CN111539686A - Transportation management monitoring method based on vehicle running record visualization - Google Patents
Transportation management monitoring method based on vehicle running record visualization Download PDFInfo
- Publication number
- CN111539686A CN111539686A CN202010324136.3A CN202010324136A CN111539686A CN 111539686 A CN111539686 A CN 111539686A CN 202010324136 A CN202010324136 A CN 202010324136A CN 111539686 A CN111539686 A CN 111539686A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- information
- transportation management
- speed
- management monitoring
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2477—Temporal data queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
Abstract
The invention discloses a transportation management monitoring method based on vehicle driving record visualization, which comprises the steps of selecting a corresponding date range, and importing historical data of the date range, wherein the historical data comprises a time label and speed information; the data cleaning comprises removing redundant time labels and abnormal speed information, and sorting the speed information after the data cleaning according to the sequence of the time labels; determining a corresponding time window unit according to actual requirements, generating two columns of characteristics of an actual date and time window unit from the time label, calculating the average speed of the vehicle in each time window unit, and generating a data table, wherein the numerical value in each cell is the average speed corresponding to the actual date and time window unit. A single chart is designed by utilizing the relation between the speed and the working condition, the rule and the change of the vehicle running path or the transportation business on the time dimension are indirectly disclosed, and an analyst can conveniently compare the business forms among the vehicles.
Description
Technical Field
The invention relates to the technical field of transportation management, in particular to a transportation management monitoring method based on vehicle running record visualization.
Background
With the popularization of electronic informatization in the modern transportation industry, the time, geography, driving modes (speed, acceleration, deceleration and the like) and vehicle state information of the vehicle running process can be traced. In the aspect of business scenes, along with the flourishing of urbanization processes and electronic commerce, the demand for distribution in the same city and intercity is obviously increased, and the characteristics of complex and changeable distribution scenes, frequent logistics activities, numerous logistics nodes and the like are presented. This type of transportation requirement is mainly fulfilled by distribution companies (having multiple vehicles) and individual transporters (having or leasing vehicles). Accordingly, the distribution company service personnel need to manage and schedule a plurality of vehicles at the same time, or the vehicle rental company needs to monitor the use conditions of a plurality of rented vehicles to reduce the risk of rental or improper use. The above scenarios all belong to a one-to-many relationship, and one person needs to monitor a plurality of management objects simultaneously. Too much monitoring information is too complicated, which is easy to cause inaccurate monitoring, manual error and other conditions, and causes related loss of a monitoring party.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a transportation management monitoring method based on vehicle running record visualization, so that at least real-time monitoring based on position information is used for monitoring a management object, the position of the current vehicle is displayed on a map, and if abnormal conditions needing attention exist, the current vehicle is reminded by text information. If the information in the spatial position and the time dimension is to be grasped simultaneously, a plurality of charts are generally used for displaying the information in different dimensions, and an analyst needs to cross-compare the plurality of charts to extract effective information so as to help decision-making judgment.
The purpose of the invention is realized by the following technical scheme:
a transportation management monitoring method based on vehicle driving record visualization comprises the following steps,
step S1: selecting a corresponding date range according to the actual demand of transportation management monitoring, and importing historical data of the date range, wherein the historical data comprises a time label and speed information;
step S2: on the basis of the step S1, performing data cleaning on the historical data, wherein the data cleaning comprises removing redundant time labels and abnormal speed information, and sequencing the speed information after the data cleaning according to the sequence of the time labels;
step S3: on the basis of the step S2, extracting the characteristics of the time label, determining a corresponding time window unit according to actual requirements, and generating two columns of characteristics of actual date and time window units from the time label;
step S4: on the basis of step S3, when the vehicle is in a running state, calculating an average speed of the vehicle in each of the time window units;
step S5: on the basis of step S4, a data table is generated, where the row index of the data table is the actual date, the column index is the time window unit, and the numerical value in each cell is the average speed corresponding to the actual date and the time window unit;
step S6: on the basis of step S5, the data table is converted into a thermodynamic diagram, where the abscissa is the actual date, the ordinate is the time window unit, the color depth of each cell represents the magnitude of the corresponding average speed, the darker the color represents the larger the average speed, and when the cell is selected, the corresponding time and average speed information can be correspondingly displayed.
Further, in step S1, the history data further includes brake pedal information and accelerator pedal information.
Further, when the vehicle is an electric vehicle, the speed information in step S1 may be replaced with the current information.
