CN109448211B - License plate issuing method, device, medium and equipment based on big data - Google Patents
License plate issuing method, device, medium and equipment based on big data Download PDFInfo
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Abstract
The invention provides a license plate issuing method and device based on big data, a computer readable storage medium and computer equipment, wherein the method comprises the following steps: collecting driving behavior data of participants who participate in the current license plate issuing; calculating the license plate hit rate of the participant according to the driving behavior data; and obtaining a license plate issuing result according to the license plate hit rate. The license plate issuing method can obviously improve the transparency and fairness of issuing the license plate, and can enable participants more suitable for obtaining the license plate to have higher license plate hit rate, thereby improving the satisfaction degree of the participants on a license plate issuing mechanism.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a license plate issuing method and device based on big data, a computer readable storage medium and computer equipment.
Background
With the rapid development of economy, the demand of people for car purchasing is more and more, and thus a part of cities have a serious traffic jam problem caused by the excessive number of cars, and therefore, the part of cities restrain the continuously increasing number of newly added cars by limiting the number of issued car license plates.
At present, random license plate shaking distribution is a main mode for issuing license plates of automobiles, but the biggest problem of the existing mode is that corruption behavior, various backbuying, backselling and intermediary behaviors are easily caused due to the fact that a license plate shaking mechanism is not transparent, license plate issuing cannot be rapidly and fairly realized, and the satisfaction degree of people on the license plate issuing mechanism is generally low.
Disclosure of Invention
In order to solve at least one of the above technical defects, the present invention provides a license plate issuing method based on big data, a corresponding device, a computer-readable storage medium, and a computer device in the following technical solutions.
The embodiment of the invention provides a license plate issuing method based on big data according to one aspect, which comprises the following steps:
collecting driving behavior data of participants who participate in the current license plate issuing;
calculating the license plate hit rate of the participant according to the driving behavior data;
and obtaining a license plate issuing result according to the license plate hit rate.
Preferably, the calculating the license plate hit rate of the participant according to the driving behavior data includes:
determining the average hit rate of the license plate issuing at the current time;
calculating a floating factor of the license plate hit rate of the participant according to the driving behavior data; the floating factor is used for adjusting and obtaining the license plate hit rate of the participant based on the average hit rate;
and calculating the license plate hit rate of the participant according to the average hit rate and the floating factor.
Further, the determining the average hit rate of the current license plate issue includes:
acquiring the number of license plates and the number of participants issued by the current license plate;
and calculating the average hit rate of the current license plate according to the number of the license plates and the number of the participants.
Preferably, the driving behavior data includes behavior data corresponding to a preset number of driving behaviors;
the calculating a floating factor of the license plate hit rate of the participant according to the driving behavior data comprises:
the behavior data of each driving behavior in the preset number of driving behaviors and the behavior data of the same driving behavior corresponding to all participants who participate in the current license plate distribution are sorted according to a preset sorting algorithm to obtain a sorting percentage corresponding to the behavior data of each driving behavior;
acquiring preset weight corresponding to each driving behavior;
calculating a floating value of the behavior data of each driving behavior according to the sequencing percentage, the preset weight and a preset reference value;
and adding the floating values to obtain a floating factor of the license plate hit rate of the participant.
Further, the floating factor is calculated by the following formula:
wherein, A iskRepresenting a percentage of the ranking of the behavior data corresponding to the driving behavior, 50% representing the reference value, the akA preset weight representing the corresponding driving behavior, and n represents the preset number.
Preferably, the driving behavior data comprises at least one of: the driving time of the congested road section, the driving speed of the high-speed area, the driving speed of the non-high-speed area and the driving lane changing frequency.
Preferably, before collecting the driving behavior data of the participants who participate in the current license plate dealing, the method comprises the following steps:
acquiring traffic data of an applicant applying for dealing with the current license plate in a preset time period;
judging whether the applicant has traffic violation behaviors in a preset time period or not according to the traffic data;
if yes, refusing the applicant to participate in the current license plate issue;
if not, the applicant is set as a participant participating in the current license plate dealing.
In addition, according to another aspect, an embodiment of the present invention provides a license plate issuing apparatus based on big data, including:
the data acquisition module is used for acquiring the driving behavior data of the participants who participate in the current license plate distribution;
the hit rate calculation module is used for calculating the license plate hit rate of the participant according to the driving behavior data;
and the license plate issuing module is used for obtaining a license plate issuing result according to the license plate hit rate.
According to yet another aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the above-mentioned big-data-based license plate issuing method.
According to yet another aspect, embodiments of the present invention provide a computer device, the computer comprising one or more processors; a memory; one or more computer programs, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, the one or more computer programs configured to: and executing the license plate issuing method based on the big data.
