A monitoring and control method for a car transporter

Abstract

This invention relates to a monitoring and control method for a car handling machine. It employs video recognition positioning for guiding the car handling machine, enabling more direct and effective global monitoring of all factors and timely adjustments. Compared to methods relying on radar scanning devices for vehicle or wheel positioning, this method offers greater stability, prevents movement errors caused by radar interference, and has lower purchase and maintenance costs. The lifting mechanism features a fixed-point rotating support lifting arm and a rotating lifting arm, eliminating the need for lateral space for storing the support lifting arm and for sliding space for the rotating lifting arm. This allows for reduction in both lateral and longitudinal dimensions, resulting in product miniaturization. Based on the structural characteristics of the lifting mechanism, during the lifting process, the rotating lifting arm only needs to rotate to lift the wheel in conjunction with the support lifting arm, eliminating the need for sliding and improving work efficiency.

Description

Technical Field

[0001] This invention relates to the field of AGV control technology, and more specifically, to a monitoring and control method for a car transporter. Background Technology

[0002] Existing automotive handling robots, particularly the gripper arms used to lift wheels, have structural and operational shortcomings.

[0003] Chinese Utility Model No. 201921522931.2 discloses a submersible car handling robot. The car handling robot includes a main frame with gripping arms symmetrically arranged on both sides of the main frame. Each gripping arm includes two robotic arm bodies connected by a linkage mechanism. Both robotic arm bodies can rotate relative to the main frame. A power unit is connected to one of the robotic arm bodies to drive its rotation and drive the other robotic arm body to rotate synchronously through the linkage mechanism. The two robotic arm bodies of each set of gripping arms complete the gripping and lifting of one tire.

[0004] The aforementioned utility model's gripping arms achieve tire gripping and lifting by rotating synchronously in opposite directions. During the gripping and lifting process, the linkage mechanism is subjected to a reaction force from the vehicle's gravity, placing high demands on the power and strength of the linkage mechanism. The gripping arms need to be aligned with the tire as closely as possible, and the relative positions of the gripping arms on both sides and the tire on the same side need to be as symmetrical as possible. If the car handling robot has a certain angle with the vehicle, the car handling robot will shift during the gripping and lifting process, leading to inaccurate positioning, increased risk of power system failure, and unstable gripping.

[0005] Chinese invention patent application 201811396254.4 discloses an omnidirectional car handling robot and its control method. The car handling robot includes a main frame with gripping units symmetrically arranged on both sides of the main frame. Each gripping unit includes a front gripping arm and a rear gripping arm. The front gripping arm includes a front robotic arm body, a rotating shaft seat, a steering push rod, a lead screw and nut seat assembly, a power failure brake, a reduction gear set, a robotic arm closing induction switch, and a robotic arm opening induction switch. The rear gripping arm includes a planetary reducer, a coupling, a lead screw and nut and guide rail slider assembly, a drive seat, a drive push rod, a rear robotic arm body, a robotic arm rotating shaft seat, and a robotic arm rotation locking mechanism.

[0006] The clamping unit of the aforementioned invention patent application includes a fixed-point rotating front clamping arm and a sliding rear clamping arm. During the clamping and lifting process, the rear clamping arm needs to perform rotation, unfolding and sliding operations, which results in a relatively complex structure, low work efficiency, and the main frame needs to reserve a certain length of space to accommodate the lead screw nut and guide rail slider assembly, resulting in a large overall size and other shortcomings.

[0007] Chinese invention patent application 201811315061.1 discloses a telescopic clamping arm type transporter, including a front transporter and a rear transporter, as well as a connecting assembly connecting the front and rear transporters. The front and rear transporters are driven by a drive mechanism. Each transporter is equipped with two telescopic clamping arms, two rotating clamping arms, a detection switch, and a controller. The telescopic clamping arms are located at the rear of the rotating clamping arms. When clamping a vehicle, the telescopic clamping arms extend and retract to both sides, and the two transporters move forward. After the telescopic clamping arms touch the car tire, the detection switch sends a signal to the controller, and the controller controls the two rotating clamping arms to rotate in the direction of the telescopic clamping arms, ultimately clamping the car tire together.

[0008] The transporter in the aforementioned patent application includes a front transporter and a rear transporter, as well as a connecting assembly connecting the front and rear transporters. Each transporter includes a telescopic clamping arm and a rotating clamping arm. Correspondingly, during operation, the transporter can only extend when it is in position, i.e., when the telescopic clamping arm of each transporter is located between two tires, resulting in low work efficiency. Moreover, the transporter needs to reserve lateral space to accommodate the telescopic clamping arm, and the front and rear transporters need to be connected as one unit through the connecting assembly, resulting in shortcomings such as large length and width dimensions.

[0009] On the other hand, existing technologies for automated car transport by robots require the robots to identify objects within their perception range to achieve self-control, i.e., single-point identification and control. This not only demands high-level identification technology but also suffers from low accuracy and weak resistance to attacks (including vulnerability to single-point attacks and easy leakage of privacy data), posing a risk of hijacking. Alternatively, identification and control can be performed through a backend system, involving data exchange such as data collection, identification results, control commands, and motion status. This approach also suffers from weak resistance to attacks and carries the risk of hijacking. Summary of the Invention

[0010] The purpose of this invention is to overcome the shortcomings of the prior art and provide a monitoring and control method for a car handling machine. Based on the structural characteristics of the lifting mechanism, it saves the need for a high-power, high-strength drive mechanism, eliminates the need for storage space in the lateral space, and eliminates the need for sliding space for the lifting mechanism. The corresponding work process is simplified, greatly improving work efficiency. Furthermore, it fundamentally changes the traditional single-point security sensitivity of information systems, making the data source and calculation process reliable, with high execution efficiency, low hardware requirements, and high recognition accuracy, enabling effective resistance to attacks and overcoming the risk of hijacking.

[0011] The technical solution of the present invention is as follows:

[0012] A monitoring and control method for a car transporter is disclosed, comprising determining the position of the vehicle to be transported and the position of the car transporter, and controlling the car transporter to move toward the vehicle based on the positions of the vehicle and the car transporter; the car transporter is provided with a lifting mechanism on each side, the lifting mechanism including a support lifting arm and a rotating lifting arm; the car transporter moves along the length of the vehicle into the bottom of the vehicle, the support lifting arm is adjusted to the support lifting working position before reaching the wheel of the vehicle and remains in position; the car transporter continues to move until the support lifting arm touches the wheel, at which point the car transporter stops; the rotating lifting arm is adjusted to the rotating lifting working position, the support lifting arm and the rotating lifting arm approach each other, forming a compression on both sides of the wheel and lifting the wheel.

[0013] Preferably, each car transporter is matched with a pair of target wheels. Multiple car transporters drive into the bottom of the vehicle from the same or different directions of travel to lift the matched target wheels. If the direction of travel of the car transporter is to pass the non-target wheels first and then reach the target wheels, the support lifting arm remains in the support lifting and storage position before the end of the non-target wheels, and is adjusted to the support lifting working position before reaching the target wheels and remains in position.

[0014] Preferably, the support lifting arm includes a rotatably mounted support base and a support rod connected to the support base. The support base is driven by a first rotation drive device to rotate about a vertical axis, thereby controlling the support lifting arm to swing back and forth between a retracted support lifting arm position and a working support lifting arm position. When the support lifting arm is in the working support lifting arm position, it has a certain rotation margin. When the support rod abuts against the wheel, the car transporter continues to move, and the support lifting arm continues to rotate by the rotation margin until the support base triggers a switch mechanism to control the car transporter to stop moving.

[0015] Alternatively, the support rod is equipped with a touch sensor. When the support arm is in the working position and touches the tire, the touch sensor is triggered to stop the car transporter from moving forward.

[0016] Alternatively, the sensing data from the switching mechanism or touch sensor can be used as input to the node devices of a peer-to-peer network. The resulting data can be obtained through collaborative computation by the peer-to-peer network, and the stepper motor or braking component of the car transporter can then perform automatic control to stop the vehicle based on the resulting data.

[0017] Preferably, the rotating lifting arm includes a support frame and several rolling components that are rolled on the support frame; the rolling components are in one or more rows. When multiple rows of rolling components are provided, the rolling components form an arc profile on the upward side relative to the supporting lifting arm, from near to far.

[0018] Preferably, visual markers are provided at the ends of both the support rod and the support frame. The visual markers are visually identified and positioned using image data. Based on the identification and positioning results of the visual markers of the support lifting arm and the rotating lifting arm, the working status of the support lifting arm and the rotating lifting arm is determined. Among them, the visual markers have direction specificity and are used to determine the running angle of the support rod or the support frame.

[0019] As a preferred method, visual recognition and visual positioning of the car transporter and the vehicle are performed using image data taken from a downward angle; when the car transporter logo and the vehicle logo overlap in the image data, the positional relationship between the car transporter and the vehicle is calculated based on the amount of the car transporter logo that is exposed above the vehicle logo.

