CN119151383B - Method, device and storage medium for evaluating thermal power in a geographical area based on supply and demand relationship - Google Patents
Method, device and storage medium for evaluating thermal power in a geographical area based on supply and demand relationship Download PDFInfo
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Abstract
The application relates to a method, a device and a storage medium for evaluating heat of a geographic area based on supply and demand relations, which are applied to the technical field of network contract vehicles and comprise the steps of simultaneously dividing time dimensions by acquiring network contract vehicle order data and driver operation service track data of a period of time, acquiring historical IPH (Internet protocol) of different time dimensions by extracting the income amount of a driver order, the service duration of the driver order and the listening duration of the driver of each time dimension, and finally fusing the historical IPH of the same time dimension with real-time IPH according to set weights.
Description
Technical Field
The invention relates to the technical field of network about vehicles, in particular to a method and a device for evaluating geographic area heating power based on supply-demand relationship and a storage medium.
Background
Geographical area thermodynamic evaluation (abbreviated as thermodynamic diagram) refers to a computing technology for evaluating cold and hot conditions of a certain class of business scenes through the size and distribution of business data, a change from low value to high value is generally represented by a visualization tool through using gradient colors from cold color to warm color, the thermodynamic diagram can show a concentrated trend or abnormal condition of data on the geospatial distribution, the thermodynamic diagram is widely applied to various fields, such as a geographic information system, bioinformatics, network flow analysis and the like, for example, in the network about vehicle industry, the thermodynamic diagram is generally used for showing the density of vehicle demands of passengers in a certain area in a city, and the thermodynamic diagram can be used for evaluating an order hot spot area.
The current industry thermodynamic diagram implementation scheme is more, but generally focuses on a certain service scene, data of a related geographic area, such as population density, traffic flow, commodity sales number and the like, which are related to geographic positions are collected, then the collected data are cleaned and calculated to ensure the accuracy and consistency of the data, the processed result data should contain geographic position information (such as longitude and latitude) and numerical values to be displayed, and finally a proper visualization tool is selected to construct the thermodynamic diagram.
Disclosure of Invention
In view of the above, the present invention aims to provide a method, a device and a storage medium for estimating the heat of a geographic area based on a supply-demand relationship, so as to solve the problem that the existing method cannot adapt to complex and diversified business scenarios due to the factors of single data dimension, limited geographic area division logic by a platform or a tool, etc.
According to a first aspect of an embodiment of the present invention, there is provided a method of estimating thermal power of a geographical area based on a supply-demand relationship, the method comprising:
A method of assessing the heating power of a geographical area based on supply and demand relationships, the method comprising:
Acquiring historical N days, current real-time network vehicle order data and driver operation service track data;
Dividing each day of the history N days into X time periods averagely, wherein the length of each time period is Y, and numbering the X time periods according to the sequence; taking time periods with the same numbers in N days of history as a time dimension, wherein the total time dimension is X, and each time dimension comprises N time periods with the same numbers;
acquiring the order placing time of all the passenger orders every day according to the historical N-day network appointment vehicle order data, and classifying the passenger orders into time periods with different numbers every day according to the order placing time;
Acquiring the income amount of the driver order and the corresponding service time length of the driver order of each passenger in each time dimension according to the network appointment vehicle order data and the driver operation service track data of the historical N days, and acquiring the hearing time length of the driver in each time period;
performing outlier replacement on the income amount of the driver order, the service time length of the driver order and the listening time length of the driver in each time period of each passenger order;
Acquiring total order income amount of each time dimension based on the replaced driver order income amount, and acquiring Directorate-General machine order service time length of each time dimension based on the replaced driver order service time length;
selecting any one time dimension and two adjacent future time dimensions as a time window, traversing X time dimensions to obtain X time windows;
Acquiring a historical IPH under the corresponding time window dimension according to the total order income amount of three time dimensions of each time window, directorate-General machine order service duration and total listening time duration;
acquiring real-time IPH under the current time dimension according to the current real-time network appointment vehicle order data and the driver operation service track data;
respectively setting a historical data weight and a real-time data weight, and obtaining a final fusion IPH under a corresponding time window dimension based on the historical IPH, the real-time IPH, the historical data weight and the real-time data weight under the same time dimension;
And performing thermal rendering on all honeycomb areas of the city under the corresponding time window dimension based on the final fusion IPH of all honeycomb areas of the city under the corresponding time window dimension and the urban maximum IPH.
Preferably, the method comprises the steps of,
The abnormal value replacement for the income amount of the driver order, the service time length of the driver order and the listening time length of the driver in each time period of each passenger order comprises the following steps:
Acquiring service IPH corresponding to each passenger order according to the income amount of the driver order and the service duration of the driver order of each passenger order;
and screening the abnormal service IPH and the abnormal driver ticket duration based on the three sigma law, replacing the corresponding driver order income amount and the driver order service duration of the abnormal service IPH with the preset standard driver order income amount and the preset standard driver order service duration, and replacing the abnormal driver ticket duration with the preset standard driver ticket duration.
