CN117949976B - Real-time positioning method and system for highway transport vehicle for transporting yellow phosphorus - Google Patents
Real-time positioning method and system for highway transport vehicle for transporting yellow phosphorus Download PDFInfo
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- OBSZRRSYVTXPNB-UHFFFAOYSA-N tetraphosphorus Chemical compound P12P3P1P32 OBSZRRSYVTXPNB-UHFFFAOYSA-N 0.000 title claims abstract description 43
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000012544 monitoring process Methods 0.000 claims abstract description 108
- 230000002159 abnormal effect Effects 0.000 claims abstract description 56
- 230000005856 abnormality Effects 0.000 claims abstract description 10
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 142
- 229910052742 iron Inorganic materials 0.000 claims description 71
- 238000004891 communication Methods 0.000 claims description 19
- 230000007613 environmental effect Effects 0.000 claims description 14
- 238000005516 engineering process Methods 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 description 10
- 230000000306 recurrent effect Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 239000000383 hazardous chemical Substances 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 125000004122 cyclic group Chemical group 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 230000006403 short-term memory Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
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Abstract
The invention relates to a real-time positioning method and a real-time positioning system for a highway transport vehicle for transporting yellow phosphorus, which relate to the technical field of transport management, wherein the system comprises the following components: the first positioning module is used for acquiring first positioning information of the road transport vehicle and uploading the first positioning information to the cloud management platform; the state monitoring module is used for acquiring state information of the road transport vehicle; the abnormality judging module is used for determining the abnormal value of the vehicle of the road transport vehicle based on the state information and the first positioning information of the road transport vehicle; the second positioning module is used for acquiring second positioning information when the abnormal value of the vehicle is larger than a preset abnormal value threshold value of the vehicle and uploading the second positioning information to the cloud management platform, wherein the accuracy of the second positioning information is higher than that of the first positioning information, and the second positioning module has the advantage of improving the safety of highway transportation of yellow phosphorus.
Description
Technical Field
The invention relates to the technical field of transportation management, in particular to a real-time positioning method and a real-time positioning system for a highway transport vehicle for transporting yellow phosphorus.
Background
The transportation of dangerous goods is a bridge and a tie which are used for connecting production, operation, storage and use of dangerous goods such as finished oil, natural gas and the like. In recent years, with the increase of the transportation amount of chemical dangerous goods year by year, the transportation business households, transportation vehicles and practitioners of road chemical dangerous goods transportation increase year by year, and the dangerous goods enterprises have small operation scale, the transportation vehicles and facility equipment are old, the management is disordered, the safety investment is insufficient, the production operation condition is poor, the road transportation of the chemical dangerous goods has great potential safety hazard, the transportation accident occurs, and serious threat is formed to social public safety.
Yellow phosphorus shipment mainly uses barreled transportation, and yellow phosphorus is packed into a closed iron barrel and is transported by an automobile or a train, and in the transportation process, the iron barrels are easy to cause yellow phosphorus leakage due to mutual collision, oxidation and combustion are generated, so that the transportation is unsafe. In the prior art, in the transportation process of yellow phosphorus, a highway transport vehicle for transporting yellow phosphorus cannot be tracked in real time, and specific information of the highway transport vehicle for transporting yellow phosphorus cannot be managed in time, so that the safety early warning capability is lacked.
Therefore, it is necessary to provide a real-time positioning method and system for highway transportation vehicles for transporting yellow phosphorus, which are used for improving the safety of highway transportation of yellow phosphorus.
Disclosure of Invention
The invention provides a real-time positioning system of a highway transport vehicle for transporting yellow phosphorus, which comprises the following components: the first positioning module is used for acquiring first positioning information of the road transport vehicle and uploading the first positioning information to the cloud management platform; the state monitoring module is used for acquiring state information of the road transport vehicle; an anomaly determination module for determining a vehicle anomaly value of the road transport vehicle based on the state information of the road transport vehicle and the first positioning information; the second positioning module is used for acquiring second positioning information when the abnormal value of the vehicle is larger than a preset abnormal value threshold value of the vehicle and uploading the second positioning information to the cloud management platform, wherein the accuracy of the second positioning information is higher than that of the first positioning information.
Further, the first positioning module obtains first positioning information of the road transport vehicle based on a GPS positioning technology.
Still further, the state monitoring module comprises a pressure monitoring unit, a vibration monitoring unit, a sound monitoring unit and a temperature monitoring unit, wherein the pressure monitoring unit is used for collecting pressure information of the interior of the iron barrel of the highway truck, the vibration monitoring unit is used for collecting vibration information of the iron barrel of the highway truck, the sound monitoring unit is used for collecting sound information of the iron barrel of the highway truck, and the temperature monitoring unit is used for collecting temperature information of the interior of the iron barrel of the highway truck; the state information of the road transport vehicle comprises pressure information of the interior of the iron barrel of the road transport vehicle, vibration information of the iron barrel of the road transport vehicle, sound information of the iron barrel of the road transport vehicle and temperature information of the interior of the iron barrel of the road transport vehicle at a plurality of time points in a monitoring period.
Furthermore, the collection frequencies of the pressure monitoring unit, the vibration monitoring unit, the sound monitoring unit and the temperature monitoring unit are adjusted in real time based on the first positioning information of the road transport vehicle.
