. However, the supply of the parking lot is closely linked with the parking demand, if the operation of the parking lot is not standardized, the operation condition of the parking lot is deteriorated, the urban traffic problem is directly aggravated, and the difficulty of comprehensively analyzing the operation condition of the parking lot by a manager in the prior art is high.
Based on this, the embodiment of the application provides a data processing method and device based on a parking lot and an electronic device, and the technical problem that analysis of the operation condition of 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 based on 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 indexes of the parking lot and index data corresponding to the indexes;
it should be noted that the data of the parking lot has many indexes, for example, the indexes include the number of vehicles entering and exiting, the average income of the parking lot, the exit balance, the traffic efficiency, and the like, and after the indexes of the parking lot are obtained, the index data corresponding to the indexes are continuously obtained.
Step S120, clustering and factor analyzing the index data to obtain a preset number of target indexes and target index data corresponding to the target indexes;
step S130, establishing an index system according to the target index and the target index data;
it should be noted that the index system may include indexes of different preset data types and target index data, where the preset data types include aspects of business income, payment condition, traffic efficiency, congestion condition, abnormal condition, and the like.
And step S140, determining the health degree index of the parking lot through an index system.
The health index indicates an actual operation condition of the parking lot, and for example, an operation condition or a payment condition of the parking lot may be determined according to the health index.
In the embodiment of the application, indexes of the parking lot and index data corresponding to the indexes can be obtained; clustering and factor analyzing the index data to obtain a preset number of target indexes and target index data corresponding to the target indexes; establishing an index system according to the target index and the target index data; and determining the health index of the parking lot through an index system. According to the scheme, the target indexes and the target index data can be obtained by utilizing a clustering method and a factor analysis method, an index system is established according to the target indexes and the target index data, and the health degree index of the parking lot is calculated through the index system.
The above steps are described in detail below.
In some embodiments, based on the step S120, clustering and factor analysis may be performed on the index data, so that the electronic device obtains a preset number of target indexes and target index data corresponding to the target indexes. As an example, the step S150 may include the steps of:
step a), clustering index data to obtain multiple types of first indexes, wherein each type of first index corresponds to multiple first index data;
and b), performing factor analysis processing on each type of first indexes to obtain a preset number of target indexes and target index data corresponding to the target indexes.
For the step a), specifically, the index data is clustered to finally obtain a plurality of groups of newly grouped first index data, and because the index data corresponds to the index, a plurality of first indexes corresponding to each group of first index data can be obtained, and the plurality of first indexes are first indexes of one class.
For the step b), performing factor analysis processing on each type of first indexes to obtain a preset number of target indexes and target index data corresponding to the target indexes, so that the target indexes with the largest influence on evaluation can be screened out from each type of indexes.
In the embodiment of the application, the index data can be clustered to obtain multiple types of first indexes, wherein each type of first index corresponds to multiple first index data; and performing factor analysis processing on each type of first indexes to obtain a preset number of target indexes and target index data corresponding to the target indexes. Therefore, the electronic device can perform clustering processing and factor analysis processing on the index data, and screen out the target index which has the greatest influence on evaluation.
In some embodiments, based on the step a), the index data may be clustered by using a sum of squared deviations method, so that the electronic device obtains multiple classes of first indexes. As an example, step a) may comprise the steps of:
step a1), clustering the index data by using a dispersion square sum method to obtain a plurality of types of first indexes.
Specifically, the sum of squared deviations method (Ward method) is based on the idea of variance analysis, if classification is correct, the sum of squared deviations between index data of the same type should be smaller, and the sum of squared deviations between classes should be larger, and clustering processing is performed on the index data by using the sum of squared deviations method, so that the first indexes of the plurality of types can be obtained.
In the embodiment of the application, the index data can be clustered by using the sum of squared deviations method to obtain the first indexes of multiple categories, so that the electronic device can perform clustering on the index data by using the sum of squared deviations method.