Preferably, the method comprises the steps of,
The screening of abnormal service IPH and abnormal driver ticket duration based on the three sigma law comprises the following steps:
Acquiring an average IPH in each time dimension according to all the service IPHs in each time dimension, and acquiring an IPH standard deviation according to the average IPH;
determining an IPH standard range according to the average IPH and the IPH standard deviation;
If any service IPH is not in the IPH standard range, the service IPH is an abnormal service IPH;
Acquiring average ticket duration in each time dimension according to all the driver ticket durations in each time dimension, and acquiring a ticket duration standard deviation according to the average ticket duration;
determining a standard range of the length of the audible bill according to the average length of the audible bill and the standard deviation of the length of the audible bill;
If any driver ticket duration is not within the ticket duration standard deviation, the driver ticket duration is abnormal driver ticket duration;
The preset standard driver order income amount, the preset standard driver order service duration and the preset standard driver ticket duration are respectively the average driver order income amount, the average driver order service duration and the average ticket duration in each time dimension.
Preferably, the method comprises the steps of,
The step of obtaining the historical IPH in the corresponding time window dimension according to the total order income amount of the three time dimensions of each time window, the Directorate-General machine order service duration and the total listening time duration comprises the following steps:
Obtaining time window order income amount according to the total order income amount of three time dimensions in each time window, obtaining time window driver order service time according to the total order time of three time dimensions in each time window, and obtaining time window order time according to the total order time of three time dimensions in each time window;
and acquiring the IPH of each time window according to the income amount of the time window order, the service duration of the time window driver order and the listening duration of the time window, and taking the IPH of each time window as the historical IPH under the dimension of the corresponding time window.
Preferably, the method comprises the steps of,
The obtaining the real-time IPH in the current time dimension according to the current real-time network vehicle order data and the driver operation service track data comprises:
Acquiring Z time periods before the current moment, wherein the length of each time period is Y;
And acquiring the total income amount of the driver orders, the total service time of the driver orders and the total listening time of the driver in the previous Z time periods according to the current real-time network vehicle order data and the driver running service track data, and acquiring the real-time IPH (in the time dimension corresponding to the previous Z time periods based on the total income amount of the driver orders, the total service time of the driver orders and the total listening time of the driver in the previous Z time periods.
Preferably, the method comprises the steps of,
The thermally rendering the corresponding time window dimensions of all the honeycomb areas of the city based on the final converged IPH and the city maximum IPH in the corresponding time window dimensions of all the honeycomb areas of the city comprises:
taking any honeycomb area of the city as a target honeycomb area;
f adjacent honeycomb areas around the target honeycomb area are obtained, and final converged IPH under the corresponding time window dimension of each adjacent F honeycomb areas are respectively obtained;
Based on the final converged IPH under the corresponding time window dimension of the adjacent F honeycomb areas and the maximum IPH of the city, respectively obtaining the thermodynamic grade of the F honeycomb areas, selecting the smallest one of the F thermodynamic grades as the thermodynamic grade under the corresponding time window dimension of the target honeycomb area, traversing all honeycomb areas of the city to obtain the thermodynamic grade under the corresponding time window dimension of all honeycomb areas, and carrying out thermodynamic rendering of the corresponding time window dimension on the city map according to the thermodynamic grade under the corresponding time window dimension of all honeycomb areas of the city.
Preferably, the method comprises the steps of,
The acquiring the network vehicle order data and the driver operation service track data in the history of N days and the real time of the day comprises the following steps:
acquiring historical N days and real-time network taxi order data and driver operation service track data on the same day through a service system, and storing through TableStore and MySQL;
Synchronizing data in MySQL into a data channel DataHub through an Alicloud data transmission tool DTS, and synchronizing data in TableStore into a data channel DataHub through real-time calculation of Flink;
Preprocessing the data in the data channel DataHub through the Flink operation, and converging and writing the preprocessed network bus order data and driver operation service track data into the data warehouse MaxCompute for subsequent calculation.
Preferably, the method further comprises:
For the heat level data of all honeycomb areas of the city, the Redis and TableStore are written in periodically and simultaneously through DataWorks data integration tools, and the honeycomb area circle selection and the honeycomb area heat level data query API are provided through the Redis Geo and TableStore indexes for downstream service;
And the driver side and other service systems inquire the heat level of the honeycomb area through the data center API to display or apply functions.
