CN114881561B - Remote on-line monitoring management cloud platform for cargo transportation vehicle based on logistics industry - Google Patents

Remote on-line monitoring management cloud platform for cargo transportation vehicle based on logistics industry Download PDF

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CN114881561B
CN114881561B CN202210500251.0A CN202210500251A CN114881561B CN 114881561 B CN114881561 B CN 114881561B CN 202210500251 A CN202210500251 A CN 202210500251A CN 114881561 B CN114881561 B CN 114881561B
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李超
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Abstract

The invention discloses a cargo transportation vehicle remote on-line monitoring management cloud platform based on logistics industry, which comprises: the system comprises a cargo carrier basic information acquisition module, a vehicle and personnel information acquisition module, a cargo transport order distribution module, a truck vehicle entrance monitoring module, a maintenance vehicle display and a storage database, wherein a preferable influence coefficient is obtained through comprehensive analysis of both driving experience of a driver and fatigue index of the driver, and a vehicle maintenance coefficient is obtained through comprehensive analysis of both wear degree of a vehicle body and dent index of the vehicle body.

Description

Remote on-line monitoring management cloud platform for cargo transportation vehicle based on logistics industry
Technical Field
The invention relates to the technical field of cargo transportation, in particular to a cargo transportation vehicle remote on-line monitoring management cloud platform based on logistics industry.
Background
Modern logistics industry is an emerging composite industry, and is rapidly developed worldwide, and has become a basic industry of global people economy and a backbone industry of service industry. At present, the logistics industry is in an accumulation stage, and along with the great application of information technology and the rising of electronic commerce, more and more cargo transportation modes mainly comprising trucks are appeared.
At present, the management of the cargo transportation vehicles lacks intelligent control and management, so the present cargo transportation vehicle management platform has the following disadvantages:
the conventional cargo transportation vehicle management platform has certain defects in the allocation management of the truck drivers, so that the analysis of the driving experience of the drivers is lacking, the analysis of the fatigue indexes of the drivers is lacking, the problem of incomplete consideration exists when the truck drivers are allocated, the irrational effect is easily caused, and meanwhile, the increase of potential safety hazards of the truck drivers in the driving process is caused by neglecting the fatigue indexes of the truck drivers, so that the accident rate is greatly improved.
The conventional cargo transportation vehicle management platform has certain defects in maintenance and management of the truck vehicles, not only ignoring analysis of the abrasion degree of the truck vehicle bodies, but also neglecting analysis of the dent index of the truck vehicle bodies, so that the vehicle maintenance coefficient cannot be obtained as reliable reference data, further, the truck vehicles cannot be maintained and adjusted in time, the service efficiency of the truck vehicles is reduced, certain economic loss is caused, and meanwhile, instability is caused for the traveling of the truck vehicles.
Disclosure of Invention
In order to overcome the defects in the background technology, the embodiment of the invention provides a remote online monitoring and managing cloud platform for a cargo transportation vehicle based on the logistics industry, which can effectively solve the problems related to the background technology.
The aim of the invention can be achieved by the following technical scheme:
cargo transportation vehicle remote on-line monitoring management cloud platform based on commodity circulation trade includes:
the basic information acquisition module of the cargo carrier is used for acquiring basic information of the cargo carrier, wherein the basic information comprises the number of the wagon vehicles corresponding to each vehicle type and the number of the wagon drivers corresponding to each vehicle type, and numbering the wagon vehicles corresponding to each vehicle type and the wagon drivers corresponding to each vehicle type;
the vehicle and personnel information acquisition module is used for monitoring the use state of each truck vehicle corresponding to each vehicle type and the working state of each truck driver corresponding to each vehicle type in real time through the intelligent camera, acquiring the rest duration of each idle truck driver corresponding to each vehicle type from the use state and the working state of each idle truck driver, and further acquiring the idle truck vehicle set corresponding to each vehicle type and the idle truck driver set corresponding to each vehicle type;
the system comprises a cargo transportation order information acquisition module, a cargo transportation order information acquisition module and a cargo management module, wherein the cargo transportation order information acquisition module is used for acquiring current cargo transportation order information, and the cargo transportation order information comprises cargo volume and cargo transportation places;
the goods transport order distribution module is used for acquiring a target transport vehicle number and a target transport truck driver number distributed by the current goods transport order from the idle truck vehicle set corresponding to each vehicle type and the idle truck driver set corresponding to each vehicle type, and further notifying the target transport truck driver of goods transport work;
the truck vehicle entrance monitoring module is used for counting the number of the distributed truck vehicles in a set time period, acquiring the driving-in and driving-out images of each distributed truck vehicle through the intelligent camera to obtain a vehicle body driving-in image and a vehicle body driving-out image corresponding to each distributed truck vehicle, analyzing corresponding vehicle maintenance coefficients of each distributed truck vehicle according to the vehicle driving-in and driving-out images, counting the number of the vehicles to be maintained according to the vehicle maintenance coefficients, and extracting the number of each vehicle to be maintained;
the maintenance vehicle display is used for displaying the number and the corresponding number of the vehicles to be maintained;
the storage database is used for storing the ages and driving ages corresponding to the truck drivers of the various vehicle types, storing the volume of the nuclear cargo corresponding to the various vehicle types, storing the driving mileage and the driving duration corresponding to the various truck drivers of the various vehicle types, storing the vehicle body surface area corresponding to the various truck drivers of the various vehicle types, storing the recommended rest duration corresponding to the ages of the various truck drivers and storing the contact modes corresponding to the various truck drivers of the various vehicle types.
