CN113469501A - Data analysis method and device, electronic device and computer equipment - Google Patents
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Abstract
According to the data analysis method, the data analysis device, the electronic device and the computer equipment, the GPS basic data of the target vehicle are obtained, the early warning index of the target vehicle is obtained based on the GPS basic data and the preset wind control operation rule, the characteristic value of the target vehicle is obtained according to the early warning index and the GPS basic data, the characteristic value is subjected to weighted statistics, and the risk score of the target vehicle is obtained. The risk assessment of the target vehicle is carried out by utilizing the weighted statistics of the early warning indexes and the GPS basic data, so that the quantification of the risk indexes is realized, and the timeliness and the accuracy of risk decision are improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a data analysis method and apparatus, an electronic apparatus, and a computer device.
Background
In the field of automobile finance, vehicle data reported by a gps (global Positioning system) device can be used for vehicle risk assessment to realize air control supervision in vehicle credit. In the loan central control supervision, the vehicle risk can be comprehensively evaluated by asset supervision personnel according to data such as equipment offline condition, alarm data, dangerous area electronic fence early warning and vehicle abnormal aggregation provided by GPS equipment. At present, on one hand, the vehicle risk assessment is carried out by operators with industry experience based on vehicle data provided by a GPS monitoring platform to comprehensively judge the vehicle risk. The method depends on subjective judgment and practical experience of operators, so the operation cost is high and the timeliness is low. On the other hand, the assessment of the vehicle risk can be realized in an online early warning mode, and a more accurate early warning index is formed through strategic online risk monitoring. Although the traditional online early warning mode reduces the subjective decision influence caused by manual operation to a great extent, the risk index is lack of quantification, so that the timeliness and the accuracy of the risk decision cannot be improved.
At present, no effective solution is provided for the problem that the vehicle risk assessment is lack of timeliness and accuracy in the related technology.
Disclosure of Invention
In view of the above, it is necessary to provide a data analysis method, an apparatus, an electronic apparatus, and a computer device.
In a first aspect, an embodiment of the present application provides a data analysis method, where the method includes:
acquiring GPS basic data of a target vehicle;
obtaining an early warning index of the target vehicle based on the GPS basic data and a preset wind control operation rule;
and obtaining a characteristic value of the target vehicle according to the early warning index and the GPS basic data, and carrying out weighted statistics on the characteristic value to obtain a risk score of the target vehicle.
In one embodiment, the acquiring GPS basic data of the target vehicle includes:
when the GPS equipment of the target vehicle gives an alarm, acquiring first original GPS data of the target vehicle reported by the GPS equipment;
calculating the first original GPS data according to a preset preliminary rule to obtain GPS basic alarm data;
acquiring second original GPS data reported by the GPS equipment of the target vehicle according to a preset period;
and obtaining the GPS basic data of the target vehicle according to the GPS basic alarm data and the second original GPS data.
The obtaining of the early warning index of the target vehicle based on the GPS basic data and the preset wind control operation rule comprises the following steps:
and calculating the GPS basic alarm data according to the wind control operation rule to obtain the early warning index.
In one embodiment, obtaining the characteristic value of the target vehicle according to the early warning indicator and the GPS basic data includes:
acquiring historical data and real-time data in the early warning index and the GPS basic data;
respectively carrying out corresponding big data analysis processing on the historical data and the real-time data to obtain an initial characteristic value of the target vehicle;
and carrying out standardization processing on the initial characteristic value to obtain the characteristic value of the target vehicle.
In one embodiment, the performing weighted statistics on the feature values to obtain the risk score of the target vehicle includes:
and according to a preset weight distribution standard, distributing corresponding feature weights for the feature values, and carrying out weighted statistics on the feature values according to the feature weights to obtain the risk score of the target vehicle.
In one embodiment, the preset weight distribution criteria include a feature category to which the feature value belongs and a preset vehicle risk correlation corresponding to the feature value.
In a second aspect, an embodiment of the present application further provides a data analysis device, where the device includes an acquisition module, an early warning calculation module, and a risk scoring module:
the acquisition module is used for acquiring GPS basic data of the target vehicle;
the early warning calculation module is used for obtaining an early warning index of the target vehicle based on the GPS basic data and a preset wind control operation rule;
and the risk scoring module is used for obtaining a characteristic value of the target vehicle according to the early warning index and the GPS basic data, and carrying out weighted statistics on the characteristic value to obtain the risk score of the target vehicle.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the method in the first aspect when executing the computer program.
