CN113962752B - Electric taxi individual behavior analysis decision-making system based on multivariate information interaction - Google Patents
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Abstract
The invention discloses an electric taxi individual behavior analysis decision system based on multivariate information interaction, which relates to the field of big data analysis and solves the technical problem that the behavior decision accuracy is low because the electric taxi cannot be accurately analyzed in the prior art; the state conversion analysis and the income analysis are carried out on the driving behaviors, and the state conversion and the income of the electric taxi in the historical driving behaviors are analyzed, so that an accurate basis is provided for real-time behavior decision, the accuracy of analysis decision is improved, the driving behaviors of the electric taxi and the state conversion of the corresponding driving behaviors are controlled, and the income rate and the operation efficiency of the electric taxi are improved.
Description
Technical Field
The invention relates to the technical field of behavior analysis decision-making, in particular to an electric taxi individual behavior analysis decision-making system based on multivariate information interaction.
Background
The electric taxi aggregator grasps the load characteristics of the electric taxies in the management range, and is the basis for the aggregator to predict and regulate the power demand level and the regulation capacity, the power demand and the regulation capacity of the electric taxi aggregate load depend on various factors, such as the number of vehicles, availability, battery types and storage capacity, more detailed factors, energy consumption rate, time for arriving at and leaving a charging station, driving distance, initial SOC, battery degradation, charge and discharge power and the like, and the main reason is that the electric taxies have more complex behavior targets and uncertain behavior space-time characteristics compared with private electric vehicles and electric buses, so that the behavior analysis decision of the electric taxies is particularly important;
however, in the prior art, the subjective consciousness of a driver cannot be accurately eliminated during the history analysis of the electric taxi, so that the accuracy of the history analysis is reduced, and the behavior analysis of the electric taxi is influenced; the behavior and the income of the electric taxi cannot be accurately analyzed in the analysis, so that the influence factors of the electric taxi cannot be determined;
in view of the above technical drawbacks, a solution is proposed.
Disclosure of Invention
The invention aims to solve the problems, the electric taxi individual behavior analysis decision system based on multi-information interaction is provided, and the real-time subjective environment of the electric taxi is analyzed, so that the influence of human subjective consciousness on the behavior of the historical electric taxi is reduced, the subjective environment of the electric taxi is shown as the environment which is not changed due to the subjective observation or cognition of the electric taxi, and the historical behavior analysis of the electric taxi is improved; the state conversion analysis and the income analysis are carried out on the driving behaviors, and the state conversion and the income of the electric taxi in the historical driving behaviors are analyzed, so that an accurate basis is provided for real-time behavior decision, the accuracy of analysis decision is improved, the driving behavior of the electric taxi is controlled to be converted corresponding to the state, and the income rate and the operation efficiency of the electric taxi are improved.
The purpose of the invention can be realized by the following technical scheme:
the electric taxi individual behavior analysis decision system based on multivariate information interaction comprises an analysis decision platform, wherein a subjective environment analysis unit, a driving behavior management unit, a charging behavior management unit, a state conversion unit, a profit conversion unit, an influence factor analysis unit, a decision gap analysis unit and a real-time behavior decision unit are arranged in the analysis decision platform;
the analysis decision platform is used for analyzing according to the behaviors of the historical electric taxies so as to make a decision on the behaviors of the real-time electric taxies; analyzing the real-time subjective environment of the electric taxi through a subjective environment analysis unit; generating a driving behavior management signal and a charging behavior management signal through analysis, and respectively sending the driving behavior management signal and the charging behavior management signal to a driving behavior management unit and a charging behavior management unit; after receiving the driving behavior management signal, the driving behavior management unit generates a driving state conversion signal and a driving profit analysis signal and respectively sends the driving state conversion signal and the driving profit analysis signal to the state conversion unit and the profit analysis unit; acquiring state conversion of historical driving behaviors of the electric taxi through a state conversion unit; analyzing the income of the driving behavior of the electric taxi in the historical analysis time period;
after receiving the charging behavior management signal, the charging behavior management unit generates a charging state conversion signal and a charging income analysis signal and respectively sends the charging state conversion signal and the charging income analysis signal to the state conversion unit and the income analysis unit; acquiring state conversion of historical charging behaviors of the electric taxi through a state conversion unit; analyzing the income of the charging behavior of the electric taxi in the historical analysis time period through an income analysis unit;
analyzing the influence factors of the corresponding electric taxi through an influence factor analysis unit; carrying out decision clearance analysis on the electric taxi through a decision clearance analysis unit; and carrying out real-time behavior decision on the electric taxi through the real-time behavior decision unit.
