CN113793105A - Special vehicle operation supervision system based on big data - Google Patents
Special vehicle operation supervision system based on big data Download PDFInfo
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- CN113793105A CN113793105A CN202111131112.7A CN202111131112A CN113793105A CN 113793105 A CN113793105 A CN 113793105A CN 202111131112 A CN202111131112 A CN 202111131112A CN 113793105 A CN113793105 A CN 113793105A
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Abstract
The invention discloses a special vehicle operation supervision system based on big data, which relates to the technical field of special vehicle operation supervision and solves the technical problem that the special vehicle in the prior art cannot carry out all-around supervision when transporting hazardous chemical substances; the method has the advantages that the proper vehicle is selected, the transportation cost of the hazardous chemical substances is reduced, the transportation safety performance is enhanced, the transportation progress is prevented from being reduced due to vehicle faults, and the increase of the abrasion of the vehicle due to the fact that the vehicle is not matched with the hazardous chemical substances transported in real time is avoided, so that the service life is indirectly shortened; the surrounding environment of the selected special vehicle in the running process is monitored, the situation that environment changes cause dangerous chemicals to be stored to cause influence, the dangerous chemicals are harmful to the periphery, and the harm of dangerous chemicals transportation cannot be controlled is prevented.
Description
Technical Field
The invention relates to the technical field of special vehicle operation supervision, in particular to a special vehicle operation supervision system based on big data.
Background
Hazardous chemicals refer to highly toxic chemicals and other chemicals which have the properties of toxicity, corrosion, explosion, combustion supporting and the like and are harmful to human bodies, facilities and the environment. The dangerous goods transport vehicle is a special vehicle for transporting dangerous goods such as petrochemical industry, explosive, firecrackers and the like, wherein the top of a container is not closed, an exhaust pipe is arranged in front, and a spark prevention device is arranged on the exhaust pipe. The dangerous goods transport vehicle is provided with an ABS system device to carry out whole-course monitoring of the transport process. The equipment is provided with anti-collision strips, and is safe and reliable.
However, in the prior art, hazardous chemical substances cannot supervise special vehicles in the transportation process, cannot perform route planning and vehicle matching before starting, and therefore transportation efficiency is reduced, and meanwhile cannot monitor after starting, supervision intensity is low in the transportation process, transportation conditions cannot be accurately judged, risks of hazardous chemical substance transportation are increased, and safety factors of surrounding environments and personnel are reduced.
In view of the above technical drawbacks, a solution is proposed.
Disclosure of Invention
The invention aims to provide a special vehicle operation supervision system based on big data, which plans a running route of a special transport vehicle and screens out a proper route according to a specific destination in real time, so that the transportation safety performance and the operation efficiency of the special transport vehicle are improved, the inaccurate route planning is prevented, the operation cost is increased, and the operation safety risk is increased; the method has the advantages that the proper vehicle is selected, the transportation cost of the hazardous chemical substances is reduced, the transportation safety performance is enhanced, the transportation progress is prevented from being reduced due to vehicle faults, and the increase of the abrasion of the vehicle due to the fact that the vehicle is not matched with the hazardous chemical substances transported in real time is avoided, so that the service life is indirectly shortened; the surrounding environment of the selected special vehicle in the running process is monitored, the situation that environment changes cause dangerous chemicals to be stored to cause influence, the dangerous chemicals are harmful to the periphery, and the harm of dangerous chemicals transportation cannot be controlled is prevented.
The purpose of the invention can be realized by the following technical scheme:
a special vehicle operation supervision system based on big data comprises an operation supervision platform, wherein a planning end and a monitoring end are arranged in the operation supervision platform, a server is arranged in the planning end, the server is in communication connection with a route analysis planning unit and a vehicle travel analysis unit, a controller is arranged in the monitoring end, and the controller is in communication connection with a real-time road condition analysis unit, an environment monitoring unit and a vehicle monitoring unit;
the planning terminal is used for analyzing and planning the transportation of the special vehicle, the server generates a route planning signal and sends the route planning signal to the route analysis planning unit, the running route of the special vehicle is planned through the route analysis planning unit, the planned route is screened out through analysis, the planned route is sent to the server, after the server receives the planned route, a vehicle analysis signal is generated and sent to the vehicle travel analysis unit, the vehicle for transporting hazardous chemical substances in real time is analyzed through the vehicle travel analysis unit, the selected special vehicle is obtained through analysis, and the selected special vehicle is sent to the server; the server generates an operation monitoring instruction after receiving the selected special vehicle and sends the operation monitoring instruction to a monitoring end;
after the monitoring end receives the operation supervision instruction, the controller generates an environment monitoring signal and sends the environment monitoring signal to the environment monitoring unit, and the environment monitoring unit is used for monitoring the surrounding environment of the selected special vehicle in the running process; monitoring the operation of the selected special vehicle through a vehicle monitoring unit; and carrying out road condition analysis on the current running road section of the selected special vehicle through a real-time road condition analysis unit.