Further, in step S2, the abnormal speed information includes: the speed is less than zero and the speed exceeds a maximum speed threshold.
Further, in step S3, the time window unit is set according to the requirement, and may be: every hour, every half hour, every fifteen minutes, or every minute.
Further, in step S4, it is determined that the vehicle is in a running state and one of the following conditions is satisfied: the speed information is not zero, and the brake pedal is away from the initial position or the accelerator pedal is away from the initial position.
Further, a specific method for determining that the vehicle is in the driving state is as follows:
when the speed information is zero, if the brake pedal leaves the initial position and the accelerator pedal leaves the initial position, the vehicle is in a running state and is in an abnormal running state, and an abnormal running alarm is sent out;
when the speed information is zero, if the brake pedal leaves the initial position and the accelerator pedal does not leave the initial position, the vehicle is in a running state and is in a normal running state;
when the speed information is zero, if the brake pedal does not leave the initial position and the accelerator pedal leaves the initial position, the vehicle is in a running state and is in an abnormal running state, and an abnormal running alarm is sent out;
when the speed information is not zero, if the brake pedal leaves the initial position and the accelerator pedal leaves the initial position, the vehicle is in a running state and is in an abnormal running state, and an abnormal running alarm is sent out;
when the speed information is not zero, if the brake pedal does not leave the initial position and the accelerator pedal leaves the initial position, the vehicle is in a running state and is in a normal running state;
when the speed information is not zero, if the brake pedal leaves the initial position and the accelerator pedal does not leave the initial position, the vehicle is in a running state and is in a normal running state;
when the speed information is not zero, if the brake pedal does not leave the initial position and the accelerator pedal does not leave the initial position, the vehicle is in a running state, and the vehicle is in a normal running state.
Further, a plurality of date intervals which are parallel to the date range in the step 1 are obtained, a speed information set, a brake pedal information set, an accelerator pedal information set, a time window unit set, an average speed set and an actual date set of the plurality of date intervals in which the vehicle normally runs are obtained in the same manner, the speed information set, the brake pedal information set, the accelerator pedal information set, the time window unit set, the average speed set and the actual date set are used as sample sets, in the sample sets, the speed information set, the brake pedal information set, the accelerator pedal information set and the time window unit set are subjected to feature fusion and then used as input, the average speed set and the actual date set are used as output, the sample sets are divided into a training set and a testing set, the training set is trained to obtain a transportation management monitoring model, the testing set is substituted into the transportation management monitoring model to obtain an optimized transportation management monitoring model, when the date range recorded in the step 1 and the date ranges recorded outside the plurality of date intervals need to be analyzed, the speed information set, the brake pedal information set, the accelerator pedal information set and the time window unit set recorded in the date range are subjected to feature fusion and then input into the optimized transportation management monitoring model as actual input information, and then the optimized transportation management monitoring model outputs a corresponding actual average speed set and an actual date set for transportation management monitoring analysis.
Further, after obtaining the actual average speed set and the actual date set, the method further comprises a verification and verification step, specifically:
when the optimized transportation management monitoring model outputs a corresponding actual average speed set and an actual date set, calculating a verification average speed set corresponding to the actual date set according to the speed information set, the brake pedal information set, the accelerator pedal information set and the time window unit corresponding to the optimized transportation management monitoring model by the method of the step S4;
comparing and judging the actual average speed set with the verification average speed set;
if the actual average speed set is equal to the verified average speed set in a one-to-one correspondence manner, the output result of the optimized transportation management monitoring model is correct;
otherwise, the output result of the optimized transportation management monitoring model is abnormal, meanwhile, an abnormal output alarm is carried out, and the verified average speed set replaces the actual average speed set output by the optimized transportation management monitoring model.
The invention has the beneficial effects that: 1. the single chart is used for supporting the analysis and understanding of the quick and large-capacity time-space dimension information, and the longitudinal comparison of the service rules of the same vehicle on the time dimension and the transverse comparison of the service forms among different vehicles can be realized. The interactive (using ECharts) visualization design can provide specific time and speed information of each node while presenting visual panoramic information contrast, thereby facilitating further deep analysis by analysts. In addition, the technology has low requirements on vehicle-mounted data acquisition equipment, only needs two-dimensional information of time and speed, indirectly reflects the similarity of a path or a transportation service scene through speed reflection working conditions, and reduces the requirements on the accuracy of each time point data by using the average speed in a given time window length (for example, per hour).