Compared with the prior art, the invention has the following beneficial effects:
according to the license plate issuing method and device based on the big data, the license plate hit rate of the participants is calculated through analyzing the driving behavior data, and the license plate issuing result is obtained through calculation according to the license plate hit rate.
In addition, the license plate issuing method and device based on big data, the computer readable storage medium and the computer device provided by the invention also calculate the floating factor of the license plate hit rate of the participant through the driving behavior data, and further calculate the license plate hit rate of the participant according to the floating factor and the preset average hit rate, and the floating factor is set to improve the accuracy of the license plate hit rate calculation, so that the participant more suitable for obtaining the license plate has higher license plate hit rate; the license plate hit rate of the participants is calculated by analyzing driving behavior data comprising the driving time of the congested road section, the driving speed of the high-speed area, the driving speed of the non-high-speed area and the driving lane change frequency, and the license plate hit rate is calculated based on the driving behavior data, so that the participants with higher urgency for obtaining the license plate and higher law-keeping degree of the driving behavior have higher license plate hit rate, and further the satisfaction degree of the participants on a license plate issuing mechanism is improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of a method for issuing a license plate based on big data according to an embodiment of the present invention;
FIG. 2 is a flowchart of a license plate hit rate calculation method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a license plate issuing device based on big data according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative only and should not be construed as limiting the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, 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. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The embodiment of the invention provides a license plate issuing method based on big data, which comprises the following steps of:
step S11: collecting the driving behavior data of the participants who participate in the current license plate dealing.
For the embodiment, the driver can collect and store the behavior data of the driver in the driving process, and the driving behavior data of the participant is a behavior data set corresponding to each driving behavior collected by the participant in the daily driving process. Wherein the driving behavior data includes, but is not limited to: traffic violation data, driving area data, driving time data, and the like.
In an actual application scenario, the existing driving behavior data acquisition technology or equipment can be adopted to acquire the behavior data of a driver in the daily driving process on an automobile.
Step S12: and calculating the license plate hit rate of the participant according to the driving behavior data.
For the embodiment, the driving behavior data can reflect the driving behaviors and habits of the participants who participate in the current license plate issue in the daily driving process, such as whether traffic violation behaviors exist, the general area and time period where the driving behaviors occur, the driving duration and the like. The driving behaviors and habits of the participants can be obtained by analyzing the driving behavior data of the participants, and the urgency degree of the participants for acquiring license plates and the law-keeping degree of the driving behaviors can be evaluated according to the driving behaviors and habits.
For the embodiment, in order to avoid the problems that the license plate is issued to the participants who do not have the requirements for obtaining the license plate, the issued license plate becomes a tool of a speculative player to cause various backbuying, resale and intermediary behaviors, and the license plate is urgently obtained to solve the problem that the participants who have the requirements for work and family life are difficult to hit, the license plate issuing system presets a license plate hit rate calculation rule for calculating the license plate hit rate of the participants according to the urgency degree of the requirements for obtaining the license plate of the participants, namely when the license plate hit rate is calculated only according to the urgency degree of the requirements, the higher the urgency degree of the requirements for obtaining the license plate is, the higher the license plate hit rate is, and the license plate hit rate corresponding to the participants who have high urgency degree of obtaining the license plate is higher than the license plate hit rate of the participants who have low urgency degree of the requirements.
For the embodiment, in order to encourage drivers to drive in a law-of-conservation mode, the license plate issuing system is preset with a license plate hit rate calculation rule for calculating the license plate hit rate of the participants according to the law-of-conservation degree of the driving behaviors of the participants, namely when the license plate hit rate is calculated only according to the law-of-conservation degree of the driving behaviors, the higher the law-of-conservation degree of the driving behaviors is, the higher the license plate hit rate is, and the license plate hit rate corresponding to the participants with the high law-of-conservation degree of the driving behaviors is higher than the license plate hit rate of the participants with the low law-of-conservation degree of the driving behaviors. The degree of the driving behavior can be embodied by the driving behaviors and habits of the participants such as driving speed, driving route and the like.
It is to be clearly noted that the license plate issuing system provided by the embodiment of the present invention may further preset other license plate hit rate calculation rules for calculating the license plate hit rate of the participant according to the driving behaviors and habits, so as to calculate the license plate hit rate matched with the driving behavior data of the participant.
Step S13: and obtaining a license plate issuing result according to the license plate hit rate.
In practical application scenarios, the number of license plates issued each time is limited, so that whether each participant hits the license plate or not needs to be calculated according to the license plate hit rate of each participant participating in the current license plate issue.
For the embodiment, the license plate issuing result is specifically a result of whether the license plate is hit by a participant who can acquire driving behavior data. In step S12, after the license plate hit rate of each participant that can acquire the driving behavior data is calculated, whether each participant hits a license plate is calculated according to the license plate hit rate of each participant, so as to obtain the license plate issue result.
