CN117952386A - Intelligent bus scheduling system based on data analysis of Internet of things - Google Patents
Intelligent bus scheduling system based on data analysis of Internet of things Download PDFInfo
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Abstract
The invention belongs to the technical field of bus scheduling management, and particularly relates to an intelligent bus scheduling system based on data analysis of the Internet of things, which comprises an Internet of things platform, a user registration login module, a bus positioning module, a bus loading monitoring module, a line scheduling allocation rationality evaluation module and a bus scheduling management end; according to the invention, through collecting the real-time positions and the number of spare seats of the corresponding buses and sending the real-time positions and the number of spare seats to the intelligent terminals of all target users, staff of the corresponding company can inquire the remaining seats and the current arrival information of the buses at any time and any place and pre-judge the commute on the sky and the off-duty, so that the commute mode is selected in advance, and the buses are reasonably analyzed through the route scheduling distribution rationality evaluation module, the corresponding routes are increased or reduced according to the need when the unreasonable signals of the buses are generated, the later-stage buses are planned more reasonably, the management difficulty of management staff is reduced, and the bus utilization effect is increased.
Description
Technical Field
The invention relates to the technical field of scheduling management of buses, in particular to an intelligent bus scheduling system based on data analysis of the Internet of things.
Background
The class scheduling refers to scheduling and adjusting the operation of the class to ensure that the operation of the class meets the regulations and meets the requirements of passengers, and for a factory, the operation of the class in the factory involves a plurality of operation lines, and the class scheduling of the corresponding factory is generally responsible for the management staff in the factory;
however, as the daily manned quantity of each operation route is not fixed, the existing operation routes are too few in manned quantity, so that the space and time seats of the buses are too many when arriving at the factory, and the front half section of the operation routes is fully loaded, so that the following staff cannot take the buses, the utilization rate of the buses is greatly reduced, the staff commute to and from work is seriously influenced, and the buses cannot be comprehensively evaluated comprehensively and early warned in time, so that the follow-up management and arrangement of the drivers are not facilitated;
In view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide an intelligent bus scheduling system based on data analysis of the Internet of things, which solves the problems that the scheduling allocation rationality of corresponding operation lines is difficult to accurately judge, the utilization rate of buses is greatly reduced, the commute of workers on duty and duty are seriously influenced, the drivers of buses cannot be comprehensively evaluated and early warned in time, the follow-up management and arrangement of the drivers are not facilitated, and the intelligent degree is low in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an intelligent bus dispatching system based on data analysis of the Internet of things comprises an Internet of things platform, a user registration login module, a bus positioning module, a bus loading monitoring module, a line dispatching allocation rationality evaluation module and a bus dispatching management end; the user registration login module is used for registering company workers, marking the registered company workers as target users, carrying out identity verification on the target users when the target users log in, and allowing the target users to log in successfully after verification is error-free; the method comprises the steps that an Internet of things platform acquires all running lines and all buses corresponding to the running lines, and a bus positioning module and a bus loading monitoring module are carried in all buses;
The method comprises the steps that a class car positioning module obtains real-time positions of corresponding class cars and generates positioning information of the class cars, the positioning information of the corresponding class cars is sent to an Internet of things platform, a class car loading monitoring module monitors the interior of the class cars, the number of spare seats of the corresponding class cars is collected in real time based on the number of people on the class cars and the number of people on the class cars, the number of spare seats of the corresponding class cars is sent to the Internet of things platform, and the Internet of things platform sends the positioning information of the corresponding class cars and the number of spare seats to intelligent terminals of all target users;
The line scheduling distribution rationality evaluation module is used for setting an evaluation period with the number of days being Q1, carrying out bus scheduling rationality analysis on all running lines when the number of days reaches Q1, generating bus scheduling unreasonable signals or bus scheduling reasonable signals of the corresponding running lines through analysis, sending the bus scheduling reasonable signals or the bus scheduling unreasonable signals of the corresponding running lines to a bus scheduling management end through an Internet of things platform, and sending early warning when the bus scheduling management end receives the bus scheduling unreasonable signals of the corresponding lines.
