A vehicle dispatch optimization system based on big data analysis

Abstract

The present invention relates to the technical field of vehicle dispatch management, and in particular to a vehicle dispatch optimization system based on big data analysis, comprising a vehicle management platform, a screening and analysis module, a vehicle supervision unit, a personnel evaluation unit, a vehicle push unit and an early warning display unit; the present invention analyzes from the perspective of the dynamic trend of dispatch efficiency so as to optimize and adjust the current vehicle dispatch decision according to information feedback so as to improve the vehicle dispatch efficiency and dispatch rationality, and on the premise of qualified dispatch efficiency, performs preliminary screening and supervision analysis on vehicles in a response area, and analyzes two points of the vehicle itself and the operator of the dispatch object by means of information feedback so as to provide data support for subsequent vehicle dispatch recommendation, and performs information fusion supervision recommendation analysis by means of information fusion so as to further understand the situation of vehicles in the dispatch object that meet the dispatch demand so as to improve the dispatch rationality of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle dispatch management, and in particular to a vehicle dispatch optimization system based on big data analysis. Background Art

[0002] Vehicle dispatching refers to the formulation of driving routes so that vehicles can pass through a series of loading and unloading points in an orderly manner under certain constraints to achieve goals such as shortest distance, minimum cost, and least time consumption. However, with the rapid development of the logistics industry, traditional vehicle management methods can no longer meet the modern park's demand for efficient, accurate, and real-time logistics; therefore, the park logistics vehicle dispatching management system came into being;

[0003] However, in the prior art, it is impossible to supervise the current vehicle dispatching efficiency, which is not conducive to the timely optimization of the current vehicle dispatching decision, thereby reducing the vehicle dispatching efficiency. It is also impossible to safely supervise the status of the dispatched vehicle itself, which leads to the vehicle itself not meeting the dispatching requirements, reducing the garbage truck dispatching efficiency. At the same time, it is impossible to score the operators, which affects the vehicle dispatching efficiency, and it is impossible to reasonably recommend the vehicle for dispatching.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a vehicle scheduling optimization system based on big data analysis to solve the above-mentioned technical defects. The present invention analyzes from the perspective of the dynamic trend of scheduling efficiency so as to optimize and adjust the current vehicle scheduling decision according to the information feedback situation to improve the vehicle scheduling efficiency and scheduling rationality. On the premise of qualified scheduling efficiency, the vehicles in the response area are preliminarily screened, supervised and analyzed, that is, the logistics vehicles are screened from the perspective of loading feasibility so that the selected objects can meet the preliminary loading requirements of cargo transportation so as to reasonably schedule the vehicles. The two points of the vehicle itself and the operator of the scheduling object are analyzed by means of information feedback so as to provide data support for subsequent vehicle scheduling recommendations. The information fusion supervision recommendation analysis is performed by means of information fusion so as to further understand the situation of vehicles in the scheduling object that meet the scheduling requirements, that is, on the premise that the feedback object is not working and the loading is satisfied, it is helpful to select the feedback object preferentially to improve the rationality of vehicle scheduling, and at the same time, the transportation risk of the feedback object is more intuitively understood.

[0006] The object of the present invention can be achieved by the following technical solutions: A vehicle dispatch optimization system based on big data analysis, including a vehicle management platform, a screening and analysis module, a vehicle supervision unit, a personnel evaluation unit, a vehicle push unit and an early warning display unit;

[0007] When the vehicle management platform generates a transport management instruction, the transport management instruction is sent to the screening and analysis module. After receiving the transport management instruction, the screening and analysis module immediately collects the vehicle status information, which includes the loading volume and loading weight, and performs preliminary vehicle screening and supervision analysis on the status information, and sends the obtained optimization signal to the early warning display unit, and sends the obtained dispatch object to the vehicle supervision unit and the personnel evaluation unit;

[0008] After receiving the dispatch object, the vehicle supervision unit immediately collects the operation risk data of the dispatched vehicle, including the vehicle assessment value and the transportation interference value, and performs vehicle performance supervision assessment analysis on the operation risk data, so as to further perform screening analysis from the perspective of the dispatched vehicle itself, and sends the obtained transportation risk assessment coefficient Pg to the vehicle push unit;