In a third aspect, an embodiment of the present application provides a computer 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 steps of the method in the first aspect are implemented.
In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method of the first aspect.
According to the data analysis method, the data analysis device, the electronic device and the computer equipment, the GPS basic data of the target vehicle are obtained, the early warning index of the target vehicle is obtained based on the GPS basic data and the preset wind control operation rule, the characteristic value of the target vehicle is obtained according to the early warning index and the GPS basic data, the characteristic value is subjected to weighted statistics, and the risk score of the target vehicle is obtained. The risk assessment of the target vehicle is carried out by utilizing the weighted statistics of the early warning indexes and the GPS basic data, so that the quantification of the risk indexes is realized, and the timeliness and the accuracy of risk decision are improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is an application scenario diagram of a data analysis method according to an embodiment of the present application;
FIG. 2 is a flow chart of a method of data analysis according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a data analysis device according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
Fig. 1 is an application scenario diagram of a data analysis method according to an embodiment of the present application, and the embodiment of the method provided in this embodiment may be applied to the application scenario shown in fig. 1. As shown in fig. 1, 12 is a server, 14 is a GPS terminal, where the server 12 may be implemented by a stand-alone server or a server cluster composed of a plurality of servers, and the GPS terminal 14 may specifically be one or more GPS devices installed in the target vehicle, such as a wired GPS device, an off-the-air GPS device, and a micro GPS device. The server 12 performs data interaction with the GPS terminal 14 through a network. The GPS base data of the target vehicle may be obtained from the GPS data provided by the GPS terminal 14, and the risk score of the target vehicle may be calculated based on the GPS base data.
In one embodiment, as shown in fig. 2, a data analysis method is provided, which is described by taking the application of the method to the terminal in fig. 1 as an example, and includes the following steps:
step S210, GPS basic data of the target vehicle is acquired.
The GPS basic data of the target vehicle contains rich driving information of the target vehicle and can be used as a data source for early warning index analysis. After analyzing and processing the GPS data provided by the GPS device installed in the target vehicle, the data obtained by filtering invalid redundant data in the GPS data may be used as the GPS base data. Because the target vehicle may include more than one type of GPS device, the GPS basic data obtained by processing the GPS data may generally include multiple types of data, such as total mileage, mileage on the day, total offline warning times, total power failure warning times, total removal warning times, fence entry and exit times on the day, real-time warning times, and the like.
Because the GPS basic data is effective data obtained after processing on the basis of the multi-source GPS data with large data quantity provided by the GPS equipment, the subsequent calculation processing is carried out on the basis of the GPS basic data, and the stability of the processing system can be improved.
Further, when an alarm occurs in a hardware module in the GPS device of the target vehicle, the original GPS alarm data actively reported by the hardware module may be acquired as the first original GPS data, and the GPS basic alarm data of the target vehicle may be obtained after the first original GPS data is processed through the above process. Wherein the warning generated by the GPS device of the target vehicle may include: dismantling alarm, power failure alarm, terminal under-voltage alarm, off-line alarm, fence in and out alarm and the like. The GPS base data can be obtained by performing data cleaning and filtering on the first raw GPS data. Additionally, the GPS basic data further includes second original GPS data reported by the GPS device according to a preset period, and specifically may include GPS positioning information and GPS power data. For example, the wired GPS device may report the positioning information periodically every 10s to 30s, and the wireless GPS device may report the positioning information periodically every one day. The GPS basic alarm data and the second original GPS data jointly form the GPS basic data of the target vehicle.
And step S220, obtaining an early warning index of the target vehicle based on the GPS basic data and a preset wind control operation rule.
The wind control operation rule can be a series of rules and verified logic which are set by an operator in advance for vehicle risk assessment. Because the warning generated by the GPS equipment of the target vehicle can be of various types, the early warning index can also comprise various types, such as overtime running, overtime parking, abnormal off-line, high-risk area stop and the like. And calculating the GPS basic alarm data in the GPS basic data of the GPS equipment according to the logic in the corresponding wind control operation rule according to different early warning indexes. For example, when the calculation type is the abnormal offline warning indicator, the corresponding wind control operation rule may be the determination of an offline area and an offline duration, that is, the offline area is divided in advance and the offline duration is set, when the vehicle enters the offline area and the time interval of the vehicle offline exceeds the offline duration, it is determined that the vehicle is abnormal offline, and at this time, the abnormal offline warning indicator may be calculated based on the GPS basic data of the vehicle.
