Truck transportation safety management method and system based on Internet of Things, and storage medium

Abstract

The invention discloses a truck transportation safety management method based on the Internet of Things, and the method comprises the steps: recording the initial weights of n regions in a carriage after the loading of transported goods is completed; recording initial images of n areas in the carriage; in the transportation process, the real-time weights of the n areas in the carriage are dynamically recorded; the change rate qm of the Lm and the change rate qm of the Wm are compared, and when qm exceeds a preset single change value q0, a carriage cargo displacement monitoring mode is started; the carriage cargo displacement monitoring mode comprises the following steps: acquiring real-time images of n areas in a carriage; and respectively comparing the offsets fj of the real-time images and the initial image of the n areas, and when fj exceeds a preset single offset value f0 or exceeds a preset total offset value F0, starting early warning. By installing various sensors, positioning devices and monitoring devices in the carriage of the truck, abnormal conditions are found in time and early warning is given out through data analysis and processing, intelligent and refined management of truck transportation is achieved, and the transportation safety level is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of truck safety operation technology, and in particular to a large truck transportation safety management method, system and storage medium based on the Internet of Things. Background Art

[0002] Trucking is a key component of the modern logistics industry, carrying large quantities of cargo over long distances and playing a vital role in the development of the national economy. With continued economic growth and the rise of industries like e-commerce, the demand for cargo transportation continues to increase, and the volume of cargo transported by trucks is also growing.

[0003] Traditional technologies make it difficult to achieve real-time, all-round monitoring of large trucks. Supervisors are unable to keep up with the status of cargo, vehicle movement, and driver performance, making it difficult to detect and correct misconduct, respond to emergencies, and ensure vehicle safety. Summary of the Invention

[0004] In order to solve the technical problems existing in the background technology, the present invention proposes a large truck transportation safety management method, system and storage medium based on the Internet of Things.

[0005] The present invention proposes a large truck transportation safety management method based on the Internet of Things, comprising:

[0006] After the transport cargo is loaded, record the initial weights of n areas in the carriage, denoted as W1, W2...W n ;Record the initial images of n areas in the car;

[0007] During transportation, the real-time weight of n areas in the carriage is dynamically recorded and recorded as L1, L2...L n ;

[0008] Comparison L m With W m The rate of change q m , when q m When the preset single change value q0 is exceeded, the carriage cargo displacement monitoring mode is activated; 1≤m≤n;

[0009] The carriage cargo displacement monitoring mode includes:

[0010] Obtain real-time images of n areas in the carriage;

[0011] Compare the offset f of the real-time image and the initial image of n areas respectively j , when f j Exceeds the preset single offset value f0 or When the preset total offset value F0 is exceeded, an early warning is activated; where 1≤j≤n.

[0012] Optionally, the offset f of the comparison between the real-time image of the ath region and the initial image is a , specifically including:

[0013] Using a feature detector to extract key points of the real-time image and the initial image of region a;

[0014] Matching the two sets of key points based on feature descriptors;

[0015] Calculate the coordinate difference (dx i ,dy i ), take the average value to get the overall offset The offset of the ath region

[0016] Among them, dx i Indicates the horizontal offset, dy i Indicates the vertical offset, 1≤a≤n.

[0017] Optionally, the starting of the carriage cargo displacement monitoring mode further includes:

[0018] During transportation, compare L b With W b The rate of change q b ,when When the preset total change value Q0 is exceeded, the carriage cargo displacement monitoring mode is activated; 1≤b≤n.

[0019] Optionally, the starting of the carriage cargo displacement monitoring mode further includes:

[0020] During transportation, the current weather conditions are obtained in real time. When rain, fog, snow, strong winds, and hail occur, the cargo displacement monitoring mode in the carriage is activated;

[0021] During transportation, the vehicle's driving status is acquired in real time. When sudden braking and / or acceleration and / or high-speed turning and / or high-speed lane changing occur, the cargo displacement monitoring mode is activated;

[0022] During transportation, road condition information is obtained in real time, and when there are bumpy roads and / or curves and / or slopes, the carriage cargo displacement monitoring mode is activated.