[0009] After receiving the dispatch object, the personnel assessment unit immediately collects the driving management data of the dispatch object operator, which includes the driving risk value, the number of violations and the total number of transport orders, and performs an operator driving safety assessment analysis on the driving management data, and sends the obtained driving impact assessment coefficient Rg to the vehicle push unit;

[0010] After receiving the transport risk assessment coefficient Pg and the driving impact assessment coefficient Rg, the vehicle push unit immediately performs information fusion supervision recommendation analysis and sends the obtained feedback object to the early warning display unit.

[0011] Preferably, the vehicle preliminary screening supervision analysis process of the screening analysis module is as follows:

[0012] Set the monitoring cycle and set it as the time threshold, obtain the dispatch station of the vehicle within the time threshold, draw a circle with the dispatch station location as the center and r1 as the radius, mark the obtained circular area as the response area, obtain the total number of dispatches in the response area within the time threshold, and obtain the qualified number of dispatches in the response area within the time threshold. The qualified number of dispatches indicates the number of dispatches corresponding to the completion of the specified task volume within the specified time. Set the ratio of the qualified number of dispatches to the total number of dispatches as the dispatch decision management coefficient, obtain the dispatch decision management coefficients of the historical m response areas within the time threshold, m is a natural number greater than zero, establish a rectangular coordinate system with the number of time thresholds as the X-axis and the dispatch decision management coefficient as the Y-axis, draw the dispatch decision management coefficient curve by drawing points, set the change trend value of the dispatch decision management coefficient curve as the dispatch optimization evaluation coefficient, and compare and analyze the dispatch optimization evaluation coefficient with the preset dispatch optimization evaluation coefficient threshold stored in the internal entry:

[0013] If the ratio between the scheduling optimization evaluation coefficient and the preset scheduling optimization evaluation coefficient threshold is less than 1, a qualified signal is generated;

[0014] If the ratio between the scheduling optimization evaluation coefficient and the preset scheduling optimization evaluation coefficient threshold is greater than or equal to 1, an optimization signal is generated.

[0015] Preferably, when the screening and analysis module generates a qualified signal:

[0016] The vehicles in the response area are marked as response vehicles, and the actual status of the response vehicles is analyzed:

[0017] If the responding vehicle is in working state, it is determined that the responding vehicle does not meet the transportation requirements, and the corresponding responding vehicle is marked as an unselectable object;

[0018] If the responding vehicle is in a waiting state, the responding vehicle is determined to meet the transportation requirements, and the corresponding responding vehicle is marked as an optional object. The loading volume and loading weight of the optional object are obtained, and the product value obtained after data normalization of the loading volume and loading weight of the optional object is set as the loading assessment value, and the loading assessment value is compared and analyzed with the preset loading assessment value threshold value recorded and stored internally:

[0019] If the loading assessment value is less than or equal to the preset loading assessment value threshold, it is determined that the optional object does not meet the transportation requirements, and the corresponding optional object is marked as an excluded object;

[0020] If the loading assessment value is greater than the preset loading assessment value threshold, the optional object is determined to meet the transportation requirements and the corresponding optional object is marked as a scheduling object.

[0021] Preferably, the vehicle performance supervision evaluation and analysis process of the vehicle supervision unit is as follows:

[0022] S1: Obtain the vehicle assessment value of the dispatch object within the time threshold. The vehicle assessment value table is the product of the transportation risk value and the driving risk value of the dispatch object after data normalization. The transportation risk value represents the proportion of the number of delay alarms in the total number of alarms of the dispatch object. The driving risk value represents the ratio of the number of failures of the dispatch object to the average length of the failure interval after data normalization.