As a preferred embodiment of the present invention, the subjective environment analysis process of the subjective environment analysis unit is as follows:
setting a label i of the electric taxi, wherein the label i is a natural number larger than 1, acquiring corresponding real-time electric quantity and real-time order increasing speed when each electric taxi selects an action, and respectively marking the corresponding real-time electric quantity and the real-time order increasing speed when each electric taxi selects the action as DLi and SDi; obtaining a historical subjective environment analysis coefficient Xi of the electric taxi through analysis;
comparing the historical subjective environment analysis coefficient of the electric taxi with the threshold range of the subjective environment analysis coefficient: if the historical subjective environment analysis coefficient of the electric taxi is within the threshold range of the subjective environment analysis coefficient, judging that the subjective environment influence is small, generating a non-subjective signal, analyzing the historical behavior of the electric taxi, generating a driving behavior management signal and a charging behavior management signal, and respectively sending the driving behavior management signal and the charging behavior management signal to a driving behavior management unit and a charging behavior management unit; and if the historical subjective environment analysis coefficient of the electric taxi is not in the subjective environment analysis coefficient threshold range, judging that the subjective environment influence is large, generating a subjective signal and not analyzing the historical behavior of the corresponding electric taxi in the subjective environment.
As a preferred embodiment of the present invention, the driving behavior management unit generates a driving state conversion signal and a driving profit analysis signal and transmits the driving state conversion signal and the driving profit analysis signal to the state conversion unit and the profit analysis unit, respectively;
after receiving the driving state conversion signal, the state conversion unit divides the state of the driving behavior into a passenger searching state and a passenger carrying state, sets the electric taxi to be analyzed as an analysis object, sets a historical analysis time period for the analysis object, acquires all passenger searching states and passenger carrying states of the analysis object in the historical analysis time period, sorts the passenger searching states and the passenger carrying states corresponding to the analysis object according to the time sequence of the historical analysis time period, constructs a historical state subset of the driving behavior, analyzes the state conversion among all adjacent subsets, divides the state conversion among the adjacent subsets into position state conversion, time state conversion and energy consumption state conversion, and marks the position state conversion corresponding conversion distance as position state conversion position data; marking the conversion time corresponding to the time state conversion as time data of the time state conversion, and marking the electric quantity correspondingly consumed by the energy consumption state conversion as energy consumption data of the energy consumption state conversion; and sending the position data, the time data and the energy consumption data to a profit analysis unit and an influence factor analysis unit.
As a preferred embodiment of the present invention, after receiving the driving profit analysis signal, the profit analysis unit uniformly marks position data of position state conversion, time data of time state conversion, and energy consumption data of energy consumption state conversion corresponding to the electric taxi as cost data, marks electric quantity loss, time loss, and manual loss corresponding to the cost data as cost data corresponding to cost, and summarizes costs corresponding to all cost data in the history state subset of driving behaviors corresponding to the electric taxi;
acquiring the cost of the electric taxi in the passenger carrying state, marking the cost as the just-needed cost, and summarizing the just-needed cost and the cost corresponding to the cost data to obtain the total cost of the electric taxi, wherein the just-needed cost comprises the cost of electricity consumption, time consumption and the like of the electric taxi in the passenger carrying state; marking the income of the electric taxi corresponding to the passenger carrying state as the total income; if the total income of the electric taxi is greater than the total cost, marking the driving behavior of the electric taxi as a positive income behavior, and if the total income of the electric taxi is not greater than the total cost, marking the driving behavior of the electric taxi as a negative income behavior; and sending the positive income behaviors or the negative income behaviors and the corresponding electric taxis to the influence factor analysis unit.
As a preferred embodiment of the present invention, the charging behavior management unit generates a charging state conversion signal and a charging benefit analysis signal and transmits the charging state conversion signal and the charging benefit analysis signal to the state conversion unit and the benefit analysis unit, respectively;
the state conversion unit divides the state of the charging behavior into a state to be charged and a state to be charged, acquires all the states to be charged and the states to be charged of the analysis object in a historical time period, sequences the states to be charged and the states to be charged of the analysis object according to the time sequence of the historical analysis time period, constructs a historical state subset of the charging behavior, analyzes the state conversion between each adjacent subsets, divides the state conversion between the adjacent subsets into the state conversion to be charged and the state conversion to be charged, marks the distance between the state conversion to be charged and the time length of queuing for charging as the data to be charged of the state conversion, and marks the electric quantity correspondingly supplemented by the state conversion as the charging data of the state conversion; and sending the data to be charged and the energy charging data to a profit analysis unit and an influence factor analysis unit.