For this embodiment, the license plate issuing system preferentially calculates whether the participants who can acquire the driving behavior data hit the license plate according to the license plate hit rate, calculates the license plate hit rate for the participants who do not have the driving behavior data after obtaining the license plate issuing result whether all the participants who can acquire the driving behavior data hit the license plate, and calculates whether the participants who do not have the driving behavior data hit the license plate. For the participants without driving behavior data, the license plate hit rate can be calculated by dividing the difference between the number of license plates issued at the current time and the number of license plates hit by the participants who can acquire driving behavior by the number of participants without driving behavior data, and the license plate hit rate calculation formula is as follows: (number of license plates when the next license plate is issued-number of license plates that can be collected that have been hit by a participant in driving behavior)/number of participants who do not have driving behavior data.
According to the license plate issuing method based on the big data, the license plate hit rate of the participants is calculated through analyzing the driving behavior data, and the license plate issuing result is calculated according to the license plate hit rate.
In one embodiment, as shown in fig. 2, the step S12 of calculating the license plate hit rate of the participant according to the driving behavior data includes:
step S121: and determining the average hit rate of the license plate issuing at the current time.
For this embodiment, the average hit rate is the number plate hit rate that is uniform among all participants in the current number plate issue, regardless of the individual driving behavior data of the participants. The average hit rate may be a default parameter preset by the license plate issuing system, or may be a parameter calculated according to the specific situation of the current license plate issuing.
Step S122: calculating a floating factor of the license plate hit rate of the participant according to the driving behavior data; the floating factor is used for adjusting and obtaining the license plate hit rate of the participant based on the average hit rate.
For this embodiment, the license plate hit rate of the participant may be taken from within a preset fluctuation range of the average hit rate, the value of the license plate hit rate is affected by a fluctuation factor of the license plate hit rate of the participant, and the fluctuation factor is used to obtain the license plate hit rate of the participant based on the average hit rate adjustment.
For the embodiment, the license plate issuing system is preset with a preset algorithm for calculating the floating factor according to the driving behavior data, the driving behavior and the habit of the participant can be obtained by analyzing the driving behavior data of the participant, the driving behavior and the habit are evaluated according to the preset algorithm to obtain the floating factor of the license plate hit rate of the participant, and the floating factor can reflect the urgent degree of the requirement of the participant for obtaining the license plate and the law-keeping degree of the driving behavior. The larger the floating factor value is, the higher the urgency degree of the participant to acquire the license plate and the law-keeping degree of the driving behavior of the participant.
Step S123: and calculating the license plate hit rate of the participant according to the average hit rate and the floating factor.
For this embodiment, the license plate hit rate calculation rule specifically includes that on the basis of the average hit rate, the floating factor is adopted to adjust the license plate hit rate of the participant, and specifically, the average hit rate is used as a reference to calculate an increment value corresponding to the floating factor, so as to calculate the license plate hit rate. The formula for calculating the hit rate of the license plate is specifically as follows: plate hit rate ═ average hit rate (1+ float factor). The higher the urgency degree of the participant in acquiring the license plate and the law-keeping degree of the driving behavior of the participant is, the higher the floating factor is, and correspondingly, the higher the license plate hit rate of the participant is.
In the embodiment, the floating factor of the license plate hit rate of the participant is calculated through the driving behavior data, the license plate hit rate of the participant is calculated according to the floating factor and the preset average hit rate, and the accuracy of the license plate hit rate calculation can be improved by setting the floating factor, so that the participant more suitable for obtaining the license plate has higher license plate hit rate, the requirement of the participant for obtaining the license plate is more easily met, and the satisfaction degree of the participant on a license plate issuing mechanism is further improved.
In one embodiment, the determining the average hit rate of the current license plate issue includes:
acquiring the number of license plates and the number of participants issued by the current license plate;
and calculating the average hit rate of the current license plate according to the number of the license plates and the number of the participants.
For this embodiment, the average hit rate is calculated according to a parameter obtained by calculating a specific situation of the current license plate issue, specifically, the number of license plates issued by the current license plate is predefined, the number of license plates and the number of participants issued by the current license plate are obtained, the number of license plates is divided by the number of participants, and the average hit rate of the current license plate issue is calculated. The formula for calculating the average hit rate is specifically as follows: average hit rate is the number of license plates/number of participants. For example, if the number of license plates issued by the next license plate is 4000 and the number of participants is 100000, the average hit rate is 4000/100000-4%.