Still further, the anomaly determination module determines a vehicle anomaly value for the road transport vehicle based on the state information of the road transport vehicle and the first positioning information, comprising: determining a state anomaly value of the road transport vehicle based on pressure information inside an iron barrel of the road transport vehicle, vibration information of the iron barrel of the road transport vehicle, sound information of the iron barrel of the road transport vehicle, and temperature information inside the iron barrel of the road transport vehicle at a plurality of time points in the one monitoring period; determining a track abnormal value of the road transport vehicle based on first positioning information of the road transport vehicle at a plurality of historical transport time points and a preset transport path; and determining the abnormal value of the vehicle of the road transport vehicle based on the abnormal value of the state of the road transport vehicle and the abnormal value of the track of the road transport vehicle.
Further, the preset transportation path is generated through the cloud management platform; the cloud management platform generates the preset transportation path based on the following flow: generating a plurality of candidate transportation routes based on the transportation start point and the transportation end point of the road transportation vehicle; predicting road condition information of the candidate transportation routes in a plurality of future time periods for each candidate transportation route; for each candidate transportation route, acquiring road transportation vehicle transportation information of the candidate transportation route in a plurality of future time periods; determining, for each of the candidate transportation routes, transportation safety information for the candidate transportation route over a plurality of future time periods; determining, for each of the candidate transportation routes, environmental information of the candidate transportation route over a plurality of future time periods; for each candidate transportation route, determining a matching value of the candidate transportation route based on road condition information, road transportation vehicle transportation information, transportation safety information and environment information of the candidate transportation route in a plurality of future time periods; when at least one candidate transportation route with the matching value larger than a first preset matching value threshold exists, the candidate transportation route with the matching value larger than the first preset matching value threshold is used as a first target transportation route, and the preset transportation route is generated based on the matching value of each first target transportation route; and when at least one candidate transportation route does not exist and the matching value of the candidate transportation routes is larger than a first preset matching value threshold, the candidate transportation route with the matching value larger than a second preset matching value threshold is used as a second target transportation route, and the second target transportation route is fused to generate the preset transportation route.
Still further, the abnormality determination module determines a state abnormality value of the road transportation vehicle based on pressure information of an inside of an iron tank of the road transportation vehicle, vibration information of the iron tank of the road transportation vehicle, sound information of the iron tank of the road transportation vehicle, and temperature information of the inside of the iron tank of the road transportation vehicle at a plurality of time points in the one monitoring period, including: generating pressure fluctuation parameters of the road transport vehicle in the monitoring period based on the pressure information of the interior of the iron drum of the road transport vehicle at a plurality of time points in the monitoring period; generating vibration fluctuation parameters of the road transport vehicle in the monitoring period based on vibration information of iron barrels of the road transport vehicle at a plurality of time points in the monitoring period; generating sound fluctuation parameters of the road transport vehicle in the monitoring period based on sound information of iron barrels of the road transport vehicle at a plurality of time points in the monitoring period; generating temperature fluctuation parameters of the road transport vehicle in the monitoring period based on temperature information of the interior of the iron barrel of the road transport vehicle at a plurality of time points in the monitoring period; and determining a state abnormal value of the road transport vehicle based on the pressure fluctuation parameter, the vibration fluctuation parameter, the sound fluctuation parameter and the temperature fluctuation parameter of the road transport vehicle in the monitoring period.
Still further, the second positioning module at least comprises a short-range communication positioning component and a machine vision positioning component.
Still further, the second positioning module obtains the second positioning information, including: acquiring short-distance communication positioning information based on first positioning information of the road transport vehicle through the short-distance communication positioning assembly; and acquiring the second positioning information through the machine vision positioning component and the short-distance communication positioning information.
The invention provides a real-time positioning method of a highway transport vehicle for transporting yellow phosphorus, which comprises the following steps: acquiring first positioning information of a road transport vehicle and uploading the first positioning information to a cloud management platform; acquiring state information of the road transport vehicle; determining a vehicle outlier of the road transport vehicle based on the state information of the road transport vehicle and the first positioning information; and acquiring second positioning information when the abnormal value of the vehicle is larger than a preset abnormal value threshold value of the vehicle, and uploading the second positioning information to a cloud management platform, wherein the accuracy of the second positioning information is higher than that of the first positioning information.
Compared with the prior art, the highway transport vehicle real-time positioning system for transporting yellow phosphorus provided by the specification has the following beneficial effects:
1. firstly, first positioning information of a highway transport vehicle is acquired and uploaded to a cloud management platform, the position of the highway transport vehicle is monitored, meanwhile, based on the state information and the first positioning information of the highway transport vehicle, whether the highway transport vehicle is abnormal or not is evaluated, when the abnormal value of the vehicle is larger than a preset abnormal value threshold value of the vehicle, second positioning information with higher precision is acquired and uploaded to the cloud management platform, data support is provided for subsequent highway transport vehicle rescue and offline management of yellow phosphorus, the state and the position of the highway transport vehicle of the yellow phosphorus are monitored simultaneously, and the safety of highway transport of the yellow phosphorus is improved;
2. The method comprises the steps of accurately and comprehensively evaluating whether the road transport vehicle is easy to be abnormal on the current running road or not according to accident occurrence frequency, road structure parameters, environment abnormal parameters and dangerous goods transportation parameters of the current running road, further, adjusting acquisition frequencies of a pressure monitoring unit, a vibration monitoring unit, a sound monitoring unit and a temperature monitoring unit in real time according to evaluation results, and improving real-time monitoring on the state of the road transport vehicle when the road transport vehicle is easy to be abnormal;
3. The candidate transportation route is evaluated in four dimensions from road condition information, road transportation vehicle transportation information, transportation safety information and environment information of the candidate transportation route in a plurality of future time periods, so that a transportation route which is more suitable for the yellow phosphorus transportation task is generated, and the safety of the yellow phosphorus transportation is improved;
4. the positioning with higher precision is realized by combining the short-distance communication positioning component with the machine vision positioning component.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a block diagram of a real-time positioning system for a highway truck for transporting yellow phosphorus, in accordance with one embodiment of the present application;
FIG. 2 is a flow chart illustrating the generation of a preset transportation path in an embodiment of the present application;
FIG. 3 is a flow chart illustrating the determination of abnormal values of states of a road transport vehicle according to an embodiment of the present application;
Fig. 4 is a flow chart of a real-time positioning method of a highway truck for transporting yellow phosphorus according to an embodiment of the present application.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below.