In some embodiments, a non-parametric K-W check may be performed on the first indicator and the first indicator data corresponding to the first indicator, so that the electronic device verifies whether the result of the clustering is reasonable. As an example, the method may further include the steps of:
step c), carrying out nonparametric K-W inspection on the first index and first index data corresponding to the first index;
and d), when the probability value of the first index data corresponding to the first index is larger than a preset threshold value, determining that the first index obtained by clustering and the target index data corresponding to the first index are reasonable.
For the step c), after the R clustering is performed, performing nonparametric K-W inspection on the first indexes of the clustered types, calculating the probability P value of the difference of the first index data corresponding to the same type of first indexes, judging whether the probability P value is greater than a standard threshold, for example, the standard threshold is 0.05, and judging whether the clustering number is reasonable.
For the step d), when the significance level of each type of first index is greater than 0.05, the first indexes in the same type have no significance difference, and the clustering is reasonable; if the significance level is less than 0.05, the significance difference between the first indexes of the same type is shown, the clustering is unreasonable, and if the significance level is unreasonable, the index data needs to be clustered again.
In the embodiment of the application, nonparametric K-W inspection can be performed on the first index and first index data corresponding to the first index; and when the probability value of the first index data corresponding to the first index is larger than a preset threshold value, determining that the first index obtained by clustering and the target index data corresponding to the first index are reasonable. Therefore, the electronic equipment can verify the clustering result and ensure a correct clustering result by using nonparametric K-W inspection.
In some embodiments, the first index data may be subjected to factor analysis processing, and a correlation coefficient matrix, a feature value, and a cumulative variance contribution rate are obtained, so that the electronic device screens out an index with the largest load of each type of factor. As an example, the step b) may include the steps of:
step b1), determining a correlation coefficient matrix of the first index data, and determining a plurality of eigenvalues of the correlation coefficient matrix;
step b2), determining the cumulative variance contribution rate corresponding to the characteristic value;
step b3), arranging a plurality of characteristic values according to a preset sequence, determining a preset number of target characteristic values according to the value range of the accumulated variance contribution rate, and establishing a factor analysis model according to common factors corresponding to the target characteristic values;
step b4), determining a target index corresponding to the target characteristic value in each type of first index and target index data corresponding to the target index.
For the above step b1), in particular, a correlation coefficient matrix R of the first index data is determinedm×mAnd determining a plurality of eigenvalues λ of the correlation coefficient matrixj(j＝1、2、3...m),λjIs the jth common factor FjThe total variance of the disclosed raw first index data.
For step b2) above, a common factor FjCumulative variance contribution to original first index data
For the step b3), the plurality of eigenvalues are arranged in a preset order from large to small, and a preset number of target eigenvalues are determined according to the value range of the cumulative variance contribution rate, for example, according to the cumulative variance contribution rate>Determining a preset number of target characteristic values according to the principle of 85%, and establishing a factor analysis model X according to common factors corresponding to the target characteristic valuesi，
Xi＝ai1F1+ai2F2+...aikFk+εiIn which X isiIs the ith index; fjIs the jth common factor; a isijIs the load of the ith index on the jth common factor (factor load); k is the common factor number.
For the step b4), determining the target index corresponding to the target characteristic value in each type of first index as the index having the most significant influence on the evaluation health degree index, and keeping the target index and the target index data corresponding to the target index.
In the embodiment of the application, a correlation coefficient matrix of the first index data can be determined, and a plurality of eigenvalues of the correlation coefficient matrix are determined; determining the cumulative variance contribution rate corresponding to the characteristic value; arranging a plurality of characteristic values according to a preset sequence based on the accumulated variance contribution rate, determining a preset number of target characteristic values according to the value range of the accumulated variance contribution rate, and establishing a factor analysis model according to common factors corresponding to the target characteristic values; and determining a target index corresponding to the target characteristic value in each type of first index and target index data corresponding to the target index. Therefore, the electronic equipment can perform factor analysis processing on the first index data so as to screen out the target index with the largest factor load in various first indexes and reject other first indexes, so that the condition that the screened target index has the most obvious influence on the evaluation result of the parking lot operation condition in the category of the index is ensured, and the information repetition of the target indexes of the same category is avoided.
