A method of monitoring a transport condition

Abstract

The application discloses a kind of transport state monitoring method, to overcome the problem that the insufficient monitoring of goods in prior art transport monitoring method causes the situation of load deviation of carriage, the application includes setting view monitoring module to carry out real-time monitoring to the shape and placement state of goods;Setting gravity detection module judges the gravity center deviation of carriage by comprehensively judging the load of vehicle body and the inclination of vehicle body;The measurement results of view monitoring module and gravity detection module are brought into risk algorithm, and the transport risk coefficient is calculated. By calculating the transport state, occupied space and displacement trajectory of goods, combined with the load-bearing condition inside the carriage, the specific load deviation of the carriage is obtained, and the transport risk coefficient of the carriage is predicted, thereby ensuring the safety of vehicle transportation.

Description

Technical Field

[0001] This invention relates to the field of transportation safety monitoring, and more particularly to a method for monitoring transportation status. Background Technology

[0002] During freight transport, improper cargo loading often leads to uneven loading of the truck bed, causing the bed to tilt and resulting in serious traffic accidents. Existing vehicle transport safety monitoring methods can calculate uneven loading of the truck bed, but they cannot predict the risks associated with uneven loading or monitor the movement of cargo inside the truck.

[0003] For example, a "Truck Off-Side Load Dynamic Detection Method and Alarm System" disclosed in Chinese patent literature, publication number CN101350136A, includes a truck off-side load dynamic detection alarm system. This system comprises: a signal sensing unit including left and right acceleration sensors; an information acquisition and control unit including a microcontroller; and an alarm notification unit. The microcontroller has a pre-programmed off-side load dynamic detection algorithm module, which presets an off-side load threshold and an alarm range. The system obtains the left and right acceleration signals of the truck through the left and right acceleration sensors. The off-side load coefficient is calculated using the off-side load dynamic detection algorithm module. The information acquisition and control unit issues a corresponding alarm command based on the difference between the off-side load coefficient and the off-side load threshold, according to the preset off-side load alarm range. This solution can calculate the off-side load, but it cannot monitor the cargo transportation status. Summary of the Invention

[0004] This invention primarily addresses the problem that existing transportation monitoring methods are insufficient in monitoring cargo, leading to uneven loading in the carriages. It provides a transportation status monitoring method that calculates the transportation status, occupied space, and displacement trajectory of cargo, and combines this with the load-bearing capacity inside the carriage to determine the specific uneven loading situation of the carriage and predict the transportation hazard factor of the carriage, thereby ensuring the safety of vehicle transportation.

[0005] The above-mentioned technical problems of the present invention are mainly solved by the following technical solutions: This invention includes a visual monitoring module for real-time monitoring of the cargo's shape and placement; a center of gravity detection module to determine the cargo's center of gravity shift based on the vehicle's load and tilt; and the integration of the measurement results from both modules into a risk algorithm to calculate a transportation hazard coefficient. By monitoring the cargo's shape and placement, information such as displacement or deformation during transportation can be obtained, facilitating the monitoring of basic transportation safety. The center of gravity detection module measures the cargo's off-center loading information and, by combining cargo information, determines the likelihood of dangerous situations such as cargo rollover during transportation, thus ensuring transportation safety.

[0006] Preferably, the real-time monitoring of the cargo's shape includes acquiring snapshot data of the cargo at various time points, modeling and deriving the difference data of the snapshot data at each time point within a set time period, and obtaining the difference data values; the real-time monitoring of the cargo's placement status includes acquiring the cargo's position data at each time point, and combining the correspondence between the time point data and the position data to derive the cargo trajectory data within the set time period. If there are significant differences in the cargo snapshots during monitoring, it indicates that the cargo may have collided or tipped over during transportation. This monitoring can help relevant personnel to promptly confirm the cargo transportation status.

[0007] Preferably, the center of gravity detection module is evenly distributed on the bottom of the carriage to acquire pressure sensing data at each preset pressure-sensitive point at a set time node in the carriage, and calculate the center of gravity offset of the carriage; the preset pressure-sensitive points include each edge vertex, each edge midpoint and the center of the carriage; this method disperses the acquisition of pressure data in the carriage, which facilitates the subsequent detailed permutation and combination to obtain specific carriage off-center load information, and can improve the accuracy of the calculation.