[0020] Preferably, the vehicle model, wheel appearance, and position of the car transporter under the vehicle are identified through visual recognition and visual positioning or collaborative computing based on peer-to-peer networks. The distances between the two sides of the car transporter and the wheels on both sides of the vehicle are determined. The support lifting arm and / or rotating lifting arm are pre-deployed at a certain preparatory angle. The distance that the support lifting arm and / or rotating lifting arm extends laterally after being deployed at the preparatory angle is less than the distance between the support lifting arm and / or rotating lifting arm and the wheel on the same side.

[0021] Preferably, the size of the car transporter is such that when the support arm of the car transporter touches the top of the wheel, the car transporter does not completely enter the bottom of the vehicle; the part of the car transporter exposed above the vehicle is used for visual identification and visual positioning; or, when the car transporter is completely obscured by the vehicle, visual identification and visual positioning are performed by the part of the support arm exposed above the vehicle.

[0022] Preferably, the car transporter moves by being driven by a stepper motor; the stepping data of the stepper motor is acquired, and a pre-trained position estimation model is created using machine learning to convert the stepping data into estimated position information; if the estimated position information is inconsistent with the actual position information obtained by visual positioning, the actual position information obtained by visual positioning shall prevail; and the stepping data, the working parameters of the sensor that acquires the stepping data, the ground friction coefficient, the estimated position information, and the actual position information are used as training samples and added to the sample library for further training and adjustment of the position estimation model.

[0023] As a preferred method, as all the truck transporters move, a dynamic dataset of friction coefficients at various locations on the ground is generated. The latest friction coefficients at each location in the dynamic dataset are then incorporated into a location estimation model for calculating the conversion of location information.

[0024] Preferably, a peer-to-peer network is used to perform non-specific feature recognition and location recognition of the target, which includes car transporters, vehicles, and other fixed or moving objects other than car transporters and vehicles.

[0025] The peer-to-peer network includes multiple node devices, and there is no master-slave relationship among all node devices. Each node device is equipped with a data acquisition device and a computing module. The data acquisition device includes at least one type of sensor, including an image acquisition device, for collecting different corresponding types of sensing data. Node devices set at different acquisition locations collect at least one point sample of the target, and the point sample is sensing data of the corresponding sensor type.

[0026] For a given node device, the collected sensing data is processed to obtain result data, which is then propagated to other node devices. Other node devices receiving the result data use it as one of the original collected data, influencing the result data of other node devices. Based on this, without needing to obtain the target's identity information, multiple node devices in the peer-to-peer network perform collaborative computation to determine each unique target as itself, achieving non-specific feature recognition and target location identification, including the distance between the two sides of the car transporter and the wheels on the same side during the process of the car transporter entering the bottom of a vehicle.

[0027] Preferably, the current node device receives the result data output by other node devices; for the current node device, the collected sensing data is combined with the result data from other node devices to calculate the result data of the current node device, and then sent to other node devices; the node devices in the peer-to-peer network perform collaborative calculations as sensing data is collected and result data is calculated.

[0028] Preferably, in a peer-to-peer network, for a specific point sample of a target, in the result data transmitted from the node device that collected the point sample to other node devices, the subsequent node devices adjust their perceptual attention based on the characteristics of the point sample, or report the characteristics of the point sample for subsequent node devices to adjust their perceptual attention; if the subsequent other node devices do not detect the characteristics of the point sample, but can determine from the characteristics of other point samples that the undetected characteristics of the point sample still belong to the target, then the undetected characteristics of the point sample are continued to be represented in the result data of the current node device and transmitted to other node devices.

[0029] As a preferred method, the method of reporting the features of the point sample for subsequent node devices to adjust the perceptual attention is as follows: based on the result data expressing the features of the point sample provided by the preceding node device, or the features of the point sample, the parameters of the data processing model of the subsequent node device are adjusted so that the subsequent node device can improve the computing power of the point sample to identify its features; or, the subsequent node device uses the perceptual attention model to match the features of the received point sample or the result data expressing the features of the point sample to adjust the computing power.

[0030] Preferably, when processing the result data output by several preceding node devices, the node device, based on the data processing model, merges the point sample features and other information described by each node device into the same target when the target described by several preceding node devices can be determined as the same target through certain common point sample features.

[0031] Preferably, when the result data received by the node device indicates that the flag used by the current node device to identify the target before the current receipt of result data is different from the flag used by other node devices to identify the target, and the flags assigned to the target by other node devices have been updated, then the flag used by the current node device to identify the target before the current receipt of result data is converted.

[0032] As a preferred method, the method for converting the flag used by the current node device to identify the target before the current reception of result data is as follows:

[0033] Replace the flag used by the current node device to identify the target before the current reception of result data with the latest flag assigned to the target by other node devices;

[0034] Alternatively, record the conversion relationship between the flag used by the current node device to identify the target before the current reception of result data and the updated flags assigned to the target by other node devices, and perform the conversion when it is necessary to reference the result data received by the current node device in the current reception.

[0035] Alternatively, node devices can deploy transformation models to perform corresponding transformations on the labels of multiple targets based on the input raw data or result data.

[0036] As a preferred option, for one or more point samples collected sequentially by node devices at different collection locations, if the feature values ​​of one or more point samples at different collection locations meet the preset similarity conditions or are determined by a specific model to have a correlation threshold, and are unique at each collection location, then it is determined that the point samples at different collection locations are correlated.

[0037] Preferably, for one or more point samples collected simultaneously by node devices at different acquisition locations, if the node devices at different acquisition locations collect data on the same spatial field, and there is only one target in the spatial field, or the collected point sample can correctly point to one of the multiple targets, then for a certain target, one or more point samples collected by node devices at different acquisition locations are correlated.

[0038] Preferably, the data acquisition device of the node device includes one or more of the following: an image acquisition device, an electromagnetic induction device, a temperature measurement device, a vibration frequency sensing device, and a lidar. The data acquired by the above devices and the three-dimensional point cloud acquired by the lidar, or the point cloud generated from images acquired by multiple image acquisition devices, are jointly calculated to obtain three-dimensional points with data. The image color, contour, lines, reflectivity, motion trend, electromagnetic characteristics, temperature, temperature change trend, vibration frequency, and vibration frequency change trend based on two-dimensional perception are used as additional attributes of the corresponding three-dimensional points to form an attributed three-dimensional point cloud. Combining electromagnetic induction, temperature law, vibration frequency change characteristics, motion correlation, and reflectivity, the correspondence between each region of the attributed three-dimensional point cloud and each part or related part of the consumer's 3D appearance is determined.

[0039] Preferably, when it is necessary to obtain the target's identity information, an identity information acquisition command is triggered. The identity information acquisition command is used as one of the inputs to participate in the calculation of the result data of the node device. By driving the node device in the peer-to-peer network that is connected to the barrier-free data collection conditions that can obtain the target's identity information to respond with the corresponding result data, the identity information of the target can be obtained.

[0040] As a preferred approach, the peer-to-peer network verifies the authenticity of the target's identity information to determine its permissions. In this approach, the node devices in the peer-to-peer network that can obtain identity information do not provide the identity information itself, but only express the verification results in the result data of the node device based on the verification requirements for the authenticity of the identity information in the received result data.

[0041] Preferably, in a peer-to-peer network, the node device capable of obtaining identity information does not provide identity information. Instead, the information source device that drives the provision of identity information establishes an encrypted information transmission channel with the node device input terminal that needs to obtain identity information, or establishes an encrypted information transmission channel using other network communication modes, and uses the identity information as one of the inputs to the node device.

[0042] Preferably, the data acquisition device includes one or more of the following: image acquisition device, audio acquisition device, temperature measurement device, vibration frequency sensing device, lidar, chemical sensor, and electromagnetic induction device.

[0043] Preferably, the component parameters of each control component of the car transporter are obtained; each control component of the car transporter, or a combination of multiple control components forming the same function, is joined to a peer-to-peer network through one or more node devices; if, based on collaborative calculation, it is determined that a certain control component or a combination of multiple control components of the car transporter needs to be automatically controlled based on the corresponding control requirements, the current node device will send control commands to the control components connected to the current node device according to the calculated result data, and control the control components to complete the control actions, including controlling the travel distance of the car transporter through the stepping distance of the stepper motor, and controlling the corresponding lateral extension distance through the preparatory angles of the unfolding of the support lifting arms and the rotating lifting arms on both sides.

[0044] Preferably, the control requirements are represented by the result data; the control unit receives the result data output by the connected node device. If a specific element in the result data indicates that the control unit needs to perform automatic control, or if the result data is used as one of the inputs to the data processing model of the node device, and it is calculated that the corresponding control unit needs to perform automatic control, then the control unit performs the corresponding control action.

[0045] Preferably, when automatic control of the control component is required, the control component combines the result data received from other node devices to calculate its own result data, and controls the control component on the control component to perform the corresponding control action based on the obtained result data.