Further, the planning process of the route analysis planning unit is as follows:
collecting a real-time transportation destination of the special vehicle, collecting a real-time origin of the special vehicle according to a real-time origin position of the special vehicle, collecting a route between the real-time transportation destination and the real-time origin, marking the route as a driving route, and setting a reference number i which is a natural number more than 1;
collecting the total driving length of each driving route, and marking the total driving length of the driving route as XSi; acquiring the ratio of the running length of the running route passing through the residential area to the running length of the running route not passing through the residential area, and marking the ratio of the running length of the running route passing through the residential area to the running length of the running route not passing through the residential area as JGi; acquiring the average traffic flow of each running route, and marking the average traffic flow of each running route as PCi; obtaining screening analysis coefficients Xi of all driving routes through analysis, and comparing the screening analysis coefficients Xi of the driving routes with a screening analysis coefficient threshold value:
if the screening analysis coefficient Xi of the driving route is larger than or equal to the screening analysis coefficient threshold value, judging that the corresponding driving route is abnormal in analysis, marking the corresponding driving route as an excluded planning route, and sending the excluded planning route to a server; and if the screening analysis coefficient Xi of the driving route is less than the screening analysis coefficient threshold value, judging that the corresponding driving route is normally analyzed, marking the corresponding driving route as a planning route, and sending the planning route to a server.
Further, the analysis process of the vehicle travel analysis unit is as follows:
collecting idle special vehicles, marking the idle special vehicles as special vehicles to be used, and setting a mark o, wherein the mark o is a natural number greater than 1; setting historical operation time, acquiring the failure times and failure frequency of the special vehicle to be used in the historical operation time, and respectively marking the failure times and failure frequency of the special vehicle to be used in the historical operation time as GSo and GPo; acquiring the average time length of the maintenance fault of the special vehicle to be used in the historical operation time, and marking the average time length of the maintenance fault of the special vehicle to be used in the historical operation time as PJo; analyzing and acquiring an analysis detection coefficient Co of the special vehicle to be used, and comparing the analysis detection coefficient of the special vehicle to be used with an analysis detection coefficient threshold value:
if the analysis detection coefficient of the special vehicle to be used is larger than or equal to the analysis detection coefficient threshold value, judging that the special vehicle to be used is unqualified in detection, and marking the corresponding special vehicle to be used as a maintenance special vehicle; if the analysis detection coefficient of the special vehicle to be used is less than the analysis detection coefficient threshold value, judging that the special vehicle to be used is qualified in detection, and marking the corresponding special vehicle to be used as a special vehicle for traveling;
collecting the weight of the dangerous chemical substances transported in real time, and analyzing the weight of the dangerous chemical substances transported in real time and the loading weight threshold value of the special trip vehicle: and if the difference value between the loading weight threshold value of the special trip vehicle and the weight of the dangerous chemical substances transported in real time does not exceed the corresponding difference threshold value, marking the corresponding special trip vehicle as a selected special trip vehicle, and sending the selected special trip vehicle to the server.
Further, the monitoring process of the environment monitoring unit is as follows:
collecting corresponding influence characteristic data of transported dangerous chemicals in real time, and analyzing the proportional relation between the influence characteristic data and the corresponding dangerous chemicals; acquiring the ratio of the positive floating times to the negative floating times of the influence characteristic data in the surrounding environment, and marking the ratio of the positive floating times to the negative floating times of the influence characteristic data in the surrounding environment as FDB; acquiring the ratio of the positive floating maximum floating value to the negative floating maximum floating value of the influence characteristic data in the surrounding environment, and respectively marking the ratio of the positive floating maximum floating value to the negative floating maximum floating value of the influence characteristic data in the surrounding environment as FDZ; analyzing the analysis ratio M of the obtained influence characteristic data in the environment, and comparing the analysis ratio M of the influence characteristic data in the environment with an analysis ratio threshold:
if the analysis ratio M of the influence characteristic data in the environment is larger than or equal to the analysis ratio threshold, judging that the environment is qualified in analysis and monitoring, generating an environment qualified signal and sending the environment qualified signal to the controller; and if the analysis ratio M of the influence characteristic data in the environment is less than the analysis ratio threshold, judging that the environment analysis monitoring is unqualified, generating an environment unqualified signal and sending the environment unqualified signal to the controller.