According to a second aspect of an embodiment of the present invention, there is provided an apparatus for estimating heat of a geographical area based on a supply-demand relationship, the apparatus comprising:
the data acquisition module is used for acquiring historical N days, current real-time network vehicle order data and driver operation service track data;
The time dimension dividing module is used for dividing each day of the history N days into X time periods, wherein the length of each time period is Y, and numbering the X time periods according to the sequence; taking time periods with the same numbers in N days of history as a time dimension, wherein the total time dimension is X, and each time dimension comprises N time periods with the same numbers;
the order classification module is used for acquiring the order placing time of all the passenger orders every day according to the historical network vehicle order data of N days and classifying the passenger orders into time periods with different numbers every day according to the order placing time;
The dimension data acquisition module is used for respectively acquiring the income amount of the driver order and the corresponding service duration of the driver order of each passenger in each time dimension according to the network vehicle order data and the driver operation service track data of the historical N days, and acquiring the hearing time duration of the driver in each time period;
The abnormal value replacement module is used for replacing abnormal values of the income amount of the driver order of each passenger order, the service time length of the driver order and the time length of the driver listening order in each time period;
The dimension average data acquisition module is used for acquiring total order income amount of each time dimension based on the replaced driver order income amount, and acquiring Directorate-General machine order service time length of each time dimension based on the replaced driver order service time length;
the time window dividing module is used for selecting any one time dimension and two adjacent future time dimensions as a time window, traversing X time dimensions and obtaining X time windows;
The historical IPH acquisition module is used for acquiring historical IPH under the corresponding time window dimension according to the total order income amount of three time dimensions of each time window, directorate-General machine order service time length and total listening time length;
The real-time IPH acquisition module is used for acquiring real-time IPH in the current time dimension according to the current real-time network vehicle order data and the driver operation service track data;
The fusion IPH acquisition module is used for respectively setting a historical data weight and a real-time data weight, and obtaining a final fusion IPH under the corresponding time window dimension based on the historical IPH, the real-time IPH, the historical data weight and the real-time data weight under the same time dimension;
the honeycomb heat acquisition module is used for acquiring the IPH of X time dimensions of the history P days of any city, selecting the largest IPH as the largest IPH of the city, and carrying out heat rendering of all honeycomb areas of the city under the corresponding time window dimensions based on the final fusion IPH of all honeycomb areas of the city and the largest IPH of the city.
According to a third aspect of embodiments of the present invention, there is provided a storage medium storing a computer program which, when executed by a master, implements the steps of the above method.
The technical scheme provided by the embodiment of the invention can comprise the following beneficial effects:
The method and the system combine the data comprehensive evaluation area vehicle demand and driver transport capacity supply relation of time, space, drivers, channels, prices, external objective factors and the like, are more fit for the cold and hot conditions of actual scenes, avoid the defect that multiple scenes such as single and multiple vehicles, single and multiple low price, single and multiple high price and the like generated under the traditional thermodynamic algorithm cannot be covered, effectively improve the achievement rate of the vehicle demand of passengers, reduce the empty driving condition of the drivers, combine the historical and real-time honeycomb statistical data of fine granularity time intervals, and can give consideration to the historical periodicity of different space areas and different time periods according to proper weight distribution, and also consider the current abrupt change of thermodynamic calculation, thereby avoiding the defect that the single and multiple scenes such as single and multiple vehicles, single and multiple low price and the like cannot be covered under the traditional thermodynamic algorithm, effectively improving the achievement rate of the vehicle demand of passengers, reducing the empty driving condition of the drivers, and solving the problem that the current abrupt change of the traditional thermodynamic algorithm is more practical, and the application is more suitable for the basis of the situation of the calculation of the single and the order, thereby improving the income of the thermodynamic level and the driver's compared with the situation.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow diagram illustrating a method of assessing thermal power of a geographic area based on supply and demand relationships, according to an example embodiment;
FIG. 2 is a schematic diagram illustrating a business data collection flow, according to another exemplary embodiment;
FIG. 3 is a schematic diagram illustrating a target honeycomb area thermal classification according to another exemplary embodiment;
FIG. 4 is a schematic diagram of a data integration presentation shown according to another exemplary embodiment;
FIG. 5 is a schematic diagram of a normal distribution of three sigma laws, shown according to another example embodiment;
