CN111815208A - Traffic index data analysis method and device and terminal equipment - Google Patents
Traffic index data analysis method and device and terminal equipment Download PDFInfo
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Abstract
The application is applicable to the technical field of data processing, and provides an analysis method, an analysis device and a terminal device for traffic index data, wherein the method comprises the following steps: capturing first real-time data corresponding to each first traffic index, and searching for a first weight coefficient corresponding to each first traffic index; calculating target real-time data of a target traffic index according to the first real-time data and the first weight coefficients; and calculating target evaluation information of the target traffic index according to the target real-time data and the fuzzy membership function of the target traffic index. The method and the device can solve the problem that the accuracy of the currently obtained evaluation information of the traffic indexes is not high to a certain extent.
Description
Technical Field
The present application belongs to the field of data processing, and in particular, relates to an analysis method, an analysis device, and a terminal device for traffic index data.
Background
With the rapid development of economy, family cars are increasingly popularized, and convenience is brought to the life of people. But at the same time also leads to increasing traffic problems in cities. Therefore, the management of the operation of traffic is becoming more and more important.
At present, traffic operation analysis methods mainly comprise a traffic travel evaluation method and a knowledge graph method. In the travel evaluation method, since the data of the traffic congestion index itself is not obtained by measurement, the evaluation information of the traffic congestion index is obtained from the traffic flow and the corresponding running speed of the road. However, the evaluation information of the traffic congestion index obtained from the traffic flow and the running speed corresponding to the road is not very accurate. Although the knowledge graph method can acquire various traffic index data, the knowledge graph method cannot analyze the traffic index data, so that the traffic running condition cannot be acquired. In addition, because the quantity of the traffic index data is huge and the data has randomness, the knowledge graph method has not been well applied to the aspect of traffic operation analysis.
Therefore, the evaluation information of the traffic index cannot be obtained according to the data corresponding to the traffic index itself according to the current traffic operation analysis method, so that the accuracy of the obtained evaluation information is not high.
Disclosure of Invention
The embodiment of the application provides an analysis method, an analysis device and a terminal device for traffic index data, which can solve the problem that the accuracy of the currently obtained evaluation information of traffic indexes is not high to a certain extent.
In a first aspect, an embodiment of the present application provides a method for analyzing traffic index data, including:
capturing first real-time data corresponding to each first traffic index, and searching for a first weight coefficient corresponding to each first traffic index;
calculating target real-time data of a target traffic index according to each first real-time data and each first weight coefficient;
and calculating target evaluation information of the target traffic index according to the target real-time data and the fuzzy membership function of the target traffic index.
In a second aspect, an embodiment of the present application provides an apparatus for analyzing traffic index data, including:
the capturing module is used for capturing first real-time data corresponding to each first traffic index and searching for a first weight coefficient corresponding to each first traffic index;
a first calculating module, configured to calculate target real-time data of a target traffic index according to each of the first real-time data and each of the first weighting coefficients;
and the second calculation module is used for calculating the target evaluation information of the target traffic index according to the target real-time data and the fuzzy membership function of the target traffic index.
In a third aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and the computer program implements the steps of the method according to the first aspect when executed by a processor.
In a fifth aspect, an embodiment of the present application provides a computer program product, which, when run on a terminal device, causes the terminal device to execute the method for analyzing traffic index data according to any one of the first aspect.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
Compared with the prior art, the embodiment of the application has the advantages that:
the application provides an analysis method of traffic index data, which comprises the steps of firstly, capturing first real-time data corresponding to each first traffic index, and searching for a first weight coefficient corresponding to each first traffic index. And then calculating target real-time data of the target traffic index according to the first real-time data and the first weight coefficients. And finally, calculating target evaluation information of the target traffic index according to the target real-time data and the fuzzy membership function of the target traffic index. In the application, although the target real-time data corresponding to the target traffic index cannot be obtained through measurement, since the first real-time data corresponding to the first traffic index is measurable, the target real-time data of the target traffic index can be obtained through calculation according to each first real-time data and each first weight coefficient, and then the target evaluation information of the target traffic index can be obtained through calculation according to the target real-time data and the fuzzy membership function corresponding to the target traffic index. Therefore, in the application, the target traffic index is quantized firstly, that is, the target real-time data of the target traffic index is obtained firstly, and then the target evaluation information of the target traffic index is calculated according to the target real-time data and the fuzzy membership function corresponding to the target traffic index, so that the obtained target evaluation information of the target traffic index is more accurate.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of an analysis method of traffic index data according to an embodiment of the present application;
fig. 2 is a schematic diagram of a relationship between a target traffic index and first traffic indexes at different levels according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an apparatus for analyzing traffic index data according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The method for analyzing traffic index data provided in the embodiment of the present application may be applied to terminal devices such as a mobile phone, a tablet computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, and a Personal Digital Assistant (PDA), and the specific type of the terminal device is not limited in any way in the embodiment of the present application.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.