As a preferred embodiment of the present invention, after receiving the charging profit analysis signal, the profit analysis unit marks the distance from the electric taxi to the charging station and the duration of queuing for charging in the to-be-charged data converted corresponding to the to-be-charged state as charging cost, marks the predicted profit as charging profit by the amount of electricity supplied in the charging data converted corresponding to the to-be-charged state of the electric taxi, simultaneously, analyzes the road condition state conversion in the charging behavior, analyzes the quantity state conversion and the congestion state conversion corresponding to the road condition state, marks the road average traffic flow rate increase corresponding to the data state conversion as traffic state data, converts the congestion state into the road average time consumption increase as congestion state data, and the traffic state data is the autovariant data of the congestion state data, and the congestion state data is the dependent data of the traffic state data, if the traffic state data exists, the congestion state data exists, otherwise, the traffic state data does not exist, the congestion state data does not exist; marking the electric quantity consumed corresponding to the average consumed time increment of the road vehicles in the congestion state data as risk income;
marking the charging income and the risk income of the electric taxi as charging behavior income, and comparing the charging cost and the charging behavior income of the electric taxi: if the charging cost of the electric taxi exceeds the charging behavior income, marking the corresponding charging behavior as a high-risk income behavior; if the charging cost of the electric taxi does not exceed the charging behavior income, marking the corresponding charging behavior as a low risk income behavior; and sending the high-risk income behaviors, the low-risk income behaviors and the corresponding electric taxis to the influence factor analysis unit.
As a preferred embodiment of the present invention, the influence factor analyzing process of the influence factor analyzing unit is as follows:
marking position data, time data and energy consumption data in the driving behaviors of the electric taxi and charging data and energy charging data to be charged in the charging behaviors as preset influence factors, marking the electric taxi corresponding to negative income behaviors and high-risk income behaviors as an abnormal object, collecting the occurrence frequency and frequency of the preset influence factors of the abnormal object in historical analysis time, if the occurrence frequency and frequency of the preset influence factors of the abnormal object in the historical analysis time exceed corresponding threshold values, marking the corresponding preset influence factors as selected influence factors, and sending the selected influence factors to a decision gap analysis unit; if the occurrence frequency and the frequency of the preset influence factors of the abnormal object in the historical analysis time do not exceed the corresponding threshold values, marking the corresponding preset influence factors as risk influence factors, and sending the risk influence factors to the decision gap analysis unit.
As a preferred embodiment of the present invention, the decision gap analysis process of the decision gap analysis unit is as follows:
marking the state conversion processes of corresponding adjacent subsets in the driving behavior historical state subset and the charging behavior historical state subset as decision gaps, respectively marking the decision gaps with abnormal income and without abnormal income of the electric taxi as abnormal gaps and normal gaps, acquiring the occurrence frequency and frequency of the abnormal gaps and the ratio of the normal gap frequency to the abnormal gaps, if the occurrence frequency and frequency of the abnormal gaps exceed corresponding thresholds and the ratio of the normal gap frequency to the abnormal gaps is less than the ratio threshold, judging that the influence of corresponding human factors on the corresponding electric taxi exceeds the influence of risk influence factors, and marking the corresponding electric taxi as a human taxi; if the occurrence frequency and the frequency of the abnormal clearance do not exceed the corresponding threshold value, and the ratio of the number of the normal clearance to the abnormal clearance is greater than the ratio threshold value, judging that the influence of the corresponding human factors of the corresponding electric taxi does not exceed the influence of the risk influence factors, and marking the corresponding electric taxi as a risk taxi;
and sending the artificial taxis and the risk taxis to the real-time behavior decision unit.
As a preferred embodiment of the invention, the real-time behavior decision unit performs real-time behavior decision analysis on the person who is the taxi and the risk taxi, and when risk influence factors exist in the surrounding environment of the real-time electric taxi, if the real-time electric taxi is the person who is the taxi, a behavior conversion risk signal is generated; if the real-time electric taxi is a risk taxi, generating a behavior conversion safety signal; when the surrounding environment of the real-time electric taxi does not have risk influence factors, if the real-time electric taxi is a taxi, generating a behavior conversion suggestion signal; and if the real-time electric taxi is a risk taxi, generating a behavior timely conversion signal.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the real-time subjective environment of the electric taxi is analyzed, so that the behavior of the historical electric taxi is judged to be influenced and reduced by human subjective consciousness, the subjectively existing environment of the electric taxi is shown as the environment which is not changed due to subjective observation or recognition of the electric taxi, and the historical behavior analysis of the electric taxi is improved; the state conversion analysis and the income analysis are carried out on the driving behaviors, and the state conversion and the income of the electric taxi in the historical driving behaviors are analyzed, so that an accurate basis is provided for real-time behavior decision, the accuracy of analysis decision is improved, the driving behavior of the electric taxi is controlled to be converted corresponding to the state, and the income rate and the operation efficiency of the electric taxi are improved;
2. according to the invention, the state conversion analysis and the income analysis are carried out on the charging behavior, and the state conversion and the income of the electric taxi in the historical charging behavior are analyzed, so that an accurate basis is provided for real-time behavior decision, and the accuracy of analysis decision is improved; the feasibility and the rationality of the charging behavior are analyzed and judged through the income of the charging behavior;
