JP2003030289A - Driving control system for vehicle, constituent article thereof, computer program and recording medium - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a driving control system for speedily performing a processing for analysis, prediction and control from various viewpoints on the driving of a vehicle. SOLUTION: The driving control system is provided with master DB30 accumulating data generated in association with the driving of the vehicle and a processor 20 processing data based on accumulated data. The processor 20 selectively performs a processing for automatically extracting a cause that a prescribed phenomenon (a traffic accident and the deterioration of a business condition) occurs from accumulated data, a processing for quantizing driving efficiency in an arbitrary period based on accumulated data, a processing for automatically extracting time limit data where the termination of a prescribed contract time limit determined at every vehicle or every driver is within the prescribed period in accumulated data, a processing for predicting the occurrence of the future phenomenon based on the content of present accumulated data and a processing for drawing up a future driving plan based on the analysis result of present accumulated data.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to, for example, a vehicle inspection and maintenance deadline, a vehicle inspection deadline, a driver's license expiration date,
It comprehensively accumulates insurance deadlines, the operating status of vehicles by each driver, the usage status of fuel, etc., the occurrence of accidents, and other various data generated in connection with the performance of other vehicle operating tasks. The present invention relates to a vehicle operation management system and its component parts that can arbitrarily extract data from a required viewpoint in consideration of relevance.

[0002]

BACKGROUND OF THE INVENTION In taxi companies, bus companies or transportation companies, a large number of drivers are involved in vehicle operations. In order to carry out operation work safely, properly and efficiently,
It is necessary for the operator of the business to properly manage the information related to vehicle operation. Conventionally, information related to vehicle operation work is managed separately and independently, and it is possible to collectively manage a plurality of information and manage one information to refer to another information mutually. It's not. Therefore, there were the following problems.

(1) When planning a policy for safe operation and efficient operation, in addition to static management information such as the number of vehicles and the number of drivers, the frequency of vehicle operation, vehicle dispatch or It is necessary to perform a comprehensive analysis in consideration of dynamic management information such as the operating status, the driving ability of the driver, the driving tendency (habit), and the daily driving status of the driver. It is necessary to consider the expiration date of the driver's license and the expiration date (expiration date) of various insurances. However, conventional systems for managing this type of information do not provide comprehensive analysis. For example, if the number of operating opportunities increases, it is natural that the chances of encountering a traffic accident are higher than when operating less frequently, and in order to objectively analyze a traffic accident,
Information such as driver's driving tendency, driving aptitude, and temporal density of operation is required, but when trying to analyze based on such circumstances, the operation status of multiple drivers on multiple dates,
It must be referenced while associating operation routes with each other. However, such a comprehensive analysis is practically impossible with a system based on individual information management.

(2) If the appearance analysis is based only on the count of the number of traffic accidents, the individual management method does not cause much problems. However, the appearance analysis alone is not sufficient for investigating the background factors that are directly linked to the formulation of traffic accident prevention measures, and is based on a comprehensive viewpoint that considers various dynamically changing information. Analysis becomes essential.
Conventionally, it has not been a comprehensive management form capable of responding to at least the investigation of such background factors.

(3) From the viewpoint of efficiently operating the operation, it is important not only to suppress the occurrence of traffic accidents but also to reduce the cost required for the operation. When a fare is generated as in a taxi company or a bus company, it may be necessary to formulate an appropriate fare and forecast sales based on the fare. However, the traditional operation management system
The information for conducting the management analysis as described above has not been accumulated, and the collected materials can only be analyzed manually. Therefore, depending on the analyst,
Since the results are different, it is difficult to obtain objective analysis results. An object of the present invention is to provide a vehicle operation management system and its component parts that can solve the above problems.

[0006]

An operation management system provided by the present invention is a database capable of sequentially accumulating a plurality of types of data generated in relation to the presence of a vehicle and / or a driver and the operation of the vehicle, and A system having a processing device that performs data processing based on accumulated data accumulated in a database. The processing device includes a first means for selecting data representing the cause of occurrence of a certain event from the accumulated data, a second means for quantifying operation efficiency in an arbitrary period based on the accumulated data, and the accumulation. A third means for selecting deadline data in which the end of a predetermined contract deadline defined for each vehicle or driver in the data is within a certain period, prediction of future event occurrence based on the contents of the current accumulated data At least one of the fourth means for performing the processing is configured to be selectively constructed. You may make it further construct | assemble the 5th means which produces a future operation plan based on the result of the total or the statistics of the said accumulation data of present conditions.

The first means specifies, for example, first data which has a significant characteristic based on a predetermined criterion with respect to the occurrence of a certain event among the accumulated data, and the first data and the first data. Means for identifying the external factor data and the essential factor data having the same characteristics, and the first data is corrected by the external factor data, and the characteristics of the first data after the correction and the essential factor data are corrected. A means for extracting the essential factor data from the accumulated data as data for indicating the cause of the occurrence of the event when the characteristics are the same or similar.

[0008] When the accumulated data accumulated in the database includes operation data representing a vehicle driving situation for each driver from a plurality of viewpoints, the first means is the operation data accumulated in the database. From
The operation data from one point of view, which has a significant feature in the occurrence of a traffic accident by a certain driver or an abnormal vehicle traveling situation corresponding thereto, is the first data, and from the other point of view, the first data. Among the operation data exhibiting the same or similar features as those described above, the operation data belonging to the external factor is specified as the external factor data, and the operation data belonging to the essential factor is specified as the essential factor data.

The accumulated data accumulated in the database includes operation data representing a vehicle driving situation for each driver from a plurality of viewpoints, and expense-related data representing various expenses required to operate a vehicle from a plurality of viewpoints. In the case of including the following, the first means has a significant feature in that the fluctuation of the cost exceeding the predetermined range has occurred from the cost related data accumulated in the database. Of the operation data and / or the expense-related data having the same or similar characteristics as the first data from the other viewpoints. Those that belong to a factor are specified as the external factor data, and those that belong to an essential factor are specified as the essential factor data.

The second means calculates, for example, at least one of an operating rate, an actual vehicle rate and a loading rate of each vehicle in a designated period based on the accumulated data accumulated in the database, and It is configured to quantify the operation efficiency based on the calculation result.

Preferably, the processing device is provided with countermeasure data holding means for holding countermeasure data created for each cause of an event predicted to occur in advance, and output means, and the first means is activated and executed. When the data indicating the cause is selected and the countermeasure data corresponding to the cause is read from the countermeasure data holding means based on the data, and the countermeasure data is output from the output means together with the data indicating the cause. To configure.

[0012] The third means is, for example, deadline data indicating deadlines for vehicle inspection, inspection and maintenance of all vehicles and car insurance, which are accumulated in the database, and expiration dates of driver's licenses for all drivers and The deadline data representing the deadline of enrollment insurance is used as a search target, and each deadline is configured to select and extract the deadline data within the designated period.