Example one
Referring to fig. 1, a method for analyzing traffic index data according to an embodiment of the present application is described below, where the method includes:
step S101, capturing first real-time data corresponding to each first traffic index, and searching for a first weight coefficient corresponding to each first traffic index.
In step S101, the first traffic index is a measurable index (which may also be referred to as a visualization index) corresponding to the first traffic index. For example, the number of vehicles leaving Shenzhen in a unit time, the number of vehicles entering Shenzhen in a unit time, and the like. When the current target traffic index needs to be evaluated, the terminal device may capture first real-time data corresponding to each first traffic index from each service system, and then search for a first weight coefficient corresponding to each first traffic index. Or the terminal device may periodically capture the first real-time data corresponding to each first traffic index from each service system. For example, each first traffic index may be a total number of vehicles traveling in the region, vehicles going out Shenzhen, vehicles going into Shenzhen, a bayonet online rate, an online number of networked signal lamps, a road growth rate, and the like. For another example, each first traffic index may be a number of patrol miles of law enforcement officers, illegal drunk driving amount, illegal local vehicle amount, number of vehicles left behind, rate of case settlement within a period of time, rate of change of traffic accidents, and the like.
In some embodiments, after capturing the first real-time data corresponding to each first traffic index, the first evaluation information of the first traffic index may be further calculated according to the first real-time data corresponding to the first traffic index and the fuzzy membership function corresponding to the first traffic index.
In this embodiment, if the first evaluation information of each first traffic index is also required to be obtained, after capturing the first real-time data corresponding to each first traffic index, the first evaluation information of the first traffic index may be calculated according to the first real-time data corresponding to the first traffic index and the fuzzy membership function corresponding to the first traffic index.
And S102, calculating target real-time data of the target traffic index according to the first real-time data and the first weight coefficients.
In step S102, the target traffic index is an index (which may also be referred to as a non-materialized index) whose corresponding data is not measurable. Such as travel indicators and law enforcement indicators. The calculation formula of the target real-time data of the target traffic index is as follows:
wherein the content of the first and second substances,Xtarget real-time data representing a target traffic indicator,x i first real-time data representing respective first traffic indicators,w j a first weighting factor representing each first traffic index,nrepresenting first trafficThe number of indices.
In some embodiments, before calculating the target real-time data of the target traffic index according to the first real-time data and the first weighting coefficients, normalization processing may be performed on the first real-time data. If the first traffic index is a forward index (the larger the data of the index is represented by the forward index, the better the evaluation is), the formula of normalization of the first real-time data of the first traffic index is as follows:
if the first traffic index is a negative index (the larger the data of the index is, the worse the evaluation is), the normalization formula of the first real-time data of the first traffic index is as follows:
wherein the content of the first and second substances,x i ' denotes each of the first real-time data after normalization.
And S103, calculating target evaluation information of the target traffic index according to the target real-time data and the fuzzy membership function corresponding to the target traffic index.
In step S103, the target evaluation information includes at least one of target score information and target level information. For example, the target score information is 0.51, and the target level information is normal. After the terminal equipment obtains the target real-time data, the target evaluation information of the traffic index can be calculated according to the target real-time data and the fuzzy membership function of the target traffic index.
In the present application, since the target traffic index may be various traffic indexes. Therefore, the target evaluation information of various target traffic indexes can be obtained through the first traffic indexes corresponding to the various target traffic indexes. Therefore, in the application, the target evaluation information (for example, the evaluation information of the travel index, the evaluation information of the law enforcement and the like) of various target traffic indexes representing the overall situation of the traffic operation management can be obtained at the same time, so that the user can know the real-time overall situation of the traffic operation management, and the efficiency of the traffic management is further improved. In addition, target evaluation information of various target traffic indexes representing the overall situation of traffic operation management can be obtained simultaneously, so that the method can also provide data support for unmanned driving.