Fig. 1 is a block diagram of a real-time positioning system of a highway truck for transporting yellow phosphorus according to an embodiment of the present application, and as shown in fig. 1, the real-time positioning system of a highway truck for transporting yellow phosphorus may include a first positioning module, a state monitoring module, an anomaly judging module, and a second positioning module.
The first positioning module can be used for acquiring first positioning information of the road transport vehicle and uploading the first positioning information to the cloud management platform.
The cloud management platform can be used for information summarizing of positions and states of a plurality of highway transport vehicles for transporting dangerous goods, and achieves remote monitoring of management personnel and transportation work for managing the plurality of dangerous goods.
In some embodiments, the first positioning module may obtain first positioning information of the road transport vehicle based on GPS positioning technology.
The state monitoring module can be used for acquiring state information of the road transport vehicle.
In some embodiments, the status monitoring module includes a pressure monitoring unit, a vibration monitoring unit, a sound monitoring unit, and a temperature monitoring unit, wherein the pressure monitoring unit is used for collecting pressure information of an interior of an iron ladle of the highway truck, the vibration monitoring unit is used for collecting vibration information of the iron ladle of the highway truck, the sound monitoring unit is used for collecting sound information of the iron ladle of the highway truck, and the temperature monitoring unit is used for collecting temperature information of the interior of the iron ladle of the highway truck.
In some embodiments, the status information of the road transport vehicle includes pressure information of the interior of the iron cask of the road transport vehicle, vibration information of the iron cask of the road transport vehicle, sound information of the iron cask of the road transport vehicle, and temperature information of the interior of the iron cask of the road transport vehicle at a plurality of points in time over a monitoring period (e.g., three minutes, five minutes, 10 minutes, etc.).
In some embodiments, the acquisition frequencies of the pressure monitoring unit, the vibration monitoring unit, the sound monitoring unit, and the temperature monitoring unit are adjusted in real time based on the first positioning information of the road transport vehicle.
Specifically, the cloud management platform may determine a current driving road of the road transport vehicle according to the first positioning information of the road transport vehicle, and acquire historical accident information, road structure information, current environment information and historical dangerous goods transportation records of the current driving road, where the historical accident information may include information of an accident occurring on the current driving road in a target period (for example, the past half year, one year, etc.), for example, accident time, accident vehicle type, accident cause, etc. The road structure information may include information of each intersection with the current driving road, for example, the position of the intersection, the number of roads of the intersection, and the like. The current environmental information may include environmental information, such as an ambient temperature, an ambient humidity, etc., of the road transport vehicle during a period of time that the current driving road is driving. The historical hazardous materials transportation record may include status information of road transport vehicles traveling over the current travel path to transport hazardous materials during travel of the current travel path. And determining the road abnormal score of the current driving road according to the historical accident information, road structure information, current environment information and historical dangerous goods transportation records of the current driving road, determining the acquisition frequency of the pressure monitoring unit, the vibration monitoring unit, the sound monitoring unit and the temperature monitoring unit on the current driving road based on the road abnormal score, transmitting the acquisition frequency of the current driving road to the state monitoring module, and adjusting the acquisition frequencies of the pressure monitoring unit, the vibration monitoring unit, the sound monitoring unit and the temperature monitoring unit in real time by the state monitoring module according to the received acquisition frequency of the current driving road. For example, the higher the road abnormality score, the higher the acquisition frequency of the pressure monitoring unit, vibration monitoring unit, sound monitoring unit, and temperature monitoring unit.
For example, the cloud management platform may determine an accident occurrence frequency based on historical accident information of the current driving road, determine a road structure parameter of the current driving road according to road structure information, determine an environmental anomaly parameter of the current driving road according to current environmental information, determine a dangerous goods transportation parameter according to a historical dangerous goods transportation record, and determine a road anomaly score based on the accident occurrence frequency, the road structure parameter, the environmental anomaly parameter, and the dangerous goods transportation parameter of the current driving road.
