Intelligent adjusting method based on cargo bundling detection

Abstract

The application discloses an intelligent adjusting method based on cargo bundling detection, and aims to solve the problem of low adjusting efficiency caused by cargo loosening during the process of fixing cargo by using a bundling belt, thereby affecting cargo transportation, and comprises the following steps: S1, fixing cargo by using a plurality of bundling belts, and dynamically obtaining pre-tightening force parameter information according to a pre-tightening force monitor arranged on the bundling belt; S2, obtaining a conversion tightness state of the bundling belt according to the pre-tightening force parameter information, and sending the conversion tightness state to a correction module; S3, the correction module calls a verification module to verify the tightness state of the bundling belt, and respectively detects a cargo offset and detects bundling belt damage, and generates to-be-corrected data; and S4, adjusting the conversion tightness state according to the to-be-corrected data, so that the cargo is in an optimal tightness state. After the cargo loosens, timely detection and adjustment are performed, meanwhile, a large amount of manpower and material resources are saved, and cargo transportation safety is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cargo binding detection, and in particular to an intelligent adjustment method based on cargo binding detection. BACKGROUND

[0002] In the field of logistics transportation, cargo is bound and fixed to prevent the cargo from falling off or colliding with each other during driving, causing loss or damage. In the process of fixing the cargo with a binding belt, the problem of cargo loosening often occurs, which is not easy to be found. After the problem occurs, a large amount of manpower and material resources will be consumed for detection before the adjustment is completed, resulting in low efficiency and affecting the transportation of the cargo.

[0003] The Chinese patent document "Intelligent logistics cargo binding system" with the publication number CN101492101B discloses an intelligent logistics cargo binding system, which includes an upper controller and a lower controller connected to the upper controller. A sensor and a power mechanism are connected to the lower controller. The sensor and the power mechanism are correspondingly arranged on a belt winding and unwinding device. An upper input device is connected to the upper controller. A lower input device is connected to the lower controller. According to the pressure signal of the sensor, the upper controller controls the power mechanism to act or stop through the lower controller or the lower controller directly. However, the Chinese patent with the publication number CN101492101B does not disclose how to detect and adjust after the problem occurs. SUMMARY

[0004] The present application solves the problem of low adjustment efficiency after the cargo loosening in the process of fixing the cargo with a binding belt, which affects the transportation of the cargo. An intelligent adjustment method based on cargo binding detection is proposed. The pre-tightening force parameter information is obtained by using a pre-tightening force monitor. The verification module is called for verification and detection. The to-be-corrected data is used for adjustment. After the cargo loosens, timely detection and adjustment are performed. At the same time, a large amount of manpower and material resources is saved to ensure the safety of cargo transportation.

[0005] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme: an intelligent adjustment method based on cargo binding detection, comprising the following steps:

[0006] S1, fixing the cargo with a plurality of binding belts, and dynamically obtaining pre-tightening force parameter information according to a pre-tightening force monitor arranged on the binding belt;

[0007] S2, obtaining the conversion tightness state of the binding belt according to the pre-tightening force parameter information, and sending the conversion tightness state to a correction module;

[0008] S3, calling a verification module by the correction module to verify the tightness state of the binding belt, and respectively detecting the cargo offset and the damage of the binding belt to generate to-be-corrected data.

[0009] S4, adjusting the conversion tightness state according to the to-be-corrected data, so that the goods are in the optimal tightness state.

[0010] In the present application, the goods are fixed by the binding belt, the pre-tightening force parameter information is obtained by detection by the pre-tightening force monitor, the initial conversion tightness state is obtained by data conversion and processing of the pre-tightening force parameter information, the verification module is directly called by the correction module for verification, overall detection, goods offset detection and binding belt damage detection are performed, the to-be-corrected data is obtained, and the initial conversion tightness state is adjusted by the to-be-corrected data; the method of the present application can perform timely detection and adjustment, save a lot of manpower and resources, and improve efficiency.