FIG. 6 is a system diagram illustrating an apparatus for assessing thermal power of a geographic area based on supply and demand relationships, according to another exemplary embodiment;
In the drawing, a 1-data acquisition module, a 2-time dimension division module, a 3-order classification module, a 4-dimension data acquisition module, a 5-outlier replacement module, a 6-dimension average data acquisition module, a 7-time window division module, an 8-history IPH acquisition module, a 9-real-time IPH acquisition module, a 10-fusion IPH acquisition module and an 11-honeycomb thermal acquisition module are shown.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
Example 1
FIG. 1 is a flow diagram illustrating a method of assessing thermal power of a geographic area based on supply and demand relationships, as shown in FIG. 1, according to an exemplary embodiment, the method comprising:
S1, acquiring historical N days, current real-time network vehicle order data and driver operation service track data;
S2, dividing each day of the history N days into X time periods averagely, wherein the length of each time period is Y, and numbering the X time periods according to the sequence; taking time periods with the same numbers in N days of history as a time dimension, wherein the total time dimension is X, and each time dimension comprises N time periods with the same numbers;
s3, acquiring the order placing time of all the passenger orders every day according to the historical N-day network vehicle order data, and classifying the passenger orders into time periods with different numbers every day according to the order placing time;
S4, acquiring the income amount of the driver order and the corresponding service duration of the driver order of each passenger order in each time dimension according to the network appointment vehicle order data and the driver operation service track data of the historical N days, and acquiring the hearing time duration of the driver in each time period;
S5, replacing abnormal values of the income amount of the driver order of each passenger order, the service time of the driver order and the listening time of the driver in each time period;
S6, acquiring total order income amount of each time dimension based on the replaced driver order income amount, and acquiring Directorate-General machine order service time length of each time dimension based on the replaced driver order service time length;
S7, selecting any one time dimension and two adjacent future time dimensions as a time window, traversing X time dimensions and obtaining X time windows;
s8, acquiring a historical IPH under the corresponding time window dimension according to the total order income amount of the three time dimensions of each time window, the service duration of the Directorate-General machine order and the total listening time duration;
S9, acquiring real-time IPH in the current time dimension according to the current real-time network vehicle order data and the driver operation service track data;
S10, respectively setting a historical data weight and a real-time data weight, and obtaining a final fusion IPH under a corresponding time window dimension based on the historical IPH, the real-time IPH, the historical data weight and the real-time data weight under the same time dimension;
s11, acquiring IPH of X time dimensions of the history P days of any city, and selecting the largest IPH as the largest IPH of the city; performing thermal rendering of all the honeycomb areas of the city in the corresponding time window dimension based on the final converged IPH and the city maximum IPH of all the honeycomb areas of the city in the corresponding time window dimension;
It can be understood that, as shown in fig. 2, the present application mainly relies on passenger vehicle order detail data and city driver taxi service track data, and the present business system stores the order data and driver data through TableStore (form storage) and MySQL (relational database), then synchronizes the data of the business database table into a data channel DataHub (metadata management platform) through an ali cloud data transmission tool DTS and real-time calculation FLink, then performs operations such as cleaning, association, disassembly, aggregation, etc. through a link operation, finally gathers and writes the order data and driver track data into a data warehouse MaxCompute for use in downstream calculation;
The application records the real-time orders and driver data of the history N (usually 4 weeks of data) day and day, after the data warehouse MaxCompute is collected by the above process, the key fields (city, coordinate, time, date type, order vs. amount, driver order service start time, driver order service end time, driver out-of-car listening state, driver coordinate reporting time, etc.) of the detail data are analyzed and converted;
Each day is divided into X time periods equally, the length of each time period is Y, and is usually 10 minutes, so that 144 time periods can be divided in one day, namely, X is 144, and the X time periods are numbered according to the sequence; taking time periods with the same number in the history of N days (4 weeks are 28 days) as a time dimension, and taking X time dimensions in total, wherein each time dimension comprises N time periods with the same number, namely 144 time dimensions, and each time dimension comprises 28 time periods with the same number;
Then, acquiring the ordering time t 0 of all the passenger orders in the 28 days, and classifying all the passenger orders into corresponding time periods according to the ordering time t 0, wherein the expression formula is as follows:
floor((HOUR(t0)*60+MINUTE(t0))/10+1)
Obtaining a passenger order in each time dimension, and obtaining corresponding income amount of a driver order for each passenger order, wherein the driver order service start time is t 1, the driver order service end time is t 1, and the driver order service duration ST is obtained according to t 1 and t 2;
when no passenger is carried on the driver side, namely when no passenger order is executed, sending a coordinate point every 30s, and acquiring a ticket listening duration LT of the driver according to all the coordinate points, wherein the ticket listening duration LT can also be understood as the waiting duration of the driver, and the expression is represented by COUNT (1) for 30s;