[0046] Preferably, the control component receives the result data output by other node devices. The principle is as follows: when the control command requires the corresponding control component to operate automatically, if the result data calculated by one or more node devices can determine the control component that needs to be operated automatically, then the corresponding control component is added to the node list for transmitting the current result data, and the one or more node devices directly transmit the result data to the control component or the node device connected to the control component; or, the control component receives the result data output by other node devices in a layer-by-layer transmission manner.

[0047] As a preferred option, based on preset conditions or algorithm output and model output, the corresponding control components are added to the node list for transmitting result data.

[0048] Preferably, the control component is a node device that connects to the execution component for a specific function. The execution feedback information of the control component of the control component is fed back to the control component and participates in the calculation of the subsequent result data of the control component.

[0049] As a preferred option, the control components of the car transporter are added to the peer-to-peer network as node devices; if an anomaly is determined to exist in the control components based on collaborative computing, the anomaly data is used as one of the inputs to participate in the calculation of the result data, and a processing solution is obtained through collaborative computing.

[0050] Preferably, each car transporter is equipped with a safety mechanism in read-only storage mode. If all the node devices connected to the control component are damaged, and a safety accident is detected based on collaborative computing, the safety mechanism of the car transporter is activated. It takes over other control components according to the preset mechanism in read-only storage, and controls the car transporter to decelerate or stop. If some braking components fail, other braking components adjust the braking force to keep the car transporter in a stable position until it stops.

[0051] Preferably, the control components of the car transporter include, but are not limited to, a switch mechanism, a touch sensor, a first rotation drive device for driving the support base to rotate, a second rotation drive device or push drive device for driving the rotating lifting arm to rotate, a stepper motor, a braking component, a steering component, and a power component.

[0052] The beneficial effects of this invention are as follows:

[0053] The monitoring and control method for a car transporter described in this invention uses video recognition positioning to guide the car transporter, enabling more direct and effective monitoring of all factors globally and timely adjustments. Compared to methods that rely on radar scanning devices to locate vehicles or wheels, this method offers greater stability, prevents movement errors caused by radar interference, and has lower purchase and maintenance costs.

[0054] The lifting mechanism of the present invention is provided with a fixed-point rotating support lifting arm and a rotating lifting arm. It does not require the reservation of storage space for storing the support lifting arm in the horizontal space, nor does it require the reservation of movement space for sliding of the rotating lifting arm. As a result, the present invention can reduce the horizontal and vertical dimensions, thereby realizing the miniaturization of the product.

[0055] After the support arm of the present invention is unfolded, it forms a fixed support through the support assembly. During the lifting process, the first rotation drive device that drives the support arm to rotate does not bear the reaction force from gravity, which can reduce the power and strength requirements of the first rotation drive device and save the cost of using high-performance devices.

[0056] Based on the structural features of the lifting mechanism of this invention, during the corresponding lifting process, the car transporter moves along the length of the vehicle to the bottom of the vehicle. The supporting lifting arm is adjusted to the supporting lifting working position before reaching the wheel of the vehicle and remains in position. The car transporter continues to move until the supporting rod touches the wheel, at which point the car transporter stops. The rotating lifting arm is then adjusted to the rotating lifting working position, bringing the supporting lifting arm and the rotating lifting arm closer together, squeezing and lifting the wheel from both sides. It can be seen that the lifting mechanism of this invention simplifies the lifting process, eliminating the need for strict control over the alignment of the lifting mechanism with the wheel (the supporting rod touching the wheel is sufficient to ensure alignment), and eliminating the need for strict control over the relative symmetry between the lifting mechanisms on both sides and the wheel on the same side (the supporting rod touching the wheel is sufficient to ensure the lifting mechanism is at the correct angle). The rotating lifting arm only requires a rotating motion to complete the lifting of the wheel with the supporting lifting arm, eliminating the need for sliding and improving work efficiency.

[0057] Furthermore, the supporting lifting arm and rotating lifting arm of the present invention can be rotated to a preparatory angle beforehand, which not only does not affect the movement of the car transporter, but also reduces the time by nearly half when lifting the wheels. Moreover, based on the peer-to-peer network provided by the present invention, based on the collaborative calculation of multi-angle sensing data (i.e., images acquired from different positions), and the coordinated control of each control component, the preparatory angle for each lifting is calculated (i.e., the corresponding preparatory angle is calculated for each actual situation of the car transporter carrying different vehicles (including the possible different distances between the two sides of the car transporter and the two wheels). Correspondingly, the supporting lifting arm and rotating lifting arm can acquire different preparatory angles and accurately deploy each time they are lifted.

[0058] In this invention, visual markers are provided at the ends of the support rod and the support frame, which are used to perform visual recognition and visual positioning of the visual markers through image data, thereby assisting in the identification and positioning of the car transporter.

[0059] This invention utilizes a peer-to-peer network for collaborative computation to perform non-specific feature recognition and location recognition on all targets within a parking area, thereby completing identity recognition and positioning. In the peer-to-peer network, there is no primary or secondary relationship between all node devices, and there are no fixed connection paths between node devices. Node devices only receive the computation results of other node devices and send out their own computation results data. The detection of events and / or the response of corresponding control components do not rely on the identification and control of a single node device, but are jointly confirmed through collaborative computation by multiple node devices in the peer-to-peer network. Furthermore, this invention does not rely on car transporters for single-vehicle identification (i.e., single-point identification), but distributes the computing function across the entire network, reducing the hardware and software requirements for single-point computing, resulting in high execution efficiency and significantly improved resistance to attacks. The relatively symmetrical information among node devices prevents illegal data tampering. Even if a single node device is physically compromised and its data is altered, the network-wide computing is a highly redundant and complex calculation with numerous multi-dimensional verifications. Therefore, the alteration of data from a single node device does not affect the overall network computing results. Moreover, it allows for rapid location of faulty and tampered node devices, ensuring the reliability of the overall network computing results. This, in turn, resolves the conflict between data sharing and information security between departments.

[0060] This invention can identify each unique target as itself without requiring specific features or identity information, achieving non-specific feature recognition. This invention performs target identification, identity verification, or event monitoring through non-specific feature recognition, resulting in high accuracy and precise location identification. This invention can identify targets and verify identities without relying on specific features, protecting privacy while simultaneously addressing issues related to transportation, education, healthcare convenience, epidemic prevention, public services, emergency response, public security, counter-terrorism, community management and services, market behavior, workplace safety, and civilized behavior.

[0061] This invention employs non-specific feature recognition, effectively preventing risks caused by theft or counterfeiting of specific features, thus significantly enhancing security. It utilizes a non-contact, passive method for seamless target identification, greatly improving ease of execution. Based on the aforementioned peer-to-peer network, this invention can be easily deployed over coverage areas ranging from hundreds of meters to hundreds of kilometers, making it suitable for various geographical locations.

[0062] In this invention, the control of the car transporter is actually manifested through the manipulation actions of the control components. The peer-to-peer network does not directly control the car transporter as a single machine. The manipulation actions of the control components are based on the calculation results obtained through collaborative computing, resulting in high response efficiency and avoiding illegal responses such as false execution or failure to execute when required due to network attacks. To prevent hijacking, this invention can also use multiple node devices to collaboratively control the control components, further improving immunity to hijacking attacks. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of the main structure of the present invention (a top view showing the main internal structure, with the support lifting arm in the support lifting and storage position and the rotating lifting arm in the rotating lifting and storage position).

[0064] Figure 2 This is a schematic diagram of the working state of the present invention. Figure 1 (The support lifting arm is in the support lifting working state position, and the rotating lifting arm is in the rotating lifting and storage state position.)

[0065] Figure 3 This is a schematic diagram of the working state of the present invention. Figure 2 (The supporting lifting arm is in the supporting lifting working position, and the rotating lifting arm is in the rotating lifting working position).

[0066] Figure 4 This is a partial schematic diagram showing the support arm in the support and storage position (showing the support structure);

[0067] Figure 5 This is a partial schematic diagram of the support arm in the support and lifting working position (showing the rotation margin);

[0068] Figure 6 This is a partial schematic diagram of the lifting mechanism completing the lifting and raising (the supporting lifting arm completes the rotation of the remaining rotation margin);

[0069] Figure 7 This is a side view of the end face of the rotating support arm (the cross-section of the support frame is a parallelogram);

[0070] Figure 8 This is a side view of the end face of the rotating support arm (the cross-section of the support frame is trapezoidal);

[0071] Figure 9 This is a schematic diagram of the implementation of the rotating support arm (the second drive device is implemented as a rotating drive device);

[0072] Figure 10 This is a schematic diagram of the implementation of the rotating support arm (the second drive device is implemented as a push drive device);

[0073] In the diagram: 10 is the fuselage, 11 is the switching mechanism, 12 is the support structure, 13 is the elastic limiting mechanism, 20 is the support lifting arm, 21 is the support base, 22 is the support rod, 23 is the first visual identifier, 30 is the rotating lifting arm, 31 is the support frame, 311 is the gear end, 312 is the crank, 32 is the rolling assembly, 33 is the second visual identifier, 40 is the drive gear, and 50 is the push drive device. Detailed Implementation

[0074] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0075] To address the shortcomings of existing technologies, such as large size, high performance requirements and numerous drive devices, low efficiency in the corresponding work process, and inherent defects in traditional single-point aggregation computing models due to their technical characteristics, this invention provides a monitoring and control method for car handling machines. This method not only reduces the number of high-performance drive devices, simplifying the structure and lowering costs; it also eliminates the need for lateral space for housing the lifting mechanism and longitudinal space for its sliding, achieving miniaturization; based on the structural characteristics of the lifting mechanism, the lifting process for the wheels is simplified, significantly improving work efficiency; furthermore, the identification and control of car handling machines, vehicles, and other objects does not rely on single-point identification, reducing the hardware and software requirements for single-point computation, resulting in high execution efficiency, significantly improved resistance to attacks, and immunity to illegal data tampering.