[0011] As preferred, the step S2 comprises the following steps:

[0012] S21, the obtained pre-tightening force parameter information is filtered and impurities are removed, and then a binding belt specification and pre-tightening force comparison model is called; S22, the filtered and impurity-removed pre-tightening force parameter information is input into the comparison model to determine the conversion tightness state of the binding belt;

[0013] S23, the conversion tightness state is sent to the correction module through data transmission, and the correction module issues a calling instruction.

[0014] In the present application, the dynamically obtained pre-tightening force parameter information needs to be processed to obtain the conversion tightness state; first, the data after filtering and impurity removal is input into the established comparison model, the conversion tightness state of the binding belt is output, and the conversion tightness state is transmitted to the correction module through wireless transmission; after reaching the correction module, the correction module is immediately triggered to start and issue a calling instruction.

[0015] As preferred, the step S3 comprises the following steps:

[0016] S31, the first image acquisition unit in the started verification module acquires the goods and the binding belt, the acquired images are spliced as a whole, and the goods and the binding belt part in the images are enhanced as a whole to determine whether the binding belt as a whole is in a tightened state;

[0017] S32, in the case that the binding belt as a whole is in the tightened state, the offset detection unit in the verification module detects the offset of the goods by applying force to the goods, and determines whether the goods offset is within a preset interval;

[0018] S33, in the case that the goods offset is within the preset interval, the second image acquisition unit in the verification module extracts features related to the binding belt and the contact point between the binding belt and the goods in the images to determine whether the binding belt is in an offset state and whether the binding belt is damaged.

[0019] In the present application, the verification module is used for verification of the tightness state, mainly through the image collected by the first image acquisition unit to determine whether the binding belt is in a tight state after splicing and local enhancement; then the cargo offset and the binding belt damage detection are carried out, the cargo offset is mainly detected by the offset detection unit, the offset detection unit can exert force on one side of the cargo, and the offset of the cargo is detected under the action of the force; the binding belt damage detection is detected by the second image acquisition unit, and the offset state and damage of the binding belt are determined by extracting relevant features.

[0020] As preferred, the step S3 further comprises:

[0021] According to the verification results of the first image acquisition unit, the offset detection unit and the second image acquisition unit in the verification module, the to-be-corrected data is divided into different categories, and the to-be-corrected data is fed back to the correction module in a parallel transmission mode.

[0022] In the present application, after the step S33 is completed, the verification module counts the verification result information of the first image acquisition unit, the offset detection unit and the second image acquisition unit, and divides the to-be-corrected data into different categories according to different verification results, and the to-be-corrected data is fed back to the correction module in time for timely adjustment.

[0023] As preferred, the establishment process of the comparison model is specifically:

[0024] The commonly used binding belt specifications are queried, and the pretightening force change function corresponding to the binding belt specifications is found out, and the mapping relationship between the pretightening force of different sizes and the binding belt tightness state is established, and the mapping relationship between the pretightening force and the binding belt tightness state is obtained from the confirmed historical data.

[0025] In the present application, when various binding belt specifications are queried and called out, the pretightening force change function corresponding to various binding belt specifications is searched out, and the change function can be displayed in the form of a chart or an approximate expression; in the case of fixed binding belt specifications, the pretightening force and the binding belt tightness state have a mapping relationship, and the mapping relationship is deep learning with the update of historical data, and is continuously optimized; the comparison model established in the present application has a deep learning function.

[0026] As preferred, the specific method for whether the binding belt is in an offset state and whether it is damaged in the step S33 is:

[0027] The extracted image splicing generates an overall image, feature extraction is performed on the overall image, features of the binding belt and the contact points between the binding belt and the goods are extracted, the extracted features about the binding belt are compared with reference objects without offset of the binding belt, and specific offset amount and whether the offset amount exceeds the specified offset range are determined; the extracted contact points between the binding belt and the goods are enhanced, the number of contact points is determined, and the number of contact points is compared with the contact points between the undamaged binding belt and the goods, if the number is different, the binding belt has been damaged.