acquiring service IPH (driver order income amount a/driver order service duration ST) of each order according to the driver order income amount a and the driver order service duration ST;
It is worth noting that IPH means statistics of revenue per hour based on unit dimension, and is generally used for business evaluation of revenue conditions;
For the corresponding servicing IPH of all passenger orders in one time dimension, the IPH maximum IPHMax and minimum IPHMin, maximum IPHMax =μ+3σ, minimum IPHMin =μ -3σ are calculated based on three sigma law, where μ represents the average of all servicing IPH in one time dimension, σ represents the standard deviation, it should be noted that, statistically, the 68-95-99.7 principle is in a normal distribution, less than the percentages within one standard deviation, two standard deviations, three standard deviations from the average, more accurate numbers are 68.27%, 95.45% and 99.73%, i.e., 3σ criterion:
the probability of the numerical distribution in (μ - σ, μ+σ) is 0.6827;
the probability of the numerical distribution in (μ -2σ, μ+2σ) is 0.9545;
The probability of the numerical distribution in (μ -3σ, μ+3σ) is 0.9973;
That is, the standard range finally determined is (μ -3σ, μ+3σ), after the standard range is determined, all the service IPHs in one time dimension are judged, if not in the standard range, the service IPH is an abnormal value, and since the service IPH is obtained through the driver order income amount a and the driver order service duration ST, for the abnormal service IPH, the corresponding driver order income amount a and the driver order service duration ST are replaced with preset standard values, and the preset standard values can be average values of the driver order income amount a and the driver order service duration ST in one time dimension, and the expression is:
IF IPH>IPH Max or IPH<IPH Min THEN a = AVG(a), ST = AVG(ST)
Similarly, for the ticket duration LT of the driver in one time dimension, the maximum value LTMax and the minimum value LTMin are obtained through three sigma laws, then the labeling range of the ticket duration LT of the driver is determined, and for the ticket duration LT of the driver which is not in the standard range, the average value of the ticket durations LT of the driver in one time dimension is replaced, and the expression is as follows:
IF LT>LTMax or LT<LTMin THEN LT = AVG(LT)
Acquiring total income amount of all orders in a time dimension, total service duration of all orders of the driver and total listening time duration based on the replaced income amount a of the orders of the driver, the service duration ST of the orders of the driver and the listening time duration LT of the driver;
Considering the ductility of time slices, correlating the data of 2 unit slices of displacement of the current time dimension slice, namely sliding for 30 minutes every 10 minutes, for example, the data of the current time dimension slice 1 (00:00-00:10), the time dimension slice 2 (00:10-00:20) and the time dimension slice 3 (00:20-00:30) need to be correlated to generate a time window, and as described above, the total time window has 144 time dimensions, the sequence numbers are 1-144, the time window is sequence numbers 1-3, the time dimension is sequence numbers 2-4, the time window is sequence numbers 143, 144 and 1, and the last time dimension is 144, 1 and 2, so that 144 time windows can be obtained;
for each time window, based on the total order income amount, the flat Directorate-General machine order service duration and the total listening time duration obtained by three time dimensions, the driver order income amount, the driver order service duration and the driver listening time duration of each time window are obtained, and meanwhile, the historical IPH under the corresponding time window dimensions is obtained according to the driver order income amount, the driver order service duration and the driver listening time duration of each time window, wherein the specific expression formula is as follows:
Time window driver order revenue amount: a' =a 0+a1+a2
Time window driver order service duration: ST' =st 0+ ST1+ ST2
Time window driver out-of-train ticket duration, LT' =lt 0+ LT1+ LT2
Time window IPH: a '/(ST ' +LT ');
Taking the current moment as a starting point, forward pouring 3 time periods, wherein each time period is 10 minutes, namely forward pouring for 30 minutes, obtaining Directorate-General orders income amount, directorate-General order service duration and total listening duration of all orders in the 30 minutes, and obtaining the real-time IPH of the 30 minutes according to the time window IPH calculation formula;
for the fusion of the historical IPH and the real-time IPH, firstly judging the time dimension of the real-time IPH to which 30 minutes belongs, wherein the 30 minutes comprise the three time dimensions, and the time window is divided into three continuous time dimensions which are one time window, so that 144 time windows are total, namely, 30 minutes can be corresponding to one time window, and the historical IPH of the historical time window corresponding to the 30 minutes can be obtained;
setting a historical data weight x and a real-time data weight y, wherein x+y=1;
According to the historical data weight x and the real-time data weight y, fusing the obtained historical IPH and the real-time IPH under the same time dimension to obtain a final fused IPH under the corresponding time window dimension, wherein the expression is as follows:
final fusion iph=history IPH x+real-time IPH y
After the calculation method of the final fusion IPH is obtained, the IPH of each time dimension of 7 days of any city history is obtained, and likewise, according to the method, each day of 7 days is divided into 144 time periods, each time period is numbered 1-144, the time periods with the same number in 7 days are divided into the same time dimension to obtain 144 time dimensions, and finally, the IPH of the 144 time dimensions is obtained, wherein the specific expression is c_IPH=sum (a)/sum (ST+LT)