[0023] S2: Obtain the transport interference value of the dispatch object within the time threshold, the transport interference value represents the product of the drive risk value and the vehicle status value of the dispatch object after data normalization processing, the drive risk value represents the ratio of the number of times that the operating temperature of the drive mechanism of the dispatch object exceeds the preset threshold and the duration corresponding to the preset threshold is greater than the preset threshold and the total number of transportation times, the vehicle status value represents the product of the mileage value and the tire wear value of the dispatch object after data normalization processing, the dispatch object is marked as g, g is a natural number greater than zero, and the vehicle evaluation value and the transport interference value are marked as YPg and YSg respectively;

[0024] S3: Obtain the transportation risk assessment coefficient Pg of each scheduling object according to the formula.

[0025] Preferably, the operator driving safety assessment analysis process of the personnel assessment unit is as follows:

[0026] SS1: Obtain the driving risk value of the dispatch object operator within the time threshold. The driving risk value represents the driving time between the operator and the dispatch object. At the same time, obtain the total driving years of the dispatch object operator within the time threshold. Obtain the product of the driving risk value of the dispatch object operator and the total driving years after data normalization, and set it as the driving fit;

[0027] SS2: Obtain the number of violations of the dispatch object operator within the driving time, obtain the violation type in the violation record corresponding to the number of violations, and set it as the driving deviation value. The violation record includes speeding and running a red light. The driving deviation value is compared and analyzed with the stored preset driving deviation value threshold, and the part of the driving deviation value that is greater than the preset driving deviation value threshold is set as the behavior risk value;

[0028] SS3: Obtain the total number of transport orders of the dispatch object operator within the driving time, and at the same time obtain the number of on-time completed orders of the dispatch object operator within the driving time, obtain the ratio between the number of on-time completed orders and the total number of transport orders, set the ratio between the number of on-time completed orders and the total number of transport orders as the order evaluation value, and label the driving fit, behavior risk value and order evaluation value as JTg, XFg and DDg respectively;

[0029] SS4: Obtain the driving impact assessment coefficient Rg of each dispatch object operator according to the formula.

[0030] Preferably, the information fusion supervision recommendation analysis process of the vehicle push unit is as follows:

[0031] Obtain the transport risk assessment coefficient Pg and driving impact assessment coefficient Rg within the time threshold, and substitute the transport risk assessment coefficient Pg and driving impact assessment coefficient Rg into the formula The scheduling recommendation evaluation coefficient of each scheduling object is obtained, where v1 is the preset error factor coefficient, v1 is greater than zero, and the scheduling recommendation evaluation coefficient Tg is compared and analyzed with the preset scheduling recommendation evaluation coefficient threshold stored in the internal entry:

[0032] If the ratio between the scheduling recommendation evaluation coefficient Tg and the preset scheduling recommendation evaluation coefficient threshold is less than 1, no signal is generated;

[0033] If the ratio between the scheduling recommendation evaluation coefficient Tg and the preset scheduling recommendation evaluation coefficient threshold is greater than or equal to 1, a feedback signal is generated, the scheduling object corresponding to the feedback signal is obtained, and it is set as the feedback object.

[0034] The beneficial effects of the present invention are as follows:

[0035] (1) The present invention analyzes from the perspective of the dynamic trend of dispatching efficiency so as to optimize and adjust the current vehicle dispatching decision according to the information feedback to improve the vehicle dispatching efficiency and dispatching rationality. On the premise that the dispatching efficiency is qualified, the vehicles in the response area are preliminarily screened and supervised for analysis, that is, the logistics vehicles are screened from the perspective of loading feasibility so that the selected objects can meet the preliminary loading requirements of cargo transportation, so as to reasonably dispatch the vehicles;

[0036] (2) The present invention analyzes two aspects, namely, the vehicle itself and the operator of the dispatch object by means of information feedback, namely, the vehicle performance supervision and evaluation analysis is performed on the operation risk data, so as to further perform screening analysis from the perspective of the dispatch vehicle itself, and the operator driving safety assessment analysis is performed on the driving management data, so as to understand the driving literacy and personal score of the operator of the dispatch object, so as to provide data support for subsequent vehicle dispatch recommendations, and the information fusion supervision recommendation analysis is performed by means of information fusion, so as to further understand the situation of vehicles in the dispatch object that meet the dispatch requirements, that is, on the premise that the feedback object is not working and the loading is satisfied, it is helpful to select the feedback object preferentially, so as to improve the rationality of vehicle dispatch, and at the same time more intuitively understand the transportation risk situation of the feedback object. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below in conjunction with the accompanying drawings;

