CN113011809B - Cargo displacement monitoring method for multi-path condition transportation - Google Patents
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Abstract
The invention discloses a cargo displacement monitoring method for multi-path condition transportation. In order to overcome the problems that the prior art only monitors the cargo transportation route, but does not monitor the cargo movement in the carriage; the invention comprises the following steps: s1: a monitoring system is built in a compartment in a partial area; the monitoring system collects acceleration of the vehicle in all directions in real time, and calculates displacement of cargoes in all areas in the carriage; s2: acquiring the variation of acceleration in unit time and the variation of cargo displacement in each area, and respectively constructing an acceleration-displacement model; s3: and comparing the acceleration-displacement model with multi-path condition historical data of a historical database in the monitoring system, judging the probability of collision or overturning damage of cargoes under the current road condition, alarming, predicting the probability of damage in the rated time, and carrying out early warning. And a multi-path condition model is established, monitoring and prediction comparison is performed in a targeted manner, and safe transportation of cargoes in a transportation environment with complex road conditions is ensured.
Description
Technical Field
The invention relates to the field of cargo displacement monitoring, in particular to a cargo displacement monitoring method for multi-path condition transportation.
Background
With the development of logistics industry, road transportation occupies an increasingly important position in daily life, and van vehicles are used as main tools for road transportation, so that monitoring of the transportation process of van vehicles is very important. But the transportation process road environment is complicated, there are road surface, muddy road etc. that jolts, the transportation process also has the process of brake, start etc. and accompanies acceleration, and the goods takes place to remove in the carriage easily to there is the danger risk of colliding with the damage.
Existing monitoring of logistics cargo is generally only aimed at tracking the macroscopic cargo transportation route, and does not monitor the movement of cargo inside the carriage. For example, a "transportation tracking and warning method and system" disclosed in chinese patent literature, its bulletin number CN102800210a, includes: the mobile monitoring unit is suitable for uploading real-time state data of the transport vehicle through the base station equipment; the monitoring background unit is suitable for acquiring real-time state data of the transport vehicle through the base station equipment, comparing the real-time state data with the reference line data, and generating an abnormality if the difference between the real-time state data and the reference line data exceeds a preset threshold value; and a transportation visual monitoring unit adapted to visually display the abnormality. There is also provided a transportation tracking and warning method comprising: step 1, initializing transportation lines and ageing requirement data; step 2, acquiring current state information of the transport vehicle in real time; and step 3, comparing the current state information with the reference carrier line data, and generating an abnormality if the current state information exceeds a preset threshold value.
This scheme only monitors the goods transportation route, and does not monitor the inside goods removal of carriage, can't monitor the collision that takes place to the relative movement of inside goods and report to the police.
Disclosure of Invention
The invention mainly solves the problems that in the prior art, only the cargo transportation route is monitored, but the cargo movement in the carriage is not monitored; the method comprises the steps of establishing a historical database of a multi-path condition transportation process, monitoring acceleration and displacement of the goods in real time in the goods transportation process, pertinently monitoring and predicting relative movement and relative movement trend of the goods in a carriage, alarming or early warning the displacement with dangerous risk, and guaranteeing safe carrying of the goods in a complex transportation environment.
The technical problems of the invention are mainly solved by the following technical proposal:
the cargo displacement monitoring method for multi-path condition transportation comprises the following steps:
s1: a monitoring system is built in a compartment in a partial area; the monitoring system collects acceleration of the vehicle in all directions in real time, and calculates displacement of cargoes in all areas in the carriage;
S2: acquiring the variation of acceleration in unit time and the variation of cargo displacement in each area, and respectively constructing an acceleration-displacement model;
S3: and comparing the acceleration-displacement model with multi-path condition historical data of a historical database in the monitoring system, judging the probability of collision or overturning damage of cargoes under the current road condition, alarming, predicting the probability of damage in the rated time, and carrying out early warning.
According to the scheme, goods in the vehicle are monitored in real time, an acceleration-displacement model is built, the built model is compared and matched with a model of multiple road conditions in a historical database, the probability of occurrence of risks such as collision or overturning existing in the current transportation situation under the current road conditions is judged in a targeted manner, and the probability is compared with a threshold value under the current road conditions to judge; according to the change trend of the acceleration and displacement, the historical data are matched, and whether the probability of danger exists is predicted. The cargo can be safely transported in the transportation environment with complex road conditions. Avoiding damage caused by collision and damage.