In one embodiment, the driving behavior data comprises behavior data corresponding to a preset number of driving behaviors;
the calculating a floating factor of the license plate hit rate of the participant according to the driving behavior data comprises:
the behavior data of each driving behavior in the preset number of driving behaviors and the behavior data of the same driving behavior corresponding to all participants who participate in the current license plate distribution are sorted according to a preset sorting algorithm to obtain a sorting percentage corresponding to the behavior data of each driving behavior; acquiring preset weight corresponding to each driving behavior; calculating a floating value of the behavior data of each driving behavior according to the sorting percentage, the preset weight and a preset reference value; and adding the floating values to obtain a floating factor of the license plate hit rate of the participant.
For this embodiment, the driving behavior data is a behavior data set corresponding to each driving behavior collected by the participant in the daily driving process, specifically, the driving behavior data includes behavior data corresponding to a preset number of driving behaviors, and the license plate issuing system calculates the license plate hit rate of the participant according to the preset behavior data of the preset number of driving behaviors. The preset number may be any positive integer such as 1, 3, 7, etc., which is not limited in this embodiment.
For the embodiment, corresponding to each driving behavior in the preset number of driving behaviors, the behavior data of all participants corresponding to the driving behaviors are sorted according to a preset sorting algorithm to obtain a sorting result. The more the ranking is, the larger the corresponding ranking percentage is, for example, the ranking percentage corresponding to the top ranking is 100%, and the ranking percentage corresponding to the last ranking is 0%. It is to be noted that the ranking algorithms corresponding to the driving behaviors may be the same or different.
For this embodiment, each driving behavior in the preset number of driving behaviors is provided with a corresponding preset weight, and the sum of the preset weights corresponding to the driving behaviors is 1.
For this embodiment, after obtaining the ranking percentage and the preset weight corresponding to a certain driving behavior of the participant, subtracting the ranking percentage from the preset reference value to obtain a difference value, and then multiplying the difference value by the preset weight to calculate the floating value of the behavior data corresponding to the driving behavior of the participant. After the floating value corresponding to the behavior data of each driving behavior in the preset number of driving behaviors of the participant is calculated by the method, the floating values are added to calculate the floating factor of the license plate hit rate of the participant.
As a preferred embodiment, the preset reference value is 50%, and the floating factor is calculated by the following formula:
wherein, A iskRepresenting a percentage of the ranking of the behavior data corresponding to the driving behavior, 50% representing the reference value, the akA preset weight representing the corresponding driving behavior, and n represents the preset number.
For this embodiment, when the ranking percentage is greater than 50%, the floating value corresponding to the behavior data of the driving behavior is a positive number, and when the ranking percentage is less than 50%, the floating value corresponding to the behavior data of the driving behavior is a negative number.
Taking the example that the driving behavior data includes the driving time of the congested road section, the preset number of driving behaviors only includes one driving behavior, that is, the driving behavior of the congested road section, and the preset weight corresponding to the driving behavior is 100%. Sequencing the congestion road section driving time of each participant from long to short, and determining the sequencing percentage corresponding to the behavior data of the congestion road section driving behaviors of the participants according to the percentage of the positions of the congestion road section driving time of the participants in the sequencing. Assuming that the sorting percentage is 61%, the preset reference value is 50%, and the average hit rate is 4%, the calculation formula of the floating factor of the license plate hit rate of the participant is specifically as follows: the float factor (61% -50%) is 100% ═ 11%. Correspondingly, the formula for calculating the hit rate of the license plate is as follows: the hit rate of the license plate is 4%. the (1+ 11%). the hit rate is 4.44%.
In one embodiment, the driving behavior data comprises at least one of: the method comprises the following steps of driving time on congested road sections, driving speed in high-speed areas, driving speed in non-high-speed areas and driving lane changing frequency.
In the following, a specific embodiment of calculating the license plate hit rate is shown, wherein the driving behavior data includes a congested road section driving time, a high speed area driving speed, a non-high speed area driving speed and a driving lane changing frequency.