[0038] Figure 1 It is a flowchart of the system of the present invention;

[0039] Figure 2 It is a partial reference diagram of the second embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Embodiment 1:

[0042] See also Figure 1 to Figure 2As shown, the present invention is a vehicle dispatch optimization system based on big data analysis, including a vehicle management platform, a screening and analysis module, a vehicle supervision unit, a personnel evaluation unit, a vehicle push unit and an early warning display unit, the vehicle management platform is connected to the screening and analysis module in a one-way communication, the screening and analysis module is connected to the vehicle supervision unit, the personnel evaluation unit and the early warning display unit in a one-way communication, the vehicle supervision unit and the personnel evaluation unit are connected to the vehicle push unit in a one-way communication, and the vehicle push unit is connected to the early warning display unit in a one-way communication;

[0043] When the vehicle management platform generates a transport management instruction, it sends the transport management instruction to the screening and analysis module. After receiving the transport management instruction, the screening and analysis module immediately collects the vehicle status information, which includes the loading volume and loading weight, and performs preliminary vehicle screening and supervision analysis on the status information, so as to reasonably dispatch the vehicle. The specific vehicle preliminary screening and supervision analysis process is as follows:

[0044] Set the monitoring cycle and set it as the time threshold, obtain the dispatch station of the vehicle within the time threshold, draw a circle with the dispatch station location as the center and r1 as the radius, mark the obtained circular area as the response area, obtain the total number of dispatches in the response area within the time threshold, and obtain the qualified number of dispatches in the response area within the time threshold. The qualified number of dispatches indicates the number of dispatches corresponding to the completion of the specified task volume within the specified time. Set the ratio of the qualified number of dispatches to the total number of dispatches as the dispatch decision management coefficient, obtain the dispatch decision management coefficients of the historical m response areas within the time threshold, m is a natural number greater than zero, establish a rectangular coordinate system with the number of time thresholds as the X-axis and the dispatch decision management coefficient as the Y-axis, draw the dispatch decision management coefficient curve by drawing points, set the change trend value of the dispatch decision management coefficient curve as the dispatch optimization evaluation coefficient, and compare and analyze the dispatch optimization evaluation coefficient with the preset dispatch optimization evaluation coefficient threshold stored in the internal entry:

[0045] If the ratio between the scheduling optimization evaluation coefficient and the preset scheduling optimization evaluation coefficient threshold is less than 1, a qualified signal is generated;

[0046] If the ratio between the dispatch optimization evaluation coefficient and the preset dispatch optimization evaluation coefficient threshold is greater than or equal to 1, an optimization signal is generated and sent to the warning display unit. After receiving the optimization signal, the warning display unit immediately displays the preset warning text corresponding to the optimization signal, so as to optimize and adjust the current vehicle dispatch decision according to the information feedback, so as to improve the vehicle dispatch efficiency and dispatch rationality;

[0047] When a qualified signal is generated, the vehicle in the response area is marked as a responding vehicle, and the actual status of the responding vehicle is discriminated and analyzed:

[0048] If the responding vehicle is in working state, it is determined that the responding vehicle does not meet the transportation requirements, and the corresponding responding vehicle is marked as an unselectable object;

[0049] If the responding vehicle is in a waiting state, the responding vehicle is determined to meet the transportation requirements, and the corresponding responding vehicle is marked as an optional object. The loading volume and loading weight of the optional object are obtained, and the product value obtained after data normalization of the loading volume and loading weight of the optional object is set as the loading assessment value, and the loading assessment value is compared and analyzed with the preset loading assessment value threshold value recorded and stored internally:

[0050] If the loading assessment value is less than or equal to the preset loading assessment value threshold, it is determined that the optional object does not meet the transportation requirements, and the corresponding optional object is marked as an excluded object;