Further, the road anomaly score is determined based on the following formula:
；
；
；
；
；
Wherein, The road abnormality score for the i-th travel road,For the occurrence frequency of the accident on the i-th travel road,As the road structure parameter of the i-th travel road,As the environmental abnormality parameter of the i-th travel road,Is the dangerous goods transportation parameter of the ith driving road,、、AndAre all the weights of the preset weight, and the weight of the whole body is equal to the preset weight,、、AndAre all the parameters which are preset, and the parameters are all the preset parameters,For the number of accidents occurring in the target period for the i-th travel road,For the length of time of the target period,The number of roads at the jth intersection, which is the ith travel road, J being the total number of intersections included in the ith travel road,For the ambient temperature of the road transport vehicle during the period of travel on the ith travel path,For the preset ambient temperature to be reached,For the ambient humidity of the road transport vehicle during the period of travel on the ith travel path,In order to set the humidity of the environment,The abnormal value of the road transport vehicle transporting dangerous goods in the kth historical dangerous goods transport record of the ith driving road in the driving process of the ith driving road is K, which is the total number of the historical dangerous goods transport records of the ith driving road.
It can be understood that from the accident occurrence frequency, road structure parameters, environment abnormality parameters and dangerous goods transportation parameters of the current driving road, four dimensions accurately and comprehensively evaluate whether the road transport vehicle is easy to abnormality on the current driving road, further, according to the evaluation result, the acquisition frequencies of the pressure monitoring unit, the vibration monitoring unit, the sound monitoring unit and the temperature monitoring unit are adjusted in real time, and when abnormality is easy to occur, the real-time performance of monitoring the state of the road transport vehicle is improved.
The anomaly determination module may be configured to determine a vehicle anomaly value for the road transport vehicle based on the status information and the first positioning information of the road transport vehicle.
In some embodiments, the anomaly determination module determines a vehicle anomaly value for the road transport vehicle based on the status information and the first positioning information of the road transport vehicle, comprising:
Determining a state anomaly value of the road transport vehicle based on pressure information inside an iron barrel of the road transport vehicle, vibration information of the iron barrel of the road transport vehicle, sound information of the iron barrel of the road transport vehicle, and temperature information inside the iron barrel of the road transport vehicle at a plurality of time points in one monitoring period;
Determining a track abnormal value of the road transport vehicle based on first positioning information of the road transport vehicle at a plurality of historical transport time points and a preset transport path, wherein the preset transport path is generated through a cloud management platform;
And determining the abnormal value of the vehicle of the road transport vehicle based on the abnormal value of the state of the road transport vehicle and the abnormal value of the track of the road transport vehicle.
Specifically, the anomaly determination module may perform weighted summation on the abnormal state value of the road transport vehicle and the abnormal track value of the road transport vehicle, to determine the abnormal vehicle value of the road transport vehicle.
FIG. 2 is a flow chart of generating a preset transportation path shown in an embodiment of the present application, as shown in FIG. 2, in some embodiments, the cloud management platform generates the preset transportation path based on the following:
generating a plurality of candidate transportation routes based on a transportation start point and a transportation end point of the road transportation vehicle;
predicting road condition information of the candidate transportation routes in a plurality of future time periods for each candidate transportation route;
for each candidate transportation route, acquiring road transportation vehicle transportation information of the candidate transportation route in a plurality of future time periods;
Determining, for each candidate transportation route, transportation safety information of the candidate transportation route over a plurality of future time periods;
for each candidate transportation route, determining environmental information, e.g., environmental temperature information, environmental humidity information, etc., of the candidate transportation route over a plurality of future time periods;
for each candidate transportation route, determining a matching value of the candidate transportation route based on road condition information, road transportation vehicle transportation information, transportation safety information and environmental information of the candidate transportation route in a plurality of future time periods;
When at least one candidate transportation route has a matching value larger than a first preset matching value threshold, taking the candidate transportation route with the matching value larger than the first preset matching value threshold as a first target transportation route, and generating a preset transportation route based on the matching value of each first target transportation route;
And when at least one candidate transportation route does not exist and the matching value of the at least one candidate transportation route is larger than the first preset matching value threshold, the candidate transportation route with the matching value larger than the second preset matching value threshold is used as a second target transportation route, and the second target transportation route is fused to generate a preset transportation route.
Specifically, the cloud management platform can predict the road condition information of the candidate transportation route in a plurality of future time periods based on the historical road condition information of the candidate transportation route through the road condition prediction model. The road condition prediction model can be a machine learning model such as an artificial neural network (ARTIFICIAL NEURAL NETWORK, ANN) model, a cyclic neural network (Recurrent Neural Networks, RNN) model, a Long Short-Term Memory (LSTM) model, a bidirectional cyclic neural network (BRNN) model and the like.
The road transport information may include the number of road transport vehicles transporting the hazardous materials in the candidate transportation route for a plurality of future time periods, etc.
The transportation safety information may include information of accidents occurring in the candidate transportation route during a target period (e.g., the past half year, one year, etc.), such as accident time, accident vehicle type, accident cause, etc.
In some embodiments, the cloud management platform may determine the matching value of the candidate transportation route based on road condition information, road transportation vehicle transportation information, transportation safety information, and environmental information of the candidate transportation route in a plurality of future time periods through a matching value prediction model, wherein the matching value prediction model may be a machine learning model such as an artificial neural network (ARTIFICIAL NEURAL NETWORK, ANN) model, a recurrent neural network (Recurrent Neural Networks, RNN) model, a Long Short-Term Memory (LSTM) model, a bi-directional recurrent neural network (BRNN) model, or the like. The matching value of the candidate transportation route can represent the possibility that the candidate transportation route is suitable for executing the yellow phosphorus transportation task, and the larger the matching value of the candidate transportation route is, the more suitable the candidate transportation route is for executing the yellow phosphorus transportation task. It will be appreciated that the better the road condition of the candidate transportation route in the plurality of future time periods, the larger the matching value of the candidate transportation route, the fewer the number of road transportation vehicles transporting dangerous goods in the candidate transportation route in the plurality of future time periods, the larger the matching value of the candidate transportation route, the fewer the number of accidents occurring in the candidate transportation route in the target period (for example, the past half year, one year, etc.), the larger the matching value of the candidate transportation route, the closer the environmental information of the candidate transportation route in the plurality of future time periods is to the optimal transportation environment corresponding to yellow phosphorus, and the larger the matching value of the candidate transportation route.