In an actual application scenario, a heavily congested road section is generally a place with a large traffic flow in a city, and a license plate issuing mechanism sets that a driver who often passes through the place and accumulates certain congested road section driving time has a high urgency degree on license plate acquisition and belongs to a crowd just needing license plate acquisition, so that participants who have long driving time in the congested road section are supposed to have a high floating value to obtain a high license plate hit rate. The calculation process of the floating value corresponding to the driving time of the congested road section specifically comprises the following steps: the preset weight corresponding to the driving behavior of the congested road section is preset to be 25%. Sequencing the congestion road section driving time of each participant from long to short, and determining the sequencing percentage corresponding to the behavior data of the congestion road section driving behaviors of the participants according to the percentage of the positions of the congestion road section driving time of the participants in the sequencing. Assuming that the sorting percentage is 61%, the preset reference value is 50%, and the average hit rate is 4%, the calculation formula of the floating value corresponding to the driving time of the congested road section of the participant is specifically as follows: value of float V1＝(61％-50％)*25％＝2.75％。
In order to encourage drivers to drive in a law-keeping mode, the speed is not exceeded in a high-speed driving area so as to improve the driving safety factor of the drivers, and a license plate issuing mechanism sets participants with slower driving speed in the high-speed area to hit license plates more easily, namely the participants with slower driving speed in the high-speed area have higher floating values so as to obtain higher license plate hitting rate. The calculation process of the floating value corresponding to the driving speed in the high-speed area specifically comprises the following steps: the driving method comprises the steps of presetting a high-speed area, wherein the preset weight corresponding to the driving behavior of the highway is 25%, and the driving speed of the high-speed area is specifically the average driving speed under the normal driving conditions of non-blocked road sections and the like in the high-speed area. And sequencing the high-speed area driving speed of each participant from slow to fast, and determining the sequencing percentage corresponding to the behavior data of the participant driving in the high-speed area according to the percentage of the position of the high-speed area driving speed of the participant in the sequencing. Assuming that the sorting percentage is 61% and the preset reference value is 50%, the calculation formula of the floating value corresponding to the driving speed of the high-speed area of the participant is specifically as follows: the float value V2 (61% -50%) 25% ═ 2.75%. It should be clearly noted that, in other embodiments, in order to avoid the influence on the normal traffic order in the high-speed area due to the excessively low driving speed in the high-speed area, the license plate issuing system may set the lower limit of the driving speed in the high-speed area according to the traffic regulation requirement, and set the corresponding sorting algorithm according to the setting that the license plate is less likely to be hit when the driving speed in the high-speed area is lower than the lower limit of the driving speed in the high-speed area.
In order to encourage drivers to drive in a law-keeping mode, the speed is not exceeded in a non-high-speed driving area so as to improve the driving safety factor of the drivers, and a license plate issuing mechanism sets participants with slower driving speeds in the non-high-speed area to hit license plates more easily, namely the participants with slower driving speeds in the non-high-speed area have higher floating values so as to obtain higher license plate hitting rate. The calculation process of the floating value corresponding to the driving speed of the non-high-speed area specifically comprises the following steps: the driving method comprises the steps that a non-high-speed area is preset, for example, the preset weight corresponding to driving behaviors in an urban area is 25%, wherein the driving speed in the non-high-speed area is specifically the average driving speed under normal driving conditions such as non-blocked road sections in the non-high-speed area. And sequencing the driving speed of the non-high-speed area of each participant from slow to fast, and determining the sequencing percentage corresponding to the behavior data of the participant driving in the high-speed area according to the percentage of the position of the driving speed of the non-high-speed area of the participant in the sequencing. Assuming that the sorting percentage is 61% and the preset reference value is 50%, the calculation formula of the floating value corresponding to the driving speed of the non-high speed area of the participant is specifically as follows: value of float V325% to 2.75% by weight (61% -50%). It should be clearly noted that, in other embodiments, in order to avoid the influence of the too low driving speed in the non-high speed area on the normal traffic order in the non-high speed area, the license plate issuing system may set the lower limit of the driving speed in the non-high speed area according to the traffic regulation requirement, and set the corresponding sorting algorithm according to the setting that the license plate is more difficult to hit if the driving speed in the non-high speed area is lower than the lower limit of the driving speed in the non-high speed area.
In order to encourage drivers to drive in a law-keeping way and avoid frequent lane changing in the driving process so as to improve the driving safety factor of the drivers, the license plate issuing mechanism sets that the participants who drive less frequently lane changing hit the license plate more easily, namely the participants who drive less frequently lane changing have higher floating values so as to obtain higher license plate hit rate. The calculation process of the floating value corresponding to the driving lane change frequency specifically comprises the following steps: the preset weight corresponding to the driving lane change behavior is preset to be 25%. And sequencing the driving lane change frequency of each participant from less to most, and determining the sequencing percentage corresponding to the behavior data of the driving lane change of the participant according to the percentage of the positions of the driving lane change frequency of the participant in the sequencing. Assuming that the sorting percentage is 61% and the preset reference value is 50%, the calculation formula of the floating value corresponding to the lane change frequency of the participant is specifically as follows: value of fluctuation V4＝(61％-50％)*25％＝2.75％。
In summary, the calculation formula of the floating factor of the license plate hit rate of the participant is specifically as follows: floating factor of V1+V2+V3+V42.75% + 2.75% + 11.75%. Correspondingly, the formula for calculating the hit rate of the license plate is as follows: the hit rate of the license plate is 4%. the (1+ 11%). the hit rate is 4.44%.