2. By adopting the neural network model, the rapid analysis of vehicle transportation can be realized, the management and monitoring intellectualization are facilitated, and meanwhile, the influence of manual errors on the analysis and monitoring results is greatly reduced.
3. Through the verification and inspection steps, the accuracy verification and inspection can be effectively carried out on the vehicle transportation analysis and monitoring process, although the speed of obtaining the actual average speed set through optimizing the transportation management and monitoring model is far faster than the calculation process of the accuracy verification and inspection step, the former acts normally, the actual average speed set is obtained through optimizing the transportation management and monitoring model normally, the latter carries out the accuracy verification and inspection step, the two do not influence each other at the same time, the latter is slow in speed, but provides corresponding guarantee for the result of the former, abnormal analysis results can be found in time, and managers in vehicle transportation can find and process in time; the method is a powerful verification tool for optimizing the transportation management monitoring model.
Drawings
FIG. 1 is a flow chart of the operation of one embodiment of the present invention;
FIG. 2 is a schematic diagram of an application of one embodiment of the present invention;
FIG. 3 is a flowchart illustrating specific steps according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
Example (b):
with the popularization of electronic informatization in the modern transportation industry, the time, geography, driving modes (acceleration, deceleration and the like) and vehicle state information of the vehicle running process can be traced. In the aspect of business scenes, along with the flourishing of urbanization processes and electronic commerce, the demand for distribution in the same city and intercity is obviously increased, and the characteristics of complex and changeable distribution scenes, frequent logistics activities, numerous logistics nodes and the like are presented. This type of transportation requirement is mainly fulfilled by distribution companies (having multiple vehicles) and individual transporters (having or leasing vehicles). Accordingly, the distribution company service personnel need to manage and schedule a plurality of vehicles at the same time, or the vehicle rental company needs to monitor the use conditions of a plurality of rented vehicles to reduce the risk of rental or improper use. The above scenarios all belong to a one-to-many relationship, and one person needs to monitor a plurality of management objects simultaneously. Too much monitoring information is too complicated, which is easy to cause inaccurate monitoring, manual error and other conditions, and causes related loss of a monitoring party. Therefore, a transportation management monitoring method based on vehicle driving record visualization is provided, so that at least real-time monitoring based on position information is used for monitoring a management object, the current position of a vehicle is displayed on a map, and if abnormal conditions needing attention exist, the current position is reminded by text information. If the information in the spatial position and the time dimension is to be grasped simultaneously, a plurality of charts are generally used for displaying the information in different dimensions, and an analyst needs to cross-compare the plurality of charts to extract effective information so as to help decision-making judgment. The specific contents are as follows,
as shown in fig. 1 and 3, the transportation management monitoring method based on vehicle driving record visualization includes the following steps,
step S1: selecting a corresponding date range according to the actual demand of transportation management monitoring, and importing historical data of the date range, wherein the historical data comprises a time label and speed information;
step S2: on the basis of the step S1, performing data cleaning on the historical data, wherein the data cleaning comprises removing redundant time labels and abnormal speed information, and sequencing the speed information after the data cleaning according to the sequence of the time labels;
step S3: on the basis of the step S2, extracting the characteristics of the time label, determining a corresponding time window unit according to actual requirements, and generating two columns of characteristics of actual date and time window units from the time label;
step S4: on the basis of step S3, when the vehicle is in a running state, calculating an average speed of the vehicle in each of the time window units;
based on the above scheme, there is a certain limitation, which is reflected in the following three aspects:
1) for the same vehicle, it is inconvenient to view the driving track in a long time dimension (such as three hours and more), and the tracks are overlapped due to round trip passing of the main nodes or road segments or similar transportation paths in each day, so that the previous historical information is covered on one hand, and the change process in the time dimension cannot be provided on the other hand.
2) The method does not support the transverse comparison among vehicles, and the multi-vehicle multi-path display is carried out on the same drawing page, so that on one hand, the comparison on the time dimension is not supported, and on the other hand, the sensitivity of an analyst to information is reduced due to node intersection or coverage.
3) Longitudinal contrast in the time dimension is not supported to reflect the rules and changes in the use process. The multiple charts respectively display information with different dimensions, and the method is limited in that the mutual correlation among the dimensions cannot be intuitively reflected, and the requirements on the information comparison and integration capability of an analyst are high.