Preferably, the monitoring system comprises an array pressure sensor, a triaxial acceleration sensor, a monitoring center and a historical database;
The pressure sensors are respectively arranged on the bottom surfaces of all areas in the carriage in an array mode and are used for monitoring cargo pressure distribution changes in all areas;
the three-axis acceleration sensor is used for monitoring the three-way acceleration of the whole vehicle;
The historical database stores standard acceleration-displacement models and historical acceleration-displacement change curves under different road conditions;
The monitoring center receives the data of cargo pressure distribution change in each area and the three-way acceleration of the whole vehicle, calculates the cargo pressure distribution change as cargo displacement change, respectively establishes acceleration-displacement models, retrieves the historical data in the historical database, and predicts and judges the probability of collision or rollover of the cargo.
Acquiring the acceleration of the whole vehicle in three directions through the three-axis acceleration, and establishing a three-dimensional model; obtaining pressure distribution of each area in a carriage through matrix pressure sensors, and calculating to obtain data of cargo displacement according to the change of the pressure distribution; and comprehensively establishing an acceleration-displacement model to match, compare and judge.
Preferably, the step S2 includes the steps of:
S21: the three-axis acceleration sensor acquires the three-way acceleration a of the vehicle in real time; the pressure sensors respectively collect the pressure distribution F kij of the goods in each area;
a＝(ax,ay,az)
wherein a x is acceleration in the x-axis direction of the vehicle;
a y is the acceleration in the y-axis direction of the vehicle;
a z is acceleration in the z-axis direction of the vehicle;
F kij is the pressure data monitored by the pressure sensor in the kth area, the ith row and the jth column in the carriage;
S22: calculating the variation delta a of the three-way acceleration and the variation delta F kij of the pressure distribution by taking the unit time T;
Wherein a' x is the acceleration in the x-axis direction of the vehicle after the time T of the unit time;
a' y is acceleration in the y-axis direction of the vehicle after the unit time T;
a' z is acceleration in the vehicle z-axis direction after the unit time T;
F' kij is the pressure data monitored by the pressure sensors in the kth area, the ith row and the jth column in the carriage after the unit time T;
S23: classifying the variation delta F kij of the pressure distribution in the same area as negative into a first class and classifying the variation delta F kij of the pressure distribution as positive into a second class;
Respectively utilizing a k-means algorithm to obtain a first type fitting coordinate (k i1,kj1) and a second type fitting coordinate (k i2,kj2) for the first type corresponding pressure sensor coordinate and the second type corresponding pressure sensor coordinate;
s24: the second type fitting coordinates and the first type fitting coordinates are differenced to obtain cargo displacement (k i,kj) of the area;
s25: and (3) correlating the change delta a of the three-way acceleration with the cargo displacement quantity (k i,kj) of each region to construct an acceleration-displacement model.
Preferably, the multipath conditions comprise road conditions of bumpy road surfaces, pebble road surfaces, muddy road surfaces, slopes and washboard roads, and the standard acceleration-displacement model in the historical database is obtained through construction of historical data of test road surfaces. And a multi-path condition model is established, the comparison of monitoring prediction is carried out in a targeted manner, and the accuracy of alarming and early warning is improved.
Preferably, the step S3 includes the steps of:
s31: comparing and matching the acceleration-displacement model constructed through association with multi-path condition historical data in a historical database, and when the similarity between the acceleration-displacement model constructed through association and the historical data in the historical database reaches a rated threshold, matching and obtaining the current road condition type;
S32: comparing the three-way acceleration a, the variation delta F kij of the pressure distribution and the cargo displacement (k i,kj) obtained by real-time monitoring and calculation with threshold data under the road condition in a historical database, and sending alarm information to a driver or related management personnel when the data obtained by monitoring and calculation are larger than the threshold value; otherwise, continuing to monitor.
The acquired acceleration-displacement model is matched with the current road condition, and the possibility of damage such as collision and overturning of goods is judged through the acquired acceleration and the calculated displacement.
Preferably, the step S3 further includes the steps of:
s33: comparing the calculated change curve of the three-way acceleration change delta a and the pressure distribution change delta F kij with the change curve under the same road condition in the historical database, and sending early warning information to a driver or related management personnel when the matching degree of the calculated change curve and the change curve under the same road condition in the historical database reaches a certain threshold value or the slope of the calculated change curve is larger than that of the change curve under the same road condition in the historical database; otherwise, continuing to monitor. The historical data under the same road condition is matched through the change quantity of the acceleration and the displacement unit time, so that the possibility of damage such as collision and overturning of cargoes is estimated.