Further, the specific analysis process of the bus scheduling rationality analysis is as follows:
Acquiring the daily operation shifts on the corresponding operation lines, carrying out average value calculation on the number of all the spare seats in the operation process of the corresponding operation shifts to obtain the average value of the spare seats, and carrying out average value calculation on the average value of the spare seats of all the operation shifts on the corresponding operation lines on the corresponding dates to obtain the operation spare position value of the shift; performing mean value calculation and variance calculation on the shift operation residual values of each day on the corresponding operation line to obtain an operation line adjustment table value and an operation line deviation value, and respectively performing numerical comparison on the operation line adjustment table value and the operation line deviation value and a preset operation line adjustment table value range and a preset operation line deviation threshold value;
If the running line schedule value is not in the preset running line schedule value range and the running line deviation value is not more than the preset running line deviation threshold value, generating a shift scheduling unreasonable signal of the corresponding running line; if the operating line adjustment table value is in the range of the preset operating line adjustment table value and the operating line adjustment deviation value does not exceed the preset operating line adjustment deviation threshold value, generating a regular bus scheduling reasonable signal of the corresponding operating line; and performing daily comparison analysis on the operation line scheduling under the other conditions.
Further, the specific analysis process of the daily comparative analysis of the operation line schedule is as follows:
The method comprises the steps of marking corresponding dates as free days, busy days or suitable days of a corresponding operation line through analysis, obtaining the number of the busy days and the number of the free days in the corresponding operation line, marking the number of the busy days and the number of the free days as busy daily measured values and free daily measured values respectively, marking the ratio of the busy daily measured values to a numerical value Q1 as a busy daily table value, and marking the ratio of the free daily measured values to the numerical value Q1 as a free daily table value; respectively comparing the busy daily value and the idle daily value with corresponding preset thresholds, and generating a bus scheduling unreasonable signal of a corresponding running line if the busy daily value or the idle daily value exceeds the corresponding preset threshold; and if the busy daily table value and the idle daily table value do not exceed the corresponding preset threshold values, generating a regular bus scheduling reasonable signal of the corresponding running line.
Further, the specific process of marking the corresponding date as the free day, busy day or suitable day of the corresponding operation line by analysis is as follows:
the class operation residual value of the corresponding operation circuit of the corresponding date is compared with the preset class operation residual value range in numerical value, and if the class operation residual value exceeds the maximum value of the preset class operation residual value range, the corresponding date is marked as the idle date of the corresponding circuit; if the spare value of the bus running does not exceed the minimum value of the range of the preset spare value of the bus running, marking the corresponding date as the busy day of the corresponding line; if the bus operation residual value is within the preset bus operation residual value range, marking the corresponding date as the proper date of the corresponding line.
Further, the internet of things platform is in communication connection with the driving comprehensive evaluation module, the driving comprehensive evaluation module obtains drivers who drive buses on all running lines, the corresponding drivers are marked as evaluation personnel i, and i is a natural number larger than 1; setting a monitoring period, analyzing the driving performance condition of the evaluator i in the monitoring period, marking the evaluator i as a training person or a qualified person through analysis, and sending the training person to a bus dispatching management end through an Internet of things platform.
Further, a specific analysis process for analyzing the driving performance status of the evaluator i in the monitoring period is as follows:
When an evaluator i drives the operation shift, acquiring the moment when the evaluator i stops at a corresponding station and marking the moment as the real stop moment, marking the time difference between the real stop moment and the preset stop moment as the stop time offset value, carrying out mean value calculation on all the stop time offsets corresponding to the operation shift to obtain the stop time offset value, and marking the number of the stop time offset values exceeding the preset stop time offset threshold in the corresponding operation shift as the stop abnormal table value;
Numerical calculation is carried out on the stop abnormal table value and the stop time analysis value to obtain a shift stop detection value, the shift stop detection value is compared with a preset shift stop detection threshold value in a numerical mode, and if the shift stop detection value exceeds the preset shift stop detection threshold value, the corresponding operation shift is marked as a non-optimal operation shift; if the shift stop detection value does not exceed the preset shift stop detection threshold, obtaining a shift driving detection value through driving performance analysis, performing numerical comparison on the shift driving detection value and the preset shift driving detection threshold, and if the shift driving detection value exceeds the preset shift driving detection threshold, marking the corresponding operation shift as a non-optimal operation shift;
The method comprises the steps of obtaining the number of non-optimal operation shifts of an evaluator i in a monitoring period, marking the number as a non-optimal execution value, marking the ratio of the non-optimal execution value to the total number of operation shifts of the evaluator i in the monitoring period as a non-optimal occupation analysis value, and carrying out numerical calculation on the non-optimal execution value and the non-optimal occupation analysis value to obtain a driving evaluation value; the driving evaluation value is compared with a preset driving evaluation threshold value in a numerical mode, and if the driving evaluation value exceeds the preset driving evaluation threshold value, an evaluator i is marked as a training person; and if the driving evaluation value exceeds a preset driving evaluation threshold value, marking the evaluation person i as a qualified person.