When there is at least one candidate transportation route with a matching value greater than a first preset matching value threshold, the first target transportation route with the largest matching value may be used as the preset transportation route.
When the matching value of at least one candidate transportation route is not greater than a first preset matching value threshold value, the second target transportation route can be subjected to subsection analysis, road condition information, road transportation vehicle transportation information, transportation safety information and environment information of each subsection of the second target transportation route in a plurality of future time periods are determined, the subsection matching value is determined, the subsection, with the matching value greater than the second preset matching value threshold value, in the second target transportation route is taken as a target subsection, the plurality of target subsections are combined, a plurality of candidate combination paths are generated, transportation time required by the road transportation vehicle for transporting yellow phosphorus through the candidate combination paths is determined, and the candidate combination path with the shortest transportation time is taken as a preset transportation path.
It can be understood that the candidate transportation route is evaluated in four dimensions from road condition information, road transportation vehicle transportation information, transportation safety information and environment information of the candidate transportation route in a plurality of future time periods, so that a transportation route more suitable for the yellow phosphorus transportation task is generated, and the safety of the yellow phosphorus transportation is improved.
In some embodiments, the anomaly determination module may determine the abnormal value of the track of the road transport vehicle based on the first positioning information of the road transport vehicle at a plurality of historical transportation time points and the preset transportation path according to the following formula:
；
Wherein, Is the abnormal value of the track of the road transport vehicle,For the first positioning information of the road transport vehicle at the h historical transport time point,For the position information corresponding to the first positioning information of the h historical transportation time point in the preset transportation path,Is the total number of historical shipping time points.
Fig. 3 is a flowchart illustrating determination of a state anomaly value of a road transportation vehicle in an embodiment of the present application, and in some embodiments, the anomaly determination module determines a state anomaly value of a road transportation vehicle based on pressure information inside an iron barrel of the road transportation vehicle, vibration information of the iron barrel of the road transportation vehicle, sound information of the iron barrel of the road transportation vehicle, and temperature information inside the iron barrel of the road transportation vehicle at a plurality of time points within a monitoring period, including:
Generating pressure fluctuation parameters of the road transport vehicle in the monitoring period based on pressure information of the interior of the iron drum of the road transport vehicle at a plurality of time points in the monitoring period;
Generating vibration fluctuation parameters of the road transport vehicle in the monitoring period based on vibration information of the iron barrels of the road transport vehicle at a plurality of time points in the monitoring period;
Generating sound fluctuation parameters of the road transport vehicle in the monitoring period based on sound information of iron barrels of the road transport vehicle at a plurality of time points in the monitoring period;
generating temperature fluctuation parameters of the road transport vehicle in the monitoring period based on temperature information of the interior of the iron barrel of the road transport vehicle at a plurality of time points in the monitoring period;
And determining the abnormal state value of the road transport vehicle based on the pressure fluctuation parameter, the vibration fluctuation parameter, the sound fluctuation parameter and the temperature fluctuation parameter of the road transport vehicle in the monitoring period.
Specifically, the anomaly determination module may calculate the pressure fluctuation parameter based on the following formula:
；
Wherein, As a parameter of the pressure fluctuation,To monitor the pressure information inside the cask of the road transport vehicle at the g-th point in the cycle,Is the preset pressure inside the iron drum of the road transport vehicle,To monitor the total number of time points included in the cycle.
The calculation modes of the vibration fluctuation parameter, the sound fluctuation parameter and the temperature fluctuation parameter are similar to those of the pressure fluctuation parameter, and more description about the calculation modes of the vibration fluctuation parameter, the sound fluctuation parameter and the temperature fluctuation parameter can be referred to the relevant description of the calculation modes of the pressure fluctuation parameter, which is not repeated here.
It will be appreciated that the four dimensions provide a comprehensive and accurate assessment of the condition of the road vehicle from pressure fluctuations, vibration fluctuations, sound fluctuations and temperature fluctuations.
In some embodiments, the anomaly determination module may weight and sum the pressure fluctuation parameter, the vibration fluctuation parameter, the sound fluctuation parameter, and the temperature fluctuation parameter of the road transport vehicle during the monitoring period to determine a status anomaly value of the road transport vehicle.
The second positioning module may be configured to obtain second positioning information when the abnormal value of the vehicle is greater than a preset abnormal value threshold of the vehicle, and upload the second positioning information to the cloud management platform, where the accuracy of the second positioning information is higher than that of the first positioning information.
In some embodiments, the second positioning module includes at least a short-range communication positioning component and a machine vision positioning component.
In some embodiments, the second positioning module obtains second positioning information, including:
acquiring short-distance communication positioning information based on first positioning information of the road transport vehicle through the short-distance communication positioning assembly;
And acquiring second positioning information through the machine vision positioning component and the short-distance communication positioning information.