Preferably, after the current road condition type is obtained, the driver is informed to be reminded through voice, if the obtained condition is wrong, the driver feeds back through an in-vehicle control screen, and the wrong data is uploaded and updated to the history database. The history database is continuously updated through feedback of the driver, and the model is optimized through learning.
The beneficial effects of the invention are as follows:
1. By monitoring cargoes in the vehicle in real time, an acceleration-displacement model is constructed to be matched with a model of multiple road conditions in a historical database in a comparison mode, so that the cargoes can be safely transported in a transportation environment of complex road conditions. Avoiding damage caused by collision and damage.
2. And a multi-path condition model is established, the comparison of monitoring prediction is carried out in a targeted manner, and the accuracy of alarming and early warning is improved.
3. And the monitoring and the prediction of the movement of goods in the vehicle are carried out through the comparison of the models, the timely warning and the early warning are carried out, the corresponding time is in time or in advance, and the loss is reduced.
Drawings
FIG. 1 is a flow chart of a cargo displacement monitoring method for multiplex transportation according to the present invention.
Detailed Description
The technical scheme of the invention is further specifically described below through examples and with reference to the accompanying drawings.
Examples:
the cargo displacement monitoring method for multi-path condition transportation in this embodiment, as shown in fig. 1, comprises the following steps:
S1: a monitoring system is established in a partial region within the cabin.
The monitoring system collects acceleration of the vehicle in all directions in real time, and calculates displacement of cargoes in all areas in the carriage. The monitoring system comprises an array pressure sensor, a triaxial acceleration sensor, a monitoring center and a historical database.
In this embodiment, the ground inside the carriage is divided into a plurality of monitoring areas, and pressure sensors are respectively arranged on the bottom surfaces of the areas inside the carriage in an array manner, and are used for monitoring the pressure distribution change of the cargo in the areas. The displacement of the cargo in each region is obtained by calculating the change in the cargo pressure in that region. The monitoring cargo displacement does not need to occupy the carriage inner space additionally, the loading of the cargo is not influenced, and the displacement monitoring has no dead angle.
The triaxial acceleration sensor is arranged on the carriage and used for monitoring the overall three-way acceleration of the vehicle.
The historical database stores standard acceleration-displacement models and historical acceleration-displacement change curves under different road conditions.
In this embodiment, the multiple road conditions include road conditions of bumpy road, pebble road, muddy road, slope and washboard road, and form conditions in starting and braking states. The standard acceleration-displacement model in the historical database is obtained through construction of historical data of the test pavement, and the data can be obtained through limited tests.
The historical acceleration-displacement change curve is a model which is correspondingly acquired and constructed in the actual transportation process, and comprises typical change curves of cargoes with different weight grades in collision or overturning under different road conditions and change curves of cargoes with different weight grades in normal operation under different road conditions.
The monitoring center is used for receiving the data of cargo pressure distribution change in each area and the three-way acceleration of the whole vehicle, calculating the cargo pressure distribution change as cargo displacement change, respectively establishing acceleration-displacement models, calling the historical data in the historical database, and predicting and judging the probability of cargo collision or rollover.
S2: and acquiring the variation of the acceleration in unit time and the variation of the cargo displacement in each area, and respectively constructing an acceleration-displacement model.
S21: the three-axis acceleration sensor acquires the three-way acceleration a of the vehicle in real time; the pressure sensors respectively collect the pressure distribution F kij of the goods in each area;
a＝(ax,ay,az)
wherein a x is acceleration in the x-axis direction of the vehicle;
a y is the acceleration in the y-axis direction of the vehicle;
a z is acceleration in the z-axis direction of the vehicle;
F kij is the pressure data monitored by the pressure sensor in the kth area, the ith row and the jth column in the carriage.
S22: calculating the variation delta a of the three-way acceleration and the variation delta F kij of the pressure distribution by taking the unit time T;
Wherein a' x is the acceleration in the x-axis direction of the vehicle after the time T of the unit time;
a' y is acceleration in the y-axis direction of the vehicle after the unit time T;
a' z is acceleration in the vehicle z-axis direction after the unit time T;
f' kij is the pressure data monitored by the pressure sensors in the kth area, the ith row and the jth column in the carriage after the unit time T.