[0028] In the present application, for the more detailed method in step S33, after the second image acquisition unit acquires the image, the multiple angle images at the same time are spliced as a whole, the spliced image is subjected to feature extraction, the extracted features are compared with reference objects without offset of the binding belt, and it is determined whether the offset amount of the binding belt exceeds the specified offset range; in addition, the extracted features about the contact points between the binding belt and the goods are subjected to enhancement processing, and the number of contact points is determined, and the contact points are compared, if the number of contact points is different from that of the undamaged contact points, the binding belt is considered to be damaged.

[0029] Preferably, the step S1 specifically comprises:

[0030] At least one pre-tightening force monitor is installed on each binding belt, and the pre-tightening force monitor periodically acquires pre-tightening force parameter information on the binding belt.

[0031] In the present application, generally, two pre-tightening force monitors are arranged on each binding belt, and after the pre-tightening force monitors acquire the pre-tightening force parameter information, the average value is calculated and outputted.

[0032] Preferably, the step S4 specifically comprises:

[0033] According to different types of data to be corrected, the correction module issues different adjustment instructions, the content of the adjustment instruction includes replacing the binding belt and adjusting the pre-tightening force of the binding belt, and the adjustment is performed according to the content of the adjustment instruction, and a warning is issued at the same time.

[0034] In the present application, the correction module issues different adjustment instructions according to the types of data to be corrected, the adjustment instruction is accompanied by specific content, mainly including replacing the binding belt, adjusting the pre-tightening force of the binding belt and adjusting the position of the binding belt, and the adjustment instruction also includes warning information, and the warning information plays a role of quick reminding.

[0035] The present application has the following beneficial effects:

[0036] 1. For the intelligent adjustment method based on cargo binding detection of the application, the pre-tightening force parameter information is obtained by using the pre-tightening force monitor, the verification module is called for verification and detection, and the to-be-corrected data is used for adjustment; after the cargo is loosened, timely detection and adjustment are performed, a large amount of manpower and material resources are saved, and the safety of cargo transportation is ensured;

[0037] 2. In the application, a contrast model with deep learning function is also established to obtain the conversion tightness state and ensure the accuracy of conversion. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is a flowchart of the intelligent adjustment method based on cargo binding detection of the application;

[0039] Figure 2 is a module schematic diagram of the intelligent adjustment method based on cargo binding detection of the application. DETAILED DESCRIPTION

[0040] Embodiment 1:

[0041] The intelligent adjustment method based on cargo binding detection of the present embodiment, referring to Figure 1 and Figure 2 , mainly includes the following steps.

[0042] Step S1, use several binding belts to fix the cargo, and dynamically obtain the pre-tightening force parameter information according to the pre-tightening force monitor arranged on the binding belt; specifically, in this step, generally, a plurality of binding belts are used to fix the cargo, the binding belt is made of non-rigid material to prevent damage to the cargo; and the pre-tightening force monitor is arranged on the binding belt, and the installation mode is not limited, which can be fixed on the binding belt and does not contact the cargo.

[0043] The pre-tightening force monitor in the application includes a monitor body and a communication module, the monitor body is internally provided with a pre-tightening force sensor, the communication module includes a wired communication unit and a wireless communication unit, the wired communication unit is externally provided with a plurality of communication universal ports, and the pre-tightening force monitor can be in communication connection with an external dispatching module or a display module.

[0044] In the application, at least one pre-tightening force monitor is installed on each binding belt, and the pre-tightening force monitor periodically collects the pre-tightening force parameter information on the binding belt; in this embodiment, if two or more pre-tightening force monitors are installed on a binding belt, the average value of the collected pre-tightening force parameter information needs to be calculated.

[0045] Step S2, obtain the conversion tightness state of the binding belt according to the pre-tightening force parameter information, and send the conversion tightness state to the correction module; specifically, this step includes the following sub-steps.

[0046] Step S21, the collected pre-tightening force parameter information is filtered and impurities are removed, and then a pre-tightening force and a binding belt specification are called to a comparison model; for the filtering and impurity removal process, the filtering and impurity removal process can be performed inside the pre-tightening force monitor or outside the pre-tightening force monitor.