More optimally, the method for numbering the truck vehicles corresponding to each vehicle type and the truck drivers corresponding to each vehicle type comprises the following steps: the truck corresponding to each model is marked as A kc ，A kc The c-th truck vehicle corresponds to the k-th truck vehicle, c=1, 2, w, c represents the truck vehicle number, k=1, 2, t, k represents the number of the truck type, and the truck driver corresponding to each truck type is labeled B kr ，B kr Denoted as kth vehicle model corresponds to the kth truck driver, r=1, 2.
More preferably, the truck vehicle use status includes use and idle, and the truck driver work status includes shipment and leisure.
More preferably, the cargo transportation order allocation module includes a cargo object transportation vehicle allocation unit, a cargo object transportation truck driver allocation unit, and an object transportation truck driver notification unit.
More preferably, the cargo object transportation vehicle allocation unit is configured to obtain the number of the object transportation vehicle allocated in the current cargo transportation order, and specifically analyze the number as follows:
extracting the cargo volume from the current cargo transportation order information, comparing the cargo volume with the corresponding nuclear cargo volume of each vehicle type stored in the storage database, analyzing the cargo volume matching vehicle type, and marking the cargo volume matching vehicle type as a specified vehicle type;
based on the specified vehicle types, acquiring a set of idle truck vehicles corresponding to the specified vehicle types from the set of idle truck vehicles corresponding to the specified vehicle types, simultaneously marking the idle truck vehicles corresponding to the specified vehicle types as target transport vehicles, and extracting numbers corresponding to the target transport vehicles.
More preferably, the cargo target freight wagon driver allocation unit is configured to obtain the target freight wagon driver number allocated in the current cargo transportation order, and specifically analyze the following:
based on the specified vehicle type, acquiring a leisure van driver set corresponding to the specified vehicle type from the leisure van driver set corresponding to each vehicle type, and calculating driving experience indexes of the leisure van drivers in the leisure van driver set corresponding to the specified vehicle type, wherein a specific calculation formula is as follows
Figure BDA0003634096250000041
ηB′ r Driving experience index, B ', expressed as the corresponding r-th leisure wagon driver of the specified vehicle type' r L、B′ r T is respectively expressed as the driving mileage and the driving duration of the specified vehicle type corresponding to the r-th idle van driver, L ' and T ' are respectively expressed as the reference driving mileage and the reference driving duration of the driver, and B ' r g is expressed as the driving age of the r-th idle van driver corresponding to the appointed vehicle type, and a1, a2 and a3 are respectively expressed as driving mileage, driving duration and influence factors corresponding to the driving age;
the method comprises the steps of extracting the rest time length of each leisure van driver corresponding to a specified vehicle type from the rest time length of each leisure van driver corresponding to each vehicle type, and calculating the fatigue index of each leisure van driver corresponding to the specified vehicle type, wherein the specific calculation formula is as follows
Figure BDA0003634096250000051
Figure BDA0003634096250000052
Fatigue index, B ', expressed as the fatigue index of the driver of the truck corresponding to the r-th leisure vehicle of the specified vehicle type' r t is the resting time length of the r idle van driver corresponding to the appointed vehicle type, b is the correction coefficient corresponding to the resting time length, and rjt' is the recommended resting time length corresponding to the age of the r idle van driver;
based on the driving experience index of the assigned vehicle type corresponding to each leisure van driver and the fatigue index of the assigned vehicle type corresponding to each leisure van driver, comprehensively analyzing to obtain the optimal influence coefficient of the assigned vehicle type corresponding to each leisure van driver, wherein the specific calculation formula is as follows
Figure BDA0003634096250000053
σB′ r The optimal influence coefficient of the r idle van driver corresponding to the appointed vehicle type is represented, and e1 and e2 are respectively represented as weight factors corresponding to driving experience indexes and fatigue indexes;
and sequencing the preferential influence coefficients of the appointed vehicle types corresponding to the leisure truck drivers according to the sequence from large to small, screening the corresponding leisure truck driver with the largest preferential influence coefficient from the preferential influence coefficients, marking the corresponding leisure truck driver as a target transportation truck driver, and simultaneously extracting the serial numbers of the target transportation truck driver.