[0051] If the loading evaluation value is greater than the preset loading evaluation value threshold, the selectable object is determined to meet the transportation requirements, the corresponding selectable object is marked as a dispatch object, and the dispatch object is sent to the vehicle supervision unit and the personnel evaluation unit, and the logistics vehicle statistics are performed with the dispatch site as the center, and then the logistics vehicles are screened from the perspective of loading feasibility, so that the selected object can meet the preliminary loading requirements of cargo transportation;

[0052] After receiving the dispatch object, the vehicle supervision unit immediately collects the operation risk data of the dispatched vehicle, including the vehicle assessment value and the transportation interference value, and performs vehicle performance supervision evaluation and analysis on the operation risk data, so as to further perform screening and analysis from the perspective of the dispatched vehicle itself, so as to provide data support for subsequent vehicle dispatch recommendations. The specific vehicle performance supervision evaluation and analysis process is as follows:

[0053] The vehicle assessment value of the dispatch object within the time threshold is obtained. The vehicle assessment value table is the product of the transportation risk value and the driving risk value of the dispatch object after data normalization. The transportation risk value represents the proportion of the number of delay alarms in the total number of alarms of the dispatch object. The driving risk value represents the ratio of the number of failures of the dispatch object to the average length of the failure interval after data normalization. It should be noted that the larger the value of the vehicle assessment value, the greater the risk of abnormal vehicle dispatch fit.

[0054] The transport interference value of the dispatch object within the time threshold is obtained. The transport interference value represents the product of the driving risk value and the vehicle status value of the dispatch object after data normalization. The driving risk value represents the ratio of the number of times that the operating temperature of the driving mechanism of the dispatch object exceeds the preset threshold and the duration corresponding to the preset threshold is greater than the number corresponding to the preset threshold and the total number of transportations. The vehicle status value represents the product of the mileage value and the tire wear value of the dispatch object after data normalization. It should be noted that the larger the value of the transport interference value, the greater the risk of abnormal vehicle dispatch fitting. The dispatch object is marked as g, where g is a natural number greater than zero. The vehicle assessment value and the transport interference value are marked as YPg and YSg respectively.

[0055] According to the formula The transport risk assessment coefficient of each dispatch object is obtained, where a1 and a2 are the preset proportional factor coefficients of the vehicle assessment value and the transport interference value respectively. The proportional factor coefficient is used to correct the deviation of various parameters in the formula calculation process, so as to make the calculation result more accurate. a3 is the preset correction factor coefficient. a1, a2 and a3 are all greater than zero. Pg is the transport risk assessment coefficient of each dispatch object. The transport risk assessment coefficient Pg is sent to the vehicle push unit.

[0056] Embodiment 2:

[0057] After receiving the dispatch object, the personnel assessment unit immediately collects the driving management data of the dispatch object operator, which includes driving risk value, number of violations and total number of transport orders, and conducts operator driving safety assessment analysis on the driving management data to understand the driving literacy and personal score of the dispatch object operator, so as to provide data support for subsequent vehicle dispatch recommendations. The specific operator driving safety assessment analysis process is as follows:

[0058] The driving risk value of the dispatch object operator within the time threshold is obtained, and the driving risk value represents the driving time between the operator and the dispatch object. At the same time, the total driving experience of the dispatch object operator within the time threshold is obtained, and the product value of the driving risk value of the dispatch object operator and the total driving experience after data normalization is obtained, and it is set as the driving fit. It should be noted that the larger the value of the driving fit, the smaller the driving management education demand of the dispatch object operator, the higher the comprehensive quality assessment of the operator, and the smaller the risk of transportation abnormality;

[0059] The number of violations of the dispatch object operator within the driving time is obtained, and the violation type in the violation record corresponding to the number of violations is obtained, and it is set as the driving deviation value. The violation record includes speeding, running a red light, etc. The driving deviation value is compared and analyzed with the stored preset driving deviation value threshold, and the part of the driving deviation value that is greater than the preset driving deviation value threshold is set as the behavior risk value. It should be noted that the larger the value of the behavior risk value, the greater the driving management education demand of the dispatch object operator and the greater the transportation abnormality risk;