When the accumulated data accumulated in the database includes operation record data and expense related data for making it possible to predict the sales amount when the profit rate is input, the fourth means can: It is configured to perform at least future sales forecast processing based on the operation record data and the expense related data.

When the accumulated data accumulated in the database includes operation data representing a vehicle driving situation for each driver, the fifth means is based on the operation data for each driver, nth time (n
Is a natural number of 1 or more), means for extracting a combination in which the n-th rest time and the n + 1-th continuous operation time after the continuous operation time are continuous, and means for calculating an accident occurrence rate for each of the extracted combinations, Of the combinations of the (n + 1) th continuous operation time in which the shortest rest time in the extracted combination is a predetermined time or more and the calculated accident occurrence rate is less than or equal to the threshold value, the travel distance when the continuous operation time is the longest time And a means for creating the operation plan including an operation model in which the start time of the first operation time of the specified combination, the break time, and the data of the traveling distance in the subsequent continuous operation are arranged in this order. And is configured.

In another operation management system of the present invention, a database in which a plurality of types of data generated in relation to the existence of a vehicle and / or a driver and the operation of the vehicle are accumulated, and an external operation terminal are used to store the data. And a processing device that performs data processing based on the accumulated data accumulated in the database. The external operation terminal makes a data processing request to the processing device via a communication line. Most of the configuration of the processing device is the same as that described above, but it is configured to notify the processing device of the execution results of the first to fourth means via the communication line. , Different from the above.

Another operation management system of the present invention receives input of a plurality of types of data generated in relation to the presence of a vehicle and / or a driver and the operation of the vehicle through an interface screen prepared for each type. The data receiving means, a means for sorting the data received by the data receiving means into predetermined corresponding items and storing the data in a predetermined database, a processing request receiving means for receiving a data processing request, and a processing request receiving means. And a processing device that performs data processing based on the accumulated data accumulated in the database upon reception of a data processing request. The processing device is different from the one described above in that the processing device is configured to notify the request source of the processing request of the execution results by the first to fourth means.

The present invention also provides a computer having a data input / output mechanism, (1) inputting a plurality of types of data generated in relation to the presence of a vehicle and / or a driver and the operation of the vehicle. Data receiving means for receiving via the interface screen prepared for each, (2) means for sorting the received data into predetermined corresponding items and storing in a database, (3) processing request receiving means for receiving a data processing request, ( 4) A process of selecting data indicating the cause of occurrence of a certain event from the accumulated data accumulated in the database upon reception of the data processing request, and the operation efficiency in an arbitrary period based on the accumulated data Quantifying process, the end of the prescribed contract deadline set for each vehicle or driver in the accumulated data is a fixed period Process of selecting the time limit data to be,
Means for selectively executing at least one process of a process of predicting a future event occurrence based on the contents of the current accumulated data, and notifying an execution result obtained thereby to a request source of the process request There is provided a computer program for operating as an operation management system having, and a computer-readable recording medium in which the computer program is recorded.

[0018]

BEST MODE FOR CARRYING OUT THE INVENTION Below, the present invention is applied to an operation management system which is installed in a company having a plurality of branch offices and business offices involved in vehicle operation and which comprehensively manages operation-related information in the entire company. An embodiment when applied is described.

<System Configuration> An example of the configuration of the operation management system of this embodiment is shown in FIG. This operation management system is realized by a computer (server) capable of two-way communication with an operation terminal operated by a person in charge of each business office or a driver via the network NET. The operation terminal is, for example, a PDA (personal dat) having a browser function.
aassistant) T1, personal computer (hereinafter,
"PC") T2, mobile phone T3 with data processing function
(Via the base station SS) and functions as data input means and / or data browsing means to the operation management system.

The operation management system includes a processor unit (CPU) and a memory (internal semiconductor memory, hard disk, portable disk, etc.) as main hardware elements, and the CPU is recorded in a memory. And a processing device 20 constructed by reading and executing the computer program, and further includes a data input device 11, an optical character reader (OCR) 12,
The display device 13 and the data output device 14 are provided as peripheral devices. Input / output of data between the processing device 20 and each peripheral device is controlled by the input / output control unit 10. A master DB 30 (DB is an abbreviation for database; the same applies hereinafter) 30 that can exchange data with each other is connected to the processing device 20, and the master DB 30 is connected to the customer D.
B31, message DB 32, and form DB 33 are connected. The customer DB 31 stores data of customers of each branch office and sales office of the company introducing this system. The message DB 32 stores messages as an example of countermeasure data corresponding to the results analyzed and evaluated by the processing device 20. The form DB 33 stores format data of various forms such as reports.

As the data input device 11, in addition to a pointing device such as a keyboard and a mouse, a voice input device can be used together. Data output device 1
Although 4 is generally a printer, it may be an external disk device. It is also possible to use an audio output device together.

The processing device 20 includes an evaluation processing section 21 for performing a comprehensive evaluation processing relating to operation management, a data editing section 22 for editing the evaluation result data, a data output section 23 for outputting the edited data, and a network NET. Communication control unit 24 for controlling bidirectional communication performed by the master DB 3
It has a function of the DB management unit 25 that exchanges data with 0 or the like.

The evaluation processing section 21 includes a traffic accident analysis processing module 211 and a management analysis processing module 21 which will be described later.
2. Operating efficiency analysis processing module 213, fare setting
The sales forecast processing module 214, the individual management processing module 215, and the operation plan creation module 216 are selectively formed.

<DB Structure> First, the master DB 30 will be described. The structure of the master DB is, for example, as shown in FIG. 2, a driver-related master file 301 that stores data related to all drivers belonging to a company and a vehicle-related master file 301 that stores all data related to vehicles owned by the company. A master file 302, an expense-related master file 303 for accumulating all expense-related data generated in the execution of operation tasks, an operation-related master file 304 for accumulating daily operation status of vehicles, and data on traffic accidents during operation Accident related master file 305
And a car insurance related master file 306 for accumulating data such as car insurance type and expiration date for each driver or vehicle. Each master file 301
.. to 306, each data is classified and accumulated from a plurality of viewpoints. An example of the contents will be described below.

(Driver-related master file) The driver-related master file 301 contains personal-related items 301a for accumulating data belonging to the driver's individual, and employment-related items for accumulating driver employment-related data. Item 301b,
License for accumulating driver's license and other qualifications
Major items such as qualification-related items 301c are included. The driver's name, occupation, affiliation, affiliation, sex, blood type, current address (contact point), family composition, etc. are recorded in the person-related item 301a. The employment-related item 301b records work history, hiring date, driver appointment date, transfer date, social insurance-related information, and the like. In the license / qualification related item 301c, data (type, acquisition date, expiration date), driving experience, accident / violation history, driving suitability / education history, etc. related to the driving license are recorded.