More optimally, the target transport truck driver notification unit is used for matching the number of the target transport truck driver corresponding to the designated vehicle type with the contact ways of the truck drivers corresponding to the vehicle types in the storage database, so as to obtain the contact way of the target transport truck driver, further send the number of the target transport vehicle to the target transport truck driver, and carry out the transport work of the current cargo transport order by the target transport truck driver.
More preferably, the analysis of the corresponding vehicle maintenance coefficients for each assigned truck vehicle is as follows:
the assigned truck vehicles are numbered 1,2 according to the assigned time sequence, and the number f is the number u;
comparing the vehicle body driving-in images and the vehicle body driving-out images corresponding to the distributed truck vehicles one by one, counting the abrasion quantity of the vehicle bodies of the distributed vehicles and the sinking quantity of the vehicle bodies of the distributed vehicles, and obtaining the corresponding abrasion areas of the abrasion parts of the vehicle bodies of the distributed vehicles and the corresponding sinking areas and sinking depths of the sinking parts of the vehicle bodies of the distributed vehicles;
calculating the corresponding vehicle wear degree of each allocated truck vehicle, wherein the specific calculation formula is as follows
Figure BDA0003634096250000061
γ f Denoted as f-th assigned truck corresponding body wear degree, f n m represents the corresponding wear area of the nth body wear in the f-th allocated wagon, n represents the number of the body wear, n=1, 2.
Calculating the vehicle body dent index of each allocated truck vehicle, wherein the specific calculation formula is as follows
Figure BDA0003634096250000062
μ f Expressed as the corresponding body dent index of the f-th allocated truck vehicle, f q m' is expressed as the corresponding vehicle body depression area of the q-th depression in the f-th allocated truck vehicle, f q h represents the vehicle body depression depth of the q-th depression in the f-th allocated truck vehicle, q represents the number of the depression, q=1, 2,.. P, fH represents the allowable depression depth of the corresponding vehicle body of the f-th allocated truck vehicle, and c1 and c2 represent the impact factors corresponding to the vehicle body depression area and the vehicle body depression depth respectively;
vehicle according to each allocated freight vehicleThe body abrasion degree and the body dent index calculate the corresponding vehicle maintenance coefficient of each allocated truck vehicle, and the specific calculation formula is that
Figure BDA0003634096250000071
ζ f The maintenance coefficient corresponding to the f-th allocated truck is expressed, and d1 and d2 are respectively expressed as the correction coefficients corresponding to the abrasion degree and the dent index of the truck body.
More optimally, the statistics of the vehicle to be maintained according to the vehicle maintenance coefficient comprises the following steps:
comparing the corresponding maintenance coefficient of each allocated truck with a preset vehicle maintenance threshold, if the corresponding maintenance coefficient of a certain allocated truck is larger than the preset vehicle maintenance threshold, marking the corresponding maintenance coefficient of the allocated truck as a vehicle needing maintenance, extracting the number of the vehicle needing maintenance, and further counting the number of the vehicles needing maintenance.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
the invention effectively solves the defect that the prior cargo transportation vehicle management platform cannot analyze the optimal influence coefficient, comprehensively analyzes the optimal influence coefficient from the driving experience index and the fatigue index of the driver, overcomes the defect that the prior art does not analyze the driving experience and the fatigue index of the driver, avoids the unreasonable caused by incomplete consideration when distributing the truck driver, and analyzes the fatigue index of the truck driver to ensure that analysis data has more rigorous and scientific basis, thereby effectively reducing the potential safety hazard of the truck driver in the driving process.