Further, after the GPS device reports the first original GPS data, the GPS basic alarm data is obtained through the processing of step S210, and the warning indicator of the target vehicle is calculated based on the GPS basic alarm data.
And step S230, obtaining a characteristic value of the target vehicle according to the early warning index and the GPS basic data, and carrying out weighted statistics on the characteristic value to obtain a risk score of the target vehicle.
Specifically, the characteristic value of the target vehicle may be obtained by performing standardization processing and screening on the early warning index of the target vehicle and the GPS basic data. The dimensions of different types of early warning indexes and GPS basic data are different. For example, in the GPS-based data, the target vehicle mileage data may be 100 kilometers, and the number of times the target vehicle is offline is 2 kilometers. Therefore, the initial characteristic value obtained according to the GPS basic data and the early warning index of the target vehicle needs to be standardized, different types of data are scaled and then unified into one reference system, and data larger than 0 is selected as the characteristic value of the target vehicle. Therefore, the range of the characteristic value of the target vehicle may be a fixed section, and specifically may be between 0 and 1. Additionally, if a certain characteristic value is a negative number, the risk of the characteristic value can be considered to be small, and the characteristic value can be ignored when performing risk evaluation based on the characteristic value.
In addition, both the GPS basic data and the early warning index comprise historical data and real-time data, the characteristic value can be obtained by respectively carrying out corresponding big data analysis processing on the historical data and the real-time data to obtain an initial characteristic value of the target vehicle, and the characteristic value of the target vehicle is obtained by carrying out the standardization processing on the initial characteristic value. The GPS basic data of different data sources and the real-time data and the historical data of the early warning indexes can be correspondingly processed. And (3) carrying out statistical analysis on the GPS basic data and the early warning indexes by utilizing big data analysis technologies such as spark calculation engine, flash stream calculation and the like, and finally screening the results after the statistical analysis so as to obtain the initial characteristic value of the target vehicle.
Further, after the characteristic value of the target vehicle is obtained, a characteristic weight may be assigned to each characteristic value, and the characteristic value may be subjected to weighted statistics according to the weight. Specifically, the criterion according to which the feature weight is assigned to the feature value is the feature type of the feature value and the vehicle risk correlation corresponding to the feature value. The vehicle risk correlation may be understood as a feature value that is close to the vehicle risk range, and the more the feature value is close to the vehicle risk range, the higher the risk of the feature value is and the higher the assigned feature weight is, and conversely, the lower the risk of the feature value is and the lower the assigned feature weight is. For example, the target vehicle has two characteristic values x1 and x2, wherein the characteristic value x1 is the vehicle mileage and the characteristic value x2 is the number of times the vehicle triggers the high-risk zone fence. According to the empirical judgment, the vehicle risk correlation of the characteristic value x2 is higher than that of the characteristic value x1, so the weight assigned by x2 is also higher than that assigned by x 1.
After assigning the corresponding feature weight to each feature value, the feature values may be weighted and averaged according to the weight, for example, after summing the products of the feature values and their corresponding weights, a ratio of the result of the weighted summation and the result of the weighted summation is calculated. The ratio belongs to the numerical value of the interval 0 to 1, and the scoring result under a certain score can be obtained in a multiple expansion mode. For example, the ratio is expanded by a percentage to obtain a value of 0-100, and the value is used as the risk score of the target vehicle, so that the user can make a corresponding wind control decision according to the quantified risk score.
In steps S210 to S230, acquiring GPS basic data of the target vehicle, obtaining an early warning index of the target vehicle based on the GPS basic data and a preset wind control operation rule, obtaining a characteristic value of the target vehicle according to the early warning index and the GPS basic data, performing weighted statistics on the characteristic value, and obtaining a risk score of the target vehicle. The risk assessment of the target vehicle is carried out by utilizing the weighted statistics of the early warning indexes and the GPS basic data, so that the quantification of the risk indexes is realized, and the timeliness and the accuracy of risk decision are improved.
In one embodiment, based on the step S210, acquiring the GPS basic data of the target vehicle specifically includes the following steps:
step S211, when the GPS equipment of the target vehicle gives an alarm, the first original GPS data of the target vehicle reported by the GPS equipment is obtained.