[0047] Step S22, the pre-tightening force parameter information after filtering and impurity removal is input to the comparison model to determine the conversion tightness state of the binding belt; specifically, the comparison model can also be arranged inside or outside the pre-tightening force monitor.

[0048] For the specific establishment process of the comparison model, first, the commonly used binding belt specifications are queried, and the pre-tightening force change function corresponding to the binding belt specifications is found, and the mapping relationship between the pre-tightening force of different sizes and the tightness state of the binding belt is established, and the mapping relationship between the pre-tightening force and the tightness state of the binding belt is obtained from the confirmed historical data. The comparison model in this embodiment can be continuously optimized according to the update of the historical data.

[0049] Step S23, the conversion tightness state is sent to the correction module through data transmission, and the correction module issues a calling instruction; after the output result of the comparison model is output, the output result, i.e. the conversion tightness state, is sent to the correction model, and the correction module is a separate peripheral unit.

[0050] Step S3, the correction module calls the verification module to verify the tightness state of the binding belt, and respectively detects the cargo offset and detects the damage of the binding belt to generate the to-be-corrected data; specifically, it includes the following sub-steps.

[0051] Reference Figure 2 Step S31, the first image acquisition unit in the verification module is called to acquire the cargo and the binding belt, the acquired image is spliced as a whole, and the cargo and the binding belt part in the image are enhanced as a whole to determine whether the binding belt as a whole is in a tightening state; specifically, after the correction module is started and called, this step is preferentially enabled, i.e. whether the binding belt as a whole is in a tightening state is detected, image acquisition is performed by using the first image acquisition unit, splicing is performed after acquisition, the cargo and the binding belt part in the spliced image are enhanced as a whole, and whether the binding belt is in a tightening state is observed; if it is in a tightening state, step S32 is started; if it is not in a tightening state, first to-be-corrected data is generated and fed back to the correction module.

[0052] The first image acquisition unit includes a plurality of area array cameras, and the plurality of area array cameras perform image splicing after image acquisition.

[0053] Step S32, in the case of the overall bundling belt being in the tightened state, the offset detection unit in the verification module detects the offset of the goods by applying force to the goods, and determines whether the offset of the goods is within the preset range; in this step, the offset detection unit includes a force applying component and a displacement detection component, which are wirelessly connected, first the force applying component of the offset detection unit applies a force, and then the displacement detection component detects the offset of the goods, if the offset of the goods is within the preset range, step S32 is started, and if the offset of the goods exceeds the preset range, second to-be-corrected data is directly generated and fed back to the correction module.

[0054] Step S33, in the case that the offset of the goods is within the preset range, the second image acquisition unit in the verification module extracts features related to the bundling belt and the contact points of the bundling belt and the goods in the image to determine whether the bundling belt is in an offset state and whether the bundling belt is damaged; in this step, the second image acquisition unit is used for acquisition and splicing, and the features are extracted and compared to determine whether the bundling belt is in an offset state and whether the bundling belt is damaged; a plurality of types of to-be-corrected data are generated in this step; in this embodiment, third to-be-corrected data, fourth to-be-corrected data and fifth to-be-corrected data are generated.

[0055] Step S3 further includes: according to the verification results of the first image acquisition unit, the offset detection unit and the second image acquisition unit in the verification module, different types of to-be-corrected data are classified, and the to-be-corrected data is fed back to the correction module in a parallel transmission manner; in the present application, according to the verification results of steps S31 to S33, different to-be-corrected data are classified, and the above-mentioned to-be-corrected data is fed back to the correction module, and the correction module adjusts according to the to-be-corrected data.

[0056] More specifically, the specific method for determining whether the bundling belt is in an offset state and whether it is damaged in step S33 is: the extracted images are spliced to generate an overall image, features of the bundling belt and the contact points of the bundling belt and the goods are extracted from the overall image, the extracted features of the bundling belt are compared with reference objects of the bundling belt without offset to determine the specific offset and whether it exceeds the specified offset range; the extracted features of the contact points of the bundling belt and the goods are enhanced to determine the number of contact points, and the number of contact points is compared with the contact points of the undamaged bundling belt and the goods, if the number is different, the bundling belt has been damaged. In this embodiment, the reference objects of the bundling belt without offset are obtained by the second image acquisition unit in the historical period; the contact points of the undamaged bundling belt and the goods are obtained from the images in the historical period and after analysis.