The invention effectively solves the defect that the current cargo transportation vehicle management platform cannot analyze the vehicle maintenance coefficient, comprehensively analyzes the wear degree of the vehicle body and the dent index of the vehicle body to obtain the vehicle maintenance coefficient, makes up the defect that the wear degree of the vehicle body and the dent index of the vehicle body are not analyzed in the prior art, provides a powerful data base for vehicle maintenance, realizes timely maintenance and adjustment of truck vehicles, avoids the reduction of the utilization rate of the truck vehicles, reduces economic loss to a great extent, and simultaneously ensures the running stability and safety of the truck vehicles.
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The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
FIG. 1 is a schematic diagram of the system module connection of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a remote on-line monitoring management cloud platform for a cargo transportation vehicle based on logistics industry, which comprises a cargo transportation company basic information acquisition module, a vehicle and personnel information acquisition module, a cargo transportation order distribution module, a truck vehicle entrance monitoring module, a maintenance vehicle display and a storage database.
The system comprises a cargo transportation company basic information acquisition module, a vehicle, a personnel information acquisition module, a cargo transportation order distribution module, a truck vehicle entrance monitoring module, a maintenance vehicle display and a storage database.
The basic information acquisition module is used for acquiring basic information of the cargo carrier, wherein the basic information comprises the number of the wagon vehicles corresponding to each vehicle type and the number of the wagon drivers corresponding to each vehicle type, and numbering the wagon vehicles corresponding to each vehicle type and the wagon drivers corresponding to each vehicle type.
More optimally, the method for numbering the truck vehicles corresponding to each vehicle type and the truck drivers corresponding to each vehicle type comprises the following steps: the truck corresponding to each model is marked as A kc ，A kc The c-th truck vehicle corresponds to the k-th truck vehicle, c=1, 2, w, c represents the truck vehicle number, k=1, 2, t, k represents the number of the truck type, and the truck driver corresponding to each truck type is labeled B kr ，B kr Denoted as kth vehicle model corresponds to the kth truck driver, r=1, 2.
The vehicle and personnel information acquisition module is used for monitoring the use state of each truck vehicle corresponding to each vehicle type and the working state of each truck driver corresponding to each vehicle type in real time through the intelligent camera, acquiring the rest duration of each idle truck driver corresponding to each vehicle type from the use state of each truck vehicle corresponding to each vehicle type, and further acquiring the idle truck vehicle set corresponding to each vehicle type and the idle truck driver set corresponding to each vehicle type.
More preferably, the truck vehicle use status includes use and idle, and the truck driver work status includes shipment and leisure.
The invention can obtain the rest time length of each leisure truck driver corresponding to each vehicle type, which is used for carrying out powerful data support for the fatigue index corresponding to each leisure truck driver, so that the analysis result is clearer, more accurate and more reliable.
And the goods transportation order information acquisition module is used for acquiring the current goods transportation order information, wherein the goods transportation order information comprises a goods volume and a goods transportation place.
More preferably, the cargo transportation order allocation module includes a cargo object transportation vehicle allocation unit, a cargo object transportation truck driver allocation unit, and an object transportation truck driver notification unit.
More preferably, the cargo object transportation vehicle allocation unit is configured to obtain the number of the object transportation vehicle allocated in the current cargo transportation order, and specifically analyze the number as follows:
extracting the cargo volume from the current cargo transportation order information, comparing the cargo volume with the corresponding nuclear cargo volume of each vehicle type stored in the storage database, analyzing the cargo volume matching vehicle type, and marking the cargo volume matching vehicle type as a specified vehicle type;
based on the specified vehicle types, acquiring a set of idle truck vehicles corresponding to the specified vehicle types from the set of idle truck vehicles corresponding to the specified vehicle types, simultaneously marking the idle truck vehicles corresponding to the specified vehicle types as target transport vehicles, and extracting numbers corresponding to the target transport vehicles.
In the embodiment of the present invention, the idle truck corresponding to the specified vehicle type is recorded as the target transport vehicle, which specifically includes: if the number of the idle truck vehicles corresponding to the specified vehicle type is larger than 1, acquiring the current oil quantity of the current oil tank of each idle truck vehicle corresponding to the specified vehicle type, and screening the idle truck vehicle corresponding to the specified vehicle type with the largest oil quantity left in the current oil tank from the current oil tank as a target transport vehicle; and if the number of the idle truck vehicles corresponding to the designated vehicle type is equal to 1, marking the idle truck vehicles corresponding to the designated vehicle type as target transport vehicles.