[0076] A monitoring and control method for a car transporter is characterized by: determining the position of the vehicle to be transported and the position of the car transporter; controlling the car transporter to move towards the vehicle based on the vehicle's position and the car transporter's position; further, determining the positions of other vehicles, other car transporters, and the status of available space, so as to determine the positions of all vehicles and all car transporters within the entire parking area, enabling multiple car transporters to work simultaneously, and collaboratively calculating the transport routes and real-time positions of all car transporters to obtain the optimal solution adapted to the real-time state.

[0077] In this invention, such as Figure 1 , Figure 2 , Figure 3As shown, the car handling machine includes a body 10, a traveling mechanism (not shown), and a lifting mechanism. Lifting mechanisms are provided on both sides of the body 10, and these mechanisms are used to lift and raise the wheels. Normally, the lifting mechanisms on both sides of the body 10 are symmetrically arranged, corresponding to the symmetrical wheels on both sides of the vehicle. Due to tolerances in the assembly process or wear and tear during use, the lifting mechanisms on both sides of the body 10 may have precision errors, which do not affect the normal use of the invention. These include minor precision errors within the allowable range, such as height difference, lateral deviation, clamping distance, and assembly tightness. The lifting mechanism includes a supporting lifting arm 20 and a rotating lifting arm 30. Both the supporting lifting arm 20 and the rotating lifting arm 30 are configured to rotate from a fixed point. That is, during the lifting and raising of the wheels, the pivot point of the supporting lifting arm 20 and the rotating lifting arm 30 does not shift from beginning to end. Correspondingly, the supporting lifting arm 20 and the rotating lifting arm 30 only swing without displacement. Specifically, the support lifting arm 20 includes a rotatably mounted support base 21 and a support rod 22 connected to the support base 21. The support base 21 is driven by a first driving device (not shown in the figure, which can be driven directly or indirectly) to rotate around a fixed point (i.e., the pivot point) with the vertical direction as the axis, thereby controlling the support lifting arm 20 to reciprocate in the planar direction between the support lifting and storage state position and the support lifting and working state position. The rotating lifting arm 30 includes a rotatably mounted support frame 31 and a plurality of rolling components 32 rolled on the support frame 31. The support frame 31 is driven by a second driving device to reciprocate in the rotating lifting and storage state position and the rotating lifting and working state position. In specific implementation, the end of the support frame 31 is rotatably mounted and driven by the second driving device (which can be driven directly or indirectly) to rotate around a fixed point (i.e., the pivot point) with the vertical direction as the axis, thereby driving the support frame 31 (and also driving the rotating lifting arm 30) to reciprocate in the planar direction between the rotating lifting and storage state position and the rotating lifting and working state position. When the support lifting arm 20 is in the support lifting working position and the rotating lifting arm 30 is in the rotating lifting working position, the distance between the support lifting arm 20 and the rotating lifting arm 30 is less than the longitudinal distance of the wheel cross-section of the same height, so that the wheel can be lifted to a suitable height through mutual movement.

[0078] To further achieve miniaturization, this invention addresses the structural requirements of the lifting mechanism during operation. Specifically, the supporting lifting arm 20 unfolds first, followed by the rotating lifting arm 30. In this embodiment, the body 10 is recessed towards the center of the rotating lifting arm 30 in its retracted position, creating a "convex" shape and providing space to accommodate the rotating lifting arm 30. When both the rotating lifting arm 30 and the supporting lifting arm 20 are in their retracted positions, the rotating lifting arm 30 is further towards the center of the body 10 than the supporting lifting arm 20. This allows the supporting lifting arm 20 and the rotating lifting arm 30 to overlap in length, thus shortening the overall length of the body 10 and the car transporter.

[0079] During operation, the car pallet truck travels along the length of the vehicle (either from the front to the rear or from the rear to the front) and enters the underside of the vehicle. The support lifting arm 20 adjusts to its supporting lifting position before reaching the vehicle's wheels (the left and right wheels closest to the truck pallet truck) and remains in place. The truck pallet truck continues to travel until the support lifting arm 20 touches the wheels, at which point it stops. The rotating lifting arm 30 is then adjusted to its rotating lifting position, bringing the support lifting arm 20 and rotating lifting arm 30 closer together, compressing and lifting the wheels from both sides. Then, following the calculated or pre-set transport route, the truck travels to the parking space assigned to the current vehicle.

[0080] Typically, a car includes a set of front wheels and a set of rear wheels. The car transporter described in this invention can be equipped with two sets of lifting mechanisms on both sides of the same body 10 for lifting the front and rear wheels at the front and rear positions. Based on the working process of this invention, if two sets of lifting mechanisms are provided on both sides of the body 10, when the rear wheels (which are the rear wheels corresponding to the direction of travel of the car transporter, and not necessarily the rear wheels of the car; when the car transporter enters the bottom of the vehicle from the front, the rear wheels are the rear wheels of the vehicle; when the car transporter enters the bottom of the vehicle from the rear, the rear wheels are the front wheels of the vehicle) are lifted, the supporting lifting arm 20 of the lifting mechanism used for lifting the rear wheels needs to be adjusted to the supporting lifting working position after passing the front wheels (which are the front wheels corresponding to the direction of travel of the car transporter). However, since different models of vehicles have different wheelbases (the distance between the front and rear wheels of a vehicle), the car transporter needs to be designed to automatically match different wheelbases. For example, the body 10 can be set as a split structure, and the front and rear bodies 10 of the split structure can be connected by an adjustable length connecting mechanism so that one car transporter can match multiple different types of vehicles.

[0081] To simplify the structure of the car transporter, reduce the size of a single car transporter, and extend its application to lifting vehicles with three or more pairs of wheels, this invention assigns each car transporter a pair of target wheels (wheels at the same position on both sides of the vehicle are considered a pair, such as a pair of front wheels and a pair of rear wheels). Multiple car transporters enter the underside of the vehicle from the same or different directions of travel (including from the front, rear, or side of the vehicle) to lift the matched target wheels. Different car transporters enter the underside of the vehicle from different directions of travel, facilitating the simultaneous lifting of all wheels. Specifically, if the car transporter's direction of travel involves passing non-target wheels before reaching the target wheels, the supporting lifting arm 20 remains in the supporting lifting and storage position before passing the non-target wheels at its end, preventing the car transporter from contacting the non-target wheels and affecting its movement. Before reaching the target wheels, it is adjusted to the supporting lifting working position and maintained in place. If multiple car lifters enter the underside of a vehicle from different directions and none of them need to pass over the non-target wheels first, i.e., they go directly to the target wheels, then each car lifter will lift the target wheels using the aforementioned working process.

[0082] If multiple car pallet trucks enter under a vehicle from the same direction, taking two car pallet trucks lifting and transporting a vehicle with two pairs of wheels as an example, each car pallet truck has only one set of lifting mechanisms on both sides, arranged one in front and one behind in the direction of travel. As the front car pallet truck enters under the vehicle in the direction of travel, its supporting lifting arm 20 remains in a retracted state before passing the front wheel of the vehicle (i.e., the non-target wheel of the front car pallet truck), and adjusts to the supporting lifting working position before reaching the rear wheel of the vehicle (i.e., the target wheel of the front car pallet truck), maintaining this position. The front car pallet truck continues to travel until its supporting lifting arm 20 abuts against the rear wheel, at which point the front car pallet truck stops. Its rotating lifting arm 30 then adjusts to the rotating lifting working position, lifting the rear wheel.

[0083] The supporting lifting arm 20 of the corresponding rear car transporter in the direction of travel is adjusted to the supporting lifting working position before reaching the front wheel (i.e. the target wheel of the rear car transporter) and remains in position; the rear car transporter continues to travel until its supporting lifting arm 20 touches the front wheel, and the rear car transporter stops; its rotating lifting arm 30 is adjusted to the rotating lifting working position to lift the front wheel.