Further, the specific analysis procedure of the driving performance analysis is as follows:
Collecting the total time length of the corresponding shift in which the shift car is in overspeed driving and marking the total time length as overspeed total time value, and collecting the total time length of the corresponding shift in which the shaking amplitude of the corresponding shift car exceeds a preset shaking amplitude threshold value and marking the total time length as overspeed total time value; and the total overspeed value and the total excessive shaking value are summed to obtain a total driving deviation value, the ratio of the total frame deviation value to the total driving duration of the corresponding operation shift is calculated to obtain a total driving deviation occupied value, and the total frame deviation value and the total frame deviation occupied value are subjected to numerical calculation to obtain a shift driving detection value.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, through collecting the real-time positions and the number of spare seats of the corresponding buses and sending the real-time positions and the number of spare seats to the intelligent terminals of all target users, staff of the corresponding company can inquire the number of the spare seats and the current arrival information of the buses at any time and any place and pre-judge the commute on the sky and the off-duty, so that the commute mode is selected in advance, and the buses are scheduled rationality analysis is carried out on all running circuits through a circuit scheduling allocation rationality evaluation module, so that when a signal of unreasonable buses scheduling is generated, the corresponding circuits are increased or reduced according to the need, the later-stage buses are planned more reasonably, the management difficulty of management staff is reduced, and the bus utilization effect is increased;
2. According to the invention, the driving performance conditions of the corresponding drivers in the monitoring period are analyzed by acquiring the drivers driving the buses on all running lines through the driving comprehensive evaluation module, the corresponding drivers are marked as the training staff or qualified staff through analysis, so that corresponding education training is carried out on the training staff in time, supervision is enhanced, the driving frequency of the buses of the corresponding training staff is temporarily reduced as required, the subsequent operation safety of the buses is ensured, the management difficulty of management staff is further reduced, the intelligent degree is high, and the targeted management of the drivers is realized.
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For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a system block diagram of a first embodiment of the present invention;
Fig. 2 is a system block diagram of a second embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one: as shown in fig. 1, the intelligent bus dispatching system based on the data analysis of the internet of things is mainly applied to bus dispatching management of receiving workers of corresponding companies on duty and off duty, and comprises an internet of things platform, a user registration login module, a bus positioning module, a bus loading monitoring module, a line dispatching allocation rationality evaluation module and a bus dispatching management end, wherein the internet of things platform is in communication connection with the user registration login module, the bus positioning module, the bus loading monitoring module, the line dispatching allocation rationality evaluation module and the bus dispatching management end through the internet of things;
the user registration login module is used for registering company workers, marking the registered company workers as target users, carrying out identity verification on the target users when the target users log in, and allowing the target users to log in successfully after verification is error-free; the identity verification mode includes password verification, mobile phone check code verification, fingerprint verification and the like; the method comprises the steps that an Internet of things platform acquires all running lines and all buses corresponding to the running lines, and a bus positioning module and a bus loading monitoring module are carried in all buses;
The method comprises the steps that a class car positioning module obtains real-time positions of corresponding class cars and generates positioning information of the class cars, the positioning information of the corresponding class cars is sent to an Internet of things platform, a class car loading monitoring module monitors the interior of the class cars, the number of spare seats of the corresponding class cars is collected in real time based on the number of people on the class cars and the number of people on the class cars, the number of spare seats of the corresponding class cars is sent to the Internet of things platform, the Internet of things platform sends the positioning information of the corresponding class cars and the number of spare seats to intelligent terminals of all target users, staff of a corresponding company can conveniently inquire the number of remaining seats and current arrival information of the class cars at any time and any place, and pre-judgment is made on commute on the sky and the down class, so that a commute mode is selected in advance;