Step S5: on the basis of step S4, a data table is generated, where the row index of the data table is the actual date, the column index is the time window unit, and the numerical value in each cell is the average speed corresponding to the actual date and the time window unit;
step S6: on the basis of step S5, the data table is converted into a thermodynamic diagram, where the abscissa is the actual date, the ordinate is the time window unit, the color depth of each cell represents the magnitude of the corresponding average speed, the darker the color represents the larger the average speed, and when the cell is selected, the corresponding time and average speed information can be correspondingly displayed. Each row of the thermodynamic diagram reflects the departure and receiving time of the vehicle on the date and the working condition of the transportation route; between each row, the regularity and variation of the traffic pattern or the transportation route of the vehicle are reflected (see the example of fig. 2 for details). The darker the corresponding cell color represents the greater the average velocity at that moment. For the No. 1 vehicle, the analyst can find the relative regularity of the vehicle transportation business. Every morning, the vehicle is taken out, the non-driving state (corresponding to the goods taking point of the warehouse) is 1-2 hours after passing through the high-speed driving section (such as city winding and high speed), then the vehicle passes through the high-speed driving section again, and after the day comes to the sun, the low-speed driving process (corresponding to distribution in the urban area) is started, and the vehicle is received at noon. Wherein no vehicle record exists for a period of 8 months in 2018, and the reason can be further analyzed by related personnel according to other record information due to rest or vehicle-mounted equipment failure. On the other hand, for the vehicle No. 2, by comparing the speed distribution thermodynamic diagram with the thermodynamic diagram corresponding to the vehicle No. 1, it can be seen that the transportation business form of the vehicle No. 2 is greatly different from that of the vehicle No. 1. The dynamics of the No. 2 vehicle is relatively disordered and has no certain rule, but the relevant information of the No. 2 vehicle can still be seen through the corresponding thermodynamic diagram, and the vehicle transportation management monitoring process can be carried out as usual.
The single chart is used for supporting the analysis and understanding of the quick and large-capacity time-space dimension information, and the longitudinal comparison of the service rules of the same vehicle on the time dimension and the transverse comparison of the service forms among different vehicles can be realized. The interactive (using ECharts) visualization design can provide specific time and speed information of each node while presenting visual panoramic information contrast, thereby facilitating further deep analysis by analysts. In addition, the technology has low requirements on vehicle-mounted data acquisition equipment, only needs two-dimensional information of time and speed, indirectly reflects the similarity of a path or a transportation service scene through speed reflection working conditions, and reduces the requirements on the accuracy of each time point data by using the average speed in a given time window length (for example, per hour).
Further, in step S1, the history data further includes brake pedal information and accelerator pedal information.
Further, when the vehicle is an electric vehicle, the speed information in step S1 may be replaced with the current information. The current information can reflect the speed information of the electric automobile.
Further, in step S2, the abnormal speed information includes: the speed is less than zero, the speed exceeds the maximum speed threshold value and the like, the speed information obviously not conforming to the actual situation is abnormal speed information and needs to be completely removed, otherwise, larger errors can be caused to the subsequent analysis.
Further, in step S3, the time window unit is set according to the requirement, and may be: every hour, every half hour, every fifteen minutes, or every minute, etc.
Further, in step S4, it is determined that the vehicle is in a running state and one of the following conditions is satisfied: the speed information is not zero, and the brake pedal is away from the initial position or the accelerator pedal is away from the initial position, and the three conditions are discussed in detail according to the actual driving condition.
Further, a specific method for determining that the vehicle is in the driving state is as follows:
when the speed information is zero, if the brake pedal leaves the initial position and the accelerator pedal leaves the initial position, the vehicle is in a running state and is in an abnormal running state, and an abnormal running alarm is sent out;
when the speed information is zero, if the brake pedal leaves the initial position and the accelerator pedal does not leave the initial position, the vehicle is in a running state and is in a normal running state;
when the speed information is zero, if the brake pedal does not leave the initial position and the accelerator pedal leaves the initial position, the vehicle is in a running state and is in an abnormal running state, and an abnormal running alarm is sent out;
when the speed information is not zero, if the brake pedal leaves the initial position and the accelerator pedal leaves the initial position, the vehicle is in a running state and is in an abnormal running state, and an abnormal running alarm is sent out;
when the speed information is not zero, if the brake pedal does not leave the initial position and the accelerator pedal leaves the initial position, the vehicle is in a running state and is in a normal running state;
when the speed information is not zero, if the brake pedal leaves the initial position and the accelerator pedal does not leave the initial position, the vehicle is in a running state and is in a normal running state;
when the speed information is not zero, if the brake pedal does not leave the initial position and the accelerator pedal does not leave the initial position, the vehicle is in a running state, and the vehicle is in a normal running state.