More preferably, the cargo target freight wagon driver allocation unit is configured to obtain the target freight wagon driver number allocated in the current cargo transportation order, and specifically analyze the following:
based on the specified vehicle type, acquiring a leisure van driver set corresponding to the specified vehicle type from the leisure van driver set corresponding to each vehicle type, and calculating driving experience indexes of the leisure van drivers in the leisure van driver set corresponding to the specified vehicle type, wherein a specific calculation formula is as follows
Figure BDA0003634096250000111
ηB′ r Driving experience index, B ', expressed as the corresponding r-th leisure wagon driver of the specified vehicle type' r L、B′ r T is respectively expressed as the driving mileage and the driving duration of the specified vehicle type corresponding to the r-th idle van driver, L ' and T ' are respectively expressed as the reference driving mileage and the reference driving duration of the driver, and B ' r g is expressed as the driving age of the r-th idle van driver corresponding to the appointed vehicle type, and a1, a2 and a3 are respectively expressed as driving mileage, driving duration and influence factors corresponding to the driving age;
the method comprises the steps of extracting the rest time length of each leisure van driver corresponding to a specified vehicle type from the rest time length of each leisure van driver corresponding to each vehicle type, and calculating the fatigue index of each leisure van driver corresponding to the specified vehicle type, wherein the specific calculation formula is as follows
Figure BDA0003634096250000112
Figure BDA0003634096250000113
Fatigue index, B ', expressed as the fatigue index of the driver of the truck corresponding to the r-th leisure vehicle of the specified vehicle type' r t is the resting time length of the r idle van driver corresponding to the appointed vehicle type, b is the correction coefficient corresponding to the resting time length, and rjt' is the recommended resting time length corresponding to the age of the r idle van driver;
based on the driving experience index of the assigned vehicle type corresponding to each leisure van driver and the fatigue index of the assigned vehicle type corresponding to each leisure van driver, comprehensively analyzing to obtain the optimal influence coefficient of the assigned vehicle type corresponding to each leisure van driver, wherein the specific calculation formula is as follows
Figure BDA0003634096250000121
σB′ r The optimal influence coefficient of the r idle van driver corresponding to the appointed vehicle type is represented, and e1 and e2 are respectively represented as weight factors corresponding to driving experience indexes and fatigue indexes;
and sequencing the preferential influence coefficients of the appointed vehicle types corresponding to the leisure truck drivers according to the sequence from large to small, screening the corresponding leisure truck driver with the largest preferential influence coefficient from the preferential influence coefficients, marking the corresponding leisure truck driver as a target transportation truck driver, and simultaneously extracting the serial numbers of the target transportation truck driver.
More optimally, the target transport truck driver notification unit is used for matching the number of the target transport truck driver corresponding to the designated vehicle type with the contact ways of the truck drivers corresponding to the vehicle types in the storage database, so as to obtain the contact way of the target transport truck driver, further send the number of the target transport vehicle to the target transport truck driver, and carry out the transport work of the current cargo transport order by the target transport truck driver.
The invention effectively solves the defect that the management platform of the current cargo transportation vehicle cannot analyze the optimal influence coefficient, obtains the optimal influence coefficient from the comprehensive analysis of the driving experience index and the fatigue index of the driver, overcomes the defect that the prior art does not have the analysis of the driving experience and the fatigue index of the driver, avoids the irrational caused by incomplete consideration when the truck driver is distributed, and has more rigorous and scientific basis on analysis of the fatigue index of the truck driver, thereby effectively reducing the potential safety hazard of the truck driver in the driving process.
The truck vehicle entrance monitoring module is used for counting the number of the distributed truck vehicles in a set time period, acquiring the driving-in and driving-out images of each distributed truck vehicle through the intelligent camera to obtain a vehicle body driving-in image and a vehicle body driving-out image corresponding to each distributed truck vehicle, analyzing the corresponding vehicle maintenance coefficients of each distributed truck vehicle according to the vehicle driving-in and driving-out images, counting the number of the vehicles to be maintained according to the vehicle maintenance coefficients, and extracting the numbers of the vehicles to be maintained.
More preferably, the analysis of the corresponding vehicle maintenance coefficients for each assigned truck vehicle is as follows:
the assigned truck vehicles are numbered 1,2 according to the assigned time sequence, and the number f is the number u;
comparing the vehicle body driving-in images and the vehicle body driving-out images corresponding to the distributed truck vehicles one by one, counting the abrasion quantity of the vehicle bodies of the distributed vehicles and the sinking quantity of the vehicle bodies of the distributed vehicles, and obtaining the corresponding abrasion areas of the abrasion parts of the vehicle bodies of the distributed vehicles and the corresponding sinking areas and sinking depths of the sinking parts of the vehicle bodies of the distributed vehicles;
calculating the corresponding vehicle wear degree of each allocated truck vehicle, wherein the specific calculation formula is as follows
Figure BDA0003634096250000131
γ f Denoted as f-th assigned truck corresponding body wear degree, f n m represents the corresponding wear area of the nth body wear in the f-th allocated wagon, n represents the number of the body wear, n=1, 2.