the circuit dispatching allocation rationality evaluation module is used for setting an evaluation period with the number of days being Q1, and preferably, Q1 is fifteen days; when the number of days reaches Q1, carrying out bus scheduling rationality analysis on all the running lines, generating bus scheduling unreasonable signals or bus scheduling unreasonable signals of the corresponding running lines through analysis, sending the bus scheduling unreasonable signals or bus scheduling unreasonable signals of the corresponding running lines to a bus scheduling management end through an Internet of things platform, sending early warning when receiving the bus scheduling unreasonable signals of the corresponding lines, and carrying out shift increase or decrease on the corresponding lines according to requirements when a manager of the bus scheduling management end receives the corresponding early warning, wherein later-stage bus planning is more reasonable, reduces management difficulty of the manager and increases bus utilization effect; the specific analysis process of the bus dispatching rationality analysis is as follows:
acquiring the daily operation shifts on the corresponding operation lines, carrying out average value calculation on the spare seat average value obtained by carrying out average value calculation on the number of all the spare seats (namely the number of the spare seats at all time points) in the operation process of the corresponding operation shifts, and carrying out average value calculation on the spare seat average value of all the operation shifts on the corresponding operation lines on the corresponding dates to obtain the operation spare position value of the shift; respectively carrying out mean value calculation and variance calculation on the shift operation residual values of each day on the corresponding operation line in the evaluation period to obtain an operation line adjustment table value and an operation line adjustment deviation value, and respectively carrying out numerical comparison on the operation line adjustment table value and the operation line adjustment deviation value and a preset operation line adjustment table value range and a preset operation line adjustment deviation threshold value;
If the operating line schedule value is not in the preset operating line schedule value range and the operating line deviation value is not more than the preset operating line deviation threshold value, indicating that the shift arrangement condition of the corresponding operating line is poor, generating a shift scheduling unreasonable signal of the corresponding operating line; if the operating line schedule value is in the range of the preset operating line schedule value and the operating line deviation value does not exceed the preset operating line deviation threshold value, indicating that the shift arrangement condition of the corresponding operating line is better, generating a shift scheduling reasonable signal of the corresponding operating line;
And carrying out daily comparison analysis on the scheduling of the operation line under the other conditions, wherein the daily comparison analysis comprises the following specific steps: marking the corresponding date as the free date, busy date or proper date of the corresponding operation line through analysis, specifically: the class operation residual value of the corresponding operation circuit of the corresponding date is compared with the preset class operation residual value range in numerical value, and if the class operation residual value exceeds the maximum value of the preset class operation residual value range, the corresponding date is marked as the idle date of the corresponding circuit; if the spare value of the bus running does not exceed the minimum value of the range of the preset spare value of the bus running, marking the corresponding date as the busy day of the corresponding line; if the spare value of the bus operation is within the range of the spare value of the preset bus operation, marking the corresponding date as the proper date of the corresponding line;
The method comprises the steps of obtaining the number of busy days and the number of idle days in a corresponding running line, marking the number of the busy days and the number of the idle days as a busy day measurement value and an idle day measurement value respectively, marking the ratio of the busy day measurement value to a numerical value Q1 as a busy day table value, and marking the ratio of the idle day measurement value to the numerical value Q1 as an idle day table value; it should be noted that, if the value of the busy daily value is too large, it indicates that the number of operating shifts of the corresponding operating line needs to be increased to solve the problem of insufficient capacity, and if the value of the idle daily value is too large, it indicates that the number of operating shifts of the corresponding operating line needs to be reduced to avoid the problem of capacity waste;
Respectively comparing the busy daily value and the idle daily value with corresponding preset thresholds, and if the busy daily value or the idle daily value exceeds the corresponding preset thresholds, indicating that the shift arrangement condition of the corresponding operation line is poor, generating a shift scheduling unreasonable signal of the corresponding operation line; and if the busy daily table value and the idle daily table value do not exceed the corresponding preset threshold values, indicating that the shift arrangement condition of the corresponding running line is better in performance, generating a shift scheduling reasonable signal of the corresponding running line.