[0023] Optionally, the starting of the carriage cargo displacement monitoring mode further includes:

[0024] During transportation, the driver's driving status is acquired in real time. When the driving status deviates from the preset state, the compartment cargo displacement monitoring mode is activated.

[0025] The present invention proposes a large truck transportation safety management system based on the Internet of Things, comprising:

[0026] The initial state recording module records the initial weight of n areas in the carriage after the cargo is loaded, and records them as W1, W2...W n ;Record the initial images of n areas in the car;

[0027] The real-time weight update module dynamically records the real-time weight of n areas in the carriage during transportation, recorded as L1, L2...L n ;

[0028] Real-time image update module, which dynamically records real-time images of n areas in the carriage during transportation;

[0029] State change analysis module, compare L m With W m The rate of change q m , when q m When the preset single change value q0 is exceeded, the instruction displacement monitoring and warning module will be activated; 1≤m≤n;

[0030] The displacement monitoring and early warning module compares the real-time images of n areas with the offset f of the initial image according to the instructions of the state change analysis module. j , when f j Exceeds the preset single offset value f0 or When the preset total offset value F0 is exceeded, an early warning is activated; where 1≤j≤n.

[0031] Optionally, the displacement monitoring and early warning module compares the offset f between the real-time image of the ath region and the initial image. a , specifically including:

[0032] Using a feature detector to extract key points of the real-time image and the initial image of region a;

[0033] Matching the two sets of key points based on feature descriptors;

[0034] Calculate the coordinate difference (dx i ,dy i ), take the average value to get the overall offset The offset of the ath region

[0035] Among them, dx i Indicates the horizontal offset, dy i Indicates the vertical offset, 1≤a≤n.

[0036] Optionally, the state change analysis module is further configured to:

[0037] During transportation, compare L b With W b The rate of change q b ,when When the preset total change value Q0 is exceeded, the carriage cargo displacement monitoring mode is activated; 1≤b≤n.

[0038] Optionally, the state change analysis module is further configured to:

[0039] During transportation, the current weather conditions are obtained in real time, and when rain and / or fog and / or snow and / or strong wind and / or hail occur, the displacement monitoring and early warning module is instructed to operate;

[0040] During transportation, the vehicle's driving status is acquired in real time. When sudden braking and / or sudden acceleration and / or high-speed turning and / or high-speed lane changing occurs, the displacement monitoring and warning module is instructed to operate;

[0041] During transportation, road condition information is obtained in real time, and when bumpy roads and / or curves and / or slopes appear, the displacement monitoring and warning module is instructed to operate;

[0042] During the transportation process, the driver's driving status is obtained in real time. When the driving status deviates from the preset state, the displacement monitoring and early warning module is instructed to operate.

[0043] The present invention also proposes a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the large truck transportation safety management method based on the Internet of Things.

[0044] From the above solutions, it can be seen that the IoT-based truck transportation safety management method and system provided by this application has at least the following beneficial effects compared with the existing technology:

[0045] By installing a variety of sensors, positioning equipment, and monitoring devices in the truck compartment, real-time information exchange between the vehicle and the management platform is achieved. IoT technology is used to obtain real-time data on the vehicle's driving status, road conditions, weather conditions, and the driver's driving status. Through data analysis and processing, abnormal situations are promptly detected and early warnings are issued, achieving intelligent and refined management of truck transportation and effectively improving transportation safety. Specifically, this application monitors the changes in cargo weight in different areas of the compartment during transportation to determine whether there is a potential risk, and uses this as a criterion for whether to enter the next monitoring mode. When the single weight value of a certain area changes significantly, or the combined weight value of multiple areas changes significantly, the potential risk is determined to be high, and displacement monitoring of the compartment is immediately initiated. That is, the difference between the real-time image and the original image of multiple areas in the compartment is compared to obtain the cargo offset. When the offset of a single area, or the total offset of multiple areas, exceeds the preset safety value, an early warning is initiated. This achieves intelligent monitoring of the entire transportation process, promptly detects potential dangers, responds to emergencies in a timely manner, and ensures the safety of truck driving. Furthermore, this application also uses four factors, namely, current weather conditions, vehicle driving status, real-time road conditions information, and driver's driving status, as the basis for judging whether to enter the vehicle compartment to obtain the displacement monitoring mode, comprehensively considering the safety issues faced by large truck transportation, reducing potential safety hazards and problems, and improving transportation safety and efficiency in a targeted manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flowchart of a large truck transportation safety management method based on the Internet of Things;

[0047] Figure 2 This is a module diagram of a large truck transportation safety management system based on the Internet of Things. DETAILED DESCRIPTION

[0048] like Figure 1 As shown, Figure 1 This is a flowchart of a large truck transportation safety management method based on the Internet of Things proposed by the present invention.