Further, a plurality of date intervals which are parallel to the date range in the step 1 are obtained, a speed information set, a brake pedal information set, an accelerator pedal information set, a time window unit set, an average speed set and an actual date set of the plurality of date intervals in which the vehicle normally runs are obtained in the same manner, the speed information set, the brake pedal information set, the accelerator pedal information set, the time window unit set, the average speed set and the actual date set are used as sample sets, in the sample sets, the speed information set, the brake pedal information set, the accelerator pedal information set and the time window unit set are subjected to feature fusion and then used as input, the average speed set and the actual date set are used as output, the sample sets are divided into a training set and a testing set, the training set is trained to obtain a transportation management monitoring model, the testing set is substituted into the transportation management monitoring model to obtain an optimized transportation management monitoring model, when the date range recorded in the step 1 and the date ranges recorded outside the plurality of date intervals need to be analyzed, the speed information set, the brake pedal information set, the accelerator pedal information set and the time window unit set recorded in the date range are subjected to feature fusion and then input into the optimized transportation management monitoring model as actual input information, and then the optimized transportation management monitoring model outputs a corresponding actual average speed set and an actual date set for transportation management monitoring analysis. By adopting the mode, the rapid analysis of vehicle transportation can be realized, the management and monitoring intellectualization are facilitated, and meanwhile, the influence of manual errors on analysis and monitoring results is reduced to a greater extent.
Further, after obtaining the actual average speed set and the actual date set, the method further comprises a verification and verification step, specifically:
when the optimized transportation management monitoring model outputs a corresponding actual average speed set and an actual date set, calculating a verification average speed set corresponding to the actual date set according to the speed information set, the brake pedal information set, the accelerator pedal information set and the time window unit corresponding to the optimized transportation management monitoring model by the method of the step S4;
comparing and judging the actual average speed set with the verification average speed set;
if the actual average speed set is equal to the verified average speed set in a one-to-one correspondence manner, the output result of the optimized transportation management monitoring model is correct;
otherwise, the output result of the optimized transportation management monitoring model is abnormal, meanwhile, an abnormal output alarm is carried out, and the verified average speed set replaces the actual average speed set output by the optimized transportation management monitoring model.
Through the verification and inspection steps, the accuracy verification and inspection can be effectively carried out on the vehicle transportation analysis and monitoring process, although the speed of obtaining the actual average speed set through optimizing the transportation management and monitoring model is far faster than the calculation process of the accuracy verification and inspection step, the former acts normally, the actual average speed set is obtained through optimizing the transportation management and monitoring model normally, the latter carries out the accuracy verification and inspection step, the two do not influence each other at the same time, the latter is slow in speed, but provides corresponding guarantee for the result of the former, abnormal analysis results can be found in time, and managers in vehicle transportation can find and process in time; the method is a powerful verification tool for optimizing the transportation management monitoring model.
Meanwhile, on the basis of the scheme, a positioning verification and inspection step is also arranged, and the method specifically comprises the following steps:
acquiring real-time geographical position information of a vehicle and map information of a place where the vehicle passes;
calculating a positioning average speed set of the vehicle according to the real-time geographic position information of the vehicle on the map information and the real-time movement information of the vehicle;
and carrying out verification and inspection on the positioning average speed set and the actual average speed set, and judging whether the positioning average speed set and the actual average speed set are consistent.