In some embodiments, an index system may be established according to the type of the target index, so that the electronic device generates a health index of the parking lot according to the index system. As an example, the step S130 may include the steps of:
step f), classifying the target indexes to generate target indexes corresponding to preset data types;
and g), establishing an index system according to the target index and the target index data corresponding to the target index.
For the step f), it should be noted that the target indexes include multiple indexes such as the turnover rate of parking spaces, the average income of a parking lot, the degree of exit balance, and the traffic efficiency, for example, the number of the target indexes is 21; the preset data types comprise aspects of operation income, payment condition, traffic efficiency, congestion condition, abnormal condition and the like, so the electronic equipment classifies the target indexes to obtain the target indexes respectively comprising the operation income, the payment condition, the traffic efficiency, the congestion condition and the abnormal condition.
And g), establishing an index system according to the corresponding relation between the preset data type and the target index and the corresponding relation between the target index and the target index data.
In the embodiment of the application, the target indexes can be classified to generate the target indexes corresponding to the preset data types; and establishing an index system according to the target index and target index data corresponding to the target index. Therefore, the electronic device can establish an index system according to the corresponding relationship between the preset data type and the target index and the corresponding relationship between the target index and the target index data.
In some embodiments, the target index data included in the index hierarchy may be calculated to cause the electronic device to determine a health index for the parking lot. As an example, the step S140 may include the steps of:
step h), calculating an index system, and determining a target index corresponding to the preset data type;
and i), carrying out weighted average processing on the target index, and generating and determining the health index of the parking lot.
For the step h), because the target index data belong to different preset data types, calculating the target index data included in each preset data type by an expert evaluation method, performing weight assignment on the target index, and determining a target index corresponding to the preset data type, illustratively, calculating the target index data included in the business income and determining a business index corresponding to the business income; calculating target index data included in the payment condition, and determining a payment index corresponding to the payment condition; calculating target index data included in the passing efficiency, and determining a passing index corresponding to the passing efficiency; calculating target index data included in the congestion condition, and determining a congestion index corresponding to the congestion condition; and calculating target index data included in the abnormal conditions, and determining abnormal indexes corresponding to the abnormal conditions.
Specifically, in the step i), the target index includes an operation index, a payment index, a traffic index, a congestion index and an abnormal index, and the operation index, the payment index, the traffic index, the congestion index and the abnormal index are subjected to weighted average processing to generate and determine a health index of the parking lot.
In the embodiment of the application, an index system can be calculated, and a target index corresponding to a preset data type is determined; and carrying out weighted average processing on the target index to generate and determine a health degree index of the parking lot. Therefore, the electronic equipment can determine the health degree index of the parking lot according to the index system, and further determine the actual operation condition of the parking lot according to the health degree index.
In some embodiments, as an example, the method may further perform a normalization process on the data, where the specific process is as follows:
when the data is a forward indicator: the larger the forward index value, the better the yard behavior. Setting: si is a value after corresponding indexes of a certain parking lot are standardized, X is a value of the indexes, M is a maximum value of the certain indexes, N is a minimum value, and a dimensionless processing formula of well-developed indexes of the parking lot is as follows: si ═ X-N)/(M-N);
when data is a negative indicator: the negative direction index is an index of which the operation index data of the parking lot has a reverse relation with the good operation condition of the parking lot, and the dimensionless processing formula of the negative direction index is as follows: is (M-X)/(M-N) wherein each letter has the same meaning as the above formula.