[0049] Reference Figure 1 The present invention proposes a large truck transportation safety management method based on the Internet of Things, comprising:

[0050] After the transport cargo is loaded, record the initial weights of n areas in the carriage, denoted as W1, W2...W n ;Record the initial images of n areas in the car;

[0051] During transportation, the real-time weight of n areas in the carriage is dynamically recorded and recorded as L1, L2...L n ;

[0052] Comparison L m With W m The rate of change q m , when q m When the preset single change value q0 is exceeded, the carriage cargo displacement monitoring mode is activated; 1≤m≤n;

[0053] The carriage cargo displacement monitoring mode includes:

[0054] Obtain real-time images of n areas in the carriage;

[0055] Compare the offset f of the real-time image and the initial image of n areas respectively j , when f j Exceeds the preset single offset value f0 or When the preset total offset value F0 is exceeded, an early warning is activated; where 1≤j≤n.

[0056] In this embodiment, the presence of potential risks is determined by monitoring the changes in the weight of cargo in different areas of the carriage during transportation, and the large changes in the single weight value in a certain area are used as a judgment indicator for whether to enter the next monitoring mode; the above step is the first step of judgment. On the one hand, to avoid misjudgment, and on the other hand, to prevent the potential risk from continuing to be too large, a second step of judgment is designed, namely: starting the carriage cargo displacement monitoring mode. The specific working content of this mode is: comparing the difference between the real-time image and the original image of multiple areas in the carriage to obtain the cargo offset. When the offset of a single area, or the total offset of multiple areas, exceeds the preset safety value, it is concluded that the potential risk does exist, and an early warning is immediately activated. At the same time, the driver and the management platform are notified, and countermeasures are taken in two threads. Intelligent monitoring is used to promptly detect and respond to potential dangers, thereby improving the safety of large truck transportation.

[0057] In a further embodiment, the offset f of comparing the real-time image of the a-th region with the initial image is a , specifically including:

[0058] Using a feature detector to extract key points of the real-time image and the initial image of region a;

[0059] Matching the two sets of key points based on feature descriptors;

[0060] Calculate the coordinate difference (dx i ,dy i ), take the average value to get the overall offset The offset of the ath region

[0061] Among them, dx i Indicates the horizontal offset, dy iIndicates the vertical offset, 1≤a≤n.

[0062] Using computer vision and image processing, we calculate the coordinate difference of matching point pairs to obtain image offset, in order to determine whether there is a potential risk of cargo displacement in area a. The specific calculation steps are as follows:

[0063] Assume the coordinates of the first key point of the initial image of region a are (x1, y1);

[0064] Assume the coordinates of the first key point of the real-time image of region a are (x2, y2);

[0065] Then the coordinate difference of the first key point is: dx i =x2-x1,dx i =x2-x1.

[0066] In actual monitoring, based on practical needs and the need to improve judgment accuracy, two or more key points can be set for Area A to reduce the error associated with analyzing a single key point and improve the overall accuracy of the judgment results. Furthermore, where possible, the initial and real-time images of Area A can be replaced with initial and real-time video, using video tracking to calculate the motion displacement of target key points to determine potential risks such as cargo displacement.

[0067] In a further embodiment, the starting of the carriage cargo displacement monitoring mode further includes:

[0068] During transportation, compare L b With W b The rate of change q b ,when When the preset total change value Q0 is exceeded, the carriage cargo displacement monitoring mode is activated; 1 ≤ b ≤ n. By comprehensively considering the sum of weight changes in multiple areas, slight shifts in the overall cargo within the carriage can be detected. To verify whether there is a potential risk, the second step, cargo displacement monitoring, is initiated. This prevents misjudgments and mitigates the escalation of potential risks, thereby comprehensively improving transportation safety.