Calculating the vehicle body dent index of each allocated truck vehicle, wherein the specific calculation formula is as follows
Figure BDA0003634096250000132
μ f Expressed as the corresponding body dent index of the f-th allocated truck vehicle, f q m' is expressed as the corresponding vehicle body depression area of the q-th depression in the f-th allocated truck vehicle, f q h represents the vehicle body depression depth of the q-th depression in the f-th allocated truck vehicle, q represents the number of the depression, q=1, 2,.. P, fH represents the allowable depression depth of the corresponding vehicle body of the f-th allocated truck vehicle, and c1 and c2 represent the impact factors corresponding to the vehicle body depression area and the vehicle body depression depth respectively;
calculating corresponding vehicle maintenance coefficients of each allocated truck vehicle according to the body abrasion degree and the body dent index of each allocated truck vehicle, wherein the specific calculation formula is as follows
Figure BDA0003634096250000141
ζ f The maintenance coefficient corresponding to the f-th allocated truck is expressed, and d1 and d2 are respectively expressed as the correction coefficients corresponding to the abrasion degree and the dent index of the truck body.
The invention effectively solves the defect that the current cargo transportation vehicle management platform cannot analyze the vehicle maintenance coefficient, comprehensively analyzes the vehicle maintenance coefficient from the two aspects of the vehicle body abrasion degree and the vehicle body dent index, overcomes the defect that the analysis of the vehicle body abrasion degree and the vehicle body dent index does not produce in the prior art, provides a powerful data base for vehicle maintenance, realizes timely maintenance and adjustment of the truck vehicle, avoids the reduction of the truck vehicle utilization rate, reduces the economic loss to a great extent, and simultaneously ensures the running stability and safety of the truck vehicle.
More optimally, the statistics of the vehicle to be maintained according to the vehicle maintenance coefficient comprises the following steps:
comparing the corresponding maintenance coefficient of each allocated truck with a preset vehicle maintenance threshold, if the corresponding maintenance coefficient of a certain allocated truck is larger than the preset vehicle maintenance threshold, marking the corresponding maintenance coefficient of the allocated truck as a vehicle needing maintenance, extracting the number of the vehicle needing maintenance, and further counting the number of the vehicles needing maintenance.
And the maintenance vehicle display is used for displaying the number and the corresponding number of the vehicles needing maintenance.
It should be noted that, in this embodiment, the number of the vehicles to be maintained and the corresponding numbers are displayed through the maintenance vehicle display, so that the manager of the cargo transportation company can learn the number of the vehicles to be maintained and the corresponding numbers in time, and implement remote dynamic guidance.
The storage database is used for storing the ages and driving ages corresponding to the truck drivers of the various vehicle types, storing the volume of the nuclear cargo corresponding to the various vehicle types, storing the driving mileage and the driving duration corresponding to the various truck drivers of the various vehicle types, storing the vehicle body surface area corresponding to the various truck drivers of the various vehicle types, storing the recommended rest duration corresponding to the ages of the various truck drivers and storing the contact modes corresponding to the various truck drivers of the various vehicle types.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.