In some embodiments, before calculating the target evaluation information of the target traffic index according to the target real-time data and the fuzzy membership function of the target traffic index, the method further comprises: acquiring a first set corresponding to each first traffic index, wherein the first set comprises a preset amount of historical data corresponding to the first traffic index; calculating target historical data of the target traffic indexes according to the historical data corresponding to the first traffic indexes and the first weight coefficients; and determining a fuzzy membership function of the target traffic index according to the target historical data.
At present, the evaluation of traffic indexes generally adopts fixed standards. For example, the evaluation standard of the traffic jam index is 0-2, and the traffic jam index is smooth; 2-4, basically smooth; 4-6, light congestion; 6-8, moderate congestion; 8-10, and severe congestion. However, since the traffic situation is different in each place, the evaluation information obtained by using the fixed standard is not very accurate. In the embodiment, the fuzzy membership function of the target traffic index is determined according to the target historical data of the target traffic index, and then the current target evaluation information of the target traffic index is calculated by using the fuzzy membership function of the target traffic index. Since the fuzzy membership function of the target traffic index is determined according to the target history data of the target traffic index, the target history data of the target traffic index is different when the local side is different. Correspondingly, when the target historical data are different, the fuzzy membership functions of the target traffic indexes obtained according to the target historical data of the target traffic indexes are different. When the fuzzy membership functions of the target traffic indexes are different, the obtained current evaluation information of the target traffic indexes is also different.
Therefore, in the present embodiment, the evaluation information of the target traffic index at different places can be determined according to the specific traffic situation at each place. Compared with the traditional evaluation information obtained by a fixed evaluation standard, the evaluation information obtained in the embodiment is more accurate.
Moreover, even in the same place, the traffic conditions in different stages are different. In the embodiment, when the current time changes, the terminal device may update the target history data of the target traffic index, so as to update the fuzzy membership function of the target traffic index. The fuzzy history function of the target traffic index can be continuously updated according to the historical data of traffic, so that the obtained current evaluation information of the target traffic index is more accurate.
In this embodiment, the fuzzy membership function of the target traffic index may be determined according to the target history data of the target traffic index. The target historical data of the target traffic indexes can be obtained by calculation according to the historical data corresponding to each first traffic index and each first weight coefficient, and the historical data corresponding to each first traffic index is obtained from each service system. Since each first traffic index has a preset number of historical data, in this embodiment, a plurality of target historical data can be obtained according to the historical data corresponding to each first traffic index and each first weight coefficient.
For example, there are q first traffic indicators, and each first traffic indicator has p pieces of historical data. Then correspondingly there are q first sets, each of which includes p historical data. Accordingly, p target history data can be obtained. p and q are preset integers.
Before calculating the target historical data of the target traffic index according to the historical data corresponding to each first traffic index and each first weight coefficient, normalization processing may be performed on the historical data corresponding to each first traffic index, where a normalization formula is the same as a normalization formula of the first real-time data corresponding to each first traffic index.
It should be noted that, since the historical data corresponding to each first traffic index comes from different business systems, the form of the historical data corresponding to each first traffic index may be different. Therefore, before the target historical data of the target traffic indexes are calculated according to the historical data corresponding to the first traffic indexes and the first weight coefficients, the historical data corresponding to the first traffic indexes are cleaned and integrated.
And after each target historical data is obtained, obtaining the fuzzy membership function of the target traffic index according to each target historical data. It should be noted that, the class user of the fuzzy membership function may be selected according to the actual situation, for example, a gradient fuzzy membership function, a gaussian fuzzy membership function, an S-type fuzzy membership function, and the like may be selected as the fuzzy membership function of the target traffic index. The present application is not specifically limited herein.
In some possible implementations, the fuzzy membership function of the target traffic index is a triangular fuzzy membership function. Correspondingly, the fuzzy membership function of the target traffic index is determined according to the target historical data, and comprises the following steps: determining the occurrence frequency of each target historical data; determining the target historical data with the most times as a middle limit parameter of a triangular fuzzy membership function; performing curve fitting according to the target historical data and the occurrence frequency of the target historical data to obtain a target function; solving the maximum value and the minimum value of the target function, determining the upper limit parameter of the triangular fuzzy membership function according to the maximum value, and determining the lower limit parameter of the triangular fuzzy membership function according to the minimum value.