[0057] Step S4, the conversion tightness state is adjusted according to the to-be-corrected data, so that the goods are in the optimal tightness state; specifically, in this step, the correction module issues different adjustment instructions according to different types of to-be-corrected data, the content of the adjustment instruction includes replacing the binding belt and adjusting the pre-tightening force of the binding belt, and the adjustment is performed according to the content of the adjustment instruction, and a warning is issued at the same time. In this embodiment, the first to-be-corrected data to the fifth to-be-corrected data generated in step S3 are different types of to-be-corrected data.

[0058] In this embodiment, for the dynamically obtained pre-tightening force parameter information, the conversion tightness state needs to be obtained after processing. First, the data after filtering and impurity removal is input into the established comparison model, the conversion tightness state of the binding belt is output, and the wireless transmission is transmitted to the correction module. After reaching the correction module, the correction module is immediately triggered to start and issue a call instruction.

[0059] In this embodiment, the tightness state is verified by using the verification module, mainly through the image captured by the first image acquisition unit to determine whether the binding belt is in a tight state after splicing and local enhancement; then the cargo offset and the binding belt damage detection are performed. The cargo offset is mainly detected by the offset detection unit, which can exert force on one side of the cargo, and detect the offset of the cargo under the action of the force. The binding belt damage detection is detected by the second image acquisition unit, and the offset state and damage of the binding belt are determined by extracting relevant features.

[0060] In this embodiment, after step S33 is completed, the verification module counts the verification result information of the first image acquisition unit, the offset detection unit and the second image acquisition unit, and divides the to-be-corrected data into different types according to different verification results. The to-be-corrected data is fed back to the correction module in time for timely adjustment.

[0061] In this embodiment, when various binding belt specifications are queried and called out, the pre-tightening force change function corresponding to various binding belt specifications is searched out at the same time. The change function can be displayed in the form of a chart or an approximate expression. In the case of fixed binding belt specifications, the pre-tightening force and the binding belt tightness state have a mapping relationship. The mapping relationship is learned in depth with the update of historical data, and is continuously optimized. The comparison model established in the present application has a deep learning function.

[0062] In the embodiment, for the more detailed method in step S33, the second image acquisition unit performs overall splicing on the multiple angle images at the same time after acquiring the images, the spliced images are subjected to feature extraction, the extracted features are compared with the reference objects without the offset of the binding belt, and it is determined whether the offset of the binding belt exceeds the specified offset range; in addition, the extracted features about the contact points of the binding belt and the goods are subjected to enhancement processing, the number of the contact points is determined, and the contact points are compared, and if the number of the contact points is different from that of the undamaged contact points, it is considered that the binding belt is damaged.

[0063] In the embodiment, generally, two pre-tightening force monitors are arranged on each binding belt, and the average value is obtained after the pre-tightening force parameter information is acquired by the pre-tightening force monitor, and the average value is outputted and converted.

[0064] In the embodiment, the correction module sends different adjustment instructions according to the types of the to-be-corrected data, the adjustment instructions are accompanied by specific contents, mainly including replacement of the binding belt, adjustment of the pre-tightening force of the binding belt, and position adjustment of the binding belt, and the sending of the adjustment instructions also includes early warning information, and the early warning information plays a role of quick reminding.

[0065] In the steps of the present application, the goods are first fixed by the binding belt, the pre-tightening force parameter information is acquired by the pre-tightening force monitor, then the pre-tightening force parameter information is subjected to data conversion and processing to obtain the initial converted tightness state, then the correction module directly calls the verification module for verification to perform overall detection, goods offset detection, and binding belt damage detection to obtain the to-be-corrected data, and finally the to-be-corrected data is used to adjust the initial converted tightness state; the method of the present application can perform timely detection and adjustment, save a large amount of manpower and resources, and improve the efficiency.