[0008] Preferably, the center of gravity detection module also includes liquid level display devices installed on each side wall of the carriage, recording the offset values ​​[hl, hr] of each liquid level display device at a set time node. The liquid level display devices can reflect the tilt information of the carriage from the side. If the offset values ​​on the left and right sides are too large, it indicates that the carriage is prone to tipping over to one side.

[0009] Preferably, the modeling data of the initial cargo snapshot is [Cx, Cy, Cz], and the modeling data at the time node when the set time phase ends is [Cxn, Cyn, Czn]. The occupied space and volume at each time node are calculated separately according to the modeling data, so as to obtain the initial occupied space SC=Cx*Cy*Cz and the final occupied space SCn=Cxn*Cyn*Czn. Similarly, the occupied space and volume data at any time node can be obtained; the difference data value is obtained as SCn-SC. The volume of cargo needs to be calculated by a more detailed algorithm, while the occupied space of cargo can be easily calculated through its modeling coordinate values. Calculating the occupied space of cargo can obtain the operation status of cargo more quickly and intuitively. If the cargo topples or deforms due to collision, rapid monitoring can be realized to prevent further danger.

[0010] Preferably, the cargo trajectory data includes the initial cargo modeling data [Cx, Cy, Cz] and the displacement modeling data [Cx+n, Cy+m, Cz+e] at the time node when the set time phase ends, and the actual front, rear, left and right displacements [n, m, e] of the cargo during operation are calculated through calculation. Calculating the front and rear displacement trajectories of cargo can reflect the appropriateness of the cargo placement rule. If the cargo is displaced by an excessively large distance during transportation, the possibility of damage will be increased.

[0011] Preferably, the pressure data at the preset pressure-sensitive points includes [W1, W2, W3, W4, W5, Wx1, Wx2, Wy1, Wy2], wherein W1, W2, W3 and W4 are respectively pressure-sensitive values at each edge vertex, Wx1, Wx2, Wy1 and Wy2 are respectively pressure-sensitive values at the midpoint of each edge, and W5 is the pressure-sensitive value at the center of the carriage; any edge vertex and midpoint are taken to calculate the average pressure respectively, which is compared with the pressure-sensitive value at the center of the carriage, so as to obtain the center of gravity deviation of the carriage at any time node, and further calculate the pressure-sensitive difference of each edge to obtain the center of gravity deviation ΔW1 of the carriage at the time node; similarly, the center of gravity deviation ΔWn at the set time node is calculated, and further the center of gravity deviation of the carriage within the set time phase is obtained. By separately calculating the dispersed edges, accurate unbalanced load information in the carriage can be obtained, which facilitates the subsequent calculation of the danger coefficient.

[0012] Preferably, the danger coefficient K is set as K=|hr-hl|+|ΔWn-ΔW1|. If (SCn-SC)*([n, m, e])>R, K is incremented by 1; if (SCn-SC)*([n, m, e])<R, K is decremented by 1, wherein R is a preset intermediate quantity. The preset liquid level display and the center of gravity deviation are taken as the basic standards of the risk coefficient. Meanwhile, when the deformation and displacement distance of the cargo are greater than the set threshold, the risk coefficient is correspondingly increased, and when they are smaller than the set threshold, the risk coefficient is correspondingly decreased.

[0013] Preferably, the vehicle compartment is also equipped with an early warning system, with a preset risk factor range of Kmin to Kmax. If the risk factor Kmin exceeds Kmax, the voice prompt device and alarm unit in the alarm system are triggered. The voice prompt device provides a risk warning of off-center loading to the driver's seat, and the alarm unit transmits high-risk transportation information to the cloud. By setting up the early warning system to determine whether the risk factor exceeds the risk threshold Kmax, if it does, it indicates that the current transportation risk is too high, and the driver needs to be reminded to adjust the transportation status in time to ensure the safe transportation of goods.