(Vehicle-related master file) The vehicle-related master file 302 is prepared in a one-to-one correspondence with the vehicle used for operation. The vehicle registration number, the initial registration date, the vehicle inspection expiration date, and the like. Information such as vehicle type, location, car insurance-related information, and vehicle purchase price is recorded.

(Expense-related master file) In the expense-related master file 303, a vehicle expense item 303a relating to the expense of the vehicle itself, a vehicle repair item 303b relating to vehicle repair expense, and an operating expense item 3 relating to operating expenses.
03c, a personnel cost item 303d relating to personnel expenses, and another expense item 303e relating to other expenses. The vehicle expense item 303a includes automobile insurance premiums, automobile tax, weight tax, vehicle depreciation expenses, land lease fees for the vehicle occupancy portion,
Property taxes are recorded. The vehicle repair item 303b includes repair parts cost, tire tube cost, consumables cost, miscellaneous expenses,
Accident costs are recorded. In the operation expense item 303c, the shipper, the number of pieces of cargo, transportation income, fuel cost, travel expenses such as meals and accommodations, road expenses such as highway expenses, etc. are recorded. In the personnel cost item 303d, salaries and allowances (proportion pay), bonuses (proportion pay), salaries and allowances (fixed pay), bonuses (fixed pay), retirement allowances, statutory welfare costs, welfare costs, etc. are recorded. It Other expense items 303e include personnel expenses such as a dispatcher, electricity, water and gas charges, land rent and property tax, depreciation expenses such as buildings, personnel expenses of management department, business tax, hire car revenue, and deposits. Interest·
Insurance money, vehicle sales expenses (difference between book value and trade-in price), interest on borrowing, etc. are recorded.

(Operation-related master file) The operation-related master file 304 includes an operation date, a driver, a destination, a driving vehicle, a leaving time, a receiving time, an operating time (categorized into driving and rest, etc.), and a running meter value (starting). Time, working hours, actual vehicle, empty vehicle) are recorded.

(Accident-related master file) In the accident-related master file 305, the date and time of an accident, the type of accident, the location of the accident, the outline of the accident, etc. are recorded for each accident.

(Auto Insurance Related Master File) The automobile insurance related master file 306 includes a car name, specifications, insurance company, security number, insurance start date, insurance maturity date, insurance type, and insurance content (personal insurance, property insurance). , Passenger accident insurance, vehicle insurance), etc. are recorded. These master files 30
Items 1 to 306 are linked to each other through common items and data, and the record data can be referred to each other.

<Data Input> A procedure for accumulating various data in the master DB 30 having the above structure will be described with reference to FIGS. Here, examples of vehicle repair-related data, vehicle operation-related data, and accident-related data will be given. However,
These data are examples. Also, the data input is
It is assumed that the operator in charge through the operation terminal performs the operation through the input interface screen that is unique to each type of data. In the data storage in the master DB 30, the processing device 20 stores the data in the corresponding master files 301 to 301.
Allocate to the corresponding items of 306.

(Vehicle Repair Related Data Input Processing) When a vehicle is repaired, the processing device 20 stores the vehicle repair related data for the vehicle in the master DB 30. FIG. 3 is a processing procedure diagram in the processing device 20 in this case. First, input of repair-related data is selected based on an instruction from the operator (S301), and an input interface screen for this data is displayed on the display of the operation terminal.
After that, based on the repair slip etc. entered by the operator, while selecting the vehicle (vehicle number, management number, vehicle name, etc.),
Input of relevant repair-related data is accepted (S30
2). Similarly, a repair item (vehicle inspection maintenance, inspection maintenance, general repair, sheet metal repair, etc.) and a repair period are selected (S303, S).
304), and an input interface screen for that is displayed on the operation terminal to accept data input of repair content and repair amount (S305, S306). When there are a plurality of repair content data, the processes of steps S305 and S306 are repeated (S307: Y). If the repair content data is not plural, or if the repair content data has been input, it is determined whether or not there is another repair slip, that is, the repair slip. If there are a plurality of repair slips, the processing from step S302 is repeated (S30).
8: Y). When the input of all the data regarding the repair slip is received (S308: N), the processing device 20 stores these data in the master DB 3 through the DB management unit 25.
It is recorded (stored) in the expense-related master file 303 of 0 (S309).

(Input processing of vehicle operation related data) FIG. 4
[Fig. 6] is a processing procedure diagram for accumulating vehicle operation-related data on a daily basis. Here, it is assumed that the data necessary for creating the daily operation report for each driver is accumulated. The operator notifies the processing device 20 that the data regarding the daily operation report is to be input, whereby the processing device 20 selects the data input process related to the daily operation report (S401). The operator is the operation content (operation date, driver, operating vehicle, destination,
Select a course, etc., and select the departure data (starting meter value,
Departure time, work content, actual vehicle / empty vehicle, product name, quantity, etc.) and arrival data (arrival meter value, arrival time, work content, empty vehicle / actual vehicle, product name, quantity, etc.) are sequentially input. When a new customer is added or the content of the customer is changed, these data are also input. Processor 20
Accepts input data at any time (S402-S
404). If there are more input items, step S4
The processing of 03 and S404 is repeated (S405: Y).
When there are no more input items, the operator inputs data at the time of returning (returning meter value, returning time, evening loading, etc.), and the processing device 20 accepts it (S406). If you need to output (print / record) the daily operation report form, the form DB3
The corresponding form is read from 3 to create a daily operation report, which is output from the data output device 14 via the input / output control unit 10 (S407: Y, S408). If there is other data regarding the daily operation report to be input, step S40
The processing of 2 to S408 is repeated (S409: Y). When the data regarding all the daily operation reports are input, the processing device 20 records (stores) these data in the operation-related master file 304 of the master DB 30 or the customer DB 31 (S410).

(Input of Accident-Related Data) FIG. 5 is a procedure diagram for inputting accident-related data. When accident-related data occurs, the operator informs the processing device 20 to input such data, whereby the processing device 20 is informed.
Selects input processing of accident-related data (S501),
Accident-related data entered by the operator (driver, operating vehicle,
An input of an accident date / time, an accident type, an accident place, an accident summary, a repair amount, etc.) is accepted (S502). When it is necessary to output an accident report based on this accident-related data (S503: Y), the corresponding form is read from the form DB 32 to create an accident report, and this is input / output control unit 10 and data output device 14. (S504). When all the accident-related data are input, the processing device 20
Is the master DB3 corresponding to each of these data.
It is recorded (stored) in the driver-related master file 301 of 0, the expense-related master file 303, or the accident-related master file 305 (S505).