[0069] In a further embodiment, the starting of the carriage cargo displacement monitoring mode further includes:

[0070] During transportation, the current weather conditions are obtained in real time. When rain, fog, snow, strong winds, and hail occur, the cargo displacement monitoring mode in the carriage is activated;

[0071] During transportation, the vehicle's driving status is acquired in real time. When sudden braking and / or acceleration and / or high-speed turning and / or high-speed lane changing occur, the cargo displacement monitoring mode is activated;

[0072] During transportation, road condition information is obtained in real time, and when there are bumpy roads and / or curves and / or slopes, the carriage cargo displacement monitoring mode is activated.

[0073] This embodiment lists various factors such as severe weather (heavy rain, heavy fog, ice and snow, etc. that reduce visibility and affect the driver's sight), dangerous driving behavior, and complex road conditions (mountainous roads, curves, steep slopes, etc., which are difficult to drive) as the basis for judging the activation of the carriage cargo displacement monitoring mode, comprehensively considering the safety issues faced by large trucks during transportation, reducing potential safety hazards and problems, and improving transportation safety and efficiency in a targeted manner.

[0074] In a further embodiment, the starting of the carriage cargo displacement monitoring mode further includes:

[0075] During transportation, the driver's driving status is acquired in real time. When the driving status deviates from the preset state, the compartment cargo displacement monitoring mode is activated.

[0076] This embodiment needs to determine whether the driver has engaged in behaviors such as fatigue driving, speeding, long-term driving, and illegal operations, to avoid traffic accidents caused by the driver's subjective bad driving habits and behaviors, monitor and manage potential risks in advance, and facilitate timely and accurate judgment and handling in emergency situations.

[0077] Reference Figure 2 The present invention proposes a large truck transportation safety management system based on the Internet of Things, including:

[0078] The initial state recording module records the initial weight of n areas in the carriage after the cargo is loaded, and records them as W1, W2...W n ;Record the initial images of n areas in the car;

[0079] The real-time weight update module dynamically records the real-time weight of n areas in the carriage during transportation, recorded as L1, L2...L n ;

[0080] Real-time image update module, which dynamically records real-time images of n areas in the carriage during transportation;

[0081] State change analysis module, compare L m With W m The rate of change q m , when q m When the preset single change value q0 is exceeded, the instruction displacement monitoring and warning module will be activated; 1≤m≤n;

[0082] The displacement monitoring and early warning module compares the real-time images of n areas with the offset f of the initial image according to the instructions of the state change analysis module. j , when f j Exceeds the preset single offset value f0 or When the preset total offset value F0 is exceeded, an early warning is activated; where 1≤j≤n.

[0083] In this embodiment, the presence of potential risks is determined by monitoring the changes in the weight of cargo in different areas of the carriage during transportation, and the large changes in the single weight value in a certain area are used as a judgment indicator for whether to enter the next monitoring mode; the above step is the first step of judgment. On the one hand, to avoid misjudgment, and on the other hand, to prevent the potential risk from continuing to be too large, a second step of judgment is designed, namely: starting the carriage cargo displacement monitoring mode. The specific working content of this mode is: comparing the difference between the real-time image and the original image of multiple areas in the carriage to obtain the cargo offset. When the offset of a single area, or the total offset of multiple areas, exceeds the preset safety value, it is concluded that the potential risk does exist, and an early warning is immediately activated. At the same time, the driver and the management platform are notified, and countermeasures are taken in two threads. Intelligent monitoring is used to promptly detect and respond to potential dangers, thereby improving the safety of large truck transportation.

[0084] In a further embodiment, the displacement monitoring and warning module compares the offset f between the real-time image of the a-th region and the initial image. a , specifically including:

[0085] Using a feature detector to extract key points of the real-time image and the initial image of region a;

[0086] Matching the two sets of key points based on feature descriptors;

[0087] Calculate the coordinate difference (dx i ,dy i ), take the average value to get the overall offset The offset of the ath region

[0088] Among them, dx i Indicates the horizontal offset, dy i Indicates the vertical offset, 1≤a≤n.