[0084] To provide a buffer after the support lifting arm 20 comes into contact with the wheel, and to stop the truck transporter from moving after contact with the wheel, such as... Figure 4 , Figure 5 , Figure 6As shown, in this invention, when the support lifting arm 20 is in the support lifting working state position, it has a certain rotational margin, that is, the support lifting arm 20 continues to rotate from the support lifting working state position until it can no longer rotate. The corresponding angle θ is, in this embodiment, the support structure 12 is parallel to the width direction of the body 10; when the support lifting arm 20 abuts against the wheel, the car transporter continues to move, and the support lifting arm 20 continues to rotate by the aforementioned rotational margin until the support seat 21 triggers the switch mechanism 11, controlling the car transporter to stop moving. In this embodiment, the switch mechanism 11 is disposed on the support structure 12, and the support structure 12 is disposed inside the body 10, and can be implemented as a blocking block with considerable strength; when the support seat 21 rotates to trigger the switch mechanism 11 and abuts against the support structure 12, the support seat 21 stops rotating, and the support lifting arm 20 is positioned. In specific implementation, the switching mechanism 11 can be implemented as a micro switch, pressure sensor, collision sensor, or other sensor or mechanical device with the same or similar functions; the rotation margin can be controlled by measuring the rotation angle, or an elastic limiting mechanism 13 can be set (set between the support base 21 and the support structure 12, which can be set on the support base 21 or the support structure 12). When the support base 21 rotates to the point where the elastic limiting mechanism 13 abuts against the support structure 12, or when the support base 21 abuts against the elastic limiting mechanism 13, the support base 21 stops rotating; when the support rod 22 abuts against the wheel and continues to move, the elastic limiting mechanism 13 is compressed, and the support base 21 continues to rotate until the switching mechanism 11 is triggered. The first driving device can be implemented as a rotary driving device to drive the support base 21 to rotate with low damping. Since the rotation power is small, the function of the elastic limiting mechanism 13 is easier to realize.

[0085] In another implementation, no rotational margin is provided. Once the support lifting arm 20 rotates to its working position, it can no longer rotate. Correspondingly, no switching mechanism 11 is provided, but a touch sensor is provided on the support rod 22. When the support lifting arm 20 is in its working position and abuts against the tire, the touch sensor is triggered, controlling the car transporter to stop moving forward. The other parts are the same as in the aforementioned implementation (i.e., with rotational margin and switching mechanism 11).

[0086] As another implementation, based on the real-time location information of the car transporter, the vehicle's location information, and the location information of the parking space matching the current vehicle, the peer-to-peer network provided by this invention is used. The sensing data from the switch mechanism 11 or the touch sensor is used as input to the node devices of the peer-to-peer network. The result data is obtained through collaborative computation via the peer-to-peer network. The stepper motor or braking component of the car transporter then performs automatic control to stop its movement based on the result data. The peer-to-peer network provided by this invention, based on collaborative computation, can determine the real-time position, orientation, travel speed, and posture of the car transporter. Simultaneously, based on the results of collaborative computation, the support lifting arm 20 and the rotating lifting arm 30 can be adjusted to the required working position.

[0087] In this embodiment, the support rod 22 is a rod with a smooth surface and wear-resistant material or a rotatable rod to reduce frictional damage to the wheels. The support seat 21 is a cuboid. When the support seat 21 is rotated to the support and lifting working position, the end of the support seat 21 extends further into the body 10 than when it is in the support and lifting storage position, so that the switching mechanism 11 can be triggered smoothly.

[0088] like Figure 7 , Figure 8 As shown, the rolling components 32 can be arranged in one or more rows. When multiple rows of rolling components 32 are provided, the rolling components 32, relative to the supporting lifting arm 20, form an arc profile on the upward-facing side from near to far, preferably to conform to the shape of the wheel after it is lifted and compressed. In this embodiment, the side of the support frame 31 facing the wheel is a slope or an arc surface, and the rolling components 32 are disposed on the slope or arc surface; the support frame 31 can be implemented as a mounting frame with a parallelogram or trapezoidal cross-section. Correspondingly, the rolling components 32 arranged in rows can form an arc profile by setting their height or selecting rolling components 32 of different diameters.

[0089] In this embodiment, as Figure 9 As shown, the second driving device is implemented as a rotary driving device (not shown in the figure), such as a rotary motor, which drives the drive gear 40 to rotate through direct or indirect transmission; one end of the support frame 31 is a gear end 311, which is driven to rotate by the drive gear 40 meshing with it; the drive gear 40 is driven to rotate about the vertical direction by the rotary driving device, thereby causing the support frame 31 to swing back and forth between the rotary lifting and storage state position and the rotary lifting working state position.

[0090] Or, such as Figure 10As shown, the second driving device is implemented as a driving device 50, such as a hydraulic cylinder or a pneumatic cylinder; one end of the support frame 31 is rotatably set; one end of the support frame 31 is set as a crank 312, and the pivot point is set on the crank 312. The driving device 50 drives the support frame 31 to swing back and forth between the rotating lifting and storage state position and the rotating lifting working state position.

[0091] To assist in determining the position and working status of the support lifting arm 20 and the rotating lifting arm 30, such as by using visual recognition and visual positioning methods, in this embodiment, visual markers are provided at the ends of both the support rod 22 and the support frame 31. Specifically, a first visual marker 23 is provided at the end of the support lifting arm 20, and a second visual marker 33 is provided at the end of the rotating lifting arm 30. The visual markers are visually recognized and positioned using image data. Based on the recognition and positioning results of the visual markers of the support lifting arm 20 and the rotating lifting arm 30, the working status of the support lifting arm 20 and the rotating lifting arm 30 is determined. Furthermore, the visual markers can be made directionally specific. Based on visual recognition, the running angle of the support rod 22 or the support frame 31 can be determined, thereby determining the posture and movement of the truck pallet truck, as well as the working status of the support lifting arm 20 and the rotating lifting arm 30.

[0092] This invention can identify vehicle type, wheel appearance, and the position of the car transporter under the vehicle through visual recognition and visual positioning or collaborative computing based on peer-to-peer networks. When visual recognition and visual positioning are used, the car transporter and vehicle are visually identified and positioned using image data captured from below. Specifically, when the car transporter's identifier (i.e., the appearance of the car transporter in the image data or frame) overlaps with the vehicle's identifier (i.e., the appearance of the vehicle in the image data or frame) in the acquired image data, the positional relationship between the car transporter and the vehicle is calculated based on the amount of the car transporter's identifier protruding from the vehicle's identifier. That is, by acquiring the actual size of the car transporter and the vehicle, as well as the size and proportion of the car transporter's identifier and the vehicle's identifier in the image data, the positional relationship between the car transporter and the vehicle in the real-world scenario can be deduced from the positional relationship between the car transporter and the vehicle, including the distance between the car transporter and the vehicle, the distance between the car transporter's two sides and the corresponding wheels on the same side, the travel distance of the car transporter under the vehicle, and the working status of the lifting mechanism. For example, once the car transporter's signage begins to overlap with the vehicle's signage, it's determined that the car transporter has begun to enter the underside of the vehicle. When the overlapping portion of the car transporter's signage and the vehicle's signage—that is, the part of the car transporter's signage protruding from the vehicle's signage—no longer changes, it's determined that the car transporter is in the correct lifting position. Correspondingly, the car transporter's size is sufficient so that when the support lifting arm 20 of the car transporter is against the wheel, the car transporter is not completely under the vehicle; the portion of the car transporter protruding from the vehicle is used for visual identification and positioning. Alternatively, the car transporter's size allows it to be completely obscured by the vehicle when in the accurate working position, but the length of the support lifting arm 20 is sufficient to extend beyond the vehicle's coverage area when the support lifting arm 20 is against the wheel; in this case, when the car transporter is completely obscured by the vehicle, visual identification and positioning are performed through the portion of the support lifting arm 20 protruding from the vehicle.

[0093] To improve work efficiency and reduce the overall lifting process time, based on the identification and positioning results, the distances between the two sides of the car transporter and the wheels on both sides of the vehicle are determined. Therefore, as the car transporter enters the underside of the vehicle and continues to move, the support lifting arm 20 and / or rotating lifting arm 30 are pre-deployed to a certain preparatory angle. The distance the support lifting arm 20 and / or rotating lifting arm 30 extends laterally from the preparatory angle is less than the distance between the support lifting arm 20 and / or rotating lifting arm 30 and the wheel on the same side. Correspondingly, the lifting process of this invention is roughly divided into two steps: deployment from the retracted position to the preparatory angle, and deployment from the preparatory angle to the working position. Specifically, the support lifting arm 20 is adjusted to a preparatory angle before its front end reaches the front edge of the wheel. Since the support lifting arm 20 needs to abut against the front surface of the wheel, the preparatory angle corresponding to the support lifting arm 20 can be less than or greater than the distance to the wheel on the same side. It is only necessary to prevent its front end from passing over the front edge of the wheel. The best effect is that when the support lifting arm 20 is extended to the support lifting working position, it is exactly abutting against the wheel. The lifting arm 30 is adjusted to a preparatory angle before its front end passes over the rear edge of the wheel. It starts to move when its front end passes over the rear edge of the wheel until it is rotated to the lifting working position, cooperating with the support lifting arm 20 to lift the wheel.