Comparing the positioning verification and inspection step with the verification and inspection step, and if the positioning verification and inspection step and the verification and inspection step are judged to be consistent, taking any consistent verification and inspection result as the standard; if the two are not consistent, an alarm is sent to a manager, and corresponding manual verification and inspection are carried out.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A transportation management monitoring method based on vehicle driving record visualization is characterized in that: comprises the following steps of (a) carrying out,
step S1: selecting a corresponding date range according to the actual demand of transportation management monitoring, and importing historical data of the date range, wherein the historical data comprises a time label and speed information;
step S2: on the basis of the step S1, performing data cleaning on the historical data, wherein the data cleaning comprises removing redundant time labels and abnormal speed information, and sequencing the speed information after the data cleaning according to the sequence of the time labels;
step S3: on the basis of the step S2, extracting the characteristics of the time label, determining a corresponding time window unit according to actual requirements, and generating two columns of characteristics of actual date and time window units from the time label;
step S4: on the basis of step S3, when the vehicle is in a running state, calculating an average speed of the vehicle in each of the time window units;
step S5: on the basis of step S4, a data table is generated, where the row index of the data table is the actual date, the column index is the time window unit, and the numerical value in each cell is the average speed corresponding to the actual date and the time window unit;
step S6: on the basis of step S5, the data table is converted into a thermodynamic diagram, where the abscissa is the actual date, the ordinate is the time window unit, the color depth of each cell represents the magnitude of the corresponding average speed, the darker the color represents the larger the average speed, and when the cell is selected, the corresponding time and average speed information can be correspondingly displayed.
2. The transportation management monitoring method based on vehicle driving record visualization according to claim 1, characterized in that: in step S1, the history data further includes brake pedal information and accelerator pedal information.
3. The transportation management monitoring method based on vehicle driving record visualization according to claim 2, characterized in that: when the vehicle is an electric vehicle, the speed information in step S1 may be replaced with the current information.
4. The transportation management monitoring method based on vehicle driving record visualization according to claim 2, characterized in that: in step S2, the abnormal speed information includes: the speed is less than zero and the speed exceeds a maximum speed threshold.
5. The transportation management monitoring method based on vehicle driving record visualization according to claim 4, characterized in that: in step S3, the time window unit is set according to the requirement, and may be: every hour, every half hour, every fifteen minutes, or every minute.
6. The transportation management monitoring method based on vehicle driving record visualization according to claim 5, characterized in that: in step S4, it is determined that the vehicle is in a running state and one of the following conditions is satisfied: the speed information is not zero, and the brake pedal is away from the initial position or the accelerator pedal is away from the initial position.
7. The transportation management monitoring method based on vehicle driving record visualization according to claim 5, characterized in that: the specific method for judging the running state of the vehicle is as follows:
when the speed information is zero, if the brake pedal leaves the initial position and the accelerator pedal leaves the initial position, the vehicle is in a running state and is in an abnormal running state, and an abnormal running alarm is sent out;
when the speed information is zero, if the brake pedal leaves the initial position and the accelerator pedal does not leave the initial position, the vehicle is in a running state and is in a normal running state;
when the speed information is zero, if the brake pedal does not leave the initial position and the accelerator pedal leaves the initial position, the vehicle is in a running state and is in an abnormal running state, and an abnormal running alarm is sent out;
when the speed information is not zero, if the brake pedal leaves the initial position and the accelerator pedal leaves the initial position, the vehicle is in a running state and is in an abnormal running state, and an abnormal running alarm is sent out;
when the speed information is not zero, if the brake pedal does not leave the initial position and the accelerator pedal leaves the initial position, the vehicle is in a running state and is in a normal running state;
when the speed information is not zero, if the brake pedal leaves the initial position and the accelerator pedal does not leave the initial position, the vehicle is in a running state and is in a normal running state;
when the speed information is not zero, if the brake pedal does not leave the initial position and the accelerator pedal does not leave the initial position, the vehicle is in a running state, and the vehicle is in a normal running state.
8. The transportation management monitoring method based on vehicle driving record visualization according to claim 7, characterized in that: obtaining a plurality of date intervals parallel to the date range in the step 1, obtaining a speed information set, a brake pedal information set, an accelerator pedal information set, a time window unit set, an average speed set and an actual date set of the plurality of date intervals in the same way, taking the speed information set, the brake pedal information set, the accelerator pedal information set, the time window unit set, the average speed set and the actual date set as sample sets, performing feature fusion on the speed information set, the brake pedal information set, the accelerator pedal information set and the time window unit set in the sample sets, taking the average speed set and the actual date set as outputs, dividing the sample sets into training sets and testing sets, training the training sets to obtain a transportation management monitoring model, substituting the testing sets into the transportation management monitoring model to test to obtain an optimized transportation management monitoring model, when the date range recorded in the step 1 and the date ranges recorded outside the plurality of date intervals need to be analyzed, the speed information set, the brake pedal information set, the accelerator pedal information set and the time window unit set recorded in the date range are subjected to feature fusion and then input into the optimized transportation management monitoring model as actual input information, and then the optimized transportation management monitoring model outputs a corresponding actual average speed set and an actual date set for transportation management monitoring analysis.