[0089] Using computer vision and image processing, we calculate the coordinate difference of matching point pairs to obtain image offset, in order to determine whether there is a potential risk of cargo displacement in area a. The specific calculation steps are as follows:

[0090] Assume the coordinates of the first key point of the initial image of region a are (x1, y1);

[0091] Assume the coordinates of the first key point of the real-time image of region a are (x2, y2);

[0092] Then the coordinate difference of the first key point is: dx i =x2-x1,dx i =x2-x1.

[0093] In actual monitoring, based on practical needs and the need to improve judgment accuracy, two or more key points can be set for Area A to reduce the error associated with analyzing a single key point and improve the overall accuracy of the judgment results. Furthermore, where possible, the initial and real-time images of Area A can be replaced with initial and real-time video, using video tracking to calculate the motion displacement of target key points to determine potential risks such as cargo displacement.

[0094] In a further embodiment, the state change analysis module is further configured to:

[0095] During transportation, compare L b With W b The rate of change q b ,when When the preset total change value Q0 is exceeded, the carriage cargo displacement monitoring mode is activated; 1 ≤ b ≤ n. By comprehensively considering the sum of weight changes in multiple areas, slight shifts in the overall cargo within the carriage can be detected. To verify whether there is a potential risk, the second step, cargo displacement monitoring, is initiated. This prevents misjudgments and mitigates the escalation of potential risks, thereby comprehensively improving transportation safety.

[0096] In a further embodiment, the state change analysis module is further configured to:

[0097] During transportation, the current weather conditions are obtained in real time, and when rain and / or fog and / or snow and / or strong wind and / or hail occur, the displacement monitoring and early warning module is instructed to operate;

[0098] During transportation, the vehicle's driving status is acquired in real time. When sudden braking and / or sudden acceleration and / or high-speed turning and / or high-speed lane changing occurs, the displacement monitoring and warning module is instructed to operate;

[0099] During transportation, road condition information is obtained in real time, and when bumpy roads and / or curves and / or slopes appear, the displacement monitoring and warning module is instructed to operate;

[0100] During the transportation process, the driver's driving status is obtained in real time. When the driving status deviates from the preset state, the displacement monitoring and early warning module is instructed to operate.

[0101] This embodiment lists various factors such as severe weather (heavy rain, heavy fog, ice and snow, etc. that reduce visibility and affect the driver's sight), dangerous driving behavior, and complex road conditions (mountainous roads, curves, steep slopes, etc., which are difficult to drive) as the basis for judging the activation of the carriage cargo displacement monitoring mode, comprehensively considering the safety issues faced by large trucks during transportation, reducing potential safety hazards and problems, and improving transportation safety and efficiency in a targeted manner.

[0102] This embodiment needs to determine whether the driver has engaged in behaviors such as fatigue driving, speeding, long-term driving, and illegal operations, to avoid traffic accidents caused by the driver's subjective bad driving habits and behaviors, monitor and manage potential risks in advance, and facilitate timely and accurate judgment and handling in emergency situations.

[0103] In a further embodiment, the present invention also proposes a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the large truck transportation safety management method based on the Internet of Things.

[0104] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A truck transportation safety management method based on the Internet of Things, characterized in that: include: After the transport cargo is loaded, record the initial weights of n areas in the carriage, denoted as W1, W2...W n ;Record the initial images of n areas in the car; During transportation, the real-time weight of n areas in the carriage is dynamically recorded and recorded as L1, L2...L n ; Comparison L m With W m The rate of change q m , when q m When the preset single change value q0 is exceeded, the carriage cargo displacement monitoring mode is activated; 1≤m≤n; The carriage cargo displacement monitoring mode includes: Obtain real-time images of n areas in the carriage; Compare the offset f of the real-time image and the initial image of n areas respectively j , when f j Exceeds the preset single offset value f0 or When the preset total offset value F0 is exceeded, an early warning is activated; where 1≤j≤n.