Claims (6)

1. Cargo transportation vehicle remote on-line monitoring management cloud platform based on commodity circulation trade, its characterized in that includes:
the basic information acquisition module of the cargo carrier is used for acquiring basic information of the cargo carrier, wherein the basic information comprises the number of the wagon vehicles corresponding to each vehicle type and the number of the wagon drivers corresponding to each vehicle type, and numbering the wagon vehicles corresponding to each vehicle type and the wagon drivers corresponding to each vehicle type;
the vehicle and personnel information acquisition module is used for monitoring the use state of each truck vehicle corresponding to each vehicle type and the working state of each truck driver corresponding to each vehicle type in real time through the intelligent camera, acquiring the rest duration of each idle truck driver corresponding to each vehicle type from the use state and the working state of each idle truck driver, and further acquiring the idle truck vehicle set corresponding to each vehicle type and the idle truck driver set corresponding to each vehicle type;
the system comprises a cargo transportation order information acquisition module, a cargo transportation order information acquisition module and a cargo management module, wherein the cargo transportation order information acquisition module is used for acquiring current cargo transportation order information, and the cargo transportation order information comprises cargo volume and cargo transportation places;
the goods transport order distribution module is used for acquiring a target transport vehicle number and a target transport truck driver number distributed by the current goods transport order from the idle truck vehicle set corresponding to each vehicle type and the idle truck driver set corresponding to each vehicle type, and further notifying the target transport truck driver of goods transport work;
the cargo transportation order distribution module comprises a cargo target transportation vehicle distribution unit, a cargo target transportation truck driver distribution unit and a target transportation truck driver notification unit;
the cargo object transportation vehicle allocation unit is used for acquiring the number of the object transportation vehicle allocated in the current cargo transportation order, and the specific analysis is as follows:
extracting the cargo volume from the current cargo transportation order information, comparing the cargo volume with the corresponding nuclear cargo volume of each vehicle type stored in the storage database, analyzing the cargo volume matching vehicle type, and marking the cargo volume matching vehicle type as a specified vehicle type;
based on the specified vehicle types, acquiring a set of idle truck vehicles corresponding to the specified vehicle types from the set of idle truck vehicles corresponding to the specified vehicle types, marking the idle truck vehicles corresponding to the specified vehicle types as target transport vehicles, and extracting numbers corresponding to the target transport vehicles;
the cargo target transportation truck driver allocation unit is used for acquiring the target transportation truck driver number allocated in the current cargo transportation order, and the specific analysis is as follows:
based on the specified vehicle type, acquiring a leisure van driver set corresponding to the specified vehicle type from the leisure van driver set corresponding to each vehicle type, and calculating driving experience indexes of the leisure van drivers in the leisure van driver set corresponding to the specified vehicle type, wherein a specific calculation formula is as follows
Figure FDA0004042586930000021
ηB r ' Driving experience index expressed as r-th leisure wagon driver corresponding to specified vehicle type, B r ′L、B r 'T' is respectively expressed as the driving mileage and the driving duration of the r idle truck driver corresponding to the designated vehicle type, L 'and T' are respectively expressed as the reference driving mileage and the reference driving duration of the driver, and B r ' g is expressed as the driving age of the r idle truck driver corresponding to the appointed vehicle type, and a1, a2 and a3 are respectively expressed as driving mileage, driving duration and influence factors corresponding to the driving age;
the method comprises the steps of extracting the rest time length of each leisure van driver corresponding to a specified vehicle type from the rest time length of each leisure van driver corresponding to each vehicle type, and calculating the fatigue index of each leisure van driver corresponding to the specified vehicle type, wherein the specific calculation formula is as follows
Figure FDA0004042586930000022
Figure FDA0004042586930000023
Expressed as fatigue index of the r-th idle van driver corresponding to the specified vehicle type, B r 't is the rest time length of the r idle truck driver corresponding to the appointed vehicle type, b is the correction coefficient corresponding to the rest time length, and rjt' is the recommended rest time length corresponding to the age of the r truck driver;
based on the driving experience index of the assigned vehicle type corresponding to each leisure van driver and the fatigue index of the assigned vehicle type corresponding to each leisure van driver, comprehensively analyzing to obtain the optimal influence coefficient of the assigned vehicle type corresponding to each leisure van driver, wherein the specific calculation formula is as follows
Figure FDA0004042586930000031
σB r ' is expressed as a preferential influence coefficient of the r-th idle van driver corresponding to the appointed vehicle type, and e1 and e2 are respectively expressed as weight factors corresponding to driving experience indexes and fatigue indexes;
sorting the preferential influence coefficients of the appointed vehicle types corresponding to the leisure truck drivers according to the sequence from large to small, screening out the corresponding leisure truck driver with the largest preferential influence coefficient from the preferential influence coefficients, marking the corresponding leisure truck driver as a target transportation truck driver, and extracting the serial number of the target transportation truck driver;
the truck vehicle entrance monitoring module is used for counting the number of the distributed truck vehicles in a set time period, acquiring the driving-in and driving-out images of each distributed truck vehicle through the intelligent camera to obtain a vehicle body driving-in image and a vehicle body driving-out image corresponding to each distributed truck vehicle, analyzing corresponding vehicle maintenance coefficients of each distributed truck vehicle according to the vehicle driving-in and driving-out images, counting the number of the vehicles to be maintained according to the vehicle maintenance coefficients, and extracting the number of each vehicle to be maintained;
the maintenance vehicle display is used for displaying the number and the corresponding number of the vehicles to be maintained;
the storage database is used for storing the ages and driving ages corresponding to the truck drivers of the various vehicle types, storing the volume of the nuclear cargo corresponding to the various vehicle types, storing the driving mileage and the driving duration corresponding to the various truck drivers of the various vehicle types, storing the vehicle body surface area corresponding to the various truck drivers of the various vehicle types, storing the recommended rest duration corresponding to the ages of the various truck drivers and storing the contact modes corresponding to the various truck drivers of the various vehicle types.