<Various evaluation / analysis processing> Next, master DB
Various evaluations using the accumulated data accumulated in 30
The contents of processing when performing analysis will be described. Evaluation and analysis
The evaluation processing unit 21 of the processing device 20 performs this. In this example, in the evaluation processing unit 21, the traffic accident analysis processing module 211, the management analysis processing module 212, the operation efficiency analysis processing module 213, the fare setting / sales forecast processing module 21.
4. An example of selectively activating and executing the driver individual management processing module 215 and the operation plan creation module 216 will be given.

(Traffic accident analysis: FIGS. 6 to 14) FIG.
It is a flowchart of a traffic accident analysis process. When the operator selects the traffic accident analysis processing through a predetermined evaluation / analysis instruction screen, the traffic accident analysis processing module 211, which is an example of the first means of the present invention, is activated to start the traffic accident analysis processing. (S601). In the traffic accident analysis process, first,
The operator is made to specify the analysis target period (S602). The operator designates the analysis target period such as the past one year, six years or the whole period, or from one specific day to another specific day or one year, three years. The traffic accident analysis processing module 211 receives the specified analysis target period as a search condition. After the acceptance of the search conditions, the traffic accident analysis processing module 211 accesses the accident-related master file 305 and automatically extracts the accident-related data for the specified period from the master file 305 (S603). When it becomes necessary to extract data from a file other than the accident-related master file 305, the linked file is accessed in a timely manner and the necessary data is automatically extracted therefrom. Then, from the extracted accident-related data for the specified period, a factor (hereinafter, referred to as a “cut end”) in which a significant feature is recognized regarding the occurrence of the accident is specified, and the related data is automatically extracted (S604). This is to analyze the characteristics of the accident occurrence situation for each cut.

First, in order to extract a cut exhibiting a significant feature regarding the occurrence of a traffic accident, a data classification item as a temporary cut is set. As data classification items that are temporary cuts, for example, by month, by day of week,
The number of accidents is summarized by time of day and age.
Such a temporary cut is set in advance in the operation diagnosis program, but may be arbitrarily set by the operator at the time of analysis, for example. Figure 7 is by month, Figure 8 is by day of the week,
FIG. 9 shows an example of the number of accidents by age. Of these, the number of accidents by day of the week is 100 for the month and 4 for the fire.
There are 0, water: 15, trees: 24, gold: 26, soil: 6, and days: 0, and there is a large difference in distribution. On the other hand, the distribution of the number of accidents by month and age does not differ as much as by day of the week. Therefore, the number of accidents by month and age exceeds a certain standard (for example, there is a difference of more than 20% from the number of other accidents ...), and it is unsuitable as a cut-out exhibiting significant characteristics. As a result, these data are removed and the day of the week is specified as a cut having a significant feature. The data regarding this day of the week becomes the first data.

When it is found that the day of the week has a significant characteristic, the data classified into the item which is the accident frequent occurrence factor A (outer factor data) is automatically extracted based on this (S605). Target items include the number of operating vehicles, operating time (per vehicle), mileage (per vehicle), and the like. Although these items are also set in advance in the operation diagnosis program, the operator may arbitrarily set them.

FIG. 10 shows an example of data (numerical value) of the number of operating vehicles for each day of the week, and FIG. 11 shows an example of data (numerical value) of the traveling distance for each day of the week. Each numerical value is an average value for each day of the week in a certain period. Since the operating time is not longer on Monday than on other days, it cannot be a significant feature and is omitted from the accident frequent occurrence factor A. Since the traveling distance on Monday is not longer than on other days (see FIG. 11), it is similarly excluded from the accident frequent occurrence factor A. On the other hand, the number of operating vehicles is, as shown in FIG. 10, month: 20, fire: 10, water: 15, wood: 1.
It is 2, Friday: 13, Saturday: 3, and Sunday: 0, and since there are many Mondays, it has significant characteristics. Therefore, as the accident frequent occurrence factor A, data of "the number of operating vehicles" is extracted.

Further, the data classified into the item of the accident frequent occurrence factor B (essential factor data) is automatically extracted based on the section of the day of the week (S606). Target items include the age structure and risk sensitivity of the driver, the number of visits per unit time (per vehicle), and the time lag from the operation plan (per vehicle: minutes). Although these items are set in advance in the operation diagnosis program, the operator may arbitrarily set them. FIG. 12 shows an example of data (numerical value) of the number of visits by day of the week per unit time, and FIG. 13 shows an example of data (numerical value) of time difference from the operation plan by day of the week. Each numerical value is an average value for each day of the week within a certain period. Although not shown, the age structure is not unique on Monday as compared with other days of the week, and the risk sensitivity is not unique on Monday as compared to other days. Be discarded. On the other hand, the number of visits is, as shown in FIG. 12, month: 25, Tuesday: 20,
Water: 21, Thursday: 14, Friday: 10, Saturday: 10 and Sunday: 0. Since Monday is relatively large, it has a significant characteristic. As for the time lag (minutes) from the operation plan, as shown in FIG. 13, Mon: 30, Tue: 10, Wed: 0, Thu: 15, Fri: 1.
2. Saturday: 0, Sunday: 0, and there are many Mondays, so it has significant characteristics. Therefore, as the accident frequent cause B,
Data that represents the "number of visits" and "time lag between operation plans" is automatically extracted.

In step S607, the above-mentioned "number of accidents by day of week" is corrected in consideration of the data of factor A of frequent accidents ("number of vehicles operating by day of week"), and the corrected "number of accidents by day of week" is corrected. Data and accident frequent occurrence factor B (“Number of visits”)
And data representing "time difference with operation plan"). Specifically, each day value of the number of accidents by day of the week shown in FIG. 8 is divided by the value of the same day of the number of operating vehicles shown in FIG. As a result, as shown in FIG. 14, the number of accidents per operating vehicle for each day of the week is as follows: Month: 5, Tuesday: 4,
Water: 1, wood: 2, gold: 2, soil: 2, sun: 0 is calculated. The calculation result (FIG. 14) is compared with the accident frequent occurrence factor B, that is, the number of visits by day of the week (FIG. 12) and the time lag with the operation plan (FIG. 13).

In step S608, it is determined from the above comparison result whether significant features remain. If significant features remain, steps S609 to S611
After passing through, if not remaining, steps S609 to S6
The processing is ended without passing through 11. In this example, the corrected "number of accidents by day of the week" shows a difference of more than 20% in comparison with Tuesday and more than that in comparison with other days of the week. This feature is essential factor data "number of visits". Since the characteristics are the same as the data characteristics of “and time lag from operation plan”, it is determined that significant characteristics remain. In this case, in step S609, the data representing the accident frequent occurrence factor B is estimated to be the data representing the true cause of the traffic accident.