Further, the operation monitoring process of the vehicle monitoring unit is as follows:
acquiring the starting time of the selected special vehicle, comparing the current time with the starting time to acquire a real-time running time, acquiring the corresponding interval distance between the parking times of the selected special vehicle and the adjacent parking positions in the real-time running time, and respectively marking the parking times of the selected special vehicle and the corresponding interval distance between the adjacent parking positions in the real-time running time as TCS and TXW; acquiring the real-time running speed of the selected special vehicle, acquiring the speed floating value of the adjacent time within the real-time running duration according to the real-time running speed of the selected special vehicle, and marking the speed floating value as XSV; the operation monitoring coefficient H of the selected special vehicle is obtained through analysis, and the operation monitoring coefficient H of the selected special vehicle is compared with an operation monitoring coefficient range threshold value:
if the operation monitoring coefficient H of the selected special vehicle is located at the operation monitoring coefficient range threshold value, judging that the selected special vehicle is qualified in operation monitoring within the real-time operation time length, generating an operation monitoring qualified signal and sending the operation monitoring qualified signal to the controller;
if the operation monitoring coefficient H of the selected special vehicle is larger than the operation monitoring coefficient range threshold value, judging that fatigue driving exists on the special vehicle selected within the real-time operation time length corresponding to a driver, generating a driving time adjusting signal and sending the driving time adjusting signal to a controller;
and if the operation monitoring coefficient H of the selected special vehicle is smaller than the operation monitoring coefficient range threshold, judging that the driving efficiency of the selected special vehicle corresponding to the driver is unqualified in the real-time operation time length, generating a driving efficiency enhancement signal and sending the driving efficiency enhancement signal to the controller.
Further, the analysis process of the real-time traffic status analysis unit is as follows:
acquiring the average running speed of the vehicle in the current running road section of the selected special vehicle and the traffic flow of the current running road section, and respectively comparing the average running speed of the vehicle in the current running road section of the selected special vehicle and the traffic flow of the current running road section with an average running speed threshold value and a traffic flow threshold value range:
if the average driving speed of the vehicles in the current driving road section is greater than the average driving speed threshold value and the traffic flow of the current driving road section is within the traffic flow threshold value range, judging that the current driving road condition is normal, generating a normal road condition analysis signal and sending the normal road condition analysis signal to the controller;
if the average driving speed of the vehicles in the current driving road section is smaller than the average driving speed threshold value or the traffic flow of the current driving road section is larger than the traffic flow threshold value range, judging that the current driving road condition is traffic jam, generating a traffic jam early warning signal and sending the traffic jam early warning signal to a controller, and after receiving the traffic jam early warning signal, the controller replaces the driving route of the selected special vehicle and marks the corresponding road section as a non-use road section;
if the average running speed of the vehicles in the current running road section is smaller than the average running speed threshold value or the traffic flow of the current running road section is smaller than the traffic flow threshold value range, judging that the current running road section is forbidden to pass, generating a forbidden early warning signal and sending the forbidden early warning signal to the controller; and the controller receives the forbidding early warning signal, then changes the running route of the selected special vehicle and marks the corresponding road section as a temporary unused road section.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the running route of the special transport vehicle is planned, and the proper route is screened out in real time according to the specific destination, so that the transportation safety performance and the running efficiency of the special transport vehicle are improved, the condition that the running cost is increased and the running safety risk is increased due to inaccurate route planning is prevented; the method has the advantages that the proper vehicle is selected, the transportation cost of the hazardous chemical substances is reduced, the transportation safety performance is enhanced, the transportation progress is prevented from being reduced due to vehicle faults, and the increase of the abrasion of the vehicle due to the fact that the vehicle is not matched with the hazardous chemical substances transported in real time is avoided, so that the service life is indirectly shortened;