In the GPS equipment of the target vehicle, various alarming modes related to vehicle risks are included, such as dismantling alarm, power failure alarm, terminal under-voltage alarm, off-line alarm, fence entering and exiting alarm and the like. Corresponding trigger conditions can be set for the alarm mode in advance, for example, low-voltage alarm of wired GPS equipment, and the corresponding trigger conditions can be poor power contact, insufficient power quantity of the power supply, or unplugged power cord, etc. The triggering condition of the removal alarm of the wireless GPS equipment can be that the removal alarm is triggered after the photosensitive module of the wireless GPS equipment receives light.
When a hardware module in the GPS equipment of the target vehicle gives an alarm, all first original GPS data of the target vehicle are acquired from different data sources, and GPS basic data with risk evaluation value can be obtained based on the first original GPS data.
Step S212, calculating the first original GPS data according to a preset preliminary rule to obtain GPS basic alarm data.
The preliminary rule may be a series of logics set in advance for data cleaning and filtering, and is used for filtering the first original GPS data to reduce the size of the whole GPS basic data. Specifically, the preset preliminary rule may be a data filtering rule set according to an actual service scenario. The filtering rule can be specifically set according to time and frequency. Because the possibility of false alarm exists in the GPS equipment or the frequency of reporting data by certain GPS equipment is high, the GPS data generated every day is excessive and the normal operation of the service is influenced, therefore, the statistical result of the original GPS data can be screened according to the time and the frequency to obtain the GPS basic data of the target vehicle.
Step S213, obtaining second original GPS data reported by the GPS device of the target vehicle according to a preset period.
In addition, the GPS device reports other non-alarm second original GPS data, such as power data and positioning information, according to a preset period.
And step S214, obtaining the GPS basic data of the target vehicle according to the GPS basic alarm data and the second original GPS data.
The second raw GPS data and the GPS base warning data together constitute GPS base data of the target vehicle.
Based on the step S220, the early warning index of the target vehicle is obtained based on the GPS basic data and the preset wind control operation rule, and the method specifically includes the following steps:
and step S221, calculating the GPS basic alarm data according to the wind control operation rule to obtain an early warning index.
Specifically, the processing logic in the wind control operation rule may be set as a corresponding service code module, and the GPS basic alarm data is used as an input parameter and input into the service code module, so as to obtain a corresponding early warning index calculated based on the wind control operation rule.
In one embodiment, based on the step S230, obtaining the characteristic value of the target vehicle according to the early warning indicator and the GPS basic data specifically includes the following steps:
and S231, acquiring historical data and real-time data in the early warning indexes and the GPS basic data.
And step S232, carrying out corresponding big data analysis processing on the historical data and the real-time data respectively to obtain an initial characteristic value of the target vehicle.
The historical data may specifically be GPS track data, historical vehicle alerts, and the like, and the processing of the historical data may be: storing the data into a Distributed File system, such as HDFS (Hadoop Distributed File System), and utilizing a spark calculation engine to obtain statistical data such as the total mileage, the total offline alarm times, the total outage alarm times, the total removal alarm times and the like of the target vehicle. The real-time data mainly includes data acquired by the GPS device in real time, such as GPS positioning data, fence entering and exiting times, warning data and the like. The data can be stored in a message queue, such as kafka, and then calculated by using a flink calculation engine, so as to finally obtain statistical data of the mileage of the target vehicle on the day, the times of entering and exiting the fence on the day, the times of real-time alarming and the like.
Through the corresponding big data analysis of the historical data and the real-time data, the time dimension of the data can be increased, and therefore the accuracy of subsequent risk assessment is improved.
In step S233, the initial characteristic value is normalized to obtain a characteristic value of the target vehicle.
In one embodiment, the weighted statistics of the feature values to obtain the risk score of the target vehicle specifically includes the following steps:
and step S234, distributing corresponding characteristic weights for the characteristic values according to a preset weight distribution standard, and carrying out weighted statistics on the characteristic values according to the characteristic weights to obtain the risk score of the target vehicle.
In one embodiment, based on the step S234, the preset weight distribution criteria includes the feature category to which the feature value belongs and the vehicle risk correlation corresponding to the preset feature value.