[0060] The total number of transport orders of the dispatch object operator within the driving time is obtained, and the number of on-time completed orders of the dispatch object operator within the driving time is obtained, and the ratio between the on-time completed orders and the total transport orders is obtained, and the ratio between the on-time completed orders and the total transport orders is set as the order evaluation value. It should be noted that the order evaluation value is an influencing parameter reflecting the transport risk of the dispatch object operator. The larger the value of the order evaluation value, the smaller the transport abnormality risk of the operator. The driving fit, behavior risk value and order evaluation value are labeled JTg, XFg and DDg respectively;

[0061] According to the formula Obtain the driving impact assessment coefficient of each dispatch object operator, where f1, f2 and f3 are preset weight factor coefficients of driving fit, behavior risk value and order assessment value respectively, f1, f2 and f3 are all greater than zero, f4 is a preset fault tolerance factor coefficient, and its value is 1.182, Rg is the driving impact assessment coefficient of each dispatch object operator, and the driving impact assessment coefficient Rg is sent to the vehicle push unit;

[0062] After receiving the transport risk assessment coefficient Pg and the driving impact assessment coefficient Rg, the vehicle push unit immediately performs information fusion supervision recommendation analysis to further understand the situation of vehicles that meet the scheduling requirements among the scheduling objects, so as to reasonably select the scheduling objects and improve the rationality of vehicle scheduling. The specific information fusion supervision recommendation analysis process is as follows:

[0063] Obtain the transport risk assessment coefficient Pg and driving impact assessment coefficient Rg within the time threshold, and substitute the transport risk assessment coefficient Pg and driving impact assessment coefficient Rg into the formula The scheduling recommendation evaluation coefficient of each scheduling object is obtained, where v1 is the preset error factor coefficient, v1 is greater than zero, and the scheduling recommendation evaluation coefficient Tg is compared and analyzed with the preset scheduling recommendation evaluation coefficient threshold stored in the internal entry:

[0064] If the ratio between the scheduling recommendation evaluation coefficient Tg and the preset scheduling recommendation evaluation coefficient threshold is less than 1, no signal is generated;

[0065] If the ratio between the scheduling recommendation evaluation coefficient Tg and the preset scheduling recommendation evaluation coefficient threshold is greater than or equal to 1, a feedback signal is generated, the scheduling object corresponding to the feedback signal is obtained, and it is set as the feedback object, and the feedback object is sent to the early warning display unit. After receiving the feedback object, the early warning display unit sorts the scheduling recommendation evaluation coefficients corresponding to the feedback object in order from large to small, and marks the feedback objects corresponding to the sorted scheduling recommendation evaluation coefficients with numbers on the display panel, and the sorting sequence number corresponds to the number one by one, thereby facilitating the selection of the feedback object on the premise that the feedback object is not working and the loading is satisfied, so as to improve the rationality of vehicle scheduling, and at the same time more intuitively understand the transportation risk of the feedback object;

[0066] In summary, the present invention analyzes from the perspective of the dynamic trend of scheduling efficiency so as to optimize and adjust the current vehicle scheduling decision according to the information feedback to improve the vehicle scheduling efficiency and scheduling rationality. On the premise of qualified scheduling efficiency, the vehicles in the response area are preliminarily screened and supervised, that is, the logistics vehicles are screened from the perspective of loading feasibility, so that the selected objects can meet the preliminary loading requirements of cargo transportation, so as to reasonably schedule the vehicles. The two points of the vehicle itself and the operator of the scheduling object are analyzed by information feedback, that is, the vehicle performance supervision and evaluation analysis of the operation risk data is carried out, so as to further screen and analyze from the perspective of the scheduling vehicle itself, and the driving safety assessment analysis of the operator is carried out on the driving management data to understand the driving literacy and personal score of the operator of the scheduling object, so as to provide data support for subsequent vehicle scheduling recommendations, and the information fusion supervision recommendation analysis is carried out by information fusion to further understand the situation of the vehicles that meet the scheduling requirements in the scheduling object, that is, on the premise that the feedback object is not working and the loading is satisfied, it is helpful to select the feedback object preferentially to improve the rationality of vehicle scheduling, and at the same time, more intuitively understand the transportation risk of the feedback object.