Obtaining the maximum value of the 7-day full-time slicing IPH of the city history, namely selecting the largest one from the IPHs of all time dimensions of the 7-day city history as the maximum IPH of the city, wherein the expression is maxIPH =max (c_IPH);
And then obtaining the final converged IPH of all the honeycomb areas in any city, and carrying out heat level judgment on each honeycomb area by combining the maximum IPH value of the city, wherein a heat level judgment formula is as follows:
rank 1:0< = IPH < = max IPH 0.25
Class 2:0.25 max IPH < = max IPH 0.50
Class 3:0.50 max IPH < = max IPH 0.75
Class 4:0.75 max IPH < IPH;
Wherein level 1 and level 2 are represented as cold regions on the map rendering and level 3 and level 4 are represented as hot regions on the map rendering;
It should be noted that, because the honeycomb performs strict meshing on the geographical area according to the H3 algorithm, the possibility that the difference between cold and hot will occur in the adjacent area in statistics will occur, but the actual network vehicle service scene is flexible and dynamic for the distance between the passenger ordering and the driver driving, so the cold and hot in the adjacent area should be progressive from the service perspective, therefore, the embodiment also introduces the calculation logic of "cold and hot diffusion", and solves the problems of "hot package cooling", "cold package heating", etc., specifically:
For the determination of the thermal level of the target honeycomb area, as shown in fig. 3, 6 honeycomb areas adjacent to the periphery of the target honeycomb area are acquired, the thermal levels of the 6 honeycomb areas are acquired according to the above method, and the smallest one of the thermal levels is selected as the thermal level of the target honeycomb area, and the specific expression is as follows:
thermal minima of peripheral honeycomb hMin =min (h 1, h2, h3, h4, h5, h 6)
Final thermal value h=if (h0 < hMin, hMin, h 0);
it is worth emphasizing that based on the method, the application can display the heat level of any honeycomb area of any city on weekdays or weekends in a multi-dimensional way;
As shown in FIG. 4, the above calculated honeycomb thermodynamic data is written into Redis and TableStore periodically and simultaneously by DataWorks data integration tool, the data center provides honeycomb sorting and honeycomb thermodynamic data query API for downstream service by Redis Geo and TableStore index, and driver side thermodynamic diagram function and other service system query honeycomb thermodynamic data by data center API for display or function application.
Example two
FIG. 6 is a system diagram illustrating an apparatus for assessing thermal power of a geographic area based on supply and demand relationships, the apparatus comprising:
The data acquisition module 1 is used for acquiring historical N days, current real-time network vehicle order data and driver operation service track data;
the time dimension dividing module 2 is used for dividing each day of the history N days into X time periods, wherein the length of each time period is Y, and numbering the X time periods according to the sequence; taking time periods with the same numbers in N days of history as a time dimension, wherein the total time dimension is X, and each time dimension comprises N time periods with the same numbers;
The order classification module 3 is used for acquiring the order placing time of all the passenger orders every day according to the historical network vehicle order data of N days and classifying the passenger orders into time periods with different numbers every day according to the order placing time;
The dimension data acquisition module 4 is used for respectively acquiring the income amount of the driver order and the corresponding service duration of the driver order of each passenger in each time dimension according to the network vehicle order data and the driver operation service track data of the historical N days, and acquiring the hearing time duration of the driver in each time period;
the abnormal value replacement module 5 is used for replacing abnormal values of the income amount of the driver order of each passenger order, the service duration of the driver order and the listening duration of the driver in each time period;
the dimension average data acquisition module 6 is used for acquiring total order income amount of each time dimension based on the replaced driver order income amount, and acquiring Directorate-General machine order service duration of each time dimension based on the replaced driver order service duration;
The time window dividing module 7 is used for selecting any one time dimension and two adjacent future time dimensions as a time window, traversing X time dimensions and obtaining X time windows;
The history IPH acquisition module 8 is used for acquiring the history IPH under the corresponding time window dimension according to the total order income amount of the three time dimensions of each time window, the Directorate-General machine order service duration and the total listening time duration;
The real-time IPH acquisition module 9 is used for acquiring real-time IPH in the current time dimension according to the current real-time network vehicle order data and the driver operation service track data;
The fusion IPH acquisition module 10 is used for respectively setting a historical data weight and a real-time data weight, and obtaining a final fusion IPH under the corresponding time window dimension based on the historical IPH, the real-time IPH, the historical data weight and the real-time data weight under the same time dimension;
The honeycomb heat acquisition module 11 is used for acquiring the IPH of X time dimensions of the history P days of any city, selecting the largest IPH as the largest IPH of the city, and carrying out heat rendering of all honeycomb areas of the city under the corresponding time window dimensions based on the final fusion IPH of all honeycomb areas of the city and the largest IPH of the city.