Embodiment two: as shown in fig. 2, the difference between the present embodiment and embodiment 1 is that the internet of things platform is in communication connection with the comprehensive driving evaluation module, the comprehensive driving evaluation module obtains the drivers who drive the buses on all the running lines, marks the corresponding drivers as an evaluation person i, and i is a natural number greater than 1; setting a monitoring period, preferably thirty days; analyzing the driving performance condition of the evaluator i in the monitoring period, marking the evaluator i as a training person or a qualified person through analysis, sending the training person to a bus dispatching management end through an Internet of things platform, timely carrying out corresponding education training and reinforcing supervision on the training person by a manager, temporarily reducing the bus driving frequency of the corresponding training person according to the requirement, thereby ensuring the subsequent bus operation safety, further reducing the management difficulty of the manager, having high intelligent degree and realizing targeted management on the driver; the specific analysis process is as follows:
When an evaluator i drives a corresponding operation shift, acquiring the moment of stopping the corresponding station by the evaluator i and marking the moment as real stopping moment, marking the time difference between the real stopping moment and the preset stopping moment as stopping deviation values, carrying out mean value calculation on all stopping deviation values of the corresponding operation shift to obtain stopping analysis values, carrying out numerical comparison on the stopping deviation values and preset stopping deviation threshold values, and marking the number of the stopping deviation values exceeding the preset stopping deviation threshold values in the corresponding operation shift as stopping abnormal table values;
Carrying out numerical calculation on the stop abnormal table value TSi and the stop time analysis value TYi through a formula TXi=a1+a2× TYi to obtain a shift stop detection value TXi, wherein a1 and a2 are preset proportionality coefficients, and the values of a1 and a2 are both larger than zero; moreover, the larger the value of the shift stop detection value TXI is, the worse the stop punctuality of the evaluator i in the corresponding operation shift is indicated; the shift stop detection value TXi is compared with a preset shift stop detection threshold value in a numerical mode, if the shift stop detection value TXi exceeds the preset shift stop detection threshold value, the situation that the stopping timeliness of an evaluator i in the corresponding operation shift is poor is indicated, and the corresponding operation shift is marked as a non-optimal operation shift;
If the shift stop detection value TXI does not exceed the preset shift stop detection threshold, collecting the total time length of the corresponding shift middle shift vehicle in overspeed driving and marking the total time length as overspeed total time value, and collecting the total time length of the corresponding shift vehicle in corresponding operation shift with the shake amplitude exceeding the preset shake amplitude threshold and marking the total time length as overspeed total time value; summing the overspeed total time value and the overspeed total time value to obtain a driving abnormal total time value, and calculating the ratio of the frame abnormal total time value to the driving total time length of the corresponding operation shift to obtain a driving abnormal time occupation value;
numerical calculation is performed on the frame abnormal total time value GSi and the frame abnormal time occupied value GZi through a formula GXi =ew1× GSi/ew2+ew2× GZi to obtain a shift driving detection value GXi; wherein, ew1 and ew2 are preset proportionality coefficients, and the ratio of the ew2 to the ew1 is more than 0; and, the larger the value of the shift driving detection value GXi is, the worse the driving performance of the evaluator i in the corresponding operation shift is, the more difficult the driving stability and safety are ensured; numerical comparison is carried out on the shift driving detection value GXi and a preset shift driving detection threshold value, if the shift driving detection value GXi exceeds the preset shift driving detection threshold value, the fact that the driving performance of the evaluator i in the corresponding operation shift is poor is indicated, and the corresponding operation shift is marked as non-optimal operation shift;
The method comprises the steps of obtaining the number of non-optimal operation shifts carried out by an evaluator i in a monitoring period, marking the number as a non-optimal execution value, marking the ratio of the non-optimal execution value to the total number of operation shifts carried out by the evaluator i in the monitoring period as a non-optimal occupation analysis value, and carrying out numerical calculation on the non-optimal execution value QYi and the non-optimal occupation analysis value QKi through a formula QFi =ry1× QYi +ry2× QKi to obtain a driving evaluation value QFi; wherein, ry1 and ry2 are preset proportional coefficients, and ry2 is more than ry1 and more than 0; and, the larger the value of the driving evaluation value QFi is, the worse the driving condition of the evaluator i in the monitoring period is comprehensively;
The driving evaluation value QFi is compared with a preset driving evaluation threshold value, if the driving evaluation value QFi exceeds the preset driving evaluation threshold value, the driving condition of the evaluator i in the monitoring period is poor in combination, and the supervision and training of the evaluator i are required to be enhanced in time, the evaluator i is marked as a training person; if the driving evaluation value QFi exceeds the preset driving evaluation threshold, which indicates that the driving condition of the evaluator i in the monitoring period is better in combination, the evaluator i is marked as a qualified person.