2. The cargo transportation vehicle remote on-line monitoring management cloud platform based on logistics industry of claim 1, wherein: the method for numbering the truck vehicles corresponding to each vehicle type and the truck drivers corresponding to each vehicle type comprises the following steps: the truck corresponding to each model is marked as A kc ，A kc The c-th truck vehicle corresponds to the k-th truck vehicle, c=1, 2, w, c represents the truck vehicle number, k=1, 2, t, k represents the number of the truck type, and the truck driver corresponding to each truck type is labeled B kr ，B kr Denoted as kth vehicle model corresponds to the kth truck driver, r=1, 2.
3. The cargo transportation vehicle remote on-line monitoring management cloud platform based on logistics industry of claim 1, wherein: the use state of the van vehicle comprises use and idle, and the working state of the van driver comprises shipment and idle.
4. The cargo transportation vehicle remote on-line monitoring management cloud platform based on logistics industry of claim 1, wherein: the target transport truck driver notification unit is used for matching the number of the target transport truck driver corresponding to the designated vehicle type with the contact information of each truck driver corresponding to each vehicle type in the storage database, so as to obtain the contact information of the target transport truck driver, further send the number of the target transport vehicle to the target transport truck driver, and carry out the transport work of the current cargo transport order by the target transport truck driver.
5. The cargo transportation vehicle remote on-line monitoring management cloud platform based on logistics industry of claim 1, wherein: the corresponding vehicle maintenance coefficients of each allocated truck vehicle are analyzed, and the specific analysis is as follows:
the assigned truck vehicles are numbered 1,2 according to the assigned time sequence, and the number f is the number u;
comparing the vehicle body driving-in images and the vehicle body driving-out images corresponding to the distributed truck vehicles one by one, counting the abrasion quantity of the vehicle bodies of the distributed vehicles and the sinking quantity of the vehicle bodies of the distributed vehicles, and obtaining the corresponding abrasion areas of the abrasion parts of the vehicle bodies of the distributed vehicles and the corresponding sinking areas and sinking depths of the sinking parts of the vehicle bodies of the distributed vehicles;
calculating the corresponding vehicle wear degree of each allocated truck vehicle, wherein the specific calculation formula is as follows
Figure FDA0004042586930000051
γ f Denoted as f-th assigned truck corresponding body wear degree, f n m represents the corresponding wear area of the nth body wear in the f-th allocated wagon, n represents the number of the body wear, n=1, 2.
Calculating the vehicle body dent index of each allocated truck vehicle, wherein the specific calculation formula is as follows
Figure FDA0004042586930000052
μ f Expressed as the corresponding body dent index of the f-th allocated truck vehicle, f q m' is expressed as the corresponding vehicle body depression area of the q-th depression in the f-th allocated truck vehicle, f q h represents the vehicle body depression depth of the q-th depression in the f-th allocated truck vehicle, q represents the number of the depression, q=1, 2,.. P, fH represents the allowable depression depth of the corresponding vehicle body of the f-th allocated truck vehicle, and c1 and c2 represent the impact factors corresponding to the vehicle body depression area and the vehicle body depression depth respectively;
calculating corresponding vehicle maintenance coefficients of each allocated truck vehicle according to the body abrasion degree and the body dent index of each allocated truck vehicle, wherein the specific calculation formula is as follows
Figure FDA0004042586930000061
ζ f The maintenance coefficient corresponding to the f-th allocated truck is expressed, and d1 and d2 are respectively expressed as the correction coefficients corresponding to the abrasion degree and the dent index of the truck body.
6. The cargo transportation vehicle remote on-line monitoring management cloud platform based on logistics industry of claim 1, wherein: the vehicle to be maintained is counted according to the vehicle maintenance coefficient, and the vehicle to be maintained is specifically:
comparing the corresponding maintenance coefficient of each allocated truck with a preset vehicle maintenance threshold, if the corresponding maintenance coefficient of a certain allocated truck is larger than the preset vehicle maintenance threshold, marking the corresponding maintenance coefficient of the allocated truck as a vehicle needing maintenance, extracting the number of the vehicle needing maintenance, and further counting the number of the vehicles needing maintenance.
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