Embodiment III:
The present embodiment provides a storage medium storing a computer program which, when executed by a master controller, implements each step in the above method;
it is to be understood that the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of techniques known in the art, discrete logic circuits with logic gates for implementing logic functions on data signals, application specific integrated circuits with appropriate combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. A method of assessing the heating power of a geographical area based on supply and demand relationships, the method comprising:
Acquiring historical N days, current real-time network vehicle order data and driver operation service track data;
Dividing each day of the history N days into X time periods averagely, wherein the length of each time period is Y, and numbering the X time periods according to the sequence; taking time periods with the same numbers in N days of history as a time dimension, wherein the total time dimension is X, and each time dimension comprises N time periods with the same numbers;
acquiring the order placing time of all the passenger orders every day according to the historical N-day network appointment vehicle order data, and classifying the passenger orders into time periods with different numbers every day according to the order placing time;
Acquiring the income amount of the driver order and the corresponding service time length of the driver order of each passenger in each time dimension according to the network appointment vehicle order data and the driver operation service track data of the historical N days, and acquiring the hearing time length of the driver in each time period;
performing outlier replacement on the income amount of the driver order, the service time length of the driver order and the listening time length of the driver in each time period of each passenger order;
Acquiring total order income amount of each time dimension based on the replaced driver order income amount, and acquiring Directorate-General machine order service time length of each time dimension based on the replaced driver order service time length;
selecting any one time dimension and two adjacent future time dimensions as a time window, traversing X time dimensions to obtain X time windows;
Acquiring a historical IPH under the corresponding time window dimension according to the total order income amount of three time dimensions of each time window, directorate-General machine order service duration and total listening time duration;
acquiring real-time IPH under the current time dimension according to the current real-time network appointment vehicle order data and the driver operation service track data;
respectively setting a historical data weight and a real-time data weight, and obtaining a final fusion IPH under a corresponding time window dimension based on the historical IPH, the real-time IPH, the historical data weight and the real-time data weight under the same time dimension;
Acquiring IPH of X time dimensions of history P days of any city, and selecting the maximum IPH as the maximum IPH of the city; performing thermal rendering of all the honeycomb areas of the city in the corresponding time window dimension based on the final converged IPH and the city maximum IPH of all the honeycomb areas of the city in the corresponding time window dimension;
the IPH represents statistics of revenue per hour on a unit dimension basis.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
The abnormal value replacement for the income amount of the driver order, the service time length of the driver order and the listening time length of the driver in each time period of each passenger order comprises the following steps:
Acquiring service IPH corresponding to each passenger order according to the income amount of the driver order and the service duration of the driver order of each passenger order;
and screening the abnormal service IPH and the abnormal driver ticket duration based on the three sigma law, replacing the corresponding driver order income amount and the driver order service duration of the abnormal service IPH with the preset standard driver order income amount and the preset standard driver order service duration, and replacing the abnormal driver ticket duration with the preset standard driver ticket duration.
3. The method of claim 2, wherein the step of determining the position of the substrate comprises,
The screening of abnormal service IPH and abnormal driver ticket duration based on the three sigma law comprises the following steps:
Acquiring an average IPH in each time dimension according to all the service IPHs in each time dimension, and acquiring an IPH standard deviation according to the average IPH;
determining an IPH standard range according to the average IPH and the IPH standard deviation;
If any service IPH is not in the IPH standard range, the service IPH is an abnormal service IPH;
Acquiring average ticket duration in each time dimension according to all the driver ticket durations in each time dimension, and acquiring a ticket duration standard deviation according to the average ticket duration;
determining a standard range of the length of the audible bill according to the average length of the audible bill and the standard deviation of the length of the audible bill;
If any driver ticket duration is not within the ticket duration standard deviation, the driver ticket duration is abnormal driver ticket duration;
The preset standard driver order income amount, the preset standard driver order service duration and the preset standard driver ticket duration are respectively the average driver order income amount, the average driver order service duration and the average ticket duration in each time dimension.
4. The method of claim 3, wherein the step of,
The step of obtaining the historical IPH in the corresponding time window dimension according to the total order income amount of the three time dimensions of each time window, the Directorate-General machine order service duration and the total listening time duration comprises the following steps:
Obtaining time window order income amount according to the total order income amount of three time dimensions in each time window, obtaining time window driver order service time according to the total order time of three time dimensions in each time window, and obtaining time window order time according to the total order time of three time dimensions in each time window;
and acquiring the IPH of each time window according to the income amount of the time window order, the service duration of the time window driver order and the listening duration of the time window, and taking the IPH of each time window as the historical IPH under the dimension of the corresponding time window.
5. The method of claim 4, wherein the step of determining the position of the first electrode is performed,
The obtaining the real-time IPH in the current time dimension according to the current real-time network vehicle order data and the driver operation service track data comprises:
Acquiring Z time periods before the current moment, wherein the length of each time period is Y;
And acquiring the total income amount of the driver orders, the total service time of the driver orders and the total listening time of the driver in the previous Z time periods according to the current real-time network vehicle order data and the driver running service track data, and acquiring the real-time IPH (in the time dimension corresponding to the previous Z time periods based on the total income amount of the driver orders, the total service time of the driver orders and the total listening time of the driver in the previous Z time periods.
6. The method of claim 2, wherein the step of determining the position of the substrate comprises,
The thermally rendering of the city in the corresponding time window dimension of all the honeycomb areas based on the final converged IPH and the city maximum IPH in the corresponding time window dimension of all the honeycomb areas of the city comprises:
taking any honeycomb area of the city as a target honeycomb area;
f adjacent honeycomb areas around the target honeycomb area are obtained, and final converged IPH under the corresponding time window dimension of each adjacent F honeycomb areas are respectively obtained;
Based on the final converged IPH under the corresponding time window dimension of the adjacent F honeycomb areas and the maximum IPH of the city, respectively obtaining the thermodynamic grade of the F honeycomb areas, selecting the smallest one of the F thermodynamic grades as the thermodynamic grade under the corresponding time window dimension of the target honeycomb area, traversing all honeycomb areas of the city to obtain the thermodynamic grade under the corresponding time window dimension of all honeycomb areas, and carrying out thermodynamic rendering of the corresponding time window dimension on the city map according to the thermodynamic grade under the corresponding time window dimension of all honeycomb areas of the city.
7. The method of claim 1, wherein the step of determining the position of the substrate comprises,
The acquiring the network vehicle order data and the driver operation service track data in the history of N days and the real time of the day comprises the following steps:
acquiring historical N days and real-time network taxi order data and driver operation service track data on the same day through a service system, and storing through TableStore and MySQL;
Synchronizing data in MySQL into a data channel DataHub through an Alicloud data transmission tool DTS, and synchronizing data in TableStore into a data channel DataHub through real-time calculation of Flink;
Preprocessing the data in the data channel DataHub through the Flink operation, and converging and writing the preprocessed network bus order data and driver operation service track data into the data warehouse MaxCompute for subsequent calculation.
8. The method as recited in claim 6, further comprising:
For the heat level data of all honeycomb areas of the city, the Redis and TableStore are written in periodically and simultaneously through DataWorks data integration tools, and the honeycomb area circle selection and honeycomb area heat level data query API is provided through RedisGeo and TableStore indexes for downstream service;
And the driver side and other service systems inquire the heat level of the honeycomb area through the data center API to display or apply functions.
9. An apparatus for estimating thermal power in a geographic area based on supply and demand relationships, the apparatus comprising:
the data acquisition module is used for acquiring historical N days, current real-time network vehicle order data and driver operation service track data;
The time dimension dividing module is used for dividing each day of the history N days into X time periods, wherein the length of each time period is Y, and numbering the X time periods according to the sequence; taking time periods with the same numbers in N days of history as a time dimension, wherein the total time dimension is X, and each time dimension comprises N time periods with the same numbers;
the order classification module is used for acquiring the order placing time of all the passenger orders every day according to the historical network vehicle order data of N days and classifying the passenger orders into time periods with different numbers every day according to the order placing time;
The dimension data acquisition module is used for respectively acquiring the income amount of the driver order and the corresponding service duration of the driver order of each passenger in each time dimension according to the network vehicle order data and the driver operation service track data of the historical N days, and acquiring the hearing time duration of the driver in each time period;
The abnormal value replacement module is used for replacing abnormal values of the income amount of the driver order of each passenger order, the service time length of the driver order and the time length of the driver listening order in each time period;
The dimension average data acquisition module is used for acquiring total order income amount of each time dimension based on the replaced driver order income amount, and acquiring Directorate-General machine order service time length of each time dimension based on the replaced driver order service time length;
the time window dividing module is used for selecting any one time dimension and two adjacent future time dimensions as a time window, traversing X time dimensions and obtaining X time windows;
The historical IPH acquisition module is used for acquiring historical IPH under the corresponding time window dimension according to the total order income amount of three time dimensions of each time window, directorate-General machine order service time length and total listening time length;
The real-time IPH acquisition module is used for acquiring real-time IPH in the current time dimension according to the current real-time network vehicle order data and the driver operation service track data;
The fusion IPH acquisition module is used for respectively setting a historical data weight and a real-time data weight, and obtaining a final fusion IPH under the corresponding time window dimension based on the historical IPH, the real-time IPH, the historical data weight and the real-time data weight under the same time dimension;
the honeycomb thermodynamic acquisition module is used for acquiring the IPH of X time dimensions of the history P days of any city, selecting the maximum IPH as the maximum IPH of the city, and carrying out thermodynamic rendering of all honeycomb areas of the city under the corresponding time window dimensions based on the final fusion IPH of all honeycomb areas of the city and the maximum IPH of the city;
the IPH represents statistics of revenue per hour on a unit dimension basis.
10. A storage medium storing a computer program which, when executed by a master, implements the steps of the method of assessing geographical area heating power based on supply and demand relations as claimed in any one of claims 1-8.
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