The traffic accident analysis processing module 211 extracts countermeasure data (message) corresponding to this cause from the message DB 32, and displays this message on the operation terminal together with the above cause. The display shows, for example, "Cause: There are many accidents on Monday, it is presumed that the dispatching plan is unreasonable, and the driver's car is in a hurry and impatience. Countermeasures: review of operation plan, leveling It should be incarnated. " In step S611, the above display contents are output as a report, if necessary. This is expected to facilitate accident prevention planning. Note that the data classification items of each of the above cuts are examples.

(Business Analysis: FIGS. 15 to 17) Next, a method of business analysis will be described. As shown in FIG. 15, when an instruction for management analysis processing is received from the operator,
The business analysis processing module 212, which is an example of the first means of the present invention, is activated and the business analysis processing is started (S15).
01). In the business analysis processing, first, the operator is made to specify the business analysis period (comparison between last year and this year) (S
1502). When the period designation from the operator is accepted, the company-wide break-even point ratio is calculated, and the ratio is evaluated based on a predetermined evaluation standard (S1503). The break-even point ratio is automatically calculated by the following procedure.

Sales-Variable costs (= operating income + fixed costs)
The marginal profit is calculated from the formula. Marginal profit-Operating profit is calculated from the fixed cost formula. The change ratio is calculated from the calculation formula of variable cost / sales amount × 100 (%). From the formula of marginal profit / sales (= 1-variation ratio),
Find the marginal profit ratio. Fixed cost / (1-variable ratio) = fixed cost / marginal profit ratio The target breakeven point sales amount is calculated. Calculate the break-even point ratio from the formula of target break-even point sales / actual sales.

The evaluation result is classified into any of red / dangerous area / somewhat anxious / healthy area according to a predetermined quantification standard.
Here, it is assumed that it falls outside the healthy range and is classified as a category of anxiety. In this case, the management analysis module 212 accesses the expense-related master file 303 and extracts expense-related data necessary to identify the cause for having gone out of the sound range (S1504). An example of expense-related data is shown in FIGS. 16 and 17. The expense-related data in the case of FIG. 16 is calculated up to the operating profit in the case of the company-wide total, the branch or the sales office, and is calculated to the direct profit in the case of individual, vehicle type, and shipper. A list is also created for individual, vehicle, business, and shipper. For each individual, the cost per working days and mileage (by live-action / empty car) is also calculated. In the example of FIG. 17, the variable cost and the fixed cost depending on the size of the transportation income are calculated.

From such expense-related data, first, the cuts that show significant features for being out of the sound range are extracted, and the above-mentioned break-even point ratio and the features of expenses and costs are analyzed for each cut ( S1505). As a cut,
There are data classification items such as cost increase / decrease by branch office, shipper, and operation mode. Of these, the data classification items that have increased or decreased over a predetermined threshold compared to the previous year are adopted, and the rest are discarded. For example, if it is found that the evaluation result of a certain branch office (referred to as “F branch office”) has deteriorated by 14.8% from the previous year, it will increase from the previous year in the expense items for the F branch office. The expense items that are present (referred to as personnel expenses) are identified, and the expense items that are relatively increasing among the personnel expenses are identified. Here, it is assumed that the salary (overtime pay) is higher than the salary (fixed salary) or the bonus. In this way, when it is found that the cause of the F branch's evaluation being deteriorated by 14.8% from the previous year is the salary (overtime pay), the management analysis module 212 relates to the salary (overtime pay). The data necessary to analyze the items, for example, the total working hours by day of the week of F branch and the increase / decrease in total working hours by day of the year compared to the previous year are extracted from the operation-related master file 304 (step S
1506). Then, the cuts exhibiting significant features are extracted, and the features of the total working hours and the breakdown thereof are analyzed for each cut. According to this analysis, for example, the total working hours of the F branch are mostly Monday. The breakdown of the total working hours on Monday shows that loading / unloading hours, inspection / maintenance, etc. are not so different from the previous year, but total running hours are large. The ratio of 130%) is extracted.

Then, the break-even point ratio / deterioration factor A (external factor data) is extracted (S1508). Specifically, a factor that coincides with the characteristic (Monday data) extracted in step S1507, that is, a factor that the total traveling distance on Monday is long (105% compared to the previous year) is extracted. Then, the feature that the total traveling time is 130% compared to the previous year, which is extracted in step S1507, is corrected to that for the total traveling distance (S1509). Specifically, the total traveling time fluctuation ratio per total traveling distance on Monday (= 130
% / 105% = 1.24) is calculated.

If significant features remain after modification (S
1510: Y), the break-even point ratio / deterioration factor B (essential factor data) is extracted (S1511). The criterion of whether or not there is a significant feature here is the management analysis module 21 in advance.
It is built in 2 as logic. For example, if the above-mentioned total running time variation ratio is assumed to be 1.12 or more as a significant feature, in the case of the above example, it is determined that a significant feature remains, and for example, "of the total running time The data that shows that there is a lot of fluctuation "is the break-even ratio / deterioration factor B
Is extracted as.

This data is analyzed to identify the true cause, and the corresponding countermeasure data is displayed in the message DB.
It is searched from 32 (S1512), and these are displayed on the operation terminal (S1513). In the case of the above example, the real cause is "being caught in a traffic jam due to the delay of the start time (departure time)", and the measure data is "after the weekend getting off and before the departure work on Monday. Is processed. ”Is displayed. If necessary, a report showing the above analysis result is output, and the process relating to management analysis ends (S1514). Note that step S1510
If no significant feature remains, it is determined that there is no special essential factor, and the process related to management analysis ends (S
1510: N).

By the above-described management analysis processing, the analysis result becomes objective. Therefore, it can be expected that the management analysis processing, which has been difficult due to its complexity in the past, can be standardized and the system construction becomes easy.

(Driving Efficiency Analysis Processing: FIG. 18) Next, processing for analyzing the driving efficiency of the vehicle will be described. Figure 1
8 shows the procedure of the processing. That is, when the instruction of the driving efficiency analysis processing is received from the operator, the driving efficiency analysis processing module 213 which is an example of the second means of the present invention is activated and the driving efficiency analysis processing is started (S180).
1).

The operation efficiency analysis processing module 213 has a period (1 month, half year, 1
Year). If there is a period setting (S180
2), the operation efficiency analysis processing module 213 is the object of analysis (company-wide integration, business establishment, individual, vehicle, shipper, etc.)
Select. After the target is selected (S1803), the operation-related master file 304 is accessed to automatically extract necessary data and a predetermined calculation is performed (S1804). For example,
Occupancy rate (occupancy rate (%) = working days / required days x 10)
0), actual vehicle rate (actual vehicle rate (%) = actual vehicle travel distance / mileage × 100), loading rate (loading rate = actual load amount / maximum load amount ×)
100) etc.