In the embodiment, the license plate hit rate of the participants is calculated by analyzing the driving behavior data comprising the driving time of the congested road section, the driving speed of the high-speed area, the driving speed of the non-high-speed area and the driving lane change frequency, and the license plate issuing result is calculated according to the license plate hit rate.
In one embodiment, the collecting the driving behavior data of the participants who participate in the current license plate dealing comprises:
acquiring traffic data of an applicant applying for dealing with the current license plate in a preset time period;
judging whether the applicant has traffic violation behaviors in a preset time period or not according to the traffic data;
if yes, refusing the applicant to participate in the current license plate issue;
if not, the applicant is set as a participant participating in the current license plate dealing.
For the embodiment, the license plate issuing system sets that the driver who has traffic violation in the preset time period does not have the qualification of participating in the issuing of the license plate at the current time. Therefore, the traffic data of an applicant applying for the dealing of the current license plate in a preset time period is required to be acquired, the traffic data is analyzed, whether a traffic violation behavior exists in the preset time period of the applicant is judged, if the traffic violation behavior exists, the applicant is refused to participate in the dealing of the current license plate, if the traffic violation behavior does not exist, the applicant is set as a participant participating in the dealing of the current license plate, and the applicant is determined to have the qualification of participating in the dealing of the current license plate. The preset time period may be any time period such as one month, one quarter, or an interval between two license plate releases, which is not limited in this embodiment. The traffic data in the preset time period is specifically behavior data which is generated by the applicant in the preset time period and is associated with traffic, such as traffic violation.
In the embodiment, by limiting the drivers who have traffic violations to participate in the current license plate issuing, the participants who have higher driving behavior law-keeping degree can have higher license plate hit rate, so that the satisfaction degree of the participants on a license plate issuing mechanism is improved, the citizen law-keeping driving can be encouraged, and the social and economic development is facilitated.
In addition, an embodiment of the present invention provides a license plate issuing device based on big data, and as shown in fig. 3, the device includes: the system comprises a data acquisition module 31, a hit rate calculation module 32 and a license plate issuing module 33; wherein, the first and the second end of the pipe are connected with each other,
the data acquisition module 31 is used for acquiring the driving behavior data of the participants who participate in the current license plate issue;
the hit rate calculation module 32 is configured to calculate a license plate hit rate of the participant according to the driving behavior data;
and the license plate issuing module 33 is configured to obtain a license plate issuing result according to the license plate hit rate.
In an embodiment, the hit rate calculating module 32 is specifically configured to:
determining the average hit rate of the license plate issuing at the current time;
calculating a floating factor of the license plate hit rate of the participant according to the driving behavior data; the floating factor is used for adjusting and obtaining the license plate hit rate of the participant based on the average hit rate;
and calculating the license plate hit rate of the participant according to the average hit rate and the floating factor.
In one embodiment, the determining the average hit rate of the license plate issue at the current time comprises:
acquiring the number of license plates and the number of participants issued by the current license plate;
and calculating the average hit rate of the current license plate according to the number of the license plates and the number of the participants.
In one embodiment, the driving behavior data comprises behavior data corresponding to a preset number of driving behaviors;
the calculating a floating factor of the license plate hit rate of the participant according to the driving behavior data comprises:
the behavior data of each driving behavior in the preset number of driving behaviors and the behavior data of the same driving behavior corresponding to all participants who participate in the current license plate issue are sorted according to a preset sorting algorithm to obtain the sorting percentage corresponding to the behavior data of each driving behavior;
acquiring preset weight corresponding to each driving behavior;
calculating a floating value of the behavior data of each driving behavior according to the sorting percentage, the preset weight and a preset reference value;
and adding the floating values to obtain a floating factor of the license plate hit rate of the participant.
In one embodiment, the float factor is calculated by the following equation:
wherein, A iskRepresenting a percentage of the ranking of the behavior data corresponding to the driving behavior, 50% representing the reference value, the aIA preset weight representing the corresponding driving behavior, and n represents the preset number.
In one embodiment, the driving behavior data comprises at least one of: the method comprises the following steps of driving time on congested road sections, driving speed in high-speed areas, driving speed in non-high-speed areas and driving lane changing frequency.
In one embodiment, the collecting the driving behavior data of the participants who participate in the current license plate dealing comprises:
acquiring traffic data of an applicant applying for dealing with the current license plate in a preset time period;
judging whether the applicant has traffic violation behaviors in a preset time period or not according to the traffic data;
if yes, refusing the applicant to participate in the current license plate issue;
if not, the applicant is set as a participant participating in the current license plate dealing.