9. The transportation management monitoring method based on vehicle driving record visualization according to claim 8, characterized in that: after obtaining the actual average speed set and the actual date set, the method further comprises a verification and verification step, which specifically comprises the following steps:
when the optimized transportation management monitoring model outputs a corresponding actual average speed set and an actual date set, calculating a verification average speed set corresponding to the actual date set according to the speed information set, the brake pedal information set, the accelerator pedal information set and the time window unit corresponding to the optimized transportation management monitoring model by the method of the step S4;
comparing and judging the actual average speed set with the verification average speed set;
if the actual average speed set is equal to the verified average speed set in a one-to-one correspondence manner, the output result of the optimized transportation management monitoring model is correct;
otherwise, the output result of the optimized transportation management monitoring model is abnormal, meanwhile, an abnormal output alarm is carried out, and the verified average speed set replaces the actual average speed set output by the optimized transportation management monitoring model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010324136.3A CN111539686B (en) | 2020-04-22 | 2020-04-22 | Transportation management monitoring method based on vehicle running record visualization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010324136.3A CN111539686B (en) | 2020-04-22 | 2020-04-22 | Transportation management monitoring method based on vehicle running record visualization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111539686A true CN111539686A (en) | 2020-08-14 |
| CN111539686B CN111539686B (en) | 2023-09-08 |
Family
ID=71980219
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010324136.3A Active CN111539686B (en) | 2020-04-22 | 2020-04-22 | Transportation management monitoring method based on vehicle running record visualization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111539686B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022262013A1 (en) * | 2021-06-15 | 2022-12-22 | 合肥维天运通信息科技股份有限公司 | Method and system for collecting and monitoring vehicle state information |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6559762B1 (en) * | 2002-01-02 | 2003-05-06 | Ford Global Technologies, Llc | Multi channel braking notification system |
| US20070273559A1 (en) * | 2006-05-26 | 2007-11-29 | Nissan Technical Center North America, Inc. | Adaptive traffic flow indicia for navigation systems |
| JP2008150011A (en) * | 2006-12-18 | 2008-07-03 | Tadashi Watanabe | Alarm device for preventing improper operation of accelerator of automobile |
| JP2008260507A (en) * | 2007-03-20 | 2008-10-30 | Katsumasa Itagaki | Emergency brake system |
| KR20110100702A (en) * | 2010-03-05 | 2011-09-15 | 한국산업기술대학교산학협력단 | Eco-driving support method and apparatus |
| JP2013151260A (en) * | 2012-01-26 | 2013-08-08 | Mitsubishi Motors Corp | Vehicle control device |
| US8694241B1 (en) * | 2010-10-05 | 2014-04-08 | Google Inc. | Visualization of traffic patterns using GPS data |
| CN103714694A (en) * | 2013-12-13 | 2014-04-09 | 广东车联网信息科技服务有限公司 | Urban traffic information access processing system |
| FR3011214A1 (en) * | 2013-09-30 | 2015-04-03 | Peugeot Citroen Automobiles Sa | METHOD AND DEVICE FOR DETECTING A LEFT TAKE-AWAY FROM A THERMALLY ENGINE VEHICLE TO START A STRATEGY FOR TAKE-OFF AID |
| CN104575102A (en) * | 2014-12-16 | 2015-04-29 | 北京中交兴路车联网科技有限公司 | Vehicle warning system and method |
| CN105045788A (en) * | 2014-05-02 | 2015-11-11 | 国民大学校产学协力团 | Method of processing and analysing vehicle driving big data and system thereof |
| CN108621859A (en) * | 2017-03-24 | 2018-10-09 | 华晨汽车集团控股有限公司 | A kind of drive control method of pure electric automobile |
-
2020
- 2020-04-22 CN CN202010324136.3A patent/CN111539686B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6559762B1 (en) * | 2002-01-02 | 2003-05-06 | Ford Global Technologies, Llc | Multi channel braking notification system |
| US20070273559A1 (en) * | 2006-05-26 | 2007-11-29 | Nissan Technical Center North America, Inc. | Adaptive traffic flow indicia for navigation systems |