In this implementation, the fuzzy membership function of the target traffic index is a triangular fuzzy membership function. The following describes a method for determining each parameter of the triangular fuzzy membership function.
Each first traffic index corresponds to a first set, and the first set comprises a preset amount of historical data corresponding to the first traffic index. Therefore, a plurality of target historical data can be obtained according to the historical data corresponding to the first traffic indexes and the first weight coefficients. After a plurality of target historical data are obtained, the occurrence frequency of each target historical data is determined.
It should be noted that the terminal device may determine whether or not there is the same target history data as the target history data in the stored target history data when obtaining each target history data. If there is target history data identical to the target history data, the target history data is not saved, and 1 is added to the number of occurrences of the target history data identical to the target history data. Or, the terminal device may obtain a plurality of target history data and check whether the same target history data exists. And if the same target historical data exist, counting the number of the same target historical data, and then establishing the relation between the number of the target historical data and storing the relation.
After obtaining the target historical data and the occurrence times corresponding to the target historical data, taking the target historical data with the largest occurrence times as a middle limit parameter of the triangular fuzzy membership functiont mid . The ratio is then calculated according to the following formulay i ：
Wherein the content of the first and second substances,n i representing target historical datat i The number of occurrences of (a) is,i≥1，Nrepresenting the total amount of target history data. It is to be noted thatn 0 =0。
In obtaining eacht i Andy i then according to eacht i Andy i performing curve fitting to obtain a target functionf(x). Derivation of an objective functionf(x)，To obtainf(x)’。Then makef(x)’=0, get the objective functionf(x)Maximum and minimum values of. Then the target historical data corresponding to the maximum value is used as the upper limit parameter of the three-fuzzy membership functiont max Taking the target historical data corresponding to the minimum value as the lower limit parameter of the three-fuzzy membership functiont min . Finally obtained triangular fuzzy membership functionT i Comprises the following steps:
it should be noted that, for the curve fitting algorithm, the user may select the curve fitting algorithm according to actual situations. For example, in the present embodiment, a least square method is selected as the curve fitting algorithm in the present embodiment. The present application is not specifically limited herein.
In other possible implementations, the target traffic index is obtained more accuratelyT i The target evaluation information of the method can also improve the triangular fuzzy membership function to obtain the improved triangular fuzzy membership function. When the target traffic index is a forward index, the improved triangular fuzzy membership functionT i ’Comprises the following steps:
when the target traffic index is a negative index, the improved triangular fuzzy membership functionT i ’Comprises the following steps:
it should be understood that the method for determining the fuzzy membership function of the target traffic index may be directly operated at the terminal device, or may be operated at other terminal devices, and after the other terminal devices are operated, the data corresponding to each parameter of the fuzzy membership function of the target traffic index is sent to the terminal device. The present application is not limited thereto.
In other embodiments, before searching for the first weight coefficient corresponding to each first traffic index, the method further includes: determining a target coefficient corresponding to each first traffic index according to the quantity of historical data in the first set corresponding to each first traffic index; determining a target ratio, wherein the target ratio is the ratio of each historical data corresponding to the first traffic index to the sum of the historical data corresponding to the first traffic index; and determining a first weight coefficient corresponding to each first traffic index according to the target ratio corresponding to each first traffic index and the target coefficient corresponding to each first traffic index.
In this embodiment, according to the number of the historical data in the first set corresponding to each first traffic index, a calculation formula for determining the target coefficient corresponding to each first traffic index is as follows:
wherein the content of the first and second substances,kfor the target coefficients corresponding to the respective first-level traffic indicators,mis the amount of historical data in the first set.
Target ratiof ij The calculation formula of (2) is as follows:
wherein the content of the first and second substances,jis shown asjThe first traffic indicator is a traffic indicator of the first type,r ij is shown asjA first traffic indexiAnd (4) historical data.
Before calculating the target ratio, the historical data corresponding to each first-level traffic index may be normalized, and the normalization formula is the same as the normalization formula of the first real-time data corresponding to each first traffic index.