The steps are that when the GPS equipment of the target vehicle gives an alarm, first original GPS data reported by the GPS equipment is obtained, the first original GPS data is calculated according to a preset preliminary rule to obtain GPS basic alarm data, invalid data can be filtered, the data redundancy is reduced, the system stability is improved, the influence of huge data volume generated by the GPS equipment due to false alarm or high reporting frequency on service use is avoided, the risk assessment efficiency is improved, the GPS basic alarm data is calculated according to a wind control operation rule to obtain an early warning index, the risk assessment value of the data is improved, basic data is provided for quantification of subsequent risk indexes, the GPS basic data is obtained according to the GPS basic alarm data and second original GPS data, and corresponding big data analysis processing is respectively carried out on historical data and real-time data in the GPS basic data and the early warning index, the method comprises the steps of obtaining an initial characteristic value of a target vehicle, improving the time dimension of data, improving the accuracy of an evaluation result, carrying out standardization processing on the initial characteristic value to obtain a characteristic value of the target vehicle, distributing corresponding characteristic weights for the characteristic value according to a preset weight distribution standard, carrying out weighted statistics on the characteristic value according to the characteristic weights to obtain a risk score of the target vehicle, and therefore quantification of vehicle risk indexes can be achieved, and timeliness and accuracy of risk decision are finally improved.
The present embodiment further provides a data analysis apparatus, which is used to implement the foregoing embodiments and preferred embodiments, and the description of the data analysis apparatus is omitted here. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Based on the same inventive concept, as shown in fig. 3, an embodiment of the present application further provides a data analysis apparatus 30, including:
an acquisition module 32, configured to acquire GPS basic data of a target vehicle;
the early warning calculation module 34 is used for obtaining an early warning index of the target vehicle based on the GPS basic data and a preset wind control operation rule; and
and the risk scoring module 36 is configured to obtain a feature value of the target vehicle according to the early warning index and the GPS basic data, perform weighted statistics on the feature value, and obtain a risk score of the target vehicle.
The data analysis device 30 obtains the early warning index of the target vehicle by obtaining the GPS basic data of the target vehicle based on the GPS basic data and the preset wind control operation rule, obtains the characteristic value of the target vehicle according to the early warning index and the GPS basic data, and performs weighted statistics on the characteristic value to obtain the risk score of the target vehicle. The risk assessment of the target vehicle is carried out by utilizing the weighted statistics of the early warning indexes and the GPS basic data, so that the quantification of the risk indexes is realized, and the timeliness and the accuracy of risk decision are improved.
In an embodiment, the obtaining module 32 is further configured to, when the GPS device of the target vehicle gives an alarm, obtain first original GPS data of the target vehicle, calculate the first original GPS data according to a preset preliminary rule to obtain GPS basic alarm data, obtain second original GPS data that is reported by the GPS device of the target vehicle according to a preset period, and obtain GPS basic data of the target vehicle according to the GPS basic alarm data and the second original GPS data.
In one embodiment, the early warning calculation module 34 is further configured to calculate the GPS basic warning data according to the wind control operation rule to obtain an early warning indicator.
In one embodiment, the risk scoring module 36 is further configured to obtain historical data and real-time data in the early warning indicator and the GPS basic data, perform corresponding big data analysis processing on the historical data and the real-time data respectively to obtain an initial characteristic value of the target vehicle, and perform normalization processing on the initial characteristic value to obtain a characteristic value of the target vehicle.
In one embodiment, the risk scoring module 36 is further configured to assign corresponding feature weights to the feature values according to preset weight assignment criteria, and perform weighted statistics on the feature values according to the feature weights to obtain the risk score of the target vehicle.
In one embodiment, the preset weight distribution criteria include a feature category to which the feature value belongs and a vehicle risk correlation corresponding to the preset feature value.
For specific limitations of the embodiment of the data analysis apparatus, reference may be made to the above limitations of the data analysis method, which are not described herein again. The modules in the data analysis device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, as shown in FIG. 4, an electronic device is provided that includes a memory and a processor. The memory has stored therein a computer program for providing computing and control capabilities to the processor of the electronic device. The memory of the electronic device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor, when executing the computer program, implements the following steps:
acquiring GPS basic data of a target vehicle;
obtaining an early warning index of the target vehicle based on GPS basic data and a preset wind control operation rule;
and obtaining a characteristic value of the target vehicle according to the early warning index and the GPS basic data, and carrying out weighted statistics on the characteristic value to obtain a risk score of the target vehicle.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
when the GPS equipment of the target vehicle gives an alarm, acquiring first original GPS data of the target vehicle reported by the GPS equipment;
calculating the first original GPS data according to a preset preliminary rule to obtain GPS basic alarm data;
acquiring second original GPS data reported by the GPS equipment of the target vehicle according to a preset period;
and obtaining the GPS basic data of the target vehicle according to the GPS basic alarm data and the second original GPS data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and calculating the GPS basic alarm data according to the wind control operation rule to obtain an early warning index.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring historical data and real-time data in early warning indexes and GPS basic data;
respectively carrying out corresponding big data analysis processing on the historical data and the real-time data to obtain an initial characteristic value of the target vehicle;
and carrying out standardization processing on the initial characteristic value to obtain the characteristic value of the target vehicle.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and distributing corresponding characteristic weights for the characteristic values according to a preset weight distribution standard, and carrying out weighted statistics on the characteristic values according to the characteristic weights to obtain the risk score of the target vehicle.
In one embodiment, the preset weight distribution criteria include a feature category to which the feature value belongs and a vehicle risk correlation corresponding to the preset feature value.
The electronic device obtains the early warning index of the target vehicle by obtaining the GPS basic data of the target vehicle and based on the GPS basic data and the preset wind control operation rule, obtains the characteristic value of the target vehicle according to the early warning index and the GPS basic data, and performs weighted statistics on the characteristic value to obtain the risk score of the target vehicle. The risk assessment of the target vehicle is carried out by utilizing the weighted statistics of the early warning indexes and the GPS basic data, so that the quantification of the risk indexes is realized, and the timeliness and the accuracy of risk decision are improved.
In one embodiment, as shown in FIG. 5, a computer device is provided, which may be a terminal. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities.
The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a data analysis method.
The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the above-described architecture is merely a block diagram of some of the structures associated with the present aspects and is not intended to limit the computing devices to which the present aspects apply, as particular computing devices may include more or less components than those described, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring GPS basic data of a target vehicle;
obtaining an early warning index of the target vehicle based on GPS basic data and a preset wind control operation rule;
and obtaining a characteristic value of the target vehicle according to the early warning index and the GPS basic data, and carrying out weighted statistics on the characteristic value to obtain a risk score of the target vehicle.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
when the GPS equipment of the target vehicle gives an alarm, acquiring first original GPS data of the target vehicle reported by the GPS equipment;
calculating the first original GPS data according to a preset preliminary rule to obtain GPS basic alarm data;
acquiring second original GPS data reported by the GPS equipment of the target vehicle according to a preset period;
and obtaining the GPS basic data of the target vehicle according to the GPS basic alarm data and the second original GPS data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and calculating the GPS basic alarm data according to the wind control operation rule to obtain an early warning index.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring historical data and real-time data in early warning indexes and GPS basic data;
respectively carrying out corresponding big data analysis processing on the historical data and the real-time data to obtain an initial characteristic value of the target vehicle;
and carrying out standardization processing on the initial characteristic value to obtain the characteristic value of the target vehicle.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and distributing corresponding characteristic weights for the characteristic values according to a preset weight distribution standard, and carrying out weighted statistics on the characteristic values according to the characteristic weights to obtain the risk score of the target vehicle.
In one embodiment, the preset weight distribution criteria include a feature category to which the feature value belongs and a vehicle risk correlation corresponding to the preset feature value.
The computer equipment obtains the early warning index of the target vehicle by obtaining the GPS basic data of the target vehicle and based on the GPS basic data and the preset wind control operation rule, obtains the characteristic value of the target vehicle according to the early warning index and the GPS basic data, and performs weighted statistics on the characteristic value to obtain the risk score of the target vehicle. The risk assessment of the target vehicle is carried out by utilizing the weighted statistics of the early warning indexes and the GPS basic data, so that the quantification of the risk indexes is realized, and the timeliness and the accuracy of risk decision are improved.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring GPS basic data of a target vehicle;
obtaining an early warning index of the target vehicle based on GPS basic data and a preset wind control operation rule;
and obtaining a characteristic value of the target vehicle according to the early warning index and the GPS basic data, and carrying out weighted statistics on the characteristic value to obtain a risk score of the target vehicle.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
when the GPS equipment of the target vehicle gives an alarm, acquiring first original GPS data of the target vehicle reported by the GPS equipment;
calculating the first original GPS data according to a preset preliminary rule to obtain GPS basic alarm data;
acquiring second original GPS data reported by the GPS equipment of the target vehicle according to a preset period;
and obtaining the GPS basic data of the target vehicle according to the GPS basic alarm data and the second original GPS data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and calculating the GPS basic alarm data according to the wind control operation rule to obtain an early warning index.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring historical data and real-time data in early warning indexes and GPS basic data;
respectively carrying out corresponding big data analysis processing on the historical data and the real-time data to obtain an initial characteristic value of the target vehicle;
and carrying out standardization processing on the initial characteristic value to obtain the characteristic value of the target vehicle.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and distributing corresponding characteristic weights for the characteristic values according to a preset weight distribution standard, and carrying out weighted statistics on the characteristic values according to the characteristic weights to obtain the risk score of the target vehicle.
In one embodiment, the preset weight distribution criteria include a feature category to which the feature value belongs and a vehicle risk correlation corresponding to the preset feature value.
The storage medium obtains the early warning index of the target vehicle by obtaining the GPS basic data of the target vehicle and based on the GPS basic data and the preset wind control operation rule, obtains the characteristic value of the target vehicle according to the early warning index and the GPS basic data, and performs weighted statistics on the characteristic value to obtain the risk score of the target vehicle. The risk assessment of the target vehicle is carried out by utilizing the weighted statistics of the early warning indexes and the GPS basic data, so that the quantification of the risk indexes is realized, and the timeliness and the accuracy of risk decision are improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, the computer program can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A data analysis method for calculating a risk score for a vehicle, the method comprising:
acquiring GPS basic data of a target vehicle;
obtaining an early warning index of the target vehicle based on the GPS basic data and a preset wind control operation rule;
and obtaining a characteristic value of the target vehicle according to the early warning index and the GPS basic data, and carrying out weighted statistics on the characteristic value to obtain a risk score of the target vehicle.
2. The method of claim 1, wherein the obtaining the GPS-based data for the target vehicle comprises:
when the GPS equipment of the target vehicle gives an alarm, acquiring first original GPS data of the target vehicle reported by the GPS equipment;
calculating the first original GPS data according to a preset preliminary rule to obtain GPS basic alarm data;
acquiring second original GPS data reported by the GPS equipment of the target vehicle according to a preset period;
and obtaining the GPS basic data of the target vehicle according to the GPS basic alarm data and the second original GPS data.
3. The method of claim 2, wherein obtaining the early warning index of the target vehicle based on the GPS basic data and a preset wind control operation rule comprises:
and calculating the GPS basic alarm data according to the wind control operation rule to obtain the early warning index.
4. The method of claim 1, wherein obtaining the characteristic value of the target vehicle from the early warning indicator and the GPS-based data comprises:
acquiring historical data and real-time data in the early warning index and the GPS basic data;
respectively carrying out corresponding big data analysis processing on the historical data and the real-time data to obtain an initial characteristic value of the target vehicle;
and carrying out standardization processing on the initial characteristic value to obtain the characteristic value of the target vehicle.
5. The method according to any one of claims 1 to 4, wherein the weighted statistics of the characteristic values to obtain the risk score of the target vehicle comprises:
and according to a preset weight distribution standard, distributing corresponding feature weights for the feature values, and carrying out weighted statistics on the feature values according to the feature weights to obtain the risk score of the target vehicle.
6. The method according to claim 5, wherein the preset weight distribution criteria comprise a feature category to which the feature value belongs and a vehicle risk correlation corresponding to the preset feature value.
7. A data analysis device for calculating a risk score of a vehicle, the device comprising an acquisition module, an early warning calculation module and a risk score module:
the acquisition module is used for acquiring GPS basic data of the target vehicle;
the early warning calculation module is used for obtaining an early warning index of the target vehicle based on the GPS basic data and a preset wind control operation rule;
and the risk scoring module is used for obtaining a characteristic value of the target vehicle according to the early warning index and the GPS basic data, and carrying out weighted statistics on the characteristic value to obtain the risk score of the target vehicle.
8. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the steps of the method according to any of claims 1 to 6.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 6 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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| US20180365770A1 (en) * | 2017-06-15 | 2018-12-20 | Alibaba Group Holding Limited | Determining a categorization value based on processing of attribute data |
| CN109754595A (en) * | 2017-11-01 | 2019-05-14 | 阿里巴巴集团控股有限公司 | Appraisal procedure, device and the interface equipment of vehicle risk |
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