[0094] If two car transporters are configured as a group, the preparatory angle corresponding to the support lifting arm 20 of the rear car transporter can be set to the same as its rotating lifting arm 30. The implementation of the front car transporter is the same as that of a single car transporter.

[0095] In this embodiment, the car transporter moves using a stepper motor. To ensure the accuracy of the car transporter's position determination, this invention obtains both the estimated position information and the actual position information obtained through visual positioning. Specifically, stepper motor stepping data is acquired, and a pre-trained position estimation model is built using machine learning to convert the stepping data into estimated position information. Furthermore, to ensure the accuracy of the estimated position information, the position estimation model is also calibrated. Specifically, if the estimated position information is inconsistent with the actual position information obtained through visual positioning, the actual position information obtained through visual positioning takes precedence. The stepping data, the operating parameters of the sensor acquiring the stepping data, the ground friction coefficient, the estimated position information, and the actual position information are used as training samples and added to a sample library for further training and adjustment of the position estimation model, ensuring that the model can meet the accuracy requirements under different environmental conditions.

[0096] Meanwhile, as all the truck handling machines accumulate work data, i.e. as they move, a dynamic dataset of friction coefficients for various locations on the ground is formed. The latest friction coefficients for each location in this dynamic dataset are then incorporated into a location estimation model for calculating the conversion of location information.

[0097] In practical implementation, traditional single-point identification methods can be used to identify car transporters, vehicles, and other fixed or moving objects at designated locations to achieve the purpose of identity verification and location information association. Alternatively, the peer-to-peer network-based collaborative computing provided by this invention can be used for non-specific feature-based identity recognition. The peer-to-peer network of this invention is based on collaborative computing, does not rely on single-point identification, and distributes computing functions across the entire network, reducing the hardware and software requirements of single-point computing, resulting in high execution efficiency and significantly improved anti-attack capabilities. The relatively symmetrical information among node devices can prevent illegal data tampering. Even if a single node device is physically compromised and its transmitted data is tampered with, the tampering of data transmitted by a single node device does not affect the overall network computing results because the network-wide computing is a highly redundant and complex calculation with multiple dimensions of verification. Furthermore, it can quickly locate the faulty and tampered node device, ensuring the credibility of the overall network computing results. Therefore, it can resolve the contradiction between data sharing and information security between departments.

[0098] The result data transmitted between node devices can be the processing result of information rather than the information itself. Therefore, the raw data collected (i.e., the perceived data) does not need to be stored. Node devices only receive the calculation results output by other node devices and send out their own calculation results. The amount of information contained in a single calculation result is insufficient to reconstruct any event or target information. A definite result can only be obtained by joint calculation of the calculation results of the entire peer-to-peer network, multi-dimensional data matrix elements, and physical space and facility correspondence. The collaborative calculation has less dependence on the information transmitted by a few node devices, which can fundamentally change the nature of traditional information technology's single-point security sensitivity.

[0099] In this invention, the acquisition of identity information and location information of car transporters, vehicles, and other fixed or moving objects besides car transporters and vehicles can be obtained through collaborative computing via the peer-to-peer network provided by this invention. Specifically, the peer-to-peer network is used to perform non-specific feature recognition and location recognition of targets, including car transporters, vehicles, and other fixed or moving objects besides car transporters and vehicles. The term "non-specific feature recognition" differs, strictly speaking, from the common understanding of "recognition." Common understanding of "recognition" refers to identifying the concrete form or specific identity information of a target, such as who it is (including name, specific information indicating the target's identity), and what it is (e.g., a car, a person). The "identification" in the "non-specific feature recognition" described in this invention refers to determining each unique target (i.e., a car transporter, a vehicle, or other fixed or moving objects besides car transporters and vehicles) as itself. That is, for a given object to be identified, its existence is unique. After implementing "non-specific feature recognition," this invention determines that the object to be identified (i.e., the target that has not been identified or confirmed) is itself, not another object to be identified. The result of "non-specific feature recognition" does not require determining the specific characteristics of the object to be identified, nor does it require determining the identity information or concrete form of the object. For example, a person is considered object A to be confirmed, and an object is considered object B to be confirmed. After implementing "non-specific feature recognition," it is not necessary to identify whether object A is a person or what their specific identity is, nor is it necessary to identify whether object B is an object or what kind of object it is; rather, it is necessary to determine that object A is object A itself, and object B is object B itself. Then, corresponding services or controls can be provided for object A or object B.

[0100] The peer-to-peer network comprises multiple node devices, all without a hierarchy, forming a decentralized network and computing architecture. Unlike traditional single-point aggregation computing models, the data transmission direction between node devices in this invention does not have a fixed, preset path relationship. In the peer-to-peer network described in this invention, a particular node device processes the collected raw data to obtain result data, and then propagates the result data to other node devices. Other node devices that receive the result data use it as one of their collected raw data, thus influencing the result data of other node devices. For ease of description, the aforementioned "particular node device" is referred to as the "current node device," and the "other node devices" are referred to as "subsequent node devices." One aspect of this influence is that the result data calculated by subsequent node devices is not entirely determined by the raw data they themselves collected, but rather jointly determined by the result data output by the current node device. Specifically, the result data output by the current node device may change the data processing model and parameters used by subsequent node devices to calculate the result data, thereby affecting the result data of subsequent node devices. For example, if the output data of the current node device is correlated with the raw data collected by subsequent node devices, it is necessary to consider the impact of the output data of the current node device on the accuracy of the output data of the subsequent node devices. Specifically, for the perception of a specific target, if the result data is calculated based solely on the raw data collected by subsequent node devices, it can only reflect the real-time (including real-time location and time) single-point result judgment of the target within the perception range of the subsequent node devices. However, the output data of the current node device reflects the direct perception data and result judgment of the target at other locations and at other times, or other indirectly related perception data and result judgments, which helps to improve the accuracy and comprehensiveness of the result data of the subsequent node devices, including superimposed calculations of the same dimension and correlation references of different dimensions.

[0101] Because there is no master-slave relationship between nodes in a peer-to-peer network, point-to-point transmission is possible. Therefore, for a given calculation result corresponding to a specific perceived data point of a target, as reflected in the output data of one node, the information is relatively symmetrical among other nodes receiving that result data. Other nodes use the received result data as input, combining it with their own sensor data to calculate their own result data. Their own result data naturally encompasses both the received result data and the information reflected by their own sensors, and is transmitted to other nodes in the next layer. Thus, for a specific perceived data point of a target, the information is relatively symmetrical across all nodes. This prevents the impact of tampering or falsification of the calculation process and results of a single node on the result data. It also serves as a means to detect faulty, tampered, or non-compliant node devices. This fundamentally solves the inherent hidden dangers of traditional information technology, namely, the false, falsified, and erroneous information caused by information asymmetry, which becomes a point of entry for fraud and cyberattacks. It also addresses the problems of poor accuracy, excessive time consumption, low credibility, and poor responsiveness in complex integrated applications. Therefore, it can truly become the information infrastructure for comprehensive management of large areas and the infrastructure for the digital economy. Unlike blockchain technology, which relies on independent computation by each node to determine the result and emphasizes the preservation of original data, this invention focuses on peer-to-peer collaborative computation among node devices. Through this collaborative computation, each node device can adjust its own data processing model (i.e., the algorithm for calculating the result data) and parameters when processing data. This adjustment is a feedback mechanism from all node devices, transforming the computation of all node devices into a unified whole. Instead of individual nodes performing calculations independently, all node devices collaboratively complete the computation. The adjustments to the node device's data processing model are objectively real and will impact subsequent data processing iterations.

[0102] Node devices are equipped with data acquisition devices (in specific implementations, these may include one or more of the following: image acquisition devices, audio acquisition devices, temperature measurement devices, vibration frequency sensing devices, lidar, chemical sensors, and electromagnetic induction devices) and a computing module. The data acquisition devices include at least one type of sensor for collecting sensing data of different corresponding types. The computing module calculates the resulting data based on a data processing model. Node devices located at different acquisition positions (i.e., at different physical installation locations) collect at least one point sample of the target; the point sample is sensing data corresponding to the sensor type. Based on this, without needing to obtain the target's identity information, multiple node devices in the peer-to-peer network perform collaborative computation to determine that each unique target is itself, achieving non-specific feature recognition. Furthermore, it enables position recognition of car transporters, vehicles, and other fixed or moving objects besides car transporters and vehicles, including the distance between the two sides of the car transporter and the wheels on the same side during the process of the car transporter entering the bottom of the vehicle.