The license plate issuing device based on big data provided by the invention can realize that: the license plate hit rate of the participants is calculated by analyzing the driving behavior data, and the license plate issuing result is obtained by calculation according to the license plate hit rate. It can also be realized that: the floating factor of the license plate hit rate of the participant is calculated through the driving behavior data, the license plate hit rate of the participant is calculated according to the floating factor and the preset average hit rate, and the accuracy of license plate hit rate calculation can be improved by setting the floating factor, so that the participant more suitable for obtaining the license plate has higher license plate hit rate; the license plate hit rate of the participants is calculated by analyzing driving behavior data comprising the driving time of the congested road section, the driving speed of the high-speed area, the driving speed of the non-high-speed area and the driving lane change frequency, and the license plate hit rate is calculated based on the driving behavior data, so that the participants with higher urgency for obtaining the license plate and higher law-keeping degree of the driving behavior have higher license plate hit rate, and further the satisfaction degree of the participants on a license plate issuing mechanism is improved.
The license plate issuing device based on big data provided by the embodiment of the invention can realize the method embodiment provided above, and for the specific function realization, reference is made to the description of the method embodiment, and no further description is given here.
In addition, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method for issuing a license plate based on big data as described in the above embodiment is implemented. The computer-readable storage medium includes, but is not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks, ROMs (Read-Only memories), RAMs (Random AcceSS memories), EPROMs (EraSable Programmable Read-Only memories), EEPROMs (Electrically EraSable Programmable Read-Only memories), flash memories, magnetic cards, or optical cards. That is, a storage device includes any medium that stores or transmits information in a form readable by a device (e.g., a computer, a cellular phone), and may be a read-only memory, a magnetic or optical disk, or the like.
The computer-readable storage medium provided by the invention can realize that: the license plate hit rate of the participants is calculated by analyzing the driving behavior data, and the license plate issuing result is obtained by calculation according to the license plate hit rate. It can also be realized that: the floating factor of the license plate hit rate of the participant is calculated through the driving behavior data, the license plate hit rate of the participant is further calculated according to the floating factor and a preset average hit rate, and the accuracy of license plate hit rate calculation can be improved by setting the floating factor, so that the participant more suitable for obtaining the license plate has higher license plate hit rate; the license plate hit rate of the participants is calculated by analyzing driving behavior data comprising the driving time of the congested road section, the driving speed of the high-speed area, the driving speed of the non-high-speed area and the driving lane change frequency, and the license plate hit rate is calculated based on the driving behavior data, so that the participants with higher urgency for obtaining the license plate and higher law-keeping degree of the driving behavior have higher license plate hit rate, and further the satisfaction degree of the participants on a license plate issuing mechanism is improved.
The computer-readable storage medium provided in the embodiments of the present invention can implement the method embodiments provided above, and for specific function implementation, reference is made to the description in the method embodiments, which is not repeated herein.
In addition, an embodiment of the present invention further provides a computer device, as shown in fig. 4. The computer device described in this embodiment may be a server, a personal computer, a network device, and other devices. The computer device comprises a processor 402, a memory 403, an input unit 404, and a display unit 405. Those skilled in the art will appreciate that the device configuration means shown in fig. 4 do not constitute a limitation of all devices and may include more or less components than those shown, or some components in combination. The memory 403 may be used to store the computer program 401 and the functional modules, and the processor 402 runs the computer program 401 stored in the memory 403 to execute various functional applications of the device and data processing. The memory may be internal or external memory, or include both internal and external memory. The memory may comprise read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), flash memory, or random access memory. The external memory may include a hard disk, a floppy disk, a ZIP disk, a usb-disk, a magnetic tape, etc. The disclosed memory includes, but is not limited to, these types of memory. The disclosed memory is by way of example only and not by way of limitation.
The input unit 404 is used for receiving input of signals and receiving keywords input by a user. The input unit 404 may include a touch panel and other input devices. The touch panel can collect touch operations (such as operations of a user on or near the touch panel by using any suitable object or accessory such as a finger, a stylus and the like) on or near the touch panel, and drive the corresponding connecting device according to a preset program; other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., play control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like. The display unit 405 may be used to display information input by a user or information provided to a user and various menus of the computer device. The display unit 405 may take the form of a liquid crystal display, an organic light emitting diode, or the like. The processor 402 is a control center of the computer device, connects various parts of the entire computer using various interfaces and lines, and performs various functions and processes data by operating or executing software programs and/or modules stored in the memory 402 and calling data stored in the memory.
As one embodiment, the computer device includes: one or more processors 402, a memory 403, one or more computer programs 401, wherein the one or more computer programs 401 are stored in the memory 403 and configured to be executed by the one or more processors 402, the one or more computer programs 401 being configured to perform the big-data based license plate issuing method of any of the above embodiments.