[0067] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0068] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. A vehicle dispatch optimization system based on big data analysis, characterized in that: It includes vehicle management platform, screening and analysis module, vehicle supervision unit, personnel assessment unit, vehicle push unit and early warning display unit; When the vehicle management platform generates a transport management instruction, the transport management instruction is sent to the screening and analysis module. After receiving the transport management instruction, the screening and analysis module immediately collects the vehicle status information, which includes the loading volume and loading weight, and performs preliminary vehicle screening and supervision analysis on the status information, and sends the obtained optimization signal to the early warning display unit, and sends the obtained dispatch object to the vehicle supervision unit and the personnel evaluation unit; After receiving the dispatch object, the vehicle supervision unit immediately collects the operation risk data of the dispatched vehicle, including the vehicle assessment value and the transportation interference value, and performs vehicle performance supervision assessment analysis on the operation risk data, so as to further perform screening analysis from the perspective of the dispatched vehicle itself, and sends the obtained transportation risk assessment coefficient Pg to the vehicle push unit; After receiving the dispatch object, the personnel assessment unit immediately collects the driving management data of the dispatch object operator, which includes the driving risk value, the number of violations and the total number of transport orders, and performs an operator driving safety assessment analysis on the driving management data, and sends the obtained driving impact assessment coefficient Rg to the vehicle push unit; After receiving the transport risk assessment coefficient Pg and the driving impact assessment coefficient Rg, the vehicle push unit immediately performs information fusion supervision recommendation analysis and sends the obtained feedback object to the early warning display unit; The vehicle performance supervision evaluation and analysis process of the vehicle supervision unit is as follows: S1: Obtain the vehicle assessment value of the dispatch object within the time threshold. The vehicle assessment value table is the product of the transportation risk value and the driving risk value of the dispatch object after data normalization. The transportation risk value represents the proportion of the number of delay alarms in the total number of alarms of the dispatch object. The driving risk value represents the ratio of the number of failures of the dispatch object to the average length of the failure interval after data normalization. S2: Obtain the transport interference value of the dispatch object within the time threshold, the transport interference value represents the product of the drive risk value and the vehicle status value of the dispatch object after data normalization processing, the drive risk value represents the ratio of the number of times that the operating temperature of the drive mechanism of the dispatch object exceeds the preset threshold and the duration corresponding to the preset threshold is greater than the preset threshold and the total number of transportation times, the vehicle status value represents the product of the mileage value and the tire wear value of the dispatch object after data normalization processing, the dispatch object is marked as g, g is a natural number greater than zero, and the vehicle evaluation value and the transport interference value are marked as YPg and YSg respectively; S3: Obtain the transportation risk assessment coefficient Pg of each scheduling object according to the formula; The information fusion supervision recommendation analysis process of the vehicle push unit is as follows: Obtain the transport risk assessment coefficient Pg and driving impact assessment coefficient Rg within the time threshold, and substitute the transport risk assessment coefficient Pg and driving impact assessment coefficient Rg into the formula The scheduling recommendation evaluation coefficient of each scheduling object is obtained, where v1 is the preset error factor coefficient, v1 is greater than zero, and the scheduling recommendation evaluation coefficient Tg is compared and analyzed with the preset scheduling recommendation evaluation coefficient threshold stored in the internal entry: If the ratio between the scheduling recommendation evaluation coefficient Tg and the preset scheduling recommendation evaluation coefficient threshold is less than 1, no signal is generated; If the ratio between the scheduling recommendation evaluation coefficient Tg and the preset scheduling recommendation evaluation coefficient threshold is greater than or equal to 1, a feedback signal is generated, the scheduling object corresponding to the feedback signal is obtained, and it is set as the feedback object.