Fig. 3 provides a schematic diagram of a parking lot based data processing apparatus. As shown in fig. 3, the parking lot-based data processing apparatus 300 includes:
the obtaining module 301 is configured to obtain an index of the parking lot and index data corresponding to the index;
the processing module 302 is configured to perform clustering processing and factor analysis processing on the index data to obtain a preset number of target indexes and target index data corresponding to the target indexes;
the establishing module 303 is configured to establish an index system according to the target index and the target index data;
and the determining module 304 is used for determining the health index of the parking lot through an index system.
In some embodiments, the processing module specifically includes:
the first processing module is used for clustering the index data to obtain multiple types of first indexes, wherein each type of first index corresponds to multiple first index data;
and the second processing module is used for performing factor analysis processing on each type of first indexes to obtain a preset number of target indexes and target index data corresponding to the target indexes.
In some embodiments, the first processing module is to:
and clustering the index data by using a dispersion square sum method to obtain a plurality of types of first indexes.
In some embodiments, after the first processing module, a verification module is further included for:
carrying out nonparametric K-W inspection on the first index and first index data corresponding to the first index;
and when the probability value of the first index data corresponding to the first index is larger than a preset threshold value, determining that the first index obtained by clustering and the target index data corresponding to the first index are reasonable.
In some embodiments, the second processing module is to:
determining a correlation coefficient matrix of the first index data, and determining a plurality of eigenvalues of the correlation coefficient matrix;
determining the cumulative variance contribution rate corresponding to the characteristic value;
arranging a plurality of characteristic values according to a preset sequence based on the accumulated variance contribution rate, determining a preset number of target characteristic values according to the value range of the accumulated variance contribution rate, and establishing a factor analysis model according to common factors corresponding to the target characteristic values;
and determining a target index corresponding to the target characteristic value in each type of first index and target index data corresponding to the target index.
In some embodiments, the setup module is to:
classifying the target indexes to generate target indexes corresponding to preset data types;
and establishing an index system according to the target index and target index data corresponding to the target index.
In some embodiments, the determining module is to:
calculating an index system, and determining a target index corresponding to a preset data type;
and carrying out weighted average processing on the target index to generate and determine a health degree index of the parking lot.
In some embodiments, as an example, the apparatus may further be configured to:
when the data is a forward indicator: the larger the forward index value, the better the yard behavior. Setting: si is a value after corresponding indexes of a certain parking lot are standardized, X is a value of the indexes, M is a maximum value of the certain indexes, N is a minimum value, and a dimensionless processing formula of well-developed indexes of the parking lot is as follows: si ═ X-N)/(M-N);
when data is a negative indicator: the negative direction index is an index of which the operation index data of the parking lot has a reverse relation with the good operation condition of the parking lot, and the dimensionless processing formula of the negative direction index is as follows: is (M-X)/(M-N) wherein each letter has the same meaning as the above formula.
The parking lot based data processing device provided by the embodiment of the application has the same technical characteristics as the parking lot based data processing method provided by the embodiment, so that the same technical problems can be solved, and the same technical effects are achieved.
As shown in fig. 4, an electronic device 400 provided in an embodiment of the present application includes a memory 401 and a processor 402, where the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps of the method provided in the foregoing embodiment.
Referring to fig. 4, the electronic device further includes: a bus 403 and a communication interface 404, the processor 402, the communication interface 404 and the memory 401 being connected by the bus 403; the processor 402 is used to execute executable modules, such as computer programs, stored in the memory 401.
The Memory 401 may include a high-speed 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 404 (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 403 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. 4, but that does not indicate only one bus or one type of bus.
The memory 401 is used for storing a program, and the processor 402 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 402, or implemented by the processor 402.
The processor 402 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 402. The Processor 402 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 401, and the processor 402 reads the information in the memory 401 and completes the steps of the method in combination with the hardware.
Corresponding to the parking lot-based data processing method, the embodiment of the application also provides a computer-readable storage medium, wherein computer-executable instructions are stored in the computer-readable storage medium, 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 parking lot-based data processing method.