After that, the analysis result is displayed on the operation terminal via the output control unit 13 (S1805). If the form is required (S1806: Yes), the form is printed (S1807). In this way, the operation efficiency of the vehicle is objectively analyzed, and the operation rate, the actual vehicle rate, the loading rate, etc. are calculated and used for the planning of the improvement of the operation efficiency.

(Vehicle management processing: FIG. 24) Processing for vehicle management, for example, processing for vehicle inspection and vehicle inspection and maintenance management will be described. The processing procedure for vehicle inspection and vehicle inspection and maintenance is as shown in FIG. That is, when the vehicle management processing instruction is received from the operator, the individual management processing module 215 that is an example of the third means of the present invention is activated, and the vehicle management processing is started (S2401).

First, it is determined whether the operator's instruction is about vehicle inspection or regular inspection of the vehicle, and one of them is selected (S2402). If the instruction is for vehicle inspection (S2402: vehicle inspection), the management target month is selected (S2411). If selected, the vehicle inspection data is extracted from the vehicle-related master file 302 (S2412), and it is searched whether or not there is a vehicle having deadline data for which the vehicle inspection expiration date is the target month and the next month (S2413). When the instruction is for inspection and maintenance (S2402: inspection), the same processing is performed (S2421 to S2423). Although the list of target vehicles is output (S2414, S2424), in the case of vehicle inspection, the current month and the next month are output (S241).
4). As a result, vehicle inspections and inspection / maintenance times that come in a certain period can be searched easily and quickly at any time, and management can be performed without missing the time.

(Driving License Management Process, Car Insurance Management Process) Similar to the vehicle management process, the individual management processing module 215 can also manage the driver's license and the car insurance process. That is, the driver's license master expiration date is retrieved from the driver-related master file 301, or the automobile insurance-related master file 306 is retrieved to extract expiration date data within the set period, The result is output together with the personal information of the corresponding driver.

(Transportation cost analysis processing) The processing of the transportation cost analysis will be described. This process also includes the process of setting a fare received from a customer when operating a vehicle and inputting a profit rate to forecast sales. FIG. 19 is a processing procedure diagram of this transportation cost analysis. When the instruction for the transportation cost analysis process is received from the operator, the fare setting / sales forecast processing module 214 as an example of the fourth means of the present invention is activated, and the transportation cost analysis process is started (S1901).

Fare setting / sales forecast processing module 214
Sets the period (one month, half a year, one year) necessary for analysis, which is input by the operator. After setting the period (S190
2) Select the analysis target (company-wide integration, business establishment, individual, vehicle, shipper, etc.). When the target is selected (S1
903), fare setting / sales forecast processing module 214
Accesses the accounting-related master file 303, automatically extracts all the data necessary for analysis, and performs a predetermined calculation (S1904). Here, the fare cost is calculated for each cargo (by distance and weight) based on past operation results. The variable cost per package is calculated from the record information of the expense-related master file 303, and the fixed cost is calculated by subtracting the variable cost from the fare cost. The result is displayed on the operation terminal in a table format as shown in FIG. 20, for example (S1
905). If a form is required (S1906: Ye
s) and print it (S1907).

When the fare setting for securing profit is made (S1908: Yes), first, the operator is made to input an arbitrary profit rate for the data of the chart shown in FIG. 21 (S1909). Profit margins can be entered by distance and weight. Then, using the fare cost as shown in FIG. 20 and the profit rate data as shown in FIG. 21, the formula of {freight rate = freight cost × (1 + profit rate)} is calculated (S191).
0). The result is displayed on the operation terminal in a table format as shown in FIG. 22, for example. If a form is required (S191
1: Y), and let the operating terminal print the form (S191)
2).

Furthermore, if there is an instruction to predict sales (S1913: Y), the fare shown in FIG. 22 is multiplied by the actual number to calculate the sales prediction (S1914), and the result is calculated, for example. It is displayed on the operation terminal in a table format as shown in FIG. 23 (S1915). If a form is required (S191
6: Y), the form is printed (S1917).

In this way, the fare cost is analyzed, and the sales forecast is calculated from the past operation record. This makes it possible to set an appropriate fare and to calculate an objective sales forecast.

(Operation Planning Process) Next, the operation plan of the vehicle (plan of continuous operation time, rest time, etc.) will be described. For example, in the transportation industry, a safe operation plan is generally made based on the experience of the dispatcher in consideration of the departure time, arrival time, continuous operation time, break time or operation route. is there. Therefore, depending on the dispatcher, the operation plan may be unreasonable or wasteful, which may cause a traffic accident, for example. At present, it is difficult to formulate a safe, unreasonable, lean and objectively appropriate operation plan. It can also take a lot of time to make a plan.

In the present embodiment, a proper operation plan can be quickly and objectively obtained, and a vehicle operation plan preparation process in which the operation status of each operation route is reflected in detail is realized. FIG. 25 shows a procedure of operation management processing. When the instruction of the operation management process is received from the operator, the operation plan creation module 216, which is an example of the fifth means of the present invention, is activated to start the operation management process (S2501).

The operation plan creation module 216 allows the operator to input the operation distance from the departure point to the arrival point, which is the object of the operation plan, through the operation terminal (S2502). When this is accepted, next, search conditions (departure time, driver's age, other factors to be considered in the operation plan, etc.) are input (S2503). Further, the period (for example, the past half year, the past year, etc.) of the operation data used for the safety operation planning is designated (S2504), and when the designation is accepted, the operation-related master file 304, the accident-related master file 305, necessary. Depending on the driver related master file 301
Etc. to obtain all the operation data necessary for creating an operation plan, in steps S2503 and S25.
Search based on the period and search conditions entered in 05 (S
2505, S2506).

The operation data referred to here is an array on the time axis of continuous operation time from the starting point to the destination and intervening break times between each continuous operation. It is data including the mileage. FIG. 26 is a diagram showing an example thereof. Regarding the operation data that an accident occurred on the way, a record at which point in the operation the accident occurred, that is, after the departure or after the number of breaks, how many hours continuous driving / km traveled It also includes records such as records. It should be noted that all the traveling distances from the departure place to the arrival place and the traveling distances from the departure place to the accident occurrence place are obtained by summing the traveling distances at each continuous operation in the meantime.