[0014] The beneficial effects of this invention are: The present invention discloses a transportation status monitoring method that monitors the transportation status of goods in real time, and simultaneously monitors the unbalanced loading situation in the carriage. The method calculates the risk coefficient of the carriage transportation based on the comprehensive data. If the coefficient is too high, a warning module is set up to provide dual reminders to the driver's seat and the cloud, thereby maximizing the prediction of the danger of goods transportation and taking timely precautions. Attached Figure Description

[0015] Figure 1 This is a flowchart of a transportation status monitoring method according to the present invention. Detailed Implementation

[0016] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0017] Example: This embodiment provides a transportation status monitoring method, such as... Figure 1 As shown, it includes the following steps: Step 1: Set up a visual monitoring module to monitor the shape and placement of goods in real time.

[0018] Real-time monitoring of the cargo's shape includes acquiring snapshot data of the cargo at various time points, modeling and obtaining the difference data of the snapshot data at each time point within a set time period, and obtaining the difference data value; real-time monitoring of the cargo's placement status includes acquiring the location data of the cargo at each time point, and combining the correspondence between the time point data and the location data to obtain the cargo trajectory data within a set time period.

[0019] The initial snapshot modeling data for the cargo is [Cx, Cy, Cz], and the modeling data at the end of the set time period is [Cxn, Cyn, Czn]. Based on the modeling data, the occupied space and volume at each time point are calculated separately to obtain the initial occupied space SC = Cx * Cy * Cz and the final occupied space SCn = Cxn * Cyn * Czn. Similarly, the occupied space and volume data at any time point can be obtained; the difference data value is SCn - SC.

[0020] Said cargo trajectory data includes initial cargo modeling data [Cx, Cy, Cz] and displacement modeling data [Cx+n, Cy+m, Cz+e] at the time node when the set practice phase ends, and the actual forward, backward, left and right displacements [n, m, e] of the cargo during operation are obtained through calculation.

[0021] Step 2: A center of gravity detection module is provided to determine the center of gravity offset of the carriage by integrating the vehicle load capacity and the vehicle body inclination.

[0022] The center of gravity detection module is evenly laid on the bottom of the carriage, acquires pressure sensing data at each preset pressure-sensitive point at set time nodes of the carriage, and calculates the center of gravity offset of the carriage; the preset pressure-sensitive points include each edge vertex, each edge midpoint and the center of the carriage.

[0023] The center of gravity detection module further comprises liquid level display devices arranged on each side wall of the carriage, which record the offset values [hl, hr] of each liquid level display device at the set time node.

[0024] The pressure data of the preset pressure-sensitive points includes [W1, W2, W3, W4, W5, Wx1, Wx2, Wy1, Wy2], wherein W1, W2, W3 and W4 are respectively pressure-sensitive values at each edge vertex, Wx1, Wx2, Wy1 and Wy2 are respectively pressure-sensitive values at each edge midpoint, and W5 is the pressure-sensitive value at the center of the carriage; the average pressure is calculated by taking any edge vertex and midpoint respectively, and compared with the pressure-sensitive value at the center of the carriage, so as to obtain the center of gravity offset of the carriage at any time node, and further calculate the pressure-sensitive difference of each side to obtain the center of gravity offset of the carriage at the time node ~W1; the center of gravity offset ~Wn at the set time node is calculated in the same way, so as to further obtain the center of gravity offset of the carriage within the set time period.

[0025] Step 3: The measurement results of the visual monitoring module and the center of gravity detection module are comprehensively substituted into a risk algorithm to calculate the transportation risk coefficient.

[0026] A risk coefficient K is set, K=|hr-hl|+|~Wn-~W1|, if (SCn-SC) / ([n, m, e])>R, then K increases by 1, if (SCn-SC) / ([n, m, e])<R, then K decreases by 1, wherein R is a preset intermediate quantity.

[0027] An early warning system is also arranged in the carriage, the preset risk coefficient range of which is Kmin~Kmax. If the risk coefficient K exceeds Kmax, the voice prompt device and the alarm prompt unit in the alarm system are triggered, the voice prompt device provides an unbalanced load risk reminder to the driver's seat, and the alarm prompt unit transmits high transportation risk information to the cloud.