S23: classifying the variation delta F kij of the pressure distribution in the same area as negative into a first class and classifying the variation delta F kij of the pressure distribution as positive into a second class; and respectively obtaining a first type fitting coordinate (k i1,kj1) and a second type fitting coordinate (k i2,kj2) by using a k-means algorithm for the first type corresponding pressure sensor coordinate and the second type corresponding pressure sensor coordinate.
S24: the second type of fit coordinates (k i2,kj2) are differenced from the first type of fit coordinates (k i1,kj1) to obtain the cargo displacement (k i,kj) of the region.
S25: the change amount deltaa of the three-way acceleration and the cargo displacement amount (k i,kj)) of each region are correlated to construct an acceleration-displacement model.
S3: and comparing the acceleration-displacement model with multi-path condition historical data of a historical database in the monitoring system, judging the probability of collision or overturning damage of cargoes under the current road condition, alarming, predicting the probability of damage in the rated time, and carrying out early warning.
S31: and comparing and matching the acceleration-displacement model constructed through association with multi-path condition historical data in a historical database, and when the similarity between the acceleration-displacement model constructed through association and the historical data in the historical database reaches a rated threshold, in the embodiment, the rated threshold is 90%, and matching to obtain the current road condition type.
The road condition types obtained through matching are fed back to a central control screen of a driver, and voice broadcasting is carried out; the driver can accurately match through voice feedback or describe the current road condition type through voice. When the driver feeds back the matching identification errors, the current road condition is updated and is updated to the history database for learning and optimizing of the history database.
S32: comparing the three-way acceleration a, the variation delta F kij of the pressure distribution and the cargo displacement (k i,kj) obtained by real-time monitoring and calculation with threshold data under the road condition in a historical database, and sending alarm information to a driver or related management personnel when the data obtained by monitoring and calculation are larger than the threshold value; otherwise, continuing to monitor.
Threshold data under different road conditions can be obtained through limited tests, and iteration is continuously optimized through actual operation data.
S33: comparing the calculated change curve of the three-way acceleration change delta a and the pressure distribution change delta F kij with the change curve under the same road condition in the historical database, and sending early warning information to a driver or related management personnel when the matching degree of the calculated change curve and the change curve under the same road condition in the historical database reaches a certain threshold value or the slope of the calculated change curve is larger than that of the change curve under the same road condition in the historical database; otherwise, continuing to monitor.
According to the scheme, the goods in the vehicle are monitored in real time, the acceleration-displacement model is constructed to be matched with the model of multiple road conditions in the historical database in a comparison mode, the goods can be safely transported in the transportation environment of the complex road conditions, and damage caused by collision and damage is avoided. And a multi-road condition model is established in the historical database, the comparison of monitoring prediction is carried out in a targeted manner, and the accuracy of warning and early warning is improved. And the monitoring and the prediction of the movement of goods in the vehicle are carried out through the comparison of the models, the timely warning and the early warning are carried out, the corresponding time is in time or in advance, and the loss is reduced.
It should be understood that the examples are only for illustrating the present application and are not intended to limit the scope of the present application. Furthermore, it should be understood that various changes and modifications can be made by one skilled in the art after reading the teachings of the present application, and such equivalents are intended to fall within the scope of the application as defined in the appended claims.
Claims (5)
1. The cargo displacement monitoring method for multi-path condition transportation is characterized by comprising the following steps of:
s1: a monitoring system is built in a compartment in a partial area; the monitoring system collects acceleration of the vehicle in all directions in real time, and calculates displacement of cargoes in all areas in the carriage;
S2: acquiring the variation of acceleration in unit time and the variation of cargo displacement in each area, and respectively constructing an acceleration-displacement model;
S3: comparing the acceleration-displacement model with multi-path condition historical data of a historical database in a monitoring system, judging the probability of collision or overturning damage of cargoes under the current road condition, alarming, predicting the probability of damage in the rated time, and carrying out early warning;
The historical database stores standard acceleration-displacement models and historical acceleration-displacement change curves under different road conditions; the historical acceleration-displacement change curve comprises typical change curves of cargoes with different weight grades which collide or topple under different road conditions, and change curves of cargoes with different weight grades which normally run under different road conditions;
The step S2 comprises the following steps:
S21: the three-axis acceleration sensor acquires the three-way acceleration a of the vehicle in real time; pressure sensors respectively collect pressure distribution of goods in each area ；
；
Wherein,Acceleration in the x-axis direction of the vehicle; /(I)Acceleration in the y-axis direction of the vehicle; /(I)Acceleration in the z-axis direction of the vehicle; /(I)The pressure data monitored by the pressure sensor in the ith row and the jth column in the kth area in the carriage;
s22: taking unit time T, calculating the variation of three-way acceleration And the amount of change in pressure distribution/>；
；
；
Wherein,Acceleration in the x-axis direction of the vehicle after the time T is a unit time; /(I)Acceleration in the y-axis direction of the vehicle after the time T is set as a unit time; /(I)Acceleration in the z-axis direction of the vehicle after the time T is a unit time; /(I)The pressure data is monitored by a pressure sensor in a kth area, an ith row and a jth column in the carriage after the time T of unit time;
s23: variation of pressure distribution in the same region Is negative and is classified as the first category, the variation of the pressure distribution/>Positive is classified as second class;
the first fitting coordinate is obtained by a k-means algorithm for the pressure sensor coordinate corresponding to the first class and the pressure sensor coordinate corresponding to the second class And second class fitting coordinates/>；
S24, obtaining the cargo displacement of the region by making difference between the second type fitting coordinates and the first type fitting coordinates；
S25: correlating the change of three-way accelerationAnd cargo displacement amount per each region ]Constructing an acceleration-displacement model;
the step S3 comprises the following steps:
s31: comparing and matching the acceleration-displacement model constructed through association with multi-path condition historical data in a historical database, and when the similarity between the acceleration-displacement model constructed through association and the historical data in the historical database reaches a rated threshold, matching and obtaining the current road condition type;
s32: three-way acceleration obtained by real-time monitoring and calculation Variation of pressure distribution/>And cargo displacement/>Comparing with threshold data under the road condition in a historical database, and sending alarm information to a driver or related management personnel when the data obtained by monitoring and calculation is larger than the threshold value; otherwise, continuing to monitor.
2. The method for monitoring the displacement of cargoes in multi-path transportation according to claim 1, wherein the monitoring system comprises a pressure sensor, a triaxial acceleration sensor, a monitoring center and a history database;
The pressure sensors are respectively arranged on the bottom surfaces of all areas in the carriage in an array mode and are used for monitoring cargo pressure distribution changes in all areas;
the three-axis acceleration sensor is used for monitoring the three-way acceleration of the whole vehicle;
The historical database stores standard acceleration-displacement models and historical acceleration-displacement change curves under different road conditions;
The monitoring center receives the data of cargo pressure distribution change in each area and the three-way acceleration of the whole vehicle, calculates the cargo pressure distribution change as cargo displacement change, respectively establishes acceleration-displacement models, retrieves the historical data in the historical database, and predicts and judges the probability of collision or rollover of the cargo.
3. The method for monitoring cargo displacement during multi-path transportation according to claim 1 or 2, wherein the multi-path situation comprises road conditions of bumpy road surfaces, stone road surfaces, muddy road surfaces, slopes and washboard roads, and the standard acceleration-displacement model in the historical database is obtained through construction of historical data of test road surfaces.
4. The method for monitoring displacement of cargo during transportation in multiple conditions according to claim 1, wherein said step S3 further comprises the steps of:
s33: the calculated change of the three-way acceleration And the amount of change in pressure distribution/>The change curve of the road condition is compared with the change curve under the same road condition in the history database, and when the matching degree of the calculated change curve and the change curve under the same road condition in the history database reaches a certain threshold value or the slope of the calculated change curve is larger than that of the change curve under the same road condition in the history database, early warning information is sent to a driver or related management staff; otherwise, continuing to monitor.
5. The method for monitoring cargo displacement in multi-path transportation according to claim 1, wherein after the current road condition is obtained, a driver is informed by voice, if the obtained condition is wrong, the driver feeds back the obtained condition through an in-vehicle control screen, and the wrong data is uploaded and updated to a history database.
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| CN115685932B (en) * | 2022-10-31 | 2023-05-02 | 青岛家哇云网络科技有限公司 | Logistics information intelligent management system and method based on big data |
| CN117172395A (en) * | 2023-08-04 | 2023-12-05 | 上海朗晖慧科技术有限公司 | Big data early warning system and method based on multi-region freight transportation |
| CN117078020B (en) * | 2023-10-12 | 2024-01-30 | 山东龙翼航空科技有限公司 | Logistics transportation data management system based on unmanned aerial vehicle |
| CN117549906B (en) * | 2024-01-10 | 2024-03-26 | 武汉易为泰汽车技术开发股份有限公司 | Automobile automatic driving control method and system based on 5G short-cut private network transmission |
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