Next, for all the operation data retrieved in step S2506, the accident occurrence rate is calculated for each continuous operation time from the beginning (departure point) (S2507).
As a result, the longest time of the first continuous operation time when the accident occurrence rate becomes equal to or less than the preset value (for example, 10%) is recorded, and the traveling distance during the continuous operation is recorded. (S2508). Next, the operation data having the first continuous operation time recorded in S2508 is searched (S2509). Then, a combination of the first break time and the subsequent continuous operation time is extracted from the operation data, and the accident rate for each combination is calculated (S2).
510). In this combination, the shortest rest time that is equal to or longer than a predetermined value (for example, 0.5 hours) and step S
The combination with the longest time of continuous operation times when the accident occurrence rate calculated in 2510 is appropriately set value (for example, 10%) or less is recorded, and the traveling distance during the continuous operation is recorded ( S2511). Step S251
The processing of 0 and S2511 is repeated until the cumulative traveling distance reaches the operating distance (S2512). All the recorded continuous operation time, rest time and mileage are arranged to obtain a safe operation model (S2513), and this is displayed on the operation terminal as a safe operation plan (S2514).

According to such an operation management process, an appropriate operation plan with a low accident rate can be easily and quickly created, and the result can be objective. In particular, if processing is performed using the operation data for each individual dispatch management base, it becomes possible to make a vehicle safety operation plan that reflects the operation status of each operation route in detail.

As described above, in the present embodiment, the master DB 30 in which a plurality of types of data generated in relation to the existence of the vehicle and / or the driver and the operation of the vehicle by the driver are accumulated, and the master DB 30 The operation management system is configured by including the processing device 20 that performs data processing based on the accumulated data accumulated in the master DB 30, and the processing device 20 includes:
A traffic accident analysis processing module 211 or a business analysis processing module 212 that automatically extracts the cause of occurrence of a certain event (traffic accident, deterioration of management situation, etc.) from the accumulated data,
An operational efficiency analysis processing module 213 for quantifying the operational efficiency in an arbitrary period based on the accumulated data, a deadline in which the end of a predetermined contract deadline defined for each vehicle or driver in the accumulated data is within a certain period. An individual management processing module 215 that automatically extracts data, a fare setting / sales forecast processing module 214 that predicts future event occurrence based on the contents of the current accumulated data, and a future forecast based on the analysis result of the current accumulated data. Since the operation plan creation module 216 for creating the operation plan is selectively activated and executed, an objective analysis of various data generated during the execution of the operation work from a plurality of viewpoints, for example, a certain event. It is also possible to understand the background factors that caused the accident, and also the time of vehicle inspection and inspection and maintenance, the expiration date of the driver's license and various insurance. It is possible to manage also to date, etc., that it is possible to overall management of the operating business,
The effect is obtained.

Since each employee of the company automatically inputs and generates data that is sequentially generated when operating the business through a predetermined input interface screen, the data is automatically classified and stored in the master DB 30. Is also negligible, and the continuity of operation is guaranteed.

In the present embodiment, an example in which the operator makes a data processing request through the operation terminal and the network NET has been described, but a mode in which a data processing request is made directly to the processing device 20 is also possible according to the present invention. Is. Further, in the case where each employee writes the operation record and the like in the form of “daily report” at the time of various data input processing, it may be dealt with by digitizing it in the OCR 12.

[0072]

As described above, according to the present invention,
It is possible to comprehensively manage various information related to a large number of vehicles, and it is possible to easily and quickly find necessary information. Further, since it is possible to refer to various kinds of information, it becomes possible to easily and quickly perform processing for analysis, prediction, and management from various viewpoints related to vehicle operation.

[Brief description of drawings]

FIG. 1 is a configuration diagram of an operation management system to which the present invention is applied.

FIG. 2 is a structural explanatory diagram of a master DB (database).

FIG. 3 is a flowchart showing an input processing procedure of vehicle repair-related data.

FIG. 4 is a flowchart showing an input processing procedure of vehicle operation-related data.

FIG. 5 is a procedure diagram of input processing of vehicle accident-related data.

FIG. 6 is a processing procedure diagram of traffic accident analysis.

FIG. 7 is a diagram showing an example of the number of accidents extracted when the monthly cut is used in the processing of the traffic accident analysis.

FIG. 8 is a diagram showing an example of the number of accidents extracted when a day of the week is cut.

FIG. 9 is a diagram showing an example of the number of accidents extracted when the age is cut.

FIG. 10 is a diagram showing an example of the number of operating vehicles by day of the week as an extraction target item of an accident frequent occurrence factor (external factor) in the processing of the traffic accident analysis shown in FIG. 6.

FIG. 11 is a diagram showing an example of vehicle travel distance for each day of the week.

FIG. 12 is a diagram showing an example of the number of visits per unit time by day of the week as an extraction target item of an accident frequent cause (essential factor) in the processing of the traffic accident analysis shown in FIG. 6.

FIG. 13 is a diagram showing an example of a time lag with a vehicle allocation plan for each day of the week.

FIG. 14 is a diagram showing an example of the number of accidents per operating vehicle by day of the week compared with the number of visits per unit time by day of the week in the processing of the traffic accident analysis shown in FIG. 6;

FIG. 15 is a processing procedure diagram of management analysis.

FIG. 16 is a chart showing an example of processing results displayed during business analysis.

FIG. 17 is a graph showing an example of the calculation result of the break-even point.

FIG. 18 is a processing procedure diagram of operation efficiency analysis.

FIG. 19 is a diagram showing an example of processing results displayed after the transportation cost analysis processing.

FIG. 20 is a diagram showing a display example of a transportation cost result in the transportation cost analysis processing.

FIG. 21 is a diagram showing a display example of a profit rate in the transportation cost analysis processing.

FIG. 22 is a diagram showing a display example of fares in the transportation cost analysis processing.

FIG. 23 is a diagram showing a sales forecast result in the transportation cost analysis processing.

FIG. 24 is a processing procedure diagram of vehicle inspection and inspection maintenance.

FIG. 25 is a processing procedure diagram of operation management.

FIG. 26 is a diagram showing an example of past operation data.

[Explanation of symbols]

10 I / O controller 11 Data input device 12 OCR 13 Display device (data output device) 14 Printer (data output device) 20 processing equipment 21 Evaluation processing unit 211 Traffic accident analysis processing module 212 Management Analysis Processing Module 213 Operation efficiency analysis processing module 214 Fare setting / sales forecast processing module 215 Individual management processing module 216 Operation plan creation processing module 22 Data editor 23 Data output section 24 Communication control unit 25 DB Management Department 30 Master DB 31 Customer DB 32 Message DB 33 Form DB T1 to T3 operating terminal SS base station NET network

Claims (14)