the method comprises the following steps of monitoring the surrounding environment of a selected special vehicle in the running process, and preventing the storage of hazardous chemicals from being influenced due to environmental changes, so that the hazardous chemicals are harmful to the periphery, and the harm of the transportation of the hazardous chemicals cannot be controlled; the road condition analysis is carried out on the current running road section of the selected special vehicle, the running road section is prevented from being blocked, the selected special vehicle cannot arrive at a transportation destination on time, the transportation efficiency is reduced, meanwhile, the time for placing the dangerous chemicals on the road is increased, and the risk of the dangerous chemicals corresponding to the surrounding environment is increased.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a special vehicle operation supervision system based on big data comprises an operation supervision platform, wherein a planning end and a monitoring end are arranged in the operation supervision platform, a server is arranged in the planning end, the server is in communication connection with a route analysis planning unit and a vehicle travel analysis unit, a controller is arranged in the monitoring end, and the controller is in communication connection with a real-time road condition analysis unit, an environment monitoring unit and a vehicle monitoring unit;
the planning end is used for carrying out analysis planning to the special vehicle transportation, the special vehicle shows the special transport vechicle for the danger article, the server generates the route planning signal and sends the route planning signal to the route analysis planning unit, the route analysis planning unit is used for planning the route of traveling of special transport vehicle, select suitable route according to specific destination in real time, thereby the transportation safety performance and the operating efficiency of special transport vehicle have been improved, it is inaccurate to prevent that the route planning from appearing, lead to the safe risk that the running cost risees and has increased the operation simultaneously, the concrete planning process is as follows:
collecting a real-time transportation destination of the special vehicle, collecting a real-time origin of the special vehicle according to a real-time origin position of the special vehicle, collecting a route between the real-time transportation destination and the real-time origin, marking the route as a driving route, and setting a reference number i which is a natural number more than 1;
collecting the total driving length of each driving route, and marking the total driving length of the driving route as XSi; acquiring the ratio of the running length of the running route passing through the residential area to the running length of the running route not passing through the residential area, and marking the ratio of the running length of the running route passing through the residential area to the running length of the running route not passing through the residential area as JGi; acquiring the average traffic flow of each running route, and marking the average traffic flow of each running route as PCi;
by the formula
Obtaining screening analysis coefficients Xi of all driving routes, wherein a1, a2 and a3 are all preset proportionality coefficients, and a1 is larger than a2 is larger than a3 is larger than 0; the screening analysis coefficient of the driving route is a numerical value used for judging the probability of the driving route as a planned route by carrying out normalization processing on the parameters of the driving route; the total running length of the running route, the ratio of the running length passing through the residential area to the running length not passing through the residential area and the average traffic flow are obtained through a formula, the larger the screening analysis coefficient of the running route is, the larger the probability that the running route is taken as a planned route is;
comparing the screening analysis coefficient Xi of the driving route with a screening analysis coefficient threshold value:
if the screening analysis coefficient Xi of the driving route is larger than or equal to the screening analysis coefficient threshold value, judging that the corresponding driving route is abnormal in analysis, marking the corresponding driving route as an excluded planning route, and sending the excluded planning route to a server;
if the screening analysis coefficient Xi of the driving route is smaller than the screening analysis coefficient threshold value, judging that the corresponding driving route is normal in analysis, marking the corresponding driving route as a planning route, and sending the planning route to a server;
after the server receives the planned route, generate vehicle analysis signal and with vehicle analysis signal transmission to vehicle trip analysis unit, vehicle trip analysis unit is used for carrying out the analysis to the vehicle of dangerization article real-time transport, select suitable vehicle, reduce dangerization article cost of transportation, strengthen transportation safety performance simultaneously, prevent that vehicle trouble from appearing and lead to the transportation progress to reduce, and avoid vehicle and real-time transport dangerization article not adaptation, thereby the wearing and tearing that lead to the vehicle increase have indirectly reduced life, concrete analytic process is as follows:
collecting idle special vehicles, marking the idle special vehicles as special vehicles to be used, and setting a mark o, wherein the mark o is a natural number greater than 1; setting historical operation time, acquiring the failure times and failure frequency of the special vehicle to be used in the historical operation time, and respectively marking the failure times and failure frequency of the special vehicle to be used in the historical operation time as GSo and GPo; acquiring the average time length of the maintenance fault of the special vehicle to be used in the historical operation time, and marking the average time length of the maintenance fault of the special vehicle to be used in the historical operation time as PJo;
by the formula