[0066] The preferred embodiments of the present application are described in detail above. It should be understood that those skilled in the art can make many modifications and changes without creative labor according to the concept of the present application. Therefore, any technical solution obtained by logical analysis, reasoning or limited experiment on the basis of the prior art according to the concept of the present application shall be within the protection scope determined by the claims.

Claims

1. A method for intelligent adjustment based on cargo binding detection, characterized in that, The method comprises the following steps: S1, fixing the goods using a plurality of binding belts, and dynamically obtaining pre-tightening force parameter information according to a pre-tightening force monitor arranged on the binding belt; S2, obtaining a conversion tightness state of the binding belt according to the pre-tightening force parameter information, and sending the conversion tightness state to a correction module; S2 comprises: filtering and removing impurities from the collected pre-tightening force parameter information, and then calling a comparison model of the binding belt specifications and the pre-tightening force; inputting the filtered and impurity-removed pre-tightening force parameter information into the comparison model to determine the conversion tightness state of the binding belt; the conversion tightness state is sent to the correction module through data transmission, and the correction module issues a calling instruction; S3, the correction module calls a verification module to verify the tightness state of the binding belt, and respectively detects the goods offset and the binding belt damage to generate to-be-corrected data; comprising: the first image acquisition unit of the verification module is called to acquire the goods and the binding belt, and the acquired image is spliced and locally enhanced to determine whether the binding belt as a whole is in a tight state; in the case that the binding belt as a whole is in a tight state, the offset detection unit of the verification module detects whether the goods offset is within a preset range by applying force to the goods; in the case that the goods offset is within the preset range, the second image acquisition unit of the verification module extracts the features of the binding belt and the contact points of the binding belt and the goods in the image to determine the offset state of the binding belt and whether it is damaged; S4, adjusting the conversion tightness state according to the to-be-corrected data to make the goods in the best tightness state.

2. The intelligent adjustment method based on cargo bundling detection according to claim 1, wherein, The step S3 further comprises: according to the verification results of the first image acquisition unit, the offset detection unit and the second image acquisition unit in the verification module, the to-be-corrected data is divided into different kinds of to-be-corrected data, and the to-be-corrected data is fed back to the correction module in a parallel transmission manner.

3. The intelligent adjustment method based on goods bundling detection according to claim 1, characterized in that, The specific method for determining whether the binding belt is in an offset state and whether it is damaged is: the extracted image is spliced to generate a whole image, the features of the binding belt and the contact points of the binding belt and the goods are extracted, the extracted features of the binding belt are compared with the reference of the binding belt without offset to determine the specific offset and whether it exceeds the specified offset range; the extracted features of the contact points of the binding belt and the goods are enhanced to determine the number of contact points, and the number of contact points is compared with the contact points of the undamaged binding belt and the goods, if the number is different, the binding belt has been damaged.

4. The intelligent adjustment method based on cargo bundling detection according to claim 1, wherein, The step S1 specifically comprises: at least one pre-tightening force monitor is installed on each binding belt, and the pre-tightening force monitor periodically collects the pre-tightening force parameter information on the binding belt.

5. The intelligent adjustment method based on cargo bundling detection according to claim 2, wherein, The step S4 specifically comprises: according to different kinds of to-be-corrected data, the correction module issues different adjustment instructions, the content of the adjustment instruction includes replacing the binding belt and adjusting the pre-tightening force of the binding belt, and the adjustment is made according to the content of the adjustment instruction, and a warning is issued at the same time.

6. The intelligent adjustment method based on cargo bundling detection according to claim 5, wherein, The adjustment instruction is accompanied by specific content such as replacing the binding belt, adjusting the pre-tightening force of the binding belt and adjusting the position of the binding belt, and the sending of the adjustment instruction also includes warning information, which plays a role in quick reminding.

7. The intelligent adjustment method based on goods bundling detection according to claim 4, characterized in that, Two pre-tightening force monitors are arranged on each binding belt, and an average value is calculated after the pre-tightening force parameter information is acquired by the pre-tightening force monitors, and the average value is outputted and converted.

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