Specifically, the target short-distance communication positioning base station may be determined based on the first positioning information of the road transport vehicle, the short-distance communication positioning information may be determined based on the interaction information between the short-distance communication positioning component on the road transport vehicle and the target short-distance communication positioning base station, the target road atlas may be determined based on the short-distance communication positioning information, the image information of the driving road of the road transport vehicle may be obtained through the machine vision positioning component, and the second positioning information of the road transport vehicle may be determined based on the image information of the driving road of the road transport vehicle and the target road atlas.
It will be appreciated that by combining the short-range communications positioning assembly with the machine vision positioning assembly, a higher accuracy of positioning is achieved.
Fig. 4 is a flowchart of a real-time positioning method of a highway truck for transporting yellow phosphorus according to an embodiment of the present application, and as shown in fig. 4, in some embodiments, a real-time positioning method of a highway truck for transporting yellow phosphorus may include the following steps.
Step 410, the first positioning information of the road transport vehicle is obtained and uploaded to the cloud management platform.
Step 420, obtain status information of the road transport vehicle.
Step 430, determining a vehicle anomaly value for the road transport vehicle based on the status information and the first positioning information of the road transport vehicle.
And step 440, acquiring second positioning information when the abnormal value of the vehicle is larger than a preset abnormal value threshold value of the vehicle, and uploading the second positioning information to the cloud management platform.
Wherein the accuracy of the second positioning information is higher than the first positioning information.
For more description of a real-time positioning method for a highway truck for transporting yellow phosphorus, reference may be made to a related description of a real-time positioning system for a highway truck for transporting yellow phosphorus, which will not be repeated herein.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (7)
1. A real-time positioning system for a highway transport vehicle for transporting yellow phosphorus, comprising:
The first positioning module is used for acquiring first positioning information of the road transport vehicle and uploading the first positioning information to the cloud management platform;
the state monitoring module is used for acquiring state information of the road transport vehicle;
An anomaly determination module for determining a vehicle anomaly value of the road transport vehicle based on the state information of the road transport vehicle and the first positioning information;
the second positioning module is used for acquiring second positioning information when the abnormal value of the vehicle is larger than a preset abnormal value threshold value of the vehicle and uploading the second positioning information to the cloud management platform, wherein the accuracy of the second positioning information is higher than that of the first positioning information;
The first positioning module acquires first positioning information of the road transport vehicle based on a GPS positioning technology;
The state monitoring module comprises a pressure monitoring unit, a vibration monitoring unit, a sound monitoring unit and a temperature monitoring unit, wherein the pressure monitoring unit is used for collecting pressure information in the iron barrel of the highway truck, the vibration monitoring unit is used for collecting vibration information of the iron barrel of the highway truck, the sound monitoring unit is used for collecting sound information of the iron barrel of the highway truck, and the temperature monitoring unit is used for collecting temperature information in the iron barrel of the highway truck;
the state information of the highway transportation vehicle comprises pressure information of the interior of the iron barrel of the highway transportation vehicle, vibration information of the iron barrel of the highway transportation vehicle, sound information of the iron barrel of the highway transportation vehicle and temperature information of the interior of the iron barrel of the highway transportation vehicle at a plurality of time points in a monitoring period;
The anomaly determination module determines a vehicle anomaly value for the road transport vehicle based on the state information of the road transport vehicle and the first positioning information, comprising:
Determining a state anomaly value of the road transport vehicle based on pressure information inside an iron barrel of the road transport vehicle, vibration information of the iron barrel of the road transport vehicle, sound information of the iron barrel of the road transport vehicle, and temperature information inside the iron barrel of the road transport vehicle at a plurality of time points in the one monitoring period;
determining a track abnormal value of the road transport vehicle based on first positioning information of the road transport vehicle at a plurality of historical transport time points and a preset transport path;
And determining the abnormal value of the road transport vehicle based on the abnormal value of the road transport vehicle and the abnormal value of the track of the road transport vehicle, specifically, carrying out weighted summation on the abnormal value of the road transport vehicle and the abnormal value of the track of the road transport vehicle, and determining the abnormal value of the road transport vehicle.
2. The real-time positioning system of a highway truck for transporting yellow phosphorus according to claim 1, wherein the collection frequencies of the pressure monitoring unit, the vibration monitoring unit, the sound monitoring unit and the temperature monitoring unit are adjusted in real time based on the first positioning information of the highway truck.
3. The real-time positioning system of a highway truck for transporting yellow phosphorus according to claim 1, wherein the preset transport path is generated by the cloud management platform;
the cloud management platform generates the preset transportation path based on the following flow:
Generating a plurality of candidate transportation routes based on the transportation start point and the transportation end point of the road transportation vehicle;
Predicting road condition information of the candidate transportation routes in a plurality of future time periods for each candidate transportation route;
for each candidate transportation route, acquiring road transportation vehicle transportation information of the candidate transportation route in a plurality of future time periods;
Determining, for each of the candidate transportation routes, transportation safety information for the candidate transportation route over a plurality of future time periods;
Determining, for each of the candidate transportation routes, environmental information of the candidate transportation route over a plurality of future time periods;
For each candidate transportation route, determining a matching value of the candidate transportation route based on road condition information, road transportation vehicle transportation information, transportation safety information and environment information of the candidate transportation route in a plurality of future time periods;
When at least one candidate transportation route with the matching value larger than a first preset matching value threshold exists, the candidate transportation route with the matching value larger than the first preset matching value threshold is used as a first target transportation route, and the preset transportation route is generated based on the matching value of each first target transportation route;
And when at least one candidate transportation route does not exist and the matching value of the candidate transportation routes is larger than a first preset matching value threshold, the candidate transportation route with the matching value larger than a second preset matching value threshold is used as a second target transportation route, and the second target transportation route is fused to generate the preset transportation route.