Obtaining an analysis detection coefficient Co of the special vehicle to be used, wherein b1, b2 and b3 are all preset proportional coefficients, b1 is greater than b2 is greater than b3 is greater than 0, and beta is an error correction factor and is 1.23; the analysis and detection coefficient of the special vehicle to be used is a numerical value for judging the qualification probability of the special vehicle to be used by normalizing the parameters of the special vehicle to be used; the larger the number of failures, the frequency of failures and the average duration of maintenance failures obtained through a formula, the larger the analysis and detection coefficient of the special vehicle to be used is, and the smaller the probability of detection qualification of the special vehicle to be used is;
comparing the analysis detection coefficient of the special vehicle to be used with an analysis detection coefficient threshold value:
if the analysis detection coefficient of the special vehicle to be used is larger than or equal to the analysis detection coefficient threshold value, judging that the special vehicle to be used is unqualified in detection, and marking the corresponding special vehicle to be used as a maintenance special vehicle; if the analysis detection coefficient of the special vehicle to be used is less than the analysis detection coefficient threshold value, judging that the special vehicle to be used is qualified in detection, and marking the corresponding special vehicle to be used as a special vehicle for traveling;
collecting the weight of the dangerous chemical substances transported in real time, and analyzing the weight of the dangerous chemical substances transported in real time and the loading weight threshold value of the special trip vehicle: if the difference value between the loading weight threshold value of the special trip vehicle and the weight of the dangerous chemical substances transported in real time does not exceed the corresponding difference threshold value, marking the corresponding special trip vehicle as a selected special trip vehicle, and sending the selected special trip vehicle to a server;
the server receives and selects to generate operation supervision instruction and send operation monitoring instruction to the monitoring end after the special-purpose vehicle is selected, the monitoring end receives operation supervision instruction after, the controller generates the environmental monitoring signal and sends the environmental monitoring signal to the environmental monitoring unit, the environmental monitoring unit is used for monitoring the all ring edge borders of selecting the special-purpose vehicle in the driving process, it leads to danger article to store there is the influence to prevent that environmental change from leading to danger article to produce harm to the periphery, and the harm of danger article transportation can't be controlled, concrete monitoring process is as follows:
collecting corresponding influence characteristic data of the transported dangerous chemicals in real time, analyzing the proportional relation between the influence characteristic data and the corresponding dangerous chemicals, and expressing the influence characteristic data as influence factors of the dangerous chemicals, such as environmental influence factors of temperature, humidity and the like; the proportional relation is expressed as an increasing relation of the influence degree of the influence characteristic data and the dangerous chemicals, and if the corresponding numerical value of the influence characteristic data rises or falls and the influence degree of the dangerous chemicals increases, the corresponding floating mark is negative floating; if the corresponding numerical value of the influence characteristic data rises or falls and the influence degree of the hazardous chemical substance is reduced, marking the corresponding floating as positive floating;
acquiring the ratio of the positive floating times to the negative floating times of the influence characteristic data in the surrounding environment, and marking the ratio of the positive floating times to the negative floating times of the influence characteristic data in the surrounding environment as FDB; acquiring the ratio of the positive floating maximum floating value to the negative floating maximum floating value of the influence characteristic data in the surrounding environment, and respectively marking the ratio of the positive floating maximum floating value to the negative floating maximum floating value of the influence characteristic data in the surrounding environment as FDZ; obtaining an analysis ratio M of the influence characteristic data in the environment by a formula M ═ alpha (FDB + FDZ), wherein alpha is an error correction factor and is 2.31;
comparing the analysis ratio M of the environmental influence characteristic data with an analysis ratio threshold value:
if the analysis ratio M of the influence characteristic data in the environment is larger than or equal to the analysis ratio threshold, judging that the environment is qualified in analysis and monitoring, generating an environment qualified signal and sending the environment qualified signal to the controller; if the analysis ratio M of the influence characteristic data in the environment is smaller than the analysis ratio threshold, judging that the environment analysis monitoring is unqualified, generating an environment unqualified signal and sending the environment unqualified signal to a controller; after the controller receives the unqualified environment signal, stopping the transportation of the selected special vehicle, and replacing the transportation road of the selected special vehicle;
after the controller received the environment qualified signal, generated vehicle monitoring signal and sent vehicle monitoring signal to vehicle monitoring unit, vehicle monitoring unit is used for selecting the operation monitoring of special-purpose vehicle, prevents to select special-purpose vehicle operation unusual to lead to that the dangerization article can't in time shift, has increased dangerization article transportation risk, and concrete operation monitoring process is as follows:
acquiring the starting time of the selected special vehicle, comparing the current time with the starting time to acquire a real-time running time, acquiring the corresponding interval distance between the parking times of the selected special vehicle and the adjacent parking positions in the real-time running time, and respectively marking the parking times of the selected special vehicle and the corresponding interval distance between the adjacent parking positions in the real-time running time as TCS and TXW; acquiring the real-time running speed of the selected special vehicle, acquiring the speed floating value of the adjacent time within the real-time running duration according to the real-time running speed of the selected special vehicle, and marking the speed floating value as XSV;
by the formula
Acquiring an operation monitoring coefficient H of the selected special vehicle, wherein f1, f2 and f3 are all preset proportionality coefficients, f1 is greater than f2 is greater than f3 is greater than 0, and e is a natural constant; comparing the operation monitoring coefficient H of the selected special vehicle with an operation monitoring coefficient range threshold value:
if the operation monitoring coefficient H of the selected special vehicle is located at the operation monitoring coefficient range threshold value, judging that the selected special vehicle is qualified in operation monitoring within the real-time operation time length, generating an operation monitoring qualified signal and sending the operation monitoring qualified signal to the controller;
if the operation monitoring coefficient H of the selected special vehicle is larger than the operation monitoring coefficient range threshold value, judging that fatigue driving exists on the special vehicle selected within the real-time operation time length corresponding to a driver, generating a driving time adjusting signal and sending the driving time adjusting signal to a controller; the controller limits the running time of the selected special vehicle after receiving the running time adjusting signal and sets the running limiting time;
if the operation monitoring coefficient H of the selected special vehicle is smaller than the operation monitoring coefficient range threshold, judging that the driving efficiency of the selected special vehicle corresponding to the driver is unqualified in the real-time operation duration, generating a driving efficiency enhancing signal and sending the driving efficiency enhancing signal to the controller; the controller generates a supervising instruction after receiving the driving efficiency enhancing signal and sends the supervising instruction to a mobile phone terminal of a corresponding driver in a form of a mobile phone short message;
after the controller receives the qualified signal of operation monitoring, generate road conditions analysis signal and with road conditions analysis signal transmission to real-time road conditions analysis unit, real-time road conditions analysis unit is used for carrying out road conditions analysis to selecting the current highway section of traveling of special-purpose vehicle, prevent that the highway section of traveling from producing and blocking up, lead to selecting the unable punctual arrival transport destination of special-purpose vehicle, the efficiency of transportation has been reduced, it is long when the road of increase dangerization article simultaneously is placed, cause the danger risk that dangerization article correspond all ring edge borders to increase, concrete analytic process is as follows:
acquiring the average running speed of the vehicle in the current running road section of the selected special vehicle and the traffic flow of the current running road section, and respectively comparing the average running speed of the vehicle in the current running road section of the selected special vehicle and the traffic flow of the current running road section with an average running speed threshold value and a traffic flow threshold value range:
if the average driving speed of the vehicles in the current driving road section is greater than the average driving speed threshold value and the traffic flow of the current driving road section is within the traffic flow threshold value range, judging that the current driving road condition is normal, generating a normal road condition analysis signal and sending the normal road condition analysis signal to the controller;
if the average driving speed of the vehicles in the current driving road section is smaller than the average driving speed threshold value or the traffic flow of the current driving road section is larger than the traffic flow threshold value range, judging that the current driving road condition is traffic jam, generating a traffic jam early warning signal and sending the traffic jam early warning signal to a controller, and after receiving the traffic jam early warning signal, the controller replaces the driving route of the selected special vehicle and marks the corresponding road section as a non-use road section;
if the average running speed of the vehicles in the current running road section is smaller than the average running speed threshold value or the traffic flow of the current running road section is smaller than the traffic flow threshold value range, judging that the current running road section is forbidden to pass, generating a forbidden early warning signal and sending the forbidden early warning signal to the controller; and the controller receives the forbidding early warning signal, then changes the running route of the selected special vehicle and marks the corresponding road section as a temporary unused road section.