The working principle of the invention is as follows: when the intelligent terminal is used, the real-time position of a corresponding class car is acquired through the class car positioning module, positioning information of the corresponding class car is generated, the class car loading monitoring module monitors the interior of the class car and acquires the number of spare seats of the corresponding class car in real time, the positioning information of the corresponding class car and the number of spare seats are sent to the intelligent terminals of all target users through the Internet of things platform, staff of a corresponding company can inquire the remaining seat number and the current arrival information of the class car at any time and any place, and pre-judgment is made on the commute of the sky and the off-duty, so that the commute mode is selected in advance; and the bus scheduling rationality analysis is carried out on all the running lines through the line scheduling allocation rationality evaluation module, the bus scheduling irrational signals or the bus scheduling rational signals of the corresponding running lines are generated through the analysis, the corresponding lines are increased or reduced according to the needs when the bus scheduling irrational signals are generated, the later-stage bus planning is more reasonable, the management difficulty of management staff is reduced, and the bus utilization effect is increased.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form 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 understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. The intelligent bus dispatching system based on the data analysis of the Internet of things is characterized by comprising an Internet of things platform, a user registration login module, a bus positioning module, a bus loading monitoring module, a line dispatching allocation rationality evaluation module and a bus dispatching management end; the user registration login module is used for registering company workers, marking the registered company workers as target users, carrying out identity verification on the target users when the target users log in, and allowing the target users to log in successfully after verification is error-free; the method comprises the steps that an Internet of things platform acquires all running lines and all buses corresponding to the running lines, and a bus positioning module and a bus loading monitoring module are carried in all buses;
The method comprises the steps that a class car positioning module obtains real-time positions of corresponding class cars and generates positioning information of the class cars, the positioning information of the corresponding class cars is sent to an Internet of things platform, a class car loading monitoring module monitors the interior of the class cars, the number of spare seats of the corresponding class cars is collected in real time based on the number of people on the class cars and the number of people on the class cars, the number of spare seats of the corresponding class cars is sent to the Internet of things platform, and the Internet of things platform sends the positioning information of the corresponding class cars and the number of spare seats to intelligent terminals of all target users;
The line scheduling distribution rationality evaluation module is used for setting an evaluation period with the number of days being Q1, carrying out bus scheduling rationality analysis on all running lines when the number of days reaches Q1, generating bus scheduling unreasonable signals or bus scheduling reasonable signals of the corresponding running lines through analysis, sending the bus scheduling reasonable signals or the bus scheduling unreasonable signals of the corresponding running lines to a bus scheduling management end through an Internet of things platform, and sending early warning when the bus scheduling management end receives the bus scheduling unreasonable signals of the corresponding lines.
2. The intelligent bus scheduling system based on the data analysis of the internet of things according to claim 1, wherein the specific analysis process of the bus scheduling rationality analysis is as follows:
Acquiring the daily operation shifts on the corresponding operation lines, carrying out average value calculation on the number of all the spare seats in the operation process of the corresponding operation shifts to obtain the average value of the spare seats, and carrying out average value calculation on the average value of the spare seats of all the operation shifts on the corresponding operation lines on the corresponding dates to obtain the operation spare position value of the shift; and carrying out mean value calculation and variance calculation on the shift operation residual values of each day on the corresponding operation line to obtain an operation line adjustment table value and an operation line adjustment deviation value;
If the running line schedule value is not in the preset running line schedule value range and the running line deviation value is not more than the preset running line deviation threshold value, generating a shift scheduling unreasonable signal of the corresponding running line; if the operating line adjustment table value is in the range of the preset operating line adjustment table value and the operating line adjustment deviation value does not exceed the preset operating line adjustment deviation threshold value, generating a regular bus scheduling reasonable signal of the corresponding operating line; and performing daily comparison analysis on the operation line scheduling under the other conditions.