4. The real-time positioning system of a highway truck for transporting yellow phosphorus according to claim 1, wherein the abnormality determination module determines the abnormal state value of the highway truck based on the pressure information of the interior of the iron barrel of the highway truck, the vibration information of the iron barrel of the highway truck, the sound information of the iron barrel of the highway truck, and the temperature information of the interior of the iron barrel of the highway truck at a plurality of time points in the one monitoring period, comprising:
Generating pressure fluctuation parameters of the road transport vehicle in the monitoring period based on the pressure information of the interior of the iron drum of the road transport vehicle at a plurality of time points in the monitoring period;
generating vibration fluctuation parameters of the road transport vehicle in the monitoring period based on vibration information of iron barrels of the road transport vehicle at a plurality of time points in the monitoring period;
Generating sound fluctuation parameters of the road transport vehicle in the monitoring period based on sound information of iron barrels of the road transport vehicle at a plurality of time points in the monitoring period;
Generating temperature fluctuation parameters of the road transport vehicle in the monitoring period based on temperature information of the interior of the iron barrel of the road transport vehicle at a plurality of time points in the monitoring period;
And determining a state abnormal value of the road transport vehicle based on the pressure fluctuation parameter, the vibration fluctuation parameter, the sound fluctuation parameter and the temperature fluctuation parameter of the road transport vehicle in the monitoring period.
5. A real-time positioning system for a highway transportation vehicle for transporting yellow phosphorus according to any one of claims 1-4 and wherein said second positioning module comprises at least a short-range communication positioning assembly and a machine vision positioning assembly.
6. The real-time positioning system of a highway truck for transporting yellow phosphorus according to claim 5, wherein the second positioning module obtains the second positioning information, and the method comprises:
acquiring short-distance communication positioning information based on first positioning information of the road transport vehicle through the short-distance communication positioning assembly;
And acquiring the second positioning information through the machine vision positioning component and the short-distance communication positioning information.
7. A real-time positioning method of a highway truck for transporting yellow phosphorus, which is applied to the real-time positioning system of the highway truck for transporting yellow phosphorus as claimed in any one of claims 1 to 6, and is characterized by comprising the following steps:
Acquiring first positioning information of a road transport vehicle, and uploading the first positioning information to a cloud management platform, and specifically acquiring the first positioning information of the road transport vehicle based on a GPS positioning technology;
Acquiring state information of the road transport vehicle;
Determining a vehicle outlier of the road transport vehicle based on the state information of the road transport vehicle and the first positioning information;
Acquiring second positioning information when the abnormal value of the vehicle is larger than a preset abnormal value threshold value of the vehicle, and uploading the second positioning information to a cloud management platform, wherein the accuracy of the second positioning information is higher than that of the first positioning information;
the state information of the highway transportation vehicle comprises pressure information of the interior of the iron barrel of the highway transportation vehicle, vibration information of the iron barrel of the highway transportation vehicle, sound information of the iron barrel of the highway transportation vehicle and temperature information of the interior of the iron barrel of the highway transportation vehicle at a plurality of time points in a monitoring period;
Determining a vehicle anomaly value for the road transport vehicle based on the state information of the road transport vehicle and the first positioning information, comprising:
Determining a state anomaly value of the road transport vehicle based on pressure information inside an iron barrel of the road transport vehicle, vibration information of the iron barrel of the road transport vehicle, sound information of the iron barrel of the road transport vehicle, and temperature information inside the iron barrel of the road transport vehicle at a plurality of time points in the one monitoring period;
determining a track abnormal value of the road transport vehicle based on first positioning information of the road transport vehicle at a plurality of historical transport time points and a preset transport path;
And determining the abnormal value of the road transport vehicle based on the abnormal value of the road transport vehicle and the abnormal value of the track of the road transport vehicle, specifically, carrying out weighted summation on the abnormal value of the road transport vehicle and the abnormal value of the track of the road transport vehicle, and determining the abnormal value of the road transport vehicle.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW200938808A (en) * | 2008-03-14 | 2009-09-16 | Chunghwa Telecom Co Ltd | Intelligent positioning calibration system method |