The computer equipment provided by the invention can realize that: the license plate hit rate of the participants is calculated by analyzing the driving behavior data, and the license plate issuing result is obtained by calculation according to the license plate hit rate. It can also be realized that: the floating factor of the license plate hit rate of the participant is calculated through the driving behavior data, the license plate hit rate of the participant is calculated according to the floating factor and the preset average hit rate, and the accuracy of license plate hit rate calculation can be improved by setting the floating factor, so that the participant more suitable for obtaining the license plate has higher license plate hit rate; the license plate hit rate of the participants is calculated by analyzing driving behavior data comprising the driving time of the congested road section, the driving speed of the high-speed area, the driving speed of the non-high-speed area and the driving lane change frequency, and the license plate hit rate is calculated based on the driving behavior data, so that the participants with higher urgency for obtaining the license plate and higher law-keeping degree of the driving behavior have higher license plate hit rate, and further the satisfaction degree of the participants on a license plate issuing mechanism is improved.
The computer device provided in the embodiment of the present invention may implement the method embodiment provided above, and for specific function implementation, reference is made to the description in the method embodiment, which is not described herein again.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (8)
1. A license plate issuing method based on big data is characterized by comprising the following steps:
collecting driving behavior data of participants who participate in the current license plate issuing; the driving behavior data comprises behavior data corresponding to a preset number of driving behaviors;
determining the average hit rate of the license plate issuing at the current time;
the behavior data of each driving behavior in the preset number of driving behaviors and the behavior data of the same driving behavior corresponding to all participants who participate in the current license plate distribution are sorted according to a preset sorting algorithm to obtain a sorting percentage corresponding to the behavior data of each driving behavior;
acquiring preset weight corresponding to each driving behavior;
calculating a floating value of the behavior data of each driving behavior according to the sorting percentage, the preset weight and a preset reference value;
adding the floating values to obtain a floating factor of the license plate hit rate of the participant;
calculating the license plate hit rate of the participant according to the average hit rate and the floating factor;
and obtaining a license plate issuing result according to the license plate hit rate.
2. The method for issuing the license plate according to claim 1, wherein the determining the average hit rate of the issuing of the license plate at the current time comprises:
acquiring the number of license plates and the number of participants issued by the current license plate;
and calculating the average hit rate of the current license plate according to the number of the license plates and the number of the participants.
3. The license plate issuing method according to claim 1, wherein the floating factor is calculated by the following formula:
wherein, A iskRepresenting a percentage of the ranking of the behavior data corresponding to the driving behavior, 50% representing the reference value, the akA preset weight representing the corresponding driving behavior, and n represents the preset number.
4. The license plate issuing method according to claim 1, wherein the driving behavior data includes at least one of: the driving time of the congested road section, the driving speed of the high-speed area, the driving speed of the non-high-speed area and the driving lane changing frequency.
5. The license plate issuing method according to claim 1, wherein before collecting driving behavior data of the participants who participate in the issuing of the license plate at the current time, the method comprises:
acquiring traffic data of an applicant applying for the current license plate issue in a preset time period;
judging whether the applicant has traffic violation behaviors in a preset time period or not according to the traffic data;
if yes, refusing the applicant to participate in the current license plate issue;
if not, the applicant is set as a participant participating in the current license plate dealing.
6. The utility model provides a license plate issue device based on big data which characterized in that includes:
the data acquisition module is used for acquiring driving behavior data of participants who participate in the current license plate issue; the driving behavior data comprises behavior data corresponding to a preset number of driving behaviors;
the hit rate calculation module is used for determining the average hit rate of the current license plate issuing; the behavior data of each driving behavior in the preset number of driving behaviors and the behavior data of the same driving behavior corresponding to all participants who participate in the current license plate distribution are sorted according to a preset sorting algorithm to obtain a sorting percentage corresponding to the behavior data of each driving behavior; acquiring preset weight corresponding to each driving behavior; calculating a floating value of the behavior data of each driving behavior according to the sorting percentage, the preset weight and a preset reference value; adding the floating values to obtain a floating factor of the license plate hit rate of the participant; calculating the license plate hit rate of the participant according to the average hit rate and the floating factor;
and the license plate issuing module is used for obtaining a license plate issuing result according to the license plate hit rate.
7. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when executed by a processor, the computer program implements the license plate issuance method according to any one of claims 1 to 5.
8. A computer device, comprising:
one or more processors;
a memory;
one or more computer programs, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, the one or more computer programs configured to: the license plate issuing method according to any one of claims 1 to 5 is performed.
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| CN111885503B (en) * | 2020-07-24 | 2022-10-11 | 深圳市元征科技股份有限公司 | License plate distribution method and related equipment |
| CN113793011A (en) * | 2021-09-07 | 2021-12-14 | 支付宝(杭州)信息技术有限公司 | License plate processing method and device |
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