3. The intelligent bus scheduling system based on the data analysis of the internet of things according to claim 2, wherein the specific analysis process of the daily comparative analysis of the operation line scheduling is as follows:
The method comprises the steps of marking corresponding dates as free days, busy days or suitable days of a corresponding operation line through analysis, obtaining the number of the busy days and the number of the free days in the corresponding operation line, marking the number of the busy days and the number of the free days as busy daily measured values and free daily measured values respectively, marking the ratio of the busy daily measured values to a numerical value Q1 as a busy daily table value, and marking the ratio of the free daily measured values to the numerical value Q1 as a free daily table value; if the busy daily table value or the idle daily table value exceeds a corresponding preset threshold value, generating a bus scheduling unreasonable signal of a corresponding running line; and if the busy daily table value and the idle daily table value do not exceed the corresponding preset threshold values, generating a regular bus scheduling reasonable signal of the corresponding running line.
4. A smart bus scheduling system based on data analysis of internet of things according to claim 3, wherein the specific process of marking the corresponding date as free, busy or suitable day of the corresponding operation line by analysis is as follows:
the class operation residual value of the corresponding operation circuit of the corresponding date is compared with the preset class operation residual value range in numerical value, and if the class operation residual value exceeds the maximum value of the preset class operation residual value range, the corresponding date is marked as the idle date of the corresponding circuit; if the spare value of the bus running does not exceed the minimum value of the range of the preset spare value of the bus running, marking the corresponding date as the busy day of the corresponding line; if the bus operation residual value is within the preset bus operation residual value range, marking the corresponding date as the proper date of the corresponding line.
5. The intelligent bus scheduling system based on the data analysis of the internet of things according to claim 1, wherein the internet of things platform is in communication connection with a comprehensive driving evaluation module, the comprehensive driving evaluation module obtains drivers driving buses on all running lines, the corresponding drivers are marked as evaluation staff i, and i is a natural number greater than 1; setting a monitoring period, analyzing the driving performance condition of the evaluator i in the monitoring period, marking the evaluator i as a training person or a qualified person through analysis, and sending the training person to a bus dispatching management end through an Internet of things platform.
6. The intelligent bus scheduling system based on internet of things data analysis according to claim 5, wherein the specific analysis process of analyzing the driving performance status of the evaluator i in the monitoring period is as follows:
When an evaluator i drives the operation shift, acquiring the moment when the evaluator i stops at a corresponding station and marking the moment as the real stop moment, marking the time difference between the real stop moment and the preset stop moment as the stop time offset value, carrying out mean value calculation on all the stop time offsets corresponding to the operation shift to obtain the stop time offset value, and marking the number of the stop time offset values exceeding the preset stop time offset threshold in the corresponding operation shift as the stop abnormal table value;
the stop abnormal table value and the stop time analysis value are subjected to numerical calculation to obtain a shift stop detection value, and if the shift stop detection value exceeds a preset shift stop detection threshold, the corresponding operation shift is marked as a non-optimal operation shift; if the shift stop detection value does not exceed the preset shift stop detection threshold, the shift driving detection value is obtained through driving performance analysis, and if the shift driving detection value exceeds the preset shift driving detection threshold, the corresponding operation shift is marked as non-optimal operation shift;
The method comprises the steps of obtaining the number of non-optimal operation shifts of an evaluator i in a monitoring period, marking the number as a non-optimal execution value, marking the ratio of the non-optimal execution value to the total number of operation shifts of the evaluator i in the monitoring period as a non-optimal occupation analysis value, and carrying out numerical calculation on the non-optimal execution value and the non-optimal occupation analysis value to obtain a driving evaluation value; if the driving evaluation value exceeds a preset driving evaluation threshold value, marking an evaluation person i as a training person; and if the driving evaluation value exceeds a preset driving evaluation threshold value, marking the evaluation person i as a qualified person.
7. The intelligent bus dispatching system based on the data analysis of the internet of things according to claim 6, wherein the specific analysis process of the driving performance analysis is as follows:
Collecting the total time length of the corresponding shift in which the shift car is in overspeed driving and marking the total time length as overspeed total time value, and collecting the total time length of the corresponding shift in which the shaking amplitude of the corresponding shift car exceeds a preset shaking amplitude threshold value and marking the total time length as overspeed total time value; and the total overspeed value and the total excessive shaking value are summed to obtain a total driving deviation value, the ratio of the total frame deviation value to the total driving duration of the corresponding operation shift is calculated to obtain a total driving deviation occupied value, and the total frame deviation value and the total frame deviation occupied value are subjected to numerical calculation to obtain a shift driving detection value.
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