| JP2008150011A (en) * | 2006-12-18 | 2008-07-03 | Tadashi Watanabe | Alarm device for preventing improper operation of accelerator of automobile |
| JP2008260507A (en) * | 2007-03-20 | 2008-10-30 | Katsumasa Itagaki | Emergency brake system |
| KR20110100702A (en) * | 2010-03-05 | 2011-09-15 | 한국산업기술대학교산학협력단 | Eco-driving support method and apparatus |
| US8694241B1 (en) * | 2010-10-05 | 2014-04-08 | Google Inc. | Visualization of traffic patterns using GPS data |
| JP2013151260A (en) * | 2012-01-26 | 2013-08-08 | Mitsubishi Motors Corp | Vehicle control device |
| FR3011214A1 (en) * | 2013-09-30 | 2015-04-03 | Peugeot Citroen Automobiles Sa | METHOD AND DEVICE FOR DETECTING A LEFT TAKE-AWAY FROM A THERMALLY ENGINE VEHICLE TO START A STRATEGY FOR TAKE-OFF AID |
| CN103714694A (en) * | 2013-12-13 | 2014-04-09 | 广东车联网信息科技服务有限公司 | Urban traffic information access processing system |
| CN105045788A (en) * | 2014-05-02 | 2015-11-11 | 国民大学校产学协力团 | Method of processing and analysing vehicle driving big data and system thereof |
| CN104575102A (en) * | 2014-12-16 | 2015-04-29 | 北京中交兴路车联网科技有限公司 | Vehicle warning system and method |
| CN108621859A (en) * | 2017-03-24 | 2018-10-09 | 华晨汽车集团控股有限公司 | A kind of drive control method of pure electric automobile |
Non-Patent Citations (2)
| Title |
|---|
| 李毅;高岩;任皓;应朝阳;李平凡;: "汽车事件记录器在道路交通事故调查中的应用研究", no. 04 * |
| 秦浠莲;: "误踩油门提示及防止装置的设计", 读书文摘, no. 12 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022262013A1 (en) * | 2021-06-15 | 2022-12-22 | 合肥维天运通信息科技股份有限公司 | Method and system for collecting and monitoring vehicle state information |
| US11954955B2 (en) | 2021-06-15 | 2024-04-09 | Hefei Weitianyuntong Information Technology Co., Ltd | Method and system for collecting and monitoring vehicle status information |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111539686B (en) | 2023-09-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109716414B (en) | Multi-mode road traffic abnormity detection method | |
| CN109754594B (en) | Road condition information acquisition method and equipment, storage medium and terminal thereof | |
| Tong et al. | A framework for developing driving cycles with on‐road driving data | |
| CN110376003B (en) | Intelligent train service life prediction method and system based on BIM | |
| WO2001069176A1 (en) | Method of monitoring vehicular mileage | |
| WO2020239503A1 (en) | Method for predicting at least one profile of the speed of a vehicle on a road network | |
| CA2619083A1 (en) | Smart inspections | |
| CN109493449B (en) | Freight train cargo state estimation method based on freight train GPS track data and high-speed transaction data | |
| Barabino et al. | Regularity diagnosis by automatic vehicle location raw data | |
| CN112785030A (en) | Ship behavior recognition system, method and platform | |
| CN111539764B (en) | Big data multiple access selection method based on submodular function | |
| CN111199247B (en) | Bus operation simulation method | |
| CN111539686B (en) | Transportation management monitoring method based on vehicle running record visualization | |
| CN111159625A (en) | Urban green wave coordination effect evaluation system | |
| KR20220138894A (en) | Prediction and recognition method of road marking information and road maintenance method | |
| CN113095387A (en) | Road risk identification method based on networking vehicle-mounted ADAS | |
| CN106204793A (en) | Vehicle maintenance predictor method | |
| Harrington et al. | Modeling in-use vehicle emissions and the effects of inspection and maintenance programs | |
| CN114971717A (en) | Commercial coupon issuing method | |
| Makarova et al. | Improving the reliability of autonomous vehicles in a branded service system using big data | |
| CN113569491A (en) | Analysis and correction method and device for wheel set size detection data | |
| Pereira et al. | Decision Support Dashboard for Traffic and Environment Analysis of a Smart City. | |
| Wang et al. | The state subdivision of public traffic vehicles based on K-means algorithm | |
| Nguyen et al. | Development of real fuel consumption models for motorcycle: a case study in Hanoi, Vietnam | |
| Barabino et al. | Web Architecture of a Web Portal for Reliability Diagnosis of Bus Regularity |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |