CN114925927A - Intelligent networking automobile data interaction system - Google Patents
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
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Abstract
The invention discloses an intelligent networked automobile data interaction system, which is characterized in that a data resource acquisition unit is used for acquiring the residual electric quantity and the power consumption value of an electric automobile in real time, then a daily target data source is recorded by means of a recording unit, a comprehensive processing unit is combined with a processing rule base to perform whole-process fusion analysis on a target position, a target node group corresponding to each planned route segment and a power consumption value Pd, and each intermittent node and the corresponding intermittent duration thereof are obtained to form a charging plan and the shortest charging time Tx. Analyzing each route according to the residual electric quantity of the user electric automobile, determining the most suitable charging position, and automatically selecting a suitable charging target node under the condition of the minimum charging times according to the residual electric quantity and the target position; finishing the planning of the whole route; the disorder of the existing user charging is avoided, and the charging is only carried out under the simple planning of the brain of the individual; the application is simple and effective, and is easy to use.
Description
Technical Field
The invention belongs to the field of electric vehicle charging planning, and particularly relates to an intelligent networked vehicle data interaction system.
Background
Along with the increase of data sources of the internet of things, data types are various, data scale and storage pressure are increased rapidly, and requirements for efficient storage and rapid processing of mass multi-source data in the data processing process are continuously improved.
For example, patent publication No. CN111391693A discloses a management control system based on electric vehicle parking charging and a control method thereof. The vehicle type identification and driving device comprises a first image acquisition module, a bottom line coverage judgment module, a position profile acquisition module, a second image acquisition module, an extraction module, an identification module, a vehicle type judgment module, a driving module and a charging module; the type recognition of the electric automobile is realized by utilizing the heating characteristic, the non-electric automobile is prevented from occupying a parking space, and the charging demand of the electric automobile is ensured, so that the utilization rate of charging equipment is improved, and the resource waste is avoided. The invention also improves the operation and maintenance efficiency and timeliness, correspondingly improves the vehicle moving efficiency, improves the overall charging efficiency of the parking lot, and greatly improves the overall operation efficiency of the large-scale parking charging station of the electric automobile.
However, the charging management is mainly guaranteed according to the charging requirement of the electric automobile, data processing and analysis are carried out on the basis of the current data, reasonable planning cannot be carried out on a plurality of destinations involved in the process of departure of a user every day, and the destination can be reached in the shortest time under the condition that the destinations are most reasonably supplemented according to the electric quantity of the automobile in each journey.
Disclosure of Invention
The invention aims to provide an intelligent networking automobile data interaction system.
The purpose of the invention can be realized by the following technical scheme:
an intelligent networked automobile data interaction system comprises a data resource acquisition unit, a catalog forming unit, a comprehensive processing unit, an input unit, an analysis unit, a processing rule base and a terminal unit;
the data resource acquisition unit is used for acquiring the residual electric quantity and the power consumption value Pd of the electric automobile in real time, and transmitting the residual electric quantity and the power consumption value Pd to the comprehensive processing unit after forming a data resource catalog through the catalog forming unit;
the input unit is used for inputting a daily target data source by a user, and the target data source comprises a plurality of target positions arranged according to an arrival sequence; the input unit is used for transmitting the target data source to the analysis unit;
the analysis unit is used for receiving the target data source transmitted by the input unit and carrying out plan analysis on the target data source to obtain a plurality of planned distance segments and target node groups corresponding to each target position;
the analysis unit is used for transmitting the target position and the target node group in each planned route segment to the comprehensive processing unit;
the comprehensive processing unit is used for receiving the target position transmitted by the analysis unit, the required time point corresponding to the target position and the target node group corresponding to each planned route segment;
and the comprehensive processing unit is used for performing whole-process fusion analysis on the target position, the target node group corresponding to each planned route segment and the power consumption value Pd by combining the processing rule base to obtain each intermittent node and the corresponding intermittent duration thereof, and a charging plan and the shortest charging time Tx which are formed by the intermittent nodes and the corresponding intermittent duration.
Further, the specific acquisition mode of the power consumption value is as follows:
the method comprises the following steps: acquiring driving records of nearly thirty times, wherein the driving records comprise driving distance and electricity consumption; the electricity consumption amount refers to the amount of electricity of the electric vehicle consumed in a single driving course; the single driving distance refers to that the user does not start the vehicle to drive again after stopping for T1;
step two: then dividing the driving distance by the electricity consumption to obtain single electricity consumption;
step three: thirty times of single power consumption is obtained and marked as Di, i is 1.. 30;
step four: automatically calculating to obtain the average value of all single power consumptions Di, and marking the average value as the average power consumption Pd;
step five: calculating the deviation value Ld by using a formula, wherein the specific calculation formula is as follows:
step six: when Ld is less than or equal to X1, marking the average power consumption Pd as a power consumption value Nd; here X1 is a preset number; otherwise, carrying out data cleaning to obtain the power consumption value Pd.
Further, the data cleaning in the sixth step is specifically as follows:
s1: sorting Di according to the sequence of Di-Pd from large to small;
s2: deleting the Di corresponding to the first sorting according to the sequence of the first sorting;
s3: obtaining the dispersion value again according to the mode of the fourth step to the fifth step, and then comparing the obtained new dispersion value with X1;
s4: when the new dispersion value is larger than X1, deleting the Di corresponding to the second sorting order, and repeating the step S3 again; if the obtained deviation value is still larger than X1, continuously deleting the corresponding value from Di until Ld is less than or equal to X1;
s5: calculating the average value of the remaining single power consumption, and marking the obtained average value as a power consumption value Pd;
s6: obtaining the power consumption value Pd.
Further, the plan analysis is specifically as follows:
s01: acquiring a plurality of target positions in a target data source;
s02: acquiring a first target position;
s03: acquiring the time required for reaching the target position according to navigation software or other software with a navigation function, and marking the time as target time and a corresponding planned route segment; in the process, the target time and the planned route section corresponding to the minimum value are selected according to the road penetration value, and the calculation mode of the road penetration value is as follows:
0.58 planned hops +0.42 target time;
in the formula, 0.58 and 0.42 are weights of corresponding factors, and are preset according to the requirements of managers;
s04: acquiring all charging piles in a path track corresponding to a planned path segment, and primarily screening the charging piles to obtain a plurality of target nodes in the planned path segment;
s05: marking the first target position as a starting point and the second target position as an end point as a second planned route segment, and repeating the steps S03-S04 to obtain a plurality of target nodes of the second planned route segment;
s06: acquiring a next target position, and repeating the steps S05-S06 to obtain planned hops corresponding to all the target positions and a plurality of target nodes corresponding to the planned hops; a plurality of target nodes form a target node group;
s07: a plurality of planned hops and a target node group are obtained.
Further, the preliminary screening manner in step S04 is:
acquiring a target track corresponding to the planned route segment, acquiring the shortest distance between the charging pile and the target track, and filtering the shortest distance exceeding X2, wherein X2 is a preset value; marking the charging piles meeting the requirements as initial selection charging piles;
if the number of the remaining primary selection charging piles exceeds 3, marking all the primary selection charging piles as primary selection nodes;
selecting a plurality of nodes capable of dividing the planned route into a plurality of approximately equally divided route sections from the primary selection nodes, wherein the approximately equally divided route sections meet the following conditions,
the distance is the X2 distance of which the corresponding distance difference in each approximately equally divided road section is not more than two times;
obtaining a plurality of groups of primary selection node groups capable of dividing a planned route into a plurality of approximately equally-divided road segments, and then obtaining the approximately equally-divided value of each primary selection node group; the approximate equipartition value is a mean value obtained after selecting two sections from the approximate equipartition road section, and the value is marked as the approximate equipartition value;
marking the approximate equi-score closest to X3 as a reasonable equi-score, and marking the primary selection node group corresponding to the reasonable equi-score as a target node group to obtain each target node in the target node group, wherein each target node comprises a charging pile;
when the approximate equally-divided road sections cannot be obtained, dividing the planned road sections into three equally-divided road sections, obtaining charging piles closest to two middle nodes, and marking the charging piles as two target nodes;
and when the number of the initially selected charging piles is not more than 3, acquiring the initially selected charging pile closest to the midpoint, and marking the initially selected charging pile as a target node.
Further, the whole-process fusion analysis method comprises the following specific steps:
SS 1: multiplying the residual electric quantity by the power consumption value Pd to obtain a value marked as torque quantity Bd;
SS 2: acquiring all target positions to obtain a planned route segment corresponding to each target position;
SS 3: adding all the planned stretches, and marking the obtained value as a total journey;
SS 4: when the total journey is less than the distance turning amount Bd, the last target node in the target node group in the last planned journey segment is automatically marked as an intermittent node without any analysis;
SS 5: when the current total travel distance is greater than the torque quantity Bd, automatically entering the next step of margin analysis; determining intermittent nodes according to the edge supplement analysis;
SS 6: sequentially accumulating the planned route sections according to the sequence to obtain an accumulated route, wherein when the accumulated route is greater than a distance turning amount Bd-X4, X4 is a preset kilometer number; removing the last accumulated planned route segment, and marking the rest as accumulated route;
SS 7: marking a target position corresponding to the last planned route segment of the accumulated route as an initial segment terminal point; acquiring the last target node of the target node group in the last planned route segment, and marking the last target node as an intermittent node;
SS 8: then, the remaining planned route segments accumulated into the accumulated route are marked as the distance after the route is filled; acquiring residual electric quantity after the intermittent node is reached, and marking the residual electric quantity as initial electric quantity Ch after charging;
SS 9: acquiring the charging electric quantity in unit time, and marking the charging electric quantity as unit charging electric quantity Dc; then marking the total journey in the distance after charging as Pc;
calculating to obtain Tx according to a formula Pc ═ (Ch + Tx × Dc) × Pd, wherein Pc, Ch, Dc and Pd are known values;
marking Tx as a shortest charging time;
SS 10: then acquiring the maximum charge capacity of the electric automobile, marking the maximum charge capacity as full-value charge capacity, acquiring the highest driving mileage of the full-value charge capacity,
SS 11: after dividing Pc by the highest driving mileage, if a remainder exists, the rounded value is marked as an inter-charging number, and if no remainder exists, the whole-section charging number is marked;
if the charging number is the whole segment, acquiring a planned route segment which falls when each highest traveling route in the route after charging arrives, acquiring a target node which is closest to the highest traveling route when the highest traveling route arrives at the target position from the initial position in the planned route segment, and marking the target node as an intermittent node; obtaining a plurality of intermittent nodes; the corresponding intermittent duration in each intermittent node is that the electric automobile is fully charged to the maximum storage capacity;
when the number of times of charging is the number of times of charging, acquiring a planned route segment which falls when each highest driving route in the route after charging arrives, acquiring a target node which is closest to the highest driving route when the highest driving route in the planned route segment arrives at the target position from the initial position, and marking the target node as an intermittent node; obtaining a plurality of intermittent nodes; the intermittent duration of the last intermittent node is enough to reach the last target position and then the vehicle can travel the distance of X4, and the intermittent duration in each of the rest intermittent nodes is the time for filling the electric vehicle to the maximum charge capacity;
SS 12: and obtaining each intermittent node and the corresponding intermittent duration thereof, and marking the intermittent nodes as charging plans.
Further, the device also comprises a display unit;
the integrated processing unit is configured to transmit the charging schedule and the shortest charging time Tx to the terminal unit, which is configured to transmit the charging schedule and the shortest charging time Tx to the display unit for real-time display.
Furthermore, the system also comprises a management unit, wherein the management unit is in communication connection with the terminal unit and is used for recording all preset numerical values.
The invention has the beneficial effects that:
according to the method, the residual electric quantity and the power consumption value of the electric automobile are obtained in real time through a data resource obtaining unit, then a daily target data source is recorded through a recording unit, and the target data source comprises a plurality of target positions which are arranged according to an arrival sequence; then, plan analysis is carried out on the target node group by using an analysis unit to obtain a plurality of sections of planned distance sections and target node groups corresponding to the target positions;
and finally, performing whole-process fusion analysis on the target position, the target node group corresponding to each planned route segment and the power consumption value Pd by using the comprehensive processing unit in combination with the processing rule base to obtain each intermittent node and the corresponding intermittent duration thereof, and forming a charging plan and the shortest charging time Tx.
Analyzing each route according to the residual electric quantity of the user electric automobile, determining the most suitable charging position, and automatically selecting a suitable charging target node under the condition of the minimum charging times according to the residual electric quantity and the target position; finishing the planning of the whole route; the disorder of the existing user charging is avoided, and the charging is only carried out under the simple planning of the brain of the individual; the application is simple and effective, and is easy to use.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
As shown in fig. 1, as an embodiment of the present application, an intelligent networked automobile data interaction system includes a data resource obtaining unit, a directory forming unit, a comprehensive processing unit, an entry unit, an analysis unit, a processing rule base, a terminal unit, a display unit, and a management unit;
the data resource acquisition unit is used for acquiring the residual electric quantity and the power consumption value of the electric automobile in real time, and the specific acquisition mode of the power consumption value is as follows:
the method comprises the following steps: acquiring driving records of nearly thirty times, wherein the driving records comprise driving distance and electricity consumption; the electricity consumption amount refers to the amount of electricity of the electric vehicle consumed in a single driving course; the single driving distance refers to that the user does not start the vehicle to drive again after stopping for T1;
step two: then dividing the driving distance by the electricity consumption to obtain single electricity consumption;
step three: acquiring thirty times of single power consumption, and marking the power consumption as Di, i being 1.. 30;
step four: automatically calculating to obtain the average value of all single power consumptions Di, and marking the average value as the average power consumption Pd;
step five: calculating the deviation value Ld by using a formula, wherein the specific calculation formula is as follows:
step six: when Ld is less than or equal to X1, marking the average power consumption Pd as a power consumption value Nd; here X1 is a predetermined value; otherwise, data cleaning is carried out, and the specific data cleaning mode is as follows:
s1: ordering Di according to the sequence of | Di-Pd | from large to small;
s2: deleting the Di corresponding to the first sorting according to the sequence of the first sorting from the first sorting;
s3: obtaining the deviation value again according to the mode of the fourth step to the fifth step, and then comparing the obtained new deviation value with X1;
s4: when the new dispersion value is larger than X1, deleting the Di corresponding to the second sorting, and repeating the step S3 again; if the obtained deviation value is still larger than X1, continuously deleting the corresponding value from Di until Ld is less than or equal to X1;
s5: calculating the average value of the remaining single power consumption, and marking the obtained average value as a power consumption value Pd;
s6: obtaining a power consumption value Pd;
the data resource acquisition unit is used for transmitting the residual electric quantity and the power consumption value Pd to the comprehensive processing unit through the catalog forming unit;
the input unit is used for inputting a daily target data source by a user, and the target data source comprises a plurality of target positions arranged according to an arrival sequence; the input unit is used for transmitting the target data source to the analysis unit, the analysis unit receives the target data source transmitted by the input unit and carries out plan analysis on the target data source, and the plan analysis specific mode is as follows:
s01: acquiring a plurality of target positions in a target data source;
s02: acquiring a first target position;
s03: acquiring the time required for reaching the target position according to navigation software or other software with a navigation function, and marking the time as target time and a corresponding planned route segment; in the process, the target time and the planned route section corresponding to the minimum value are selected according to the road penetration value, and the calculation mode of the road penetration value is as follows:
0.58 planned hops +0.42 target time;
in the formula, 0.58 and 0.42 are weights of corresponding factors, and are preset according to the requirements of managers;
s04: all the charging piles in the path tracks corresponding to the planned path segments are obtained and preliminarily screened, and the preliminary screening mode is as follows:
acquiring a target track corresponding to the planned route segment, acquiring the shortest distance between the charging pile and the target track, and filtering the shortest distance exceeding X2, wherein X2 is a preset value; marking the charging piles meeting the requirements as initial selection charging piles;
if the number of the remaining primary selection charging piles exceeds 3, marking all the primary selection charging piles as primary selection nodes;
selecting a plurality of nodes capable of dividing the planned route into a plurality of approximately equally divided route sections from the primary selection nodes, wherein the approximately equally divided route sections meet the following conditions,
the distance is the X2 distance of which the corresponding distance difference in each approximately equally divided road section is not more than two times;
obtaining a plurality of groups of primary selection node groups capable of dividing a planned route into a plurality of approximately equally-divided road segments, and then obtaining the approximately equally-divided value of each primary selection node group; the approximate equipartition value is a mean value obtained after selecting two sections from the approximate equipartition road section, and the value is marked as the approximate equipartition value;
marking the approximate isobaric value closest to X3 as a reasonable isobaric value, and marking the primary selection node group corresponding to the reasonable isobaric value as a target node group to obtain each target node in the target node group, wherein each target node comprises a charging pile;
when the approximate equally-divided road sections are not obtained, dividing the planned road sections into three equally-divided road sections, obtaining the charging piles closest to the middle two nodes, and marking the charging piles as two target nodes;
when the number of the primary selection charging piles is not more than 3, acquiring the primary selection charging pile closest to the midpoint, and marking the primary selection charging pile as a target node;
s05: marking the first target position as a starting point and the second target position as an end point as a second planned route segment, and repeating the steps S03-S04 to obtain a plurality of target nodes of the second planned route segment;
s06: acquiring the next target position, and repeating the steps S05-S06 to obtain planned hops corresponding to all the target positions and a plurality of target nodes corresponding to the planned hops; a plurality of target nodes form a target node group;
s07: obtaining a plurality of planned hops and a target node group;
the analysis unit is used for transmitting the target position and the target node group in each planned route segment to the comprehensive processing unit, and the comprehensive processing unit receives the target position transmitted by the analysis unit, the corresponding required time point and the target node group corresponding to each planned route segment;
the comprehensive processing unit is used for carrying out whole-process fusion analysis on the target position, the target node group corresponding to each planned route segment and the power consumption value Pd by combining the processing rule base, and the specific mode of the whole-process fusion analysis is as follows:
SS 1: multiplying the residual electric quantity by the power consumption value Pd to obtain a value marked as torque quantity Bd;
SS 2: acquiring all target positions to obtain planned hops corresponding to each target position;
SS 3: adding all the planned stretches, and marking the obtained value as a total journey;
SS 4: when the total journey is less than the distance turning amount Bd, the last target node in the target node group in the last planned journey segment is automatically marked as an intermittent node without any analysis;
SS 5: when the current total travel distance is greater than the torque quantity Bd, automatically entering the next step of margin analysis; determining intermittent nodes according to the edge supplement analysis;
SS 6: sequentially accumulating the planned route sections according to the sequence to obtain an accumulated route, wherein when the accumulated route is greater than a distance turning amount Bd-X4, X4 is a preset kilometer number; removing the last accumulated planned route segment, and marking the rest as the accumulated route segment;
SS 7: marking a target position corresponding to the last planned route segment of the accumulated route as an initial segment terminal point; acquiring the last target node of the target node group in the last planned route segment, and marking the last target node as an intermittent node;
SS 8: then, the planned route segments which are remained and accumulated in the accumulated route are marked as the route after charging; acquiring residual electric quantity after the intermittent node is reached, and marking the residual electric quantity as initial electric quantity Ch after charging;
SS 9: acquiring the charging electric quantity in unit time, and marking the charging electric quantity as unit charging electric quantity Dc; then marking the total journey in the distance after charging as Pc;
calculating to obtain Tx according to a formula Pc ═ (Ch + Tx × Dc) × Pd, wherein Pc, Ch, Dc and Pd are known values;
marking Tx as the shortest charging time;
SS 10: then the maximum charge capacity of the electric automobile is obtained and marked as full charge capacity, the highest driving mileage of the full charge capacity is obtained,
SS 11: after dividing Pc by the highest driving mileage, if a remainder exists, the value obtained by rounding is marked as an inter-charging number, and if no remainder exists, the value is marked as an integral-charging number;
if the charging number is the whole segment, acquiring a planned route segment which falls when each highest traveling route in the route after charging arrives, acquiring a target node which is closest to the highest traveling route when the highest traveling route arrives at the target position from the initial position in the planned route segment, and marking the target node as an intermittent node; obtaining a plurality of intermittent nodes; the corresponding intermittent duration in each intermittent node is that the electric automobile is fully charged to the maximum charge capacity;
when the number of times of charging is the number of times of charging, acquiring a planned route segment which falls when each highest driving route in the route after charging arrives, acquiring a target node which is closest to the highest driving route when the highest driving route in the planned route segment arrives at the target position from the initial position, and marking the target node as an intermittent node; obtaining a plurality of intermittent nodes; the intermittent duration of the last intermittent node is enough to reach the last target position and then the vehicle can travel the distance of X4, and the intermittent duration in each of the rest intermittent nodes is the time for filling the electric vehicle to the maximum charge capacity;
SS 12: and obtaining each intermittent node and the corresponding intermittent duration thereof, and marking the intermittent nodes as charging plans.
The integrated processing unit is used for transmitting the charging plan and the shortest charging time Tx to the terminal unit, and the terminal unit is used for transmitting the charging plan and the shortest charging time Tx to the display unit for real-time display.
The management unit is in communication connection with the terminal unit and is used for recording all preset values.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (8)
1. The utility model provides an intelligence networking car data interaction system which characterized in that includes:
the data resource acquisition unit is used for acquiring the residual electric quantity and the power consumption value Pd of the electric automobile in real time, and transmitting the residual electric quantity and the power consumption value Pd to the comprehensive processing unit after forming a data resource catalog through the catalog forming unit;
the input unit is used for inputting a daily target data source by a user, and the target data source comprises a plurality of target positions arranged according to an arrival sequence;
the analysis unit is used for receiving the target data source transmitted by the input unit and carrying out planning analysis on the target data source to obtain a plurality of planned distance segments and target node groups corresponding to each target position;
the analysis unit is used for transmitting the target position and the target node group in each planned route segment to the comprehensive processing unit;
and the comprehensive processing unit is used for performing whole-process fusion analysis on the target position, the target node group corresponding to each planned route segment and the power consumption value Pd by combining the processing rule base to obtain a charging plan formed by each intermittent node and the corresponding intermittent duration.
2. The intelligent networked automobile data interaction system according to claim 1, wherein the specific electricity consumption value acquisition mode is as follows:
the method comprises the following steps: acquiring nearly thirty driving records, wherein each driving record comprises a driving distance and electricity consumption; the electricity consumption amount refers to the amount of electricity of the electric vehicle consumed in a single driving course; the single driving distance refers to that the user does not start the vehicle to drive again after stopping for T1;
step two: then dividing the driving distance by the electricity consumption to obtain single electricity consumption;
step three: thirty times of single power consumption is obtained and marked as Di, i is 1.. 30;
step four: automatically calculating to obtain the average value of all single power consumptions Di, and marking the average value as the average power consumption Pd;
step five: calculating the deviation value Ld by using a formula, wherein the specific calculation formula is as follows:
step six: when Ld is less than or equal to X1, marking the average power consumption Pd as a power consumption value Nd; here X1 is a predetermined value; otherwise, carrying out data cleaning to obtain the power consumption value Pd.
3. The intelligent networked automobile data interaction system according to claim 2, wherein the data cleaning is specifically performed by:
s1: ordering Di according to the sequence of | Di-Pd | from large to small;
s2: deleting the Di corresponding to the first sorting according to the sequence of the first sorting from the first sorting;
s3: obtaining the dispersion value again according to the mode of the fourth step to the fifth step, and then comparing the obtained new dispersion value with X1;
s4: when the new dispersion value is larger than X1, deleting the Di corresponding to the second sorting, and repeating the step S3 again; if the obtained deviation value is still larger than X1, continuing deleting the corresponding value from Di until Ld is less than or equal to X1;
s5: calculating the average value of the remaining single power consumption, and marking the obtained average value as a power consumption value Pd;
s6: obtaining the power consumption value Pd.
4. The intelligent networked automobile data interaction system according to claim 1, wherein the plan analysis is specifically performed by:
s01: acquiring a plurality of target positions in a target data source;
s02: acquiring a first target position;
s03: acquiring the time required for reaching the target position according to navigation software or other software with a navigation function, and marking the time as target time and a corresponding planned route segment; in the process, the target time and the planned route section corresponding to the minimum value are selected according to the road penetration value, and the calculation mode of the road penetration value is as follows:
0.58 planned hops +0.42 target time;
in the formula, 0.58 and 0.42 are weights of corresponding factors, and are preset according to the requirements of managers;
s04: acquiring all charging piles in a path track corresponding to a planned path segment, and primarily screening the charging piles to obtain a plurality of target nodes in the planned path segment;
s05: marking the first target position as a starting point and the second target position as an end point as a planned route segment II, and repeating the steps S03-S04 to obtain a plurality of target nodes of the planned route segment II;
s06: acquiring a next target position, and repeating the steps S05-S06 to obtain planned hops corresponding to all the target positions and a plurality of target nodes corresponding to the planned hops; a plurality of target nodes form a target node group;
s07: a plurality of planned hops and a target node group are obtained.
5. The intelligent networked automobile data interaction system according to claim 4, wherein the preliminary screening mode is as follows:
acquiring a target track corresponding to the planned route segment, acquiring the shortest distance between the charging pile and the target track, and filtering the shortest distance exceeding X2, wherein X2 is a preset value; marking the charging piles meeting the requirements as initial selection charging piles;
if the number of the remaining primary selection charging piles exceeds 3, marking all the primary selection charging piles as primary selection nodes;
selecting a plurality of nodes capable of dividing the planned route into a plurality of approximately equally divided route sections from the primary selection nodes, wherein the approximately equally divided route sections meet the following conditions,
the distance is the X2 distance of which the corresponding distance difference in each approximately equally divided road section does not exceed two times;
obtaining a plurality of groups of primary selection node groups capable of dividing a planned route into a plurality of approximately equally-divided road segments, and then obtaining the approximately equally-divided value of each primary selection node group; the approximate equipartition value is a mean value obtained after selecting two sections from the approximate equipartition road section, and the value is marked as the approximate equipartition value;
marking the approximate equi-score closest to X3 as a reasonable equi-score, and marking the primary selection node group corresponding to the reasonable equi-score as a target node group to obtain each target node in the target node group, wherein each target node comprises a charging pile;
when the approximate equally-divided road sections cannot be obtained, dividing the planned road sections into three equally-divided road sections, obtaining charging piles closest to two middle nodes, and marking the charging piles as two target nodes;
and when the number of the primary selection charging piles is not more than 3, acquiring the primary selection charging pile closest to the midpoint, and marking the primary selection charging pile as a target node.