[Claims] 1. A database capable of sequentially accumulating a plurality of types of data generated in relation to the presence of a vehicle and / or a driver and operation of the vehicle, and data processing based on the accumulated data accumulated in this database. And a processing device, wherein the processing device also selects, from the accumulated data, data indicating the cause of occurrence of a certain event from the accumulated data, and data that selects operation efficiency from the accumulated data during a predetermined period. The second means for quantifying the term and the third means for selecting the term data in which the end of the predetermined contract term defined for each vehicle or driver in the accumulated data is within a certain period, A vehicle operation management system configured to selectively construct at least one means of a fourth means for predicting the occurrence of future events based on the content of the accumulated data. Stem. 2. The processing device is configured to construct a fifth means for creating a future operation plan based on a result of aggregation or statistics of the current accumulated data. Operation management system. 3. The first means identifies the first data having a significant characteristic based on a predetermined criterion with respect to the occurrence of a certain event in the accumulated data, and the first data is stored as the first data. Means for identifying the external factor data and the essential factor data exhibiting the same characteristics, and the first data is corrected by the external factor data, and the characteristics of the first data after the correction and the essential factor data are corrected. Means for comparing the characteristics with each other and specifying the essential factor data as data for indicating the cause of the occurrence of the event when the two are the same or similar. The operation management system according to item 1. 4. The accumulated data accumulated in the database includes operation data representing a vehicle driving situation for each driver from a plurality of viewpoints, and the first means accumulates in the database. Among the operation data that is present, the operation data from one viewpoint, which has a significant characteristic for the occurrence of a traffic accident by a driver or an abnormal vehicle traveling situation corresponding to the driver, is the first data, and Of the operation data having the same or similar characteristics as the first data from the viewpoint, those that belong to the external factor are identified as the external factor data, and those that belong to the essential factor are identified as the essential factor data. The operation management system according to claim 3, wherein the operation management system is configured to operate. 5. The operation data in which the accumulated data accumulated in the database expresses a vehicle driving situation for each driver from a plurality of viewpoints, and the expense-related information in which various expenses required for operating a vehicle are expressed from a plurality of viewpoints. The first means has a significant characteristic that an expense fluctuation exceeding a predetermined range has occurred from the expense-related data accumulated in the database. Of the operation data and / or the expense-related data having the same or similar characteristics as the first data in another aspect, with the operation data and / or the expense-related data in one aspect as the first data The operation management system according to claim 3, wherein the operation management system is configured to identify those belonging to an external factor as the external factor data and those belonging to an essential factor as the essential factor data. . 6. The second means calculates at least one of an operating rate, an actual vehicle rate, and a loading rate of each vehicle in a designated period based on the accumulated data accumulated in the database. The operation management system according to claim 1, wherein the operation management system is configured to quantify the operation efficiency based on a calculation result. 7. The processing device comprises: countermeasure data holding means for holding countermeasure data created for each cause of an event whose occurrence is predicted in advance; and output means, and the first means is activated and executed. When the data representing the cause is selected, the countermeasure data corresponding to the cause is read from the countermeasure data holding means based on the data, and the countermeasure data is output from the output means together with the data representing the cause. The operation management system according to claim 1, which is configured. 8. The third means includes deadline data representing deadlines for vehicle inspection and inspection and maintenance of all vehicles stored in the database, expiration dates of driver's licenses of all drivers, and deadlines of enrollment insurance. 2. The operation management system according to claim 1, wherein the deadline data representing the time limit is set as a search target, and each deadline is configured to select and extract the deadline data within the designated period. 9. The accumulated data stored in the database includes operation result data and expense related data for predicting sales when a profit rate is input, and the fourth means. The operation management system according to claim 1, characterized in that the prediction processing of at least future sales is performed based on the operation record data and the expense related data. 10. The accumulated data accumulated in the database includes operation data indicating a vehicle driving situation for each driver, and the fifth means includes the operation data for each driver. Means for extracting a combination of the n-th break time and the n + 1-th continuous operation time after the n-th (n is a natural number of 1 or more) continuous operation time by the driver, and for each extracted combination Of the combinations of means for calculating the accident rate and the n + 1st continuous operation time in which the shortest break time in the extracted combination for a predetermined time or more and the calculated accident rate is less than or equal to the threshold value, the continuous The means for identifying the mileage when the driving time is the longest, the start time of the first driving time of the identified combination, the rest time and the data of the mileage at the continuous driving time after that are listed in this order. The operation management system according to claim 2, further comprising means for creating the operation plan including the operation model arranged in the above. 11. A database capable of sequentially accumulating a plurality of types of data generated in relation to the presence of a vehicle and / or a driver and operation of the vehicle, and accumulated data accumulated in the database via an external operation terminal. A processing device that performs data processing based on the above, wherein the external operation terminal makes a data processing request to the processing device via a communication line, and the processing device is configured to detect that an event has occurred. First means for selecting data indicating the cause from the accumulated data, second means for quantifying operation efficiency in a predetermined period based on the data selected from the accumulated data, the vehicle or driver in the accumulated data The third means for selecting the deadline data in which the end of the predetermined contract deadline set for each is within a certain period, the future means based on the contents of the current accumulated data. At least one means of a fourth means for predicting the occurrence of an event is selectively constructed, and the execution result obtained by the execution of the means is notified to the processing device via the communication line. It is a vehicle operation management system. 12. Data reception means for receiving input of a plurality of types of data generated in relation to the presence of a vehicle and / or a driver and operation of the vehicle through an interface screen prepared for each type, and this data reception. The data received by the means are sorted into predetermined items and stored in a predetermined database, the processing request receiving means for receiving the data processing request, and the processing request receiving means for receiving the data processing request A processing device that performs data processing based on accumulated data accumulated in the database, wherein the processing device selects, from the accumulated data, data representing a cause of occurrence of an event, The second means for quantifying the operation efficiency in a predetermined period based on the data selected from the accumulated data, the accumulated data Third means for selecting deadline data in which the end of a predetermined contract deadline defined for each vehicle or driver falls within a certain period, and prediction of future event occurrence based on the contents of the current accumulated data A fourth means for performing the operation of the vehicle, which is configured to selectively construct at least one means, and to notify the request source of the processing request of the execution result obtained by executing the means. Management system. 13. An interface screen provided with a computer having a data input / output mechanism for inputting a plurality of types of data generated in association with the presence of a vehicle and / or a driver and the operation of the vehicle for each type. Through a data receiving means, a means for sorting the received data into predetermined corresponding items and accumulating in a predetermined database, a processing request receiving means for receiving a data processing request, and an opportunity for receiving the data processing request, A process of selecting data indicating the cause of occurrence of a certain event from the accumulated data accumulated in the database, a process of quantifying operation efficiency in an arbitrary period based on the accumulated data, the vehicle in the accumulated data, or Select deadline data that the end of the prescribed contract deadline set for each driver is within a certain period Processing, prediction processing for future events occur based on the contents of the stored data of the current, at least 1
A computer program for operating as an operation management system, comprising means for selectively executing one of the processes and notifying a request source of the process request of the execution result obtained thereby. 14. A computer-readable recording medium in which the computer program according to claim 13 is recorded.