After the target coefficient and the target ratio corresponding to each first-level traffic index are obtained, the first weight coefficient corresponding to each first-level traffic index is determined according to the following formulaw j ：
Wherein the content of the first and second substances,nthe number of the first traffic indexes is represented.
After obtaining the first weight coefficient, if there is a preset weightλ j Then, the first weight coefficient is adjusted according to the preset weightw j Obtaining a second weight coefficientw j ’. Adjusting the first weight coefficient according to the preset weight to obtain a second weight coefficient by an adjustment formula:
in this case, the target real-time data of the target traffic index is calculated according to each first real-time data and each second weighting coefficient, and the target historical data of the target traffic index is calculated according to the historical data corresponding to each first traffic index and each second weighting coefficient.
After the first weight coefficient is obtained, there may be a case where the first weight coefficient is 0. When the first weight coefficient is 0, the first weight coefficient is adjusted according to the following formula to obtain a third weight coefficientw j ’’：
In this case, the target real-time data of the target traffic index is calculated according to each first real-time data and each third weighting coefficient, and the target historical data of the target traffic index is calculated according to the historical data corresponding to each first traffic index and each third weighting coefficient.
In other embodiments, the first traffic indicator includes at least one level of traffic indicator. Correspondingly, calculating target real-time data of the target traffic index according to the first real-time data and the first weight coefficients comprises the following steps: starting from the first-level first traffic indexes, calculating first real-time data of the first-level first traffic indexes according to first real-time data of each first traffic index of the current level and first weight coefficients of each first traffic index of the current level; and calculating target real-time data of the target traffic indexes according to the first real-time data of each first traffic index of the last stage and the first weight coefficient of each first traffic index of the last stage.
In this embodiment, in order to obtain the target real-time data of the target traffic index more accurately, the traffic index may be classified into levels, and each level of traffic index may be quantized. At this time, the first traffic index includes at least one level of traffic index. Then, starting from the first-level first traffic indexes, calculating first real-time data of the first-level first traffic indexes according to the first real-time data of each first traffic index of the current level and the first weight coefficient of each first traffic index of the current level; and calculating target real-time data of the target traffic indexes according to the first real-time data of each first traffic index of the last stage and the first weight coefficient of each first traffic index of the last stage.
For example, as shown in fig. 2, the first-level first traffic indexes are the total number of vehicles traveling in the region, the number of vehicles entering and exiting the Shenzhen, the growth rate of the reserved vehicle quantity, the growth rate of the number of drivers, and the like. The first traffic index of the first level, namely the first traffic index of the second level, corresponding to the total number of regional traveling vehicles, is a dynamic demand index for urban traveling; the first traffic index of the first level, namely the first traffic index of the second level, corresponding to the number of vehicles entering and exiting the Shenzhen is a dynamic travel demand index of the city boundary; the first traffic index of the first level, namely the first traffic index of the second level, corresponding to the increase rate of the motor vehicle holding amount and the increase rate of the number of the drivers is a static demand index for travel. The dynamic demand index of urban trip, the dynamic demand index of urban boundary trip and the upper-level index of the static demand index of trip, namely the last-level traffic index is the demand index. The target traffic index is a trip index.
First, calculating first real-time data of a second-level first traffic index of an urban trip dynamic demand index according to a first-level first traffic index of the total number of regional trip vehicles and a corresponding first weight coefficient; calculating first real-time data of a second-level first traffic index 'market region boundary dynamic trip demand index' according to the first-level first traffic index 'number of vehicles entering and exiting Shenzhen' and a first weight coefficient corresponding to the first-level first traffic index 'number of vehicles entering and exiting Shenzhen'; and calculating first real-time data of a second-stage first traffic index 'travel static demand index' according to the first-stage first traffic index 'motor vehicle remaining quantity growth rate and driver quantity growth rate' and respective corresponding first weight coefficients.
Then, the first real-time data of the last-stage first traffic index "demand index" is calculated according to the first real-time data of the second-stage first traffic index "city trip dynamic demand index, city boundary dynamic trip demand index and trip static demand index" and the respective corresponding first weight coefficients (for the sake of simplicity, the calculation process of the first real-time data of the last-stage first index "road operation index and supply index" is omitted here). And finally, calculating target real-time data of a target traffic index 'travel index' according to the last-stage first traffic index 'demand index, road operation index and supply index' and respective corresponding first weight coefficients.