3. according to the method, the influence factors of the electric taxi corresponding to the negative income behaviors and the high-risk income behaviors are analyzed, and the influence factors of the electric taxi are accurately analyzed, so that the accuracy of real-time behavior decision is improved; and performing decision gap analysis on the historical state subset of the driving behaviors and the historical state subset of the charging behaviors, so as to judge whether the reason of the abnormal income of the electric taxi is an artificial factor or an influencing factor, and accurately analyzing the influence of the influencing factor on the electric taxi.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the electric taxi individual behavior analysis decision system based on multivariate information interaction includes an analysis decision platform, and a subjective environment analysis unit, a driving behavior management unit, a charging behavior management unit, a state conversion unit, a profit conversion unit, an influence factor analysis unit, a decision gap analysis unit and a real-time behavior decision unit are arranged in the analysis decision platform;
the analysis decision platform is used for analyzing according to the behaviors of the historical electric taxies so as to make a decision on the behaviors of the real-time electric taxies, and the behaviors of the electric taxies are divided into driving behaviors and charging behaviors;
the subjective environment analysis unit is used for analyzing the real-time subjective environment of the electric taxi, so that the behavior of the historical electric taxi is judged to be influenced and reduced by human subjective consciousness, the subjective environment of the electric taxi is expressed as the environment which is not changed due to subjective observation or cognition of the electric taxi, the historical behavior analysis of the electric taxi is improved, and the specific subjective environment analysis process is as follows:
setting a label i of the electric taxi, wherein the label i is a natural number larger than 1, acquiring corresponding real-time electric quantity and real-time order increasing speed when each electric taxi selects an action, and respectively marking the corresponding real-time electric quantity and the real-time order increasing speed when each electric taxi selects the action as DLi and SDi; obtaining a historical subjective environment analysis coefficient Xi of the electric taxi through analysis;
comparing the historical subjective environment analysis coefficient of the electric taxi with the threshold range of the subjective environment analysis coefficient:
if the historical subjective environment analysis coefficient of the electric taxi is within the threshold range of the subjective environment analysis coefficient, judging that the subjective environment influence is small, generating a non-subjective signal, analyzing the historical behavior of the electric taxi, generating a driving behavior management signal and a charging behavior management signal, and respectively sending the driving behavior management signal and the charging behavior management signal to a driving behavior management unit and a charging behavior management unit;
if the historical subjective environment analysis coefficient of the electric taxi is not in the subjective environment analysis coefficient threshold range, judging that the subjective environment influence is large, generating a subjective signal and not analyzing the historical behavior of the corresponding electric taxi in the subjective environment;
after receiving the driving behavior management signal, the driving behavior management unit generates a driving state conversion signal and a driving income analysis signal and respectively sends the driving state conversion signal and the driving income analysis signal to the state conversion unit and the income analysis unit, the driving behavior is subjected to state conversion analysis and income analysis, and the state conversion and the income of the electric taxi in historical driving behavior are analyzed, so that an accurate basis is provided for real-time behavior decision, the accuracy of analysis decision is improved, the driving behavior of the electric taxi is controlled to correspond to the state conversion, and the income rate and the operation efficiency of the electric taxi are improved;
after the state conversion unit receives the driving state conversion signal, the state conversion unit collects the state conversion of the historical driving behavior of the electric taxi, and acquires the data change generated by the state conversion through collecting the state conversion, so that the judgment standard of the income analysis is accurately collected, the income analysis accuracy is improved, and the specific state conversion process is as follows:
dividing the states of the driving behaviors into a passenger searching state and a passenger carrying state, setting an electric taxi to be analyzed as an analysis object, setting a historical analysis time period for the analysis object, acquiring all passenger searching states and passenger carrying states of the analysis object in the historical analysis time period, sequencing the passenger searching states and the passenger carrying states corresponding to the analysis object according to the time sequence of the historical analysis time period, constructing historical state subsets of the driving behaviors, analyzing state conversion among all adjacent subsets, dividing the state conversion among the adjacent subsets into position state conversion, time state conversion and energy consumption state conversion, and marking the conversion distance corresponding to the position state conversion as position data of the position state conversion; marking the conversion time corresponding to the time state conversion as time data of the time state conversion, and marking the electric quantity correspondingly consumed by the energy consumption state conversion as energy consumption data of the energy consumption state conversion; the position data, the time data and the energy consumption data are sent to a profit analysis unit and an influence factor analysis unit;
the income analysis unit analyzes the income of the driving behavior of the electric taxi in the historical analysis time period after receiving the driving income analysis signal, and the feasibility and the behavior efficiency of the driving behavior are judged through the income analysis of the driving behavior, and the specific income analysis process is as follows:
uniformly marking position data of position state conversion, time data of time state conversion and energy consumption data of energy consumption state conversion corresponding to the electric taxi as cost data, marking electric quantity loss, time loss and manual loss corresponding to the cost data as cost corresponding to the cost data, and summarizing the corresponding cost of all the cost data in the historical state subset of driving behaviors corresponding to the electric taxi;
acquiring the cost of the electric taxi in the passenger carrying state, marking the cost as the just-needed cost, and summarizing the just-needed cost and the cost corresponding to the cost data to obtain the total cost of the electric taxi, wherein the just-needed cost comprises the cost of electricity consumption, time consumption and the like of the electric taxi in the passenger carrying state; marking the income of the electric taxi corresponding to the passenger carrying state as the total income; if the total income of the electric taxi is greater than the total cost, marking the driving behavior of the electric taxi as a positive income behavior, and if the total income of the electric taxi is not greater than the total cost, marking the driving behavior of the electric taxi as a negative income behavior; sending the positive income behaviors or the negative income behaviors and the corresponding electric taxis to an influence factor analysis unit;
after receiving the charging behavior management signal, the charging behavior management unit generates a charging state conversion signal and a charging income analysis signal and respectively sends the charging state conversion signal and the charging income analysis signal to the state conversion unit and the income analysis unit; the state conversion analysis and the income analysis are carried out on the charging behaviors, and the state conversion and the income of the electric taxi in the historical charging behaviors are analyzed, so that an accurate basis is provided for real-time behavior decision, and the accuracy of analysis decision is improved;
after the state conversion unit receives the charging state conversion signal, the state conversion unit collects the state conversion of the historical charging behavior of the electric taxi and judges the data change generated by the charging state conversion, so that the income analysis accuracy is accurately improved, and the specific state conversion process is as follows:
dividing the state of a charging behavior into a to-be-charged state and a charging state, acquiring all the to-be-charged states and the charging states of an analysis object in a historical time period, sequencing the to-be-charged states and the charging states corresponding to the analysis object according to the time sequence of the historical analysis time period, constructing historical state subsets of the charging behavior, analyzing state conversion between every two adjacent subsets, dividing the state conversion between the adjacent subsets into the to-be-charged state conversion and charging state conversion, marking the distance of the to-be-charged state conversion corresponding to the driving to a charging station and the time length of queuing charging as to-be-charged state converted to the to-be-charged data, and marking the electric quantity correspondingly supplemented by the charging state conversion as charging data of the charging state conversion; sending the data to be charged and the energy charging data to a profit analysis unit and an influence factor analysis unit;
the income analysis unit receives the income analysis signal of charging after, carries out the analysis with the income of electric taxi charging action in the historical analysis time quantum, through the income analysis judgement charging action's of the action of charging feasibility and rationality, concrete income analysis process as follows:
marking the distance from the electric taxi to the charging station in the charging data converted corresponding to the charging state and the time length of queuing for charging as charging cost, marking the predicted income of the electric taxi for supplementing the electric quantity in the charging data converted corresponding to the charging state as charging income, meanwhile, the conversion of the road condition states of the electric taxi in the charging behavior is analyzed, the quantity state conversion and the congestion state conversion of the corresponding road condition states are analyzed, the average traffic flow increasing speed of the road corresponding to the data state conversion is marked as flow state data, the average time consumption increasing amount of the road vehicle corresponding to the congestion state conversion is marked as congestion state data, the flow state data is self-changing data of the congestion state data, the congestion state data is dependent data of the flow state data, if the flow state data exists, the congestion state data exists, otherwise, the congestion state data does not exist; marking the electric quantity consumed corresponding to the average consumed time increment of the road vehicles in the congestion state data as risk income; according to the method, the risk income and the charging income are obtained by averaging historical data of the corresponding electric taxi and the real-time road, and the method has the same reliability as a mobile phone map estimated time principle;
marking the charging income and the risk income of the electric taxi as charging behavior income, and comparing the charging cost and the charging behavior income of the electric taxi: if the charging cost of the electric taxi exceeds the charging behavior income, marking the corresponding charging behavior as a high-risk income behavior; if the charging cost of the electric taxi does not exceed the charging behavior income, marking the corresponding charging behavior as a low risk income behavior; sending the high-risk income behaviors, the low-risk income behaviors and the corresponding electric taxis to an influence factor analysis unit;
the influence factor analysis unit is used for analyzing influence factors of the electric taxi corresponding to the negative income behaviors and the high-risk income behaviors, and accurately analyzing the influence factors of the electric taxi, so that the accuracy of real-time behavior decision is improved, and the specific influence factor analysis process is as follows:
marking position data, time data and energy consumption data in the driving behaviors of the electric taxi and charging data and energy charging data to be charged in the charging behaviors as preset influence factors, marking the electric taxi corresponding to negative income behaviors and high-risk income behaviors as an abnormal object, collecting the occurrence frequency and frequency of the preset influence factors of the abnormal object in historical analysis time, if the occurrence frequency and frequency of the preset influence factors of the abnormal object in the historical analysis time exceed corresponding threshold values, marking the corresponding preset influence factors as selected influence factors, and sending the selected influence factors to a decision gap analysis unit; if the occurrence frequency and the frequency of the preset influence factors of the abnormal object in the historical analysis time do not exceed the corresponding threshold values, marking the corresponding preset influence factors as risk influence factors, and sending the risk influence factors to a decision gap analysis unit;
the decision clearance analysis unit is used for carrying out decision clearance analysis on the historical driving behavior state subset and the historical charging behavior state subset, so that whether the reason of the abnormal income of the electric taxi is an artificial factor or an influencing factor is judged, the influence of the influencing factor on the electric taxi is accurately analyzed, and the specific decision clearance analysis process is as follows:
marking the state conversion processes of corresponding adjacent subsets in the driving behavior historical state subset and the charging behavior historical state subset as decision gaps, respectively marking the decision gaps with abnormal income and without abnormal income of the electric taxi as abnormal gaps and normal gaps, acquiring the occurrence frequency and frequency of the abnormal gaps and the ratio of the normal gap frequency to the abnormal gaps, if the occurrence frequency and frequency of the abnormal gaps exceed corresponding thresholds and the ratio of the normal gap frequency to the abnormal gaps is less than the ratio threshold, judging that the influence of corresponding human factors on the corresponding electric taxi exceeds the influence of risk influence factors, and marking the corresponding electric taxi as a human taxi; if the occurrence frequency and the frequency of the abnormal clearance do not exceed the corresponding threshold value, and the ratio of the number of the normal clearance to the abnormal clearance is greater than the ratio threshold value, judging that the influence of the corresponding human factors of the corresponding electric taxi does not exceed the influence of the risk influence factors, and marking the corresponding electric taxi as a risk taxi;
sending the artificial taxi and the risk taxi to a real-time behavior decision unit;
the real-time behavior decision unit is used for carrying out real-time behavior decision analysis on the taxi as a person and the risk taxi, and generating a behavior conversion risk signal if the real-time electric taxi is the taxi as the person when risk influence factors exist in the surrounding environment of the real-time electric taxi; if the real-time electric taxi is a risk taxi, generating a behavior conversion safety signal; when the surrounding environment of the real-time electric taxi does not have risk influence factors, if the real-time electric taxi is a taxi, generating a behavior conversion suggestion signal; and if the real-time electric taxi is a risk taxi, generating a behavior timely conversion signal.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions;
when the system is used, analysis is carried out according to the behaviors of the historical electric taxis through the analysis decision platform, so that real-time electric taxi behaviors are decided; analyzing the real-time subjective environment of the electric taxi through a subjective environment analysis unit; the driving behavior management unit generates a driving state conversion signal and a driving profit analysis signal and respectively sends the driving state conversion signal and the driving profit analysis signal to the state conversion unit and the profit analysis unit; acquiring state conversion of historical driving behaviors of the electric taxi through a state conversion unit; analyzing the income of the driving behavior of the electric taxi in the historical analysis time period;
the charging behavior management unit generates a charging state conversion signal and a charging income analysis signal and respectively sends the charging state conversion signal and the charging income analysis signal to the state conversion unit and the income analysis unit; acquiring state conversion of historical charging behaviors of the electric taxi through a state conversion unit; analyzing the income of the charging behavior of the electric taxi in the historical analysis time period through an income analysis unit; analyzing the influence factors of the corresponding electric taxi through an influence factor analysis unit; carrying out decision clearance analysis on the electric taxi through a decision clearance analysis unit; and carrying out real-time behavior decision on the electric taxi through the real-time behavior decision unit.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. The electric taxi individual behavior analysis decision system based on multi-information interaction is characterized by comprising an analysis decision platform, wherein a subjective environment analysis unit, a driving behavior management unit, a charging behavior management unit, a state conversion unit, a profit conversion unit, an influence factor analysis unit, a decision gap analysis unit and a real-time behavior decision unit are arranged in the analysis decision platform;
the analysis decision platform is used for analyzing according to the behaviors of the historical electric taxies so as to make a decision on the behaviors of the real-time electric taxies; analyzing the real-time subjective environment of the electric taxi through a subjective environment analysis unit; generating a driving behavior management signal and a charging behavior management signal through analysis, and respectively sending the driving behavior management signal and the charging behavior management signal to a driving behavior management unit and a charging behavior management unit; after receiving the driving behavior management signal, the driving behavior management unit generates a driving state conversion signal and a driving profit analysis signal and respectively sends the driving state conversion signal and the driving profit analysis signal to the state conversion unit and the profit analysis unit; acquiring state conversion of historical driving behaviors of the electric taxi through a state conversion unit; analyzing the income of the driving behavior of the electric taxi in the historical analysis time period;
after receiving the charging behavior management signal, the charging behavior management unit generates a charging state conversion signal and a charging income analysis signal and respectively sends the charging state conversion signal and the charging income analysis signal to the state conversion unit and the income analysis unit; acquiring state conversion of historical charging behaviors of the electric taxi through a state conversion unit; analyzing the income of the charging behavior of the electric taxi in the historical analysis time period through an income analysis unit;
analyzing the influence factors of the corresponding electric taxi through an influence factor analysis unit; carrying out decision clearance analysis on the electric taxi through a decision clearance analysis unit; carrying out real-time behavior decision on the electric taxi through a real-time behavior decision unit;
the subjective environment analysis process of the subjective environment analysis unit is as follows:
setting a label i of the electric taxi, wherein the label i is a natural number larger than 1, acquiring corresponding real-time electric quantity and real-time order increasing speed when each electric taxi selects an action, and respectively marking the corresponding real-time electric quantity and the real-time order increasing speed when each electric taxi selects the action as DLi and SDi; obtaining a historical subjective environment analysis coefficient Xi of the electric taxi through analysis;
comparing the historical subjective environment analysis coefficient of the electric taxi with the threshold range of the subjective environment analysis coefficient: if the historical subjective environment analysis coefficient of the electric taxi is within the threshold range of the subjective environment analysis coefficient, judging that the subjective environment influence is small, generating a non-subjective signal, analyzing the historical behavior of the electric taxi, generating a driving behavior management signal and a charging behavior management signal, and respectively sending the driving behavior management signal and the charging behavior management signal to a driving behavior management unit and a charging behavior management unit; and if the historical subjective environment analysis coefficient of the electric taxi is not in the subjective environment analysis coefficient threshold range, judging that the subjective environment influence is large, generating a subjective signal and not analyzing the historical behavior of the corresponding electric taxi in the subjective environment.
2. The electric taxi individual behavior analysis and decision system based on multivariate information interaction as claimed in claim 1, wherein the driving behavior management unit generates a driving state conversion signal and a driving profit analysis signal and respectively transmits the driving state conversion signal and the driving profit analysis signal to the state conversion unit and the profit analysis unit;
after receiving the driving state conversion signal, the state conversion unit divides the state of the driving behavior into a passenger searching state and a passenger carrying state, sets the electric taxi to be analyzed as an analysis object, sets a historical analysis time period for the analysis object, acquires all passenger searching states and passenger carrying states of the analysis object in the historical analysis time period, sorts the passenger searching states and the passenger carrying states corresponding to the analysis object according to the time sequence of the historical analysis time period, constructs a historical state subset of the driving behavior, analyzes the state conversion among all adjacent subsets, divides the state conversion among the adjacent subsets into position state conversion, time state conversion and energy consumption state conversion, and marks the position state conversion corresponding conversion distance as position state conversion position data; marking the conversion time corresponding to the time state conversion as time data of the time state conversion, and marking the electric quantity correspondingly consumed by the energy consumption state conversion as energy consumption data of the energy consumption state conversion; and sending the position data, the time data and the energy consumption data to a profit analysis unit and an influence factor analysis unit.
3. The electric taxi individual behavior analysis decision system based on multivariate information interaction as claimed in claim 2, wherein after receiving the driving profit analysis signal, the profit analysis unit uniformly marks position data of position state conversion, time data of time state conversion and energy consumption data of energy consumption state conversion corresponding to the electric taxi as cost data, marks electric quantity loss, time loss and manual loss corresponding to the cost data as cost data corresponding to cost, and summarizes the costs corresponding to all the cost data in the history state subset of driving behaviors corresponding to the electric taxi;
collecting the cost of the electric taxi corresponding to the passenger carrying state, marking the cost as the just-needed cost, and summarizing the just-needed cost and the cost corresponding to the cost data to obtain the total cost of the electric taxi; marking the income of the electric taxi corresponding to the passenger carrying state as the total income; if the total income of the electric taxi is greater than the total cost, marking the driving behavior of the electric taxi as a positive income behavior, and if the total income of the electric taxi is not greater than the total cost, marking the driving behavior of the electric taxi as a negative income behavior; and sending the positive income behaviors or the negative income behaviors and the corresponding electric taxis to the influence factor analysis unit.
4. The electric taxi individual behavior analysis decision system based on multivariate information interaction as claimed in claim 1, wherein the charging behavior management unit generates a charging state conversion signal and a charging income analysis signal and respectively sends the charging state conversion signal and the charging income analysis signal to the state conversion unit and the income analysis unit;
the state conversion unit divides the state of the charging behavior into a state to be charged and a state to be charged, acquires all the states to be charged and the states to be charged of the analysis object in a historical time period, sequences the states to be charged and the states to be charged of the analysis object according to the time sequence of the historical analysis time period, constructs a historical state subset of the charging behavior, analyzes the state conversion between each adjacent subsets, divides the state conversion between the adjacent subsets into the state conversion to be charged and the state conversion to be charged, marks the distance between the state conversion to be charged and the time length of queuing for charging as the data to be charged of the state conversion, and marks the electric quantity correspondingly supplemented by the state conversion as the charging data of the state conversion; and sending the data to be charged and the energy charging data to a profit analysis unit and an influence factor analysis unit.
5. The electric taxi individual behavior analysis and decision system based on multivariate information interaction as claimed in claim 4, wherein the profit analysis unit marks the distance from the electric taxi to the charging station and the duration of queuing for charging in the to-be-charged data converted corresponding to the to-be-charged state as charging cost after receiving the charging profit analysis signal, marks the predicted profit of the electric taxi for supplementing the electric quantity in the charging data converted corresponding to the charging state as charging profit, analyzes the conversion of the road condition state of the electric taxi in the charging behavior, analyzes the conversion of the quantity state and the conversion of the congestion state corresponding to the road condition state, marks the road average traffic flow rate increase rate corresponding to the data state conversion as flow state data, converts the congestion state into the road vehicle average consumed time increase rate corresponding to the congestion state as congestion state data, and the flow rate state data is the self-variable data of the congestion state data, the congestion state data is dependent data of the flow state data, if the flow state data exists, the congestion state data exists, otherwise, the congestion state data does not exist; marking the electric quantity consumed corresponding to the average consumed time increment of the road vehicles in the congestion state data as risk income;
marking the charging income and the risk income of the electric taxi as charging behavior income, and comparing the charging cost and the charging behavior income of the electric taxi: if the charging cost of the electric taxi exceeds the charging behavior income, marking the corresponding charging behavior as a high-risk income behavior; if the charging cost of the electric taxi does not exceed the charging behavior income, marking the corresponding charging behavior as a low risk income behavior; and sending the high-risk income behaviors, the low-risk income behaviors and the corresponding electric taxis to the influence factor analysis unit.
6. The electric taxi individual behavior analysis and decision making system based on multivariate information interaction as claimed in claim 1, wherein the influence factor analysis process of the influence factor analysis unit is as follows:
marking position data, time data and energy consumption data in the driving behaviors of the electric taxi and charging data and energy charging data to be charged in the charging behaviors as preset influence factors, marking the electric taxi corresponding to negative income behaviors and high-risk income behaviors as an abnormal object, collecting the occurrence frequency and frequency of the preset influence factors of the abnormal object in historical analysis time, if the occurrence frequency and frequency of the preset influence factors of the abnormal object in the historical analysis time exceed corresponding threshold values, marking the corresponding preset influence factors as selected influence factors, and sending the selected influence factors to a decision gap analysis unit; if the occurrence frequency and the frequency of the preset influence factors of the abnormal object in the historical analysis time do not exceed the corresponding threshold values, marking the corresponding preset influence factors as risk influence factors, and sending the risk influence factors to the decision gap analysis unit.
7. The electric taxi individual behavior analysis decision system based on multivariate information interaction as claimed in claim 1, wherein the decision gap analysis process of the decision gap analysis unit is as follows:
marking the state conversion processes of corresponding adjacent subsets in the driving behavior historical state subset and the charging behavior historical state subset as decision gaps, respectively marking the decision gaps with abnormal income and without abnormal income of the electric taxi as abnormal gaps and normal gaps, acquiring the occurrence frequency and frequency of the abnormal gaps and the ratio of the normal gap frequency to the abnormal gaps, if the occurrence frequency and frequency of the abnormal gaps exceed corresponding thresholds and the ratio of the normal gap frequency to the abnormal gaps is less than the ratio threshold, judging that the influence of corresponding human factors on the corresponding electric taxi exceeds the influence of risk influence factors, and marking the corresponding electric taxi as a human taxi; if the occurrence frequency and the frequency of the abnormal clearance do not exceed the corresponding threshold value, and the ratio of the number of the normal clearance to the abnormal clearance is greater than the ratio threshold value, judging that the influence of the corresponding human factors of the corresponding electric taxi does not exceed the influence of the risk influence factors, and marking the corresponding electric taxi as a risk taxi;
and sending the artificial taxis and the risk taxis to the real-time behavior decision unit.
8. The electric taxi individual behavior analysis decision system based on multivariate information interaction as claimed in claim 1, wherein the real-time behavior decision unit performs real-time behavior decision analysis on the person-oriented taxi and the risk taxi, and generates a behavior transformation risk signal if the real-time electric taxi is the person-oriented taxi when risk influence factors exist in the surrounding environment of the real-time electric taxi; if the real-time electric taxi is a risk taxi, generating a behavior conversion safety signal; when the surrounding environment of the real-time electric taxi does not have risk influence factors, if the real-time electric taxi is a taxi, generating a behavior conversion suggestion signal; and if the real-time electric taxi is a risk taxi, generating a behavior timely conversion signal.
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