The parking lot-based data processing device 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 stored in a storage medium and including 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 parking lot based data processing method 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 parking lot based data processing method, the method comprising:
acquiring indexes of the parking lot and index data corresponding to the indexes;
clustering and factor analyzing the index data to obtain a preset number of target indexes and target index data corresponding to the target indexes;
establishing an index system according to the target index and the target index data;
and determining the health degree index of the parking lot through the index system.
2. The parking lot-based data processing method according to claim 1, wherein the step of performing clustering processing and factor analysis processing on the index data to obtain a preset number of target indexes and target index data corresponding to the target indexes comprises:
clustering the index data to obtain multiple types of first indexes, wherein each type of first indexes corresponds to multiple first index data;
and performing factor analysis processing on each type of first indexes to obtain a preset number of target indexes and target index data corresponding to the target indexes.
3. The parking lot-based data processing method according to claim 2, wherein the step of clustering the index data to obtain multiple classes of first indexes comprises:
and clustering the index data by using a dispersion square sum method to obtain a plurality of types of first indexes.
4. The parking lot-based data processing method according to claim 1, wherein after the step of clustering the index data to obtain multiple classes of first indexes, wherein each class of the first indexes corresponds to multiple first index data, the method further comprises:
carrying out nonparametric K-W inspection on the first index and first index data corresponding to the first index;
and when the probability value of first index data corresponding to the first index is larger than a preset threshold value, determining that the first index obtained by clustering and target index data corresponding to the first index are reasonable.
5. The parking lot-based data processing method according to claim 2, wherein the step of performing factor analysis processing on each type of the first indexes to obtain a preset number of target indexes and target index data corresponding to the target indexes comprises:
determining a correlation coefficient matrix of the first index data, and determining a plurality of eigenvalues of the correlation coefficient matrix;
determining the cumulative variance contribution rate corresponding to the characteristic value;
arranging a plurality of characteristic values according to a preset sequence, determining a preset number of target characteristic values according to the value range of the cumulative variance contribution rate, and establishing a factor analysis model according to common factors corresponding to the target characteristic values;
and determining a target index corresponding to the target characteristic value and target index data corresponding to the target index in each type of first index.
6. The parking lot-based data processing method according to claim 1, wherein the step of establishing an index system according to the target index and the target index data comprises:
classifying the target indexes to generate target indexes corresponding to preset data types;
and establishing an index system according to the target index and target index data corresponding to the target index.
7. The parking lot based data processing method of claim 6, wherein the step of determining the health index of the parking lot through the index system comprises:
calculating the index system, and determining a target index corresponding to the preset data type;
and carrying out weighted average processing on the target index to generate and determine the health degree index of the parking lot.
8. A parking lot-based data processing apparatus, comprising:
the acquisition module is used for acquiring indexes of the parking lot and index data corresponding to the indexes;
the processing module is used for carrying out clustering processing and factor analysis processing on the index data to obtain a preset number of target indexes and target index data corresponding to the target indexes;
the establishing module is used for establishing an index system according to the target index and the target index data;
and the determining module is used for determining the health degree index of the parking lot through the index system.
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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| CN104899679A (en) * | 2015-05-11 | 2015-09-09 | 上海交通大学 | City cbd parking lot comprehensive evaluation method |
| US20170330205A1 (en) * | 2016-05-16 | 2017-11-16 | Cerebri AI Inc. | Business artificial intelligence management engine |
| CN109583797A (en) * | 2019-01-28 | 2019-04-05 | 吉林大学 | A kind of Commercial Complex fuzzy clustering method obtained towards parking formation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104899679A (en) * | 2015-05-11 | 2015-09-09 | 上海交通大学 | City cbd parking lot comprehensive evaluation method |
| US20170330205A1 (en) * | 2016-05-16 | 2017-11-16 | Cerebri AI Inc. | Business artificial intelligence management engine |
| CN109583797A (en) * | 2019-01-28 | 2019-04-05 | 吉林大学 | A kind of Commercial Complex fuzzy clustering method obtained towards parking formation |
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Application publication date: 20210903 |