2. A vehicle dispatch optimization system based on big data analysis according to claim 1, characterized in that: The vehicle preliminary screening supervision analysis process of the screening analysis module is as follows: Set the monitoring cycle and set it as the time threshold, obtain the dispatch station of the vehicle within the time threshold, draw a circle with the dispatch station location as the center and r1 as the radius, mark the obtained circular area as the response area, obtain the total number of dispatches in the response area within the time threshold, and obtain the qualified number of dispatches in the response area within the time threshold. The qualified number of dispatches indicates the number of dispatches corresponding to the completion of the specified task volume within the specified time. Set the ratio of the qualified number of dispatches to the total number of dispatches as the dispatch decision management coefficient, obtain the dispatch decision management coefficients of the historical m response areas within the time threshold, m is a natural number greater than zero, establish a rectangular coordinate system with the number of time thresholds as the X-axis and the dispatch decision management coefficient as the Y-axis, draw the dispatch decision management coefficient curve by drawing points, set the change trend value of the dispatch decision management coefficient curve as the dispatch optimization evaluation coefficient, and compare and analyze the dispatch optimization evaluation coefficient with the preset dispatch optimization evaluation coefficient threshold stored in the internal entry: If the ratio between the scheduling optimization evaluation coefficient and the preset scheduling optimization evaluation coefficient threshold is less than 1, a qualified signal is generated; If the ratio between the scheduling optimization evaluation coefficient and the preset scheduling optimization evaluation coefficient threshold is greater than or equal to 1, an optimization signal is generated.

3. A vehicle dispatch optimization system based on big data analysis according to claim 2, characterized in that: When the screening analysis module generates a qualified signal: The vehicles in the response area are marked as response vehicles, and the actual status of the response vehicles is analyzed: If the responding vehicle is in working state, it is determined that the responding vehicle does not meet the transportation requirements, and the corresponding responding vehicle is marked as an unselectable object; If the responding vehicle is in a waiting state, the responding vehicle is determined to meet the transportation requirements, and the corresponding responding vehicle is marked as an optional object. The loading volume and loading weight of the optional object are obtained, and the product value obtained after data normalization of the loading volume and loading weight of the optional object is set as the loading assessment value, and the loading assessment value is compared and analyzed with the preset loading assessment value threshold value recorded and stored internally: If the loading assessment value is less than or equal to the preset loading assessment value threshold, it is determined that the optional object does not meet the transportation requirements, and the corresponding optional object is marked as an excluded object; If the loading assessment value is greater than the preset loading assessment value threshold, the optional object is determined to meet the transportation requirements and the corresponding optional object is marked as a scheduling object.

4. The vehicle dispatch optimization system based on big data analysis according to claim 1 is characterized in that: The operator driving safety assessment analysis process of the personnel assessment unit is as follows: SS1: Obtain the driving risk value of the dispatch object operator within the time threshold. The driving risk value represents the driving time between the operator and the dispatch object. At the same time, obtain the total driving years of the dispatch object operator within the time threshold. Obtain the product of the driving risk value of the dispatch object operator and the total driving years after data normalization, and set it as the driving fit; SS2: Obtain the number of violations of the dispatch object operator within the driving time, obtain the violation type in the violation record corresponding to the number of violations, and set it as the driving deviation value. The violation record includes speeding and running a red light. The driving deviation value is compared and analyzed with the stored preset driving deviation value threshold, and the part of the driving deviation value that is greater than the preset driving deviation value threshold is set as the behavior risk value; SS3: Obtain the total number of transport orders of the dispatch object operator within the driving time, and at the same time obtain the number of on-time completed orders of the dispatch object operator within the driving time, obtain the ratio between the number of on-time completed orders and the total number of transport orders, set the ratio between the number of on-time completed orders and the total number of transport orders as the order evaluation value, and label the driving fit, behavior risk value and order evaluation value as JTg, XFg and DDg respectively; SS4: Obtain the driving impact assessment coefficient Rg of each dispatch object operator according to the formula.

Images