In other embodiments, when a problem occurs, in order that the user may quickly locate the place where the problem is located, after the first real-time data of the first traffic index at the upper level is obtained, the second evaluation information of the first traffic index at the upper level may be further calculated according to the first real-time data of the first traffic index at the upper level and the fuzzy membership function of the first traffic index at the upper level. At this moment, the traffic indexes of each level have corresponding evaluation information. When the grade information in the evaluation information of the target traffic index is abnormal, the user can search the grade information in the evaluation information of the traffic index of which layer is abnormal layer by layer, so that the position where the problem is located can be quickly located.
It should be understood that the fuzzy membership function of each first traffic index may be calculated in the same way as the fuzzy membership function of the target traffic index, or in a different way. The present application is not specifically limited herein.
It should be understood that the above is merely illustrative of the hierarchy of the first traffic indicator and is not limiting of the hierarchy of the first traffic indicator. The user may set the number of levels in the first traffic index according to an actual situation, which is not specifically limited herein.
In this embodiment, traffic indexes are graded, then, from the first traffic indexes whose corresponding data are measurable, the first real-time data of the first traffic index at the last level is deduced layer by layer upward according to the first real-time data of each first traffic index at the current level and the first weight coefficients of each first traffic index at the current level, and finally, the target real-time data of the target traffic index is obtained, so that the traffic indexes at each level are quantized. Therefore, the target real-time data of the embodiment is more accurate than the target real-time data directly obtained from the first real-time data of the first-level traffic index.
In other embodiments, the present application further comprises: and if the target evaluation information meets the preset condition, executing a preset prompting operation. For example, if the target level information in the target evaluation information is abnormal, the terminal device informs the user of the occurrence of a problem by executing a preset prompt operation, so that the user can find and solve the problem in time. The preset prompting operation includes but is not limited to voice prompting, short message prompting, text displaying on a display screen and the like. The user may select the information according to actual conditions, and the application is not specifically limited herein.
In other embodiments, the terminal device may further display the first real-time data of the first traffic indicator and the corresponding first evaluation information thereof, the target real-time data of the target traffic indicator and the corresponding target evaluation information thereof in a form of a graph, so that the first real-time data of the first traffic indicator and the corresponding first evaluation information thereof, the target real-time data of the target traffic indicator and the corresponding target evaluation information thereof, and other data are more intuitive, and thus, a user can more easily understand the data. Meanwhile, the terminal device can also count the change conditions of various traffic indexes, classify various evaluation information (for example, which traffic indexes have good grade information), and display the statistical result and the classification result in the form of a chart.
In other embodiments, the terminal device may further send data, such as the first evaluation information corresponding to the first real-time data of the first traffic indicator, the target real-time data of the target traffic indicator, and the corresponding target evaluation information, to other traffic service platforms through an Application Programming Interface (API), or the other traffic service platforms actively acquire the data from the terminal device through the API, so that the other traffic service platforms may also acquire the data of various traffic indicators and the corresponding evaluation information.
In summary, the present application provides a method for analyzing traffic index data, which first captures first real-time data corresponding to each first traffic index, and searches for a first weight coefficient corresponding to each first traffic index. And then calculating target real-time data of the target traffic index according to the first real-time data and the first weight coefficients. And finally, calculating target evaluation information of the target traffic index according to the target real-time data and the fuzzy membership function corresponding to the target traffic index. In the application, although the target real-time data corresponding to the target traffic index cannot be obtained through measurement, since the first real-time data corresponding to the first traffic index is measurable, the target real-time data of the target traffic index can be obtained through calculation according to each first real-time data and each first weight coefficient, and then the target evaluation information of the target traffic index can be obtained through calculation according to the target real-time data and the fuzzy membership function corresponding to the target traffic index. Therefore, in the application, the target traffic index is quantized firstly, that is, the target real-time data of the target traffic index is obtained firstly, and then the target evaluation information of the target traffic index is calculated according to the target real-time data and the fuzzy membership function corresponding to the target traffic index, so that the obtained target evaluation information of the target traffic index is more accurate.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Example two
Fig. 3 shows an example of a traffic index data analysis device, and for convenience of explanation, only the part related to the embodiment of the present application is shown. The apparatus 300 comprises:
the capturing module 301 is configured to capture first real-time data corresponding to each first traffic index, and search for a first weight coefficient corresponding to each first traffic index.