2. The method for managing large truck transportation safety based on the Internet of Things according to claim 1 is characterized in that: The offset f between the real-time image of the ath region and the initial image is compared a , specifically including: Using a feature detector to extract key points of the real-time image and the initial image of region a; Matching the two sets of key points based on feature descriptors; Calculate the coordinate difference (dx i ,dy i ), take the average value to get the overall offset The offset of the ath region Among them, dx i Indicates the horizontal offset, dy i Indicates the vertical offset, 1≤a≤n.

3. The method for managing truck transportation safety based on the Internet of Things according to claim 1 is characterized in that: The starting carriage cargo displacement monitoring mode further includes: During transportation, compare L b With W b The rate of change q b ,when When the preset total change value Q0 is exceeded, the carriage cargo displacement monitoring mode is activated; 1≤b≤n.

4. The method for managing large truck transportation safety based on the Internet of Things according to claim 1 is characterized in that: The starting carriage cargo displacement monitoring mode further includes: During transportation, the current weather conditions are obtained in real time. When rain, fog, snow, strong winds, and hail occur, the cargo displacement monitoring mode in the carriage is activated; During transportation, the vehicle's driving status is acquired in real time. When sudden braking and / or acceleration and / or high-speed turning and / or high-speed lane changing occur, the cargo displacement monitoring mode is activated; During transportation, road condition information is obtained in real time, and when there are bumpy roads and / or curves and / or slopes, the carriage cargo displacement monitoring mode is activated.

5. The method for managing large truck transportation safety based on the Internet of Things according to claim 1 is characterized in that: The starting carriage cargo displacement monitoring mode further includes: During transportation, the driver's driving status is acquired in real time. When the driving status deviates from the preset state, the compartment cargo displacement monitoring mode is activated.

6. A large truck transportation safety management system based on the Internet of Things, characterized by: include: The initial state recording module records the initial weight of n areas in the carriage after the cargo is loaded, and records them as W1, W2...W n ;Record the initial images of n areas in the car; The real-time weight update module dynamically records the real-time weight of n areas in the carriage during transportation, recorded as L1, L2...L n ; Real-time image update module, which dynamically records real-time images of n areas in the carriage during transportation; State change analysis module, compare L m With W m The rate of change q m , when q m When the preset single change value q0 is exceeded, the instruction displacement monitoring and warning module will be activated; 1≤m≤n; The displacement monitoring and early warning module compares the real-time images of n areas with the offset f of the initial image according to the instructions of the state change analysis module. j , when f j Exceeds the preset single offset value f0 or When the preset total offset value F0 is exceeded, an early warning is activated; where 1≤j≤n.

7. The large truck transportation safety management system based on the Internet of Things according to claim 6 is characterized in that: The displacement monitoring and early warning module compares the offset f between the real-time image of area a and the initial image a , specifically including: Using a feature detector to extract key points of the real-time image and the initial image of region a; Matching the two sets of key points based on feature descriptors; Calculate the coordinate difference (dx i ,dy i ), take the average value to get the overall offset The offset of the ath region Among them, dx i Indicates the horizontal offset, dy i Indicates the vertical offset, 1≤a≤n.

8. The large truck transportation safety management system based on the Internet of Things according to claim 6 is characterized in that: The state change analysis module is also used for: During transportation, compare L b With W b The rate of change q b ,when When the preset total change value Q0 is exceeded, the carriage cargo displacement monitoring mode is activated; 1≤b≤n.

9. The large truck transportation safety management system based on the Internet of Things according to claim 6 is characterized in that: The state change analysis module is also used for: During transportation, the current weather conditions are obtained in real time, and when rain and / or fog and / or snow and / or strong wind and / or hail occur, the displacement monitoring and early warning module is instructed to operate; During transportation, the vehicle's driving status is acquired in real time. When sudden braking and / or sudden acceleration and / or high-speed turning and / or high-speed lane changing occurs, the displacement monitoring and warning module is instructed to operate; During transportation, road condition information is obtained in real time, and when bumpy roads and / or curves and / or slopes appear, the displacement monitoring and warning module is instructed to operate; During the transportation process, the driver's driving status is obtained in real time. When the driving status deviates from the preset state, the displacement monitoring and early warning module is instructed to operate.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the large truck transportation safety management method based on the Internet of Things as described in any one of claims 1 to 5.

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