[0103] Specifically, taking a given node device as the current node device, and considering the data transmission between its preceding and subsequent node devices (in this invention, preceding and subsequent node devices are only used to describe their sequential relationship with the current node device in the current calculation and data transmission process, and do not imply any necessary sequential or priority relationship between them), the current node device receives the result data output by other node devices (including preceding node devices), and subsequent node devices receive the result data output by other node devices (including the current node device). For the current node device, the collected sensing data is combined with the result data from other node devices (including preceding node devices) to calculate the result data of the current node device, and this result data is sent to other node devices (including subsequent node devices). Similarly, the working process of subsequent node devices is the same as that of the current node device, and preceding node devices also receive the result data from the preceding node devices of their predecessors and perform the same working process as the current node device; that is, the node devices in the peer-to-peer network perform the same working process. Furthermore, the node devices in the peer-to-peer network perform collaborative calculations as sensing data is collected and result data is calculated. In this process, the output data of a certain node device is only received and used as input by the subsequent layer of node devices, and the output data of the subsequent layer of node devices will cover the output data of the preceding layer of node devices (including the aforementioned node device).

[0104] In a peer-to-peer network, all events are processed synchronously, and it is not necessary to explicitly produce phased results such as what event was discovered or what the specific content of the event is. In a peer-to-peer network, only the sensor's perception and the corresponding execution device's response are explicit. All other intermediate processes are processed simultaneously through collaborative computing. That is, during the operation of this invention, the intermediate process of event discovery is imperceptible. As collaborative computing proceeds and the node device obtains the result data, the corresponding execution device automatically responds and executes.

[0105] To further ensure the trustworthiness of the data source and computation process, in this invention, all node devices encrypt their computational results based on an encrypted consensus mechanism, obtaining encrypted results, which are then sent to other node devices. The encrypted consensus mechanism includes one or more consensus mechanisms, with different mechanisms corresponding to changes in the encryption algorithm structure and parameters of the node devices.

[0106] Node devices communicate using standard-sized data packets (i.e., result data or calculation results). In this invention, the node devices in the peer-to-peer network are similar to human neurons. Just as each neuron does not transmit specific data directly describing external events, the node devices do not output raw data. Instead, they process the raw data acquired by connected sensors and data acquisition devices into standard-sized data packets (i.e., result data or calculation results, similar to nerve impulses in neurons) based on their own data processing model (similar to the biological characteristics of nerve cells). The information contained in a single data packet is insufficient to reconstruct any event or target information. A definite result can only be obtained through collaborative computation involving the calculation results across the entire peer-to-peer network, multi-dimensional data matrix elements, and the correspondence between physical space and facilities. Collaborative computation has little dependence on the data output by a few node devices, and it simultaneously processes all requests received or initiated by all node devices. It is a collaborative verification computation of highly multi-dimensional related information, thereby fundamentally changing the traditional single-point security sensitivity of information systems.

[0107] To ensure data integrity and the effective execution of collaborative computing, this invention deploys a QoS mechanism in the peer-to-peer network, which prioritizes the transmission quality of result data between node devices.

[0108] In practical implementation, the peer-to-peer network can be configured using one or a combination of 4G, 5G, or MESH modes to suit different application scenarios. The optimal solution is achieved by considering factors such as feasibility and cost. The MESH mode is based on the LTE standard, communicating at the LTE physical layer. Data is carried by a customized frame structure, and interaction is performed using a dedicated wireless communication protocol. Customizing the frame structure for peer-to-peer network computing and employing a proprietary wireless communication protocol developed for urban cluster peer-to-peer network computing further enhances its security and reliability. Furthermore, the wireless algorithm is fully adaptable to the multipath channel environment controlled by a consensus mechanism required for peer-to-peer network computing. Communication distances range from 100 meters to 10 kilometers within cities, and up to 120 kilometers in the field using omnidirectional antennas. In this embodiment, the Mesh network communication distance is 50-150 meters between indoor nodes and 50 meters to 120 kilometers between outdoor nodes, with each node capable of connecting to 65,535 nodes. In addition, when networking in 4G and 5G modes, there is no limit to the communication distance, and the number of node devices that can be connected depends on the computing power of the computing chip and the communication latency.

[0109] In a peer-to-peer network, for a specific point sample of an object to be identified, the resulting data transmitted from the node that collected the point sample to other nodes allows subsequent nodes to adjust their perceptual attention based on the features of that point sample (it's not necessary for the feature of the point sample to be included in the resulting data; rather, the feature of the point sample participates in the computation of the preceding node, so that the resulting data of the preceding node can be used as input to the data processing model of the subsequent node, allowing the subsequent node's data processing model to adjust the perceptual attention during computation); or, the features of the point sample can be reported for subsequent nodes to adjust their perceptual attention (the feature of the point sample is directly described in the resulting data). If other subsequent nodes do not detect the feature of the point sample, but can determine from the features of other point samples that the undetected feature of the point sample still belongs to the object to be identified, then the undetected feature of the point sample is continued to be described in the resulting data of the current node and transmitted to other nodes. For example, if a preceding node device senses the color of an object A to be identified, but the current node device does not sense the color of the object A to be identified, but it can be determined from the sensing data of other node devices that there is another object A to be identified besides other objects to be identified, then the color of the object A to be identified that has not been sensed will still be represented in the result data of the current node device.

[0110] In this embodiment, the method for reporting the features of the point sample for subsequent node devices to adjust the perceptual attention is as follows: adjusting the parameters of the data processing model of the subsequent node device based on the features of the point sample provided by the preceding node device, so that the subsequent node device can improve the computing power of the point sample to identify its features; or, the subsequent node device uses the perceptual attention model to match the features of the received point sample to adjust the computing power.

[0111] The “feature” mentioned above has a different meaning from the “feature recognition” in the prior art. The “feature recognition” in the prior art usually refers to information that can determine the identity of a target, while the “feature” in this invention represents a kind of perceived data belonging to the object to be identified, such as coordinates, colors belonging to the object to be identified, etc. The “non-specific feature recognition” of the object to be identified cannot be directly completed by the “feature” perceived by a single point.

[0112] In this embodiment, the method for reporting the features of the point sample for subsequent node devices to adjust the perceptual attention is as follows: based on the result data expressing the features of the point sample provided by the preceding node device (in this invention, the features of the point sample are usually not provided themselves, but expressed in the result data), or the features of the point sample (i.e. the features of the point sample itself), the parameters of the data processing model of the subsequent node device are adjusted so that the subsequent node device can improve the computing power of the point sample to identify its features; or, the subsequent node device uses the perceptual attention model to match the features of the received point sample or the result data expressing the features of the point sample to adjust the computing power.

[0113] When a node device processes the output data from several preceding node devices, based on the data processing model, if the objects to be identified described by several preceding node devices can be determined to be the same target through certain common point sample features, the point sample features and other information described by each node device are merged into the same target. For example, point sample features in physical space that almost completely overlap at the same time can be determined to be the same target.

[0114] When the result data received by a node device indicates that the flag used by the current node device to identify the object to be identified before the current reception of result data is different from the flags used by other node devices to identify the object to be identified, and the flags assigned to the object by other node devices have been updated, then the flag used by the current node device to identify the object to be identified before the current reception of result data is converted. Specifically, the method for converting the flag used by the current node device to identify the object to be identified before the current reception of result data is as follows:

[0115] The flag used by the current node device to identify the object to be identified before the current reception of result data is replaced with the latest flag assigned to the object by other node devices; this is a simpler implementation of the present invention.

[0116] Alternatively, the conversion relationship between the flag used by the current node device to identify the object to be identified before the current receiving result data and the updated flags assigned to the object by other node devices can be recorded, and the conversion can be performed when the current node device's current receiving result data needs to be referenced; this is a relatively complex implementation method provided by the present invention.

[0117] Alternatively, the node device can deploy a conversion model to perform corresponding conversions on the labels of multiple objects to be identified based on the input raw data or result data; this is a more complex implementation provided by the present invention.

[0118] In this invention, in order to improve the effectiveness of "non-specific feature recognition", for one or more point samples collected successively by node devices at different collection locations, if the feature values ​​of one or more point samples at different collection locations meet the preset similarity conditions or are determined by a specific model to have a correlation threshold, and are unique at each collection location, then it is determined that the point samples at different collection locations are correlated.

[0119] On the other hand, for one or more point samples collected simultaneously by node devices at different collection locations, if the node devices at different collection locations collect data on the same spatial field, and there is only one object to be identified in the spatial field, or the collected point sample can correctly point to one of the multiple objects to be identified, then for a certain object to be identified, one or more point samples collected by node devices at different collection locations are correlated.