The working principle of the invention is as follows: a special vehicle operation supervision system based on big data is characterized in that during operation, analysis planning is carried out on special vehicle transportation through a planning end, a running route of a special transportation vehicle is planned through a route analysis planning unit, a vehicle for transporting hazardous chemicals in real time is analyzed through a vehicle travel analysis unit, a selected special vehicle is obtained through analysis, and the selected special vehicle is sent to a server; the server generates an operation monitoring instruction after receiving the selected special vehicle and sends the operation monitoring instruction to a monitoring end;
after the monitoring end receives the operation supervision instruction, the controller generates an environment monitoring signal and sends the environment monitoring signal to the environment monitoring unit, and the environment monitoring unit is used for monitoring the surrounding environment of the selected special vehicle in the running process; monitoring the operation of the selected special vehicle through a vehicle monitoring unit; and carrying out road condition analysis on the current running road section of the selected special vehicle through a real-time road condition analysis unit.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula which obtains the latest real situation by acquiring a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
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 (6)
1. A special vehicle operation supervision system based on big data is characterized by comprising an operation supervision platform, wherein a planning end and a monitoring end are arranged in the operation supervision platform, a server is arranged in the planning end, the server is in communication connection with a route analysis planning unit and a vehicle travel analysis unit, a controller is arranged in the monitoring end, and the controller is in communication connection with a real-time road condition analysis unit, an environment monitoring unit and a vehicle monitoring unit;
the planning terminal is used for analyzing and planning the transportation of the special vehicle, the server generates a route planning signal and sends the route planning signal to the route analysis planning unit, the running route of the special vehicle is planned through the route analysis planning unit, the planned route is screened out through analysis, the planned route is sent to the server, after the server receives the planned route, a vehicle analysis signal is generated and sent to the vehicle travel analysis unit, the vehicle for transporting hazardous chemical substances in real time is analyzed through the vehicle travel analysis unit, the selected special vehicle is obtained through analysis, and the selected special vehicle is sent to the server; the server generates an operation monitoring instruction after receiving the selected special vehicle and sends the operation monitoring instruction to a monitoring end;
after the monitoring end receives the operation supervision instruction, the controller generates an environment monitoring signal and sends the environment monitoring signal to the environment monitoring unit, and the environment monitoring unit is used for monitoring the surrounding environment of the selected special vehicle in the running process; monitoring the operation of the selected special vehicle through a vehicle monitoring unit; and carrying out road condition analysis on the current running road section of the selected special vehicle through a real-time road condition analysis unit.
2. The big data-based special vehicle operation supervision system according to claim 1, wherein the planning process of the route analysis planning unit is as follows:
collecting a real-time transportation destination of the special vehicle, collecting a real-time origin of the special vehicle according to a real-time origin position of the special vehicle, collecting a route between the real-time transportation destination and the real-time origin, marking the route as a driving route, and setting a reference number i which is a natural number more than 1;
collecting the total driving length of each driving route, and marking the total driving length of the driving route as XSi; acquiring the ratio of the running length of the running route passing through the residential area to the running length of the running route not passing through the residential area, and marking the ratio of the running length of the running route passing through the residential area to the running length of the running route not passing through the residential area as JGi; acquiring the average traffic flow of each running route, and marking the average traffic flow of each running route as PCi; obtaining screening analysis coefficients Xi of all driving routes through analysis, and comparing the screening analysis coefficients Xi of the driving routes with a screening analysis coefficient threshold value:
if the screening analysis coefficient Xi of the driving route is larger than or equal to the screening analysis coefficient threshold value, judging that the corresponding driving route is abnormal in analysis, marking the corresponding driving route as an excluded planning route, and sending the excluded planning route to a server; and if the screening analysis coefficient Xi of the driving route is less than the screening analysis coefficient threshold value, judging that the corresponding driving route is normally analyzed, marking the corresponding driving route as a planning route, and sending the planning route to a server.
3. The big data-based special vehicle operation supervision system according to claim 1, wherein the analysis process of the vehicle travel analysis unit is as follows:
collecting idle special vehicles, marking the idle special vehicles as special vehicles to be used, and setting a mark o, wherein the mark o is a natural number greater than 1; setting historical operation time, acquiring the failure times and failure frequency of the special vehicle to be used in the historical operation time, and respectively marking the failure times and failure frequency of the special vehicle to be used in the historical operation time as GSo and GPo; acquiring the average time length of the maintenance fault of the special vehicle to be used in the historical operation time, and marking the average time length of the maintenance fault of the special vehicle to be used in the historical operation time as PJo; analyzing and acquiring an analysis detection coefficient Co of the special vehicle to be used, and comparing the analysis detection coefficient of the special vehicle to be used with an analysis detection coefficient threshold value:
if the analysis detection coefficient of the special vehicle to be used is larger than or equal to the analysis detection coefficient threshold value, judging that the special vehicle to be used is unqualified in detection, and marking the corresponding special vehicle to be used as a maintenance special vehicle; if the analysis detection coefficient of the special vehicle to be used is less than the analysis detection coefficient threshold value, judging that the special vehicle to be used is qualified in detection, and marking the corresponding special vehicle to be used as a special vehicle for traveling;
collecting the weight of the dangerous chemical substances transported in real time, and analyzing the weight of the dangerous chemical substances transported in real time and the loading weight threshold value of the special trip vehicle: and if the difference value between the loading weight threshold value of the special trip vehicle and the weight of the dangerous chemical substances transported in real time does not exceed the corresponding difference threshold value, marking the corresponding special trip vehicle as a selected special trip vehicle, and sending the selected special trip vehicle to the server.