The first calculating module 302 is configured to calculate target real-time data of the target traffic indicator according to each first real-time data and each first weight coefficient.
The second calculating module 303 is configured to calculate target evaluation information of the target traffic index according to the target real-time data and the fuzzy membership function of the target traffic index.
Optionally, the apparatus 300 further comprises:
and the historical data acquisition module is used for acquiring a first set corresponding to each first traffic index, and the first set comprises a preset amount of historical data corresponding to the first traffic index.
And the third calculation module is used for calculating the target historical data of the target traffic indexes according to the historical data corresponding to the first traffic indexes and the first weight coefficients.
And the determining module is used for determining the fuzzy membership function of the target traffic index according to the target historical data.
Optionally, the fuzzy membership function of the target traffic index is a triangular fuzzy membership function.
Accordingly, a module is determined comprising:
a number-of-occurrences determination unit for determining the number of occurrences of each target history data.
And the first parameter determining unit is used for determining the target historical data with the largest occurrence frequency as the middle limit parameter of the triangular fuzzy membership function.
And the target function determining unit is used for performing curve fitting according to the target historical data and the occurrence frequency of the target historical data to obtain a target function.
And the second parameter determining unit is used for solving the maximum value and the minimum value of the target function, determining the upper limit parameter of the triangular fuzzy membership function according to the maximum value and determining the lower limit parameter of the triangular fuzzy membership function according to the minimum value.
Optionally, the apparatus 300 further comprises:
the target coefficient determining module is used for determining a target coefficient corresponding to each first traffic index according to the quantity of historical data in the first set corresponding to each first traffic index;
and the target ratio determining module is used for determining a target ratio, wherein the target ratio is the ratio of each historical data corresponding to the first traffic index to the sum of the historical data corresponding to the first traffic index.
And the first weight coefficient determining module is used for determining the first weight coefficient corresponding to each first traffic index according to the target ratio corresponding to each first traffic index and the target coefficient corresponding to each first traffic index.
Optionally, the first traffic indicator comprises at least one level of traffic indicator.
Accordingly, the first calculation module 302 is configured to perform:
starting from the first-level first traffic indexes, calculating first real-time data of the first-level first traffic indexes according to first real-time data of each first traffic index of the current level and first weight coefficients of each first traffic index of the current level;
and calculating target real-time data of the target traffic indexes according to the first real-time data of each first traffic index of the last stage and the first weight coefficient of each first traffic index of the last stage.
Optionally, the second calculation module 303 is configured to perform:
and calculating first evaluation information of the first traffic index according to the first real-time data corresponding to the first traffic index and the fuzzy membership function of the first traffic index.
Optionally, the apparatus 300 further comprises:
and the preset prompting module is used for executing preset prompting operation if the target evaluation information meets the preset condition.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the method embodiment of the present application, and specific reference may be made to a part of the method embodiment, which is not described herein again.
EXAMPLE III
Fig. 4 is a schematic diagram of a terminal device provided in the third embodiment of the present application. As shown in fig. 4, the terminal device 400 of this embodiment includes: a processor 401, a memory 402 and a computer program 403 stored in the memory 402 and executable on the processor 401. The steps in the various method embodiments described above are implemented when the processor 401 executes the computer program 403 described above. Alternatively, the processor 401 implements the functions of the modules/units in the device embodiments when executing the computer program 403.
Illustratively, the computer program 403 may be divided into one or more modules/units, which are stored in the memory 402 and executed by the processor 401 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 403 in the terminal device 400. For example, the computer program 403 may be divided into a capture module, a first calculation module, and a second calculation module, and each module has the following specific functions:
capturing first real-time data corresponding to each first traffic index, and searching for a first weight coefficient corresponding to each first traffic index;
calculating target real-time data of a target traffic index according to the first real-time data and the first weight coefficients;
and calculating target evaluation information of the target traffic index according to the target real-time data and the fuzzy membership function of the target traffic index.
The terminal device may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that fig. 4 is merely an example of a terminal device 400 and does not constitute a limitation of terminal device 400 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input output devices, network access devices, buses, etc.