6. The intelligent networked automobile data interaction system according to claim 1, wherein the whole-process fusion analysis is specifically as follows:
SS 1: multiplying the residual electric quantity by the power consumption value Pd to obtain a value marked as a torque value Bd;
SS 2: acquiring all target positions to obtain planned hops corresponding to each target position;
SS 3: adding all the planned stretches, and marking the obtained value as a total journey;
SS 4: when the total journey is less than the distance turning amount Bd, the last target node in the target node group in the last planned journey segment is automatically marked as an intermittent node without any analysis;
SS 5: when the current total travel distance is greater than the torque Bd, automatically entering the next marginal supplement analysis; determining intermittent nodes according to edge supplement analysis;
SS 6: sequentially accumulating the planned route segments according to the sequence to obtain an accumulated route, wherein when the accumulated route is greater than a distance turning amount Bd-X4, X4 is a preset kilometer number; removing the last accumulated planned route segment, and marking the rest as accumulated route;
SS 7: marking a target position corresponding to the last planned route segment of the accumulated route as an initial segment terminal point; acquiring the last target node of the target node group in the last planned route segment, and marking the last target node as an intermittent node;
SS 8: then, the planned route segments which are remained and accumulated in the accumulated route are marked as the route after charging; acquiring residual electric quantity after the intermittent node is reached, and marking the residual electric quantity as initial electric quantity Ch after charging;
SS 9: acquiring the charging electric quantity in unit time, and marking the charging electric quantity as unit charging electric quantity Dc; then marking the total journey in the distance after charging as Pc;
calculating to obtain Tx according to a formula Pc (Ch + Tx Dc) Pd, wherein Pc, Ch, Dc and Pd are known values;
marking Tx as the shortest charging time;
SS 10: then the maximum charge capacity of the electric automobile is obtained and marked as full charge capacity, the highest driving mileage of the full charge capacity is obtained,
SS 11: after dividing Pc by the highest driving mileage, if a remainder exists, the value obtained by rounding is marked as an inter-charging number, and if no remainder exists, the value is marked as an integral-charging number;
if the charging number is the whole segment, acquiring a planned route segment which falls when each highest traveling route in the route after charging arrives, acquiring a target node which is closest to the highest traveling route when the highest traveling route arrives at the target position from the initial position in the planned route segment, and marking the target node as an intermittent node; obtaining a plurality of intermittent nodes; the corresponding intermittent duration in each intermittent node is that the electric automobile is fully charged to the maximum charge capacity;
when the number of times of charging is the number of times of charging, acquiring a planned route segment which falls when each highest driving route in the route after charging arrives, acquiring a target node which is closest to the highest driving route when the highest driving route in the planned route segment arrives at the target position from the initial position, and marking the target node as an intermittent node; obtaining a plurality of intermittent nodes; the intermittent duration of the last intermittent node is enough to reach the last target position and then the vehicle can travel the distance of X4, and the intermittent duration in each of the rest intermittent nodes is the time for filling the electric vehicle to the maximum charge capacity;
SS 12: and obtaining each intermittent node and the corresponding intermittent duration thereof, and marking the intermittent nodes as charging plans.
7. The intelligent networked automobile data interaction system according to claim 1, further comprising a display unit;
the integrated processing unit is configured to transmit the charging schedule and the shortest charging time Tx to the terminal unit, which is configured to transmit the charging schedule and the shortest charging time Tx to the display unit for real-time display.
8. The intelligent networked automobile data interaction system according to claim 1, further comprising a management unit, wherein the management unit is in communication connection with the terminal unit and is used for recording all preset values.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117096955A (en) * | 2023-10-08 | 2023-11-21 | 南京允能日新智慧能源有限公司 | Distributed photovoltaic cluster operation control system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117096955A (en) * | 2023-10-08 | 2023-11-21 | 南京允能日新智慧能源有限公司 | Distributed photovoltaic cluster operation control system |
| CN117096955B (en) * | 2023-10-08 | 2024-03-19 | 南京允能日新智慧能源有限公司 | Distributed photovoltaic cluster operation control system |
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