4. The big data-based special vehicle operation supervision system according to claim 1, wherein the monitoring process of the environment monitoring unit is as follows:
collecting corresponding influence characteristic data of transported dangerous chemicals in real time, and analyzing the proportional relation between the influence characteristic data and the corresponding dangerous chemicals; acquiring the ratio of the positive floating times to the negative floating times of the influence characteristic data in the surrounding environment, and marking the ratio of the positive floating times to the negative floating times of the influence characteristic data in the surrounding environment as FDB; acquiring the ratio of the positive floating maximum floating value to the negative floating maximum floating value of the influence characteristic data in the surrounding environment, and respectively marking the ratio of the positive floating maximum floating value to the negative floating maximum floating value of the influence characteristic data in the surrounding environment as FDZ; analyzing the analysis ratio M of the obtained influence characteristic data in the environment, and comparing the analysis ratio M of the influence characteristic data in the environment with an analysis ratio threshold:
if the analysis ratio M of the influence characteristic data in the environment is larger than or equal to the analysis ratio threshold, judging that the environment is qualified in analysis and monitoring, generating an environment qualified signal and sending the environment qualified signal to the controller; and if the analysis ratio M of the influence characteristic data in the environment is less than the analysis ratio threshold, judging that the environment analysis monitoring is unqualified, generating an environment unqualified signal and sending the environment unqualified signal to the controller.
5. The big data-based special vehicle operation supervision system according to claim 1, characterized in that the operation monitoring process of the vehicle monitoring unit is as follows:
acquiring the starting time of the selected special vehicle, comparing the current time with the starting time to acquire a real-time running time, acquiring the corresponding interval distance between the parking times of the selected special vehicle and the adjacent parking positions in the real-time running time, and respectively marking the parking times of the selected special vehicle and the corresponding interval distance between the adjacent parking positions in the real-time running time as TCS and TXW; acquiring the real-time running speed of the selected special vehicle, acquiring the speed floating value of the adjacent time within the real-time running duration according to the real-time running speed of the selected special vehicle, and marking the speed floating value as XSV; the operation monitoring coefficient H of the selected special vehicle is obtained through analysis, and the operation monitoring coefficient H of the selected special vehicle is compared with an operation monitoring coefficient range threshold value:
if the operation monitoring coefficient H of the selected special vehicle is located at the operation monitoring coefficient range threshold value, judging that the selected special vehicle is qualified in operation monitoring within the real-time operation time length, generating an operation monitoring qualified signal and sending the operation monitoring qualified signal to the controller;
if the operation monitoring coefficient H of the selected special vehicle is larger than the operation monitoring coefficient range threshold value, judging that fatigue driving exists on the special vehicle selected within the real-time operation time length corresponding to a driver, generating a driving time adjusting signal and sending the driving time adjusting signal to a controller;
and if the operation monitoring coefficient H of the selected special vehicle is smaller than the operation monitoring coefficient range threshold, judging that the driving efficiency of the selected special vehicle corresponding to the driver is unqualified in the real-time operation time length, generating a driving efficiency enhancement signal and sending the driving efficiency enhancement signal to the controller.
6. The system as claimed in claim 1, wherein the real-time traffic status analysis unit analyzes the following steps:
acquiring the average running speed of the vehicle in the current running road section of the selected special vehicle and the traffic flow of the current running road section, and respectively comparing the average running speed of the vehicle in the current running road section of the selected special vehicle and the traffic flow of the current running road section with an average running speed threshold value and a traffic flow threshold value range:
if the average driving speed of the vehicles in the current driving road section is greater than the average driving speed threshold value and the traffic flow of the current driving road section is within the traffic flow threshold value range, judging that the current driving road condition is normal, generating a normal road condition analysis signal and sending the normal road condition analysis signal to the controller;
if the average driving speed of the vehicles in the current driving road section is smaller than the average driving speed threshold value or the traffic flow of the current driving road section is larger than the traffic flow threshold value range, judging that the current driving road condition is traffic jam, generating a traffic jam early warning signal and sending the traffic jam early warning signal to a controller, and after receiving the traffic jam early warning signal, the controller replaces the driving route of the selected special vehicle and marks the corresponding road section as a non-use road section;
if the average running speed of the vehicles in the current running road section is smaller than the average running speed threshold value or the traffic flow of the current running road section is smaller than the traffic flow threshold value range, judging that the current running road section is forbidden to pass, generating a forbidden early warning signal and sending the forbidden early warning signal to the controller; and the controller receives the forbidding early warning signal, then changes the running route of the selected special vehicle and marks the corresponding road section as a temporary unused road section.
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