| CN102147964A (en) * | 2010-02-05 | 2011-08-10 | 新光国际航太科技股份有限公司 | System and method of transport vehicle for monitoring and managing temperature of goods carried |
| CN105354992A (en) * | 2015-11-05 | 2016-02-24 | 青岛中科软件股份有限公司 | Hazardous chemical substance transportation whole-process monitoring system |
| CN108827274A (en) * | 2018-04-24 | 2018-11-16 | 骁越科技（青岛）有限公司 | A kind of outdoor version AGV navigator fix technology and device |
| CN209993006U (en) * | 2019-04-01 | 2020-01-24 | 南京财经大学 | Cold chain grain transportation system of traceing back based on thing networking |
| CN110763277A (en) * | 2019-11-11 | 2020-02-07 | 李蔚田 | Real-time logistics positioning monitoring system based on Beidou satellite system |
| CN112285754A (en) * | 2020-10-16 | 2021-01-29 | 中冶赛迪上海工程技术有限公司 | Vehicle multidimensional tracking and positioning method and system |
| CN112677325A (en) * | 2021-01-08 | 2021-04-20 | 南京维拓科技股份有限公司 | Commercial concrete transportation monitoring and early warning method |
| WO2021195902A1 (en) * | 2020-03-30 | 2021-10-07 | 华为技术有限公司 | Method and apparatus for controlling positioning confidence score |
| CN114912879A (en) * | 2022-06-07 | 2022-08-16 | 公安部第三研究所 | Hazardous chemicals automobile transportation safety monitoring system based on full factor management |
| CN115065941A (en) * | 2022-07-20 | 2022-09-16 | 郑州铁路职业技术学院 | Safety communication device for logistics transport vehicle |
| CN115392818A (en) * | 2021-10-19 | 2022-11-25 | 上海闵东国际物流有限公司 | GPS-based freight tracking transportation management system |
| CN116629730A (en) * | 2023-06-12 | 2023-08-22 | 朱建柏 | Intelligent real-time monitoring system for digital perception of whole process of dangerous waste |
| CN117196450A (en) * | 2023-08-16 | 2023-12-08 | 成都立创亿联科技有限公司 | Article management system and method based on positioning monitoring |
| CN117289594A (en) * | 2023-09-15 | 2023-12-26 | 四川中烟工业有限责任公司 | Method and system for controlling water content of outlet of cut tobacco drying based on mode discrimination |
| CN117558129A (en) * | 2023-11-14 | 2024-02-13 | 长沙云软信息技术有限公司 | Intelligent positioning system and method for expressway construction |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160238406A1 (en) * | 2015-02-13 | 2016-08-18 | The Burtner Group, LLC | Refrigerated transport container wireless remote tracking and management systems and methods |
| KR102502235B1 (en) * | 2015-07-16 | 2023-02-21 | 삼성전자주식회사 | Logistic monitoring system and the method |
| KR20220031846A (en) * | 2020-09-04 | 2022-03-14 | 현대자동차주식회사 | Production factory unmanned transfer system and method |
-
2024
- 2024-03-26 CN CN202410349947.7A patent/CN117949976B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW200938808A (en) * | 2008-03-14 | 2009-09-16 | Chunghwa Telecom Co Ltd | Intelligent positioning calibration system method |
| CN102147964A (en) * | 2010-02-05 | 2011-08-10 | 新光国际航太科技股份有限公司 | System and method of transport vehicle for monitoring and managing temperature of goods carried |
| CN105354992A (en) * | 2015-11-05 | 2016-02-24 | 青岛中科软件股份有限公司 | Hazardous chemical substance transportation whole-process monitoring system |
| CN108827274A (en) * | 2018-04-24 | 2018-11-16 | 骁越科技（青岛）有限公司 | A kind of outdoor version AGV navigator fix technology and device |
| CN209993006U (en) * | 2019-04-01 | 2020-01-24 | 南京财经大学 | Cold chain grain transportation system of traceing back based on thing networking |
| CN110763277A (en) * | 2019-11-11 | 2020-02-07 | 李蔚田 | Real-time logistics positioning monitoring system based on Beidou satellite system |
| WO2021195902A1 (en) * | 2020-03-30 | 2021-10-07 | 华为技术有限公司 | Method and apparatus for controlling positioning confidence score |
| CN112285754A (en) * | 2020-10-16 | 2021-01-29 | 中冶赛迪上海工程技术有限公司 | Vehicle multidimensional tracking and positioning method and system |
| CN112677325A (en) * | 2021-01-08 | 2021-04-20 | 南京维拓科技股份有限公司 | Commercial concrete transportation monitoring and early warning method |
| CN115392818A (en) * | 2021-10-19 | 2022-11-25 | 上海闵东国际物流有限公司 | GPS-based freight tracking transportation management system |
| CN114912879A (en) * | 2022-06-07 | 2022-08-16 | 公安部第三研究所 | Hazardous chemicals automobile transportation safety monitoring system based on full factor management |
| CN115065941A (en) * | 2022-07-20 | 2022-09-16 | 郑州铁路职业技术学院 | Safety communication device for logistics transport vehicle |
| CN116629730A (en) * | 2023-06-12 | 2023-08-22 | 朱建柏 | Intelligent real-time monitoring system for digital perception of whole process of dangerous waste |
| CN117196450A (en) * | 2023-08-16 | 2023-12-08 | 成都立创亿联科技有限公司 | Article management system and method based on positioning monitoring |
| CN117289594A (en) * | 2023-09-15 | 2023-12-26 | 四川中烟工业有限责任公司 | Method and system for controlling water content of outlet of cut tobacco drying based on mode discrimination |
| CN117558129A (en) * | 2023-11-14 | 2024-02-13 | 长沙云软信息技术有限公司 | Intelligent positioning system and method for expressway construction |
Non-Patent Citations (3)
| Title |
|---|
| Smart Railway Infrastructure: Fusion of IoT, RFID and VANET for Enhanced Safety and Operational Efficiency;T Kamakshi;《023 Second International Conference on Advances in Computational Intelligence and Communication (ICACIC)》;20240219;pp. 1-6 * |
| 典型危险货物罐式箱铁路运输安全监控技术研究;杨文韬;《铁道货运》;20201225;第38卷(第12期);第78-83页 * |
| 露天矿无人驾驶运输***安全技术;白仁喜;《工矿自动化》;20231115;第49卷(第S2期);第165-170页 * |
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