The Processor 401 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware card, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 402 may be an internal storage unit of the terminal device 400, such as a hard disk or a memory of the terminal device 400. The memory 402 may also be an external storage device of the terminal device 400, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 400. Further, the memory 402 may include both an internal storage unit and an external storage device of the terminal device 400. The memory 402 is used to store the computer programs and other programs and data required by the terminal device. The memory 402 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned functions may be distributed as different functional units and modules according to needs, that is, the internal structure of the apparatus may be divided into different functional units or modules to implement all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the above modules or units is only one logical function division, and there may be other division manners in actual implementation, for example, a plurality of units or plug-ins 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 through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of 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 integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units described above, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above method embodiments may be implemented by a computer program, which may be stored in a computer readable storage medium and executed by a processor, so as to implement the steps of the above method embodiments. The computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file or some intermediate form. The computer readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, software distribution medium, etc. It should be noted that the computer readable medium described above may include content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media that does not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A method for analyzing traffic index data is characterized by comprising the following steps:
capturing first real-time data corresponding to each first traffic index, and searching for a first weight coefficient corresponding to each first traffic index;
calculating target real-time data of a target traffic index according to the first real-time data and the first weight coefficients;
and calculating target evaluation information of the target traffic index according to the target real-time data and the fuzzy membership function of the target traffic index.
2. The method for analyzing traffic index data according to claim 1, further comprising, before the calculating the evaluation information of the target traffic index according to the target real-time data and the fuzzy membership function of the target traffic index:
acquiring a first set corresponding to each first traffic index, wherein the first set comprises a preset amount of historical data corresponding to the first traffic index;
calculating target historical data of the target traffic indexes according to the historical data corresponding to the first traffic indexes and the first weight coefficients;
and determining a fuzzy membership function of the target traffic index according to the target historical data.
3. The method of analyzing traffic index data according to claim 2, wherein the fuzzy membership function of the target traffic index is a triangular fuzzy membership function;
the determining a fuzzy membership function of the target traffic index according to the target historical data comprises:
determining the occurrence number of each target historical data;
determining the target historical data with the largest occurrence number as a middle limit parameter of the triangular fuzzy membership function;
performing curve fitting according to the target historical data and the occurrence frequency of the target historical data to obtain a target function;
solving the maximum value and the minimum value of the target function, determining the upper limit parameter of the triangular fuzzy membership function according to the maximum value, and determining the lower limit parameter of the triangular fuzzy membership function according to the minimum value.
4. The method for analyzing traffic index data according to claim 2, wherein before the searching for the first weighting factor corresponding to each of the first traffic indexes, the method further comprises:
determining a target coefficient corresponding to each first traffic index according to the quantity of historical data in a first set corresponding to each first traffic index;
determining a target ratio, wherein the target ratio is the ratio of each historical data corresponding to the first traffic index to the sum of the historical data corresponding to the first traffic index;
and determining a first weight coefficient corresponding to each first traffic index according to the target ratio corresponding to each first traffic index and the target coefficient corresponding to each first traffic index.
5. The method of analyzing traffic index data of claim 1, wherein the first traffic index comprises at least one level of traffic index;
correspondingly, the calculating target real-time data of the target traffic index according to each first real-time data and each first weight coefficient includes:
starting from the first-level first traffic indexes, calculating first real-time data of the first-level first traffic indexes according to first real-time data of each first traffic index of the current level and first weight coefficients of each first traffic index of the current level;
and calculating target real-time data of the target traffic indexes according to the first real-time data of each first traffic index of the last stage and the first weight coefficient of each first traffic index of the last stage.
6. The method of analyzing traffic index data according to claim 1, further comprising:
and calculating first evaluation information of the first traffic index according to first real-time data corresponding to the first traffic index and the fuzzy membership function of the first traffic index.
7. The method of analyzing traffic index data according to claim 1, further comprising:
and if the target evaluation information meets a preset condition, executing a preset prompting operation.
8. An apparatus for analyzing traffic index data, comprising:
the capturing module is used for capturing first real-time data corresponding to each first traffic index and searching for a first weight coefficient corresponding to each first traffic index;
the first calculation module is used for calculating target real-time data of a target traffic index according to each first real-time data and each first weight coefficient;
and the second calculation module is used for calculating the target evaluation information of the target traffic index according to the target real-time data and the fuzzy membership function of the target traffic index.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1-7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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