[0028] It should be understood that the embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

Claims

1. A method for monitoring transportation status, characterized in that, include: The visual monitoring module is set up to monitor the shape and placement of goods in real time. The initial snapshot modeling data of the goods is [Cx, Cy, Cz], and the modeling data at the end of the set time period is [Cxn, Cyn, Czn]. The occupied space and volume at each time point are calculated separately based on the modeling data. The initial occupied space SC is the product of the horizontal coordinate Cx, the vertical coordinate Cy, and the vertical coordinate Cz of the initial snapshot modeling data of the goods. The final occupied space SCn is the product of the horizontal coordinate Cxn, the vertical coordinate Cyn, and the vertical coordinate Czn of the modeling data at the end of the set time period. Similarly, the occupied space and volume data at any time point are obtained. The difference data value is the difference between the final occupied space SCn and the initial occupied space SC; A center of gravity detection module is set up to determine the shift of the vehicle's center of gravity by combining the vehicle's load capacity and tilt angle. The measurement results from the combined visual monitoring module and the center of gravity detection module are input into the risk algorithm to calculate the transportation hazard coefficient.

2. The transportation status monitoring method according to claim 1, characterized in that, The real-time monitoring of the cargo's shape includes acquiring snapshot data of the cargo at various time points, modeling and obtaining the difference data of the snapshot data at each time point within a set time period, and obtaining the difference data value; the real-time monitoring of the cargo's placement status includes acquiring the location data of the cargo at each time point, and combining the correspondence between the time point data and the location data to obtain the cargo trajectory data within a set time period.

3. The transportation status monitoring method according to claim 1, characterized in that, The center of gravity detection module is evenly laid on the bottom of the carriage to acquire pressure sensing data of each preset pressure point at a set time node of the carriage and calculate the center of gravity offset of the carriage; the preset pressure points include each edge vertex, each edge midpoint and the center of the carriage.

4. The transportation status monitoring method according to claim 1, characterized in that, The center of gravity detection module also includes liquid level display devices installed on each side wall of the carriage, which record the offset values ​​[hl, hr] of each liquid level display device at a set time node.

5. The transportation status monitoring method according to claim 2, characterized in that, The cargo trajectory data includes initial cargo modeling data [Cx, Cy, Cz] and displacement modeling data [Cx+n, Cy+m, Cz+e] at the time node where the practice phase ends. The actual forward, backward, left and right displacements [n, m, e] of the cargo during operation are calculated.

6. The transportation status monitoring method according to claim 3, characterized in that, The pressure data of the preset pressure-sensitive points includes [W1, W2, W3, W4, W5, Wx1, Wx2, Wy1, Wy2], where W1, W2, W3, and W4 are the pressure values ​​at the vertices of each edge, Wx1, Wx2, Wy1, and Wy2 are the pressure values ​​at the midpoints of each edge, and W5 is the pressure value at the center of the carriage. The average pressure is calculated for any edge vertex and midpoint, and compared with the pressure value at the center of the carriage to obtain the center-of-gravity shift of the carriage at any given time point. The pressure difference between each edge is further calculated to obtain the center-of-gravity shift ~W1 of the carriage at that time point. Similarly, the center-of-gravity shift ~Wn at the set time point is calculated to obtain the center-of-gravity shift of the carriage within the set time period.

7. A transportation status monitoring method according to claim 4, 5, or 6, characterized in that, A danger coefficient K is set, where K=|hr-hl|+|~Wn -~ W1|. If (SCn-SC)*([n, m, e])>R, increment K by 1; if (SCn-SC)*([n, m, e])<R, decrement K by 1, wherein R is a preset intermediate variable.

8. The transportation status monitoring method according to claim 7, characterized in that, An early warning system is further arranged in the carriage, and the preset danger coefficient range of the early warning system is Kmin~Kmax. If the danger coefficient K exceeds Kmax, a voice prompting device and an alarm prompting unit in an alarm system are triggered, wherein the voice prompting device provides an unbalanced load risk reminder to a driving position, and the alarm prompting unit transmits high transportation risk information to a cloud end.

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