[0120] In this invention, the data acquisition device of the node device includes one or more combinations of an image acquisition device, an electromagnetic induction device, a temperature measurement device, and a vibration frequency sensing device, and a lidar. The data acquired by the aforementioned devices (i.e., one or more combinations of the image acquisition device, electromagnetic induction device, temperature measurement device, and vibration frequency sensing device) and the three-dimensional point cloud acquired by the lidar, or the point cloud generated from images acquired by multiple image acquisition devices, are jointly calculated to obtain three-dimensional points with data. The image color, contour, lines, reflectivity, motion trend, electromagnetic characteristics, temperature, temperature change trend, vibration frequency, and vibration frequency change trend based on two-dimensional perception are used as additional attributes of the corresponding three-dimensional points to constitute an attributed three-dimensional point cloud. Combining electromagnetic induction, temperature patterns, vibration frequency change characteristics, motion correlation (different motion correlations exhibited by different materials such as ropes and fabrics), and reflectivity, the correspondence between each region of the attributed three-dimensional point cloud and each part or related part of the 3D appearance of the object to be identified is determined. This embodiment utilizes the attributes and correlations of attributed 3D point clouds to determine the relationships between points, the correspondence between the regions to which each related point belongs and each part or related part of the 3D appearance of the object to be identified, and can more accurately determine the point sample features belonging to the object to be identified, thereby improving the efficiency and accuracy of "non-specific feature recognition".

[0121] In the process of "non-specific feature recognition," this invention can also acquire the identity information of the object to be identified when necessary. Specifically, when it is determined that the identity information of the object to be identified needs to be acquired, an identity information acquisition command is triggered. This command is used as one of the inputs in the calculation of the result data of the node device. By driving the node device connected to the peer-to-peer network with barrier-free data collection conditions capable of acquiring the identity information of the object to be identified to respond with the corresponding result data, the identity information of the object to be identified is acquired. The acquisition of identity information is also the result of collaborative calculation; that is, the determination that identity information needs to be acquired triggers the acquisition of identity information, rather than being additionally triggered by a specific request command. Based on this invention, if permission calculation is triggered by a request command, in most cases, it can be completed without acquiring identity information. Only in a few cases, when it is found that permission calculation cannot be completed without acquiring identity information, is the determination that identity information needs to be acquired generated according to implementation requirements. For example, if collaborative computing reveals that a person's identity information exists in several location-specific QR code registration systems, package pickup registration systems, or consumer registration systems, and prior authorization from the person or legal access to these systems is obtained, then the peer-to-peer network can drive node devices connected to these systems via barrier-free data collection. The obtained information is then transmitted to the peer-to-peer network through each node device for information comparison and to provide accurate identity information. Based on this, the present invention can also minimize the possibility of identity tampering with a system.

[0122] Specifically, the peer-to-peer network determines the permissions of the target by verifying the authenticity of the identity information. Nodes in the peer-to-peer network capable of acquiring identity information may choose not to provide the identity information (or may provide it depending on implementation requirements), but instead express the verification result in their own result data based solely on the verification requirements for the identity information's authenticity found in the received result data. In other words, in this invention, even when a node capable of acquiring identity information does not provide it, the verification result is expressed in its own result data based solely on the verification requirements for the identity information's authenticity found in the received result data.

[0123] When a node device in a peer-to-peer network that can obtain identity information does not provide identity information, the information source device that drives the provision of identity information establishes an encrypted file transmission channel with the input terminal of the node device that needs to obtain identity information, or establishes an encrypted information transmission channel using other network communication modes; and uses the identity information as one of the inputs of the node device.

[0124] When necessary, in order to meet the needs of other traditional computing modes for raw data, such as the need for evidence preservation in traditional evidence presentation, in this embodiment, the node settings can be equipped with a data storage device for storing the raw data sensed by the sensor.

[0125] In practical implementation, the node device can also be equipped with leakage protection and other functions in its power supply. The node device can also provide various communication interfaces, including fiber optic interfaces and wireless communication interfaces; it can also provide a data interface for connecting external storage devices. The node device can be powered by solar energy or mains power. When implemented outdoors, the node device can be installed on poles such as streetlights (without crossarms, mounted on the main pole, or integrated into the lampshade); in pole-less areas, if implemented indoors, it can be wall-mounted or integrated into the ceiling.

[0126] When this invention is implemented indoors and outdoors, the node devices, as artificial intelligence facilities installed in public spaces, can serve as digital economy infrastructure for urban clusters, providing 24 / 7 seamless coverage. Through collaborative computing across node devices, vehicle identification at any location within the coverage area can achieve near 100% accuracy, with location identification accuracy related to sensor accuracy.

[0127] In this invention, the architecture of a peer-to-peer computing network consists of nodes of the same type and function. Each node dynamically adjusts its data processing model in real time according to the network's consensus mechanism. The raw data collected by the data acquisition devices (including sensors, cameras, etc.) connected to each node is processed and encrypted by the node according to its own data processing model, generating byte-level processing and encryption results (i.e., result data). This result data is then sent to other node devices (the computational and encryption results received by the current node from other node devices are also considered part of the raw data collected by the current node). Therefore, the effect of the raw data sensed by each sensor will propagate exponentially among a massive number of peer-to-peer node devices. If each node sends its result data to 100 surrounding node devices, after four units of time, hundreds of millions of node devices will be affected by the event sensed by that sensor. In this computing model, information is relatively symmetrical and immune to tampering and forgery. It fundamentally solves the inherent hidden dangers of traditional information technology, namely, the false, forged, and erroneous information caused by information asymmetry, which in turn become entry points for fraud and cyberattacks, as well as the problems of long cycles, poor accuracy, and poor adaptability in complex and integrated applications. In turn, it truly becomes an information infrastructure for comprehensive management of large areas and a digital economy infrastructure.

[0128] This invention utilizes collaborative computing in a peer-to-peer network. When the results of this collaborative computing can identify an event, the event discovery is complete. In this embodiment, the discovery of an event by the peer-to-peer network includes the content of the event, the location of the event, and the corresponding response. In a peer-to-peer network, all events are processed synchronously; it is not necessarily necessary to explicitly produce staged outputs such as what event was discovered or what its specific content is. In a peer-to-peer network, only the sensor's perception and the corresponding execution device's response are explicitly defined. All other intermediate processes are processed simultaneously through collaborative computing. That is, during the operation of this invention, the intermediate process of event discovery is imperceptible; it is as the collaborative computing progresses, the node devices obtain the result data, and the corresponding execution devices automatically respond and execute.

[0129] The discovery of events in this invention can include events that may affect the operation of the car transporter, such as the entry of non-parking vehicles, the entry of pedestrians, and the occurrence of accidents. These events can be regarded as the external environment, and corresponding external environment data can be obtained. Then, the events corresponding to the external environment are added to the collaborative computing of the peer-to-peer network to calculate the travel control for each car transporter in real time, including orientation, travel speed, posture and movement.

[0130] In this invention, the control components of the car transporter include, but are not limited to, a switch mechanism 11, a touch sensor, a first rotation drive device for driving the support base 21 to rotate, a second rotation drive device or push drive device 50 for driving the rotating lifting arm 30 to rotate, a stepper motor, a braking component, a steering component, and a power component. Each control component is used to perform a corresponding function. Each control component of the car transporter, or a combination of multiple control components forming the same function, is joined to a peer-to-peer network through one or more node devices. To prevent hijacking, this invention can use multiple node devices to collaboratively control the control components, further improving immunity to hijacking attacks.

[0131] In this invention, the automatic control required for car handling, such as the control requirements for the travel control of the car transporter, and the control requirements for the support lifting arm 20 and the rotating lifting arm 30 (e.g., the control requirements for the unfolding angle), can be regarded as a request command, such as orientation, travel speed, posture movement, and control of the swing angle of the support lifting arm 20 or the rotating lifting arm 30 from the storage position, the preparatory angle to the working position. The response to the request command includes various different situations such as "requirement-execution", "request-response", or others. When the result data calculated by one or more node devices in the peer-to-peer network matches the request command, the result corresponding to the request command is represented in the result data output by one or more node devices, according to preset conditions or a pre-deployed program or a data processing model deployed on the node device. If, based on collaborative computing, it is determined that the current node device needs to respond to the request command, the current node device will send an instruction to the execution device connected to the current node device according to the calculated result data, controlling the execution device to complete the response action; that is, the "requirement-execution" situation. In this invention, if a certain control component or a combination of control components of the car transporter is determined to require automatic control based on corresponding control requirements based on collaborative calculation, the current node device will send control commands to the control components connected to the current node device according to the calculated result data, and control the control components to complete the control actions, including controlling the travel distance of the car transporter by the stepping distance of the stepper motor, and controlling the corresponding lateral extension distance by the preparatory angle of the unfolding of the support lifting arms 20 and the rotating lifting arms 30 on both sides.

[0132] Based on peer-to-peer collaborative computing, the execution device can act as one of the node devices. As collaborative computing progresses, when the result data obtained by the execution device corresponds to the request command and can be used to perform the relevant operation, the execution device completes the response to the request command. In this invention, control requirements are represented by result data; the control component receives the result data output by the connected node device. If a specific element in the result data indicates that the control component needs to perform automatic control, or if the result data is used as one of the inputs to the node device's data processing model and the calculation determines that the corresponding control component needs to perform automatic control, then the control component executes the corresponding control action.

[0133] When automatic control is required, the control component combines the received output data from other node devices to calculate its own result data. Based on this result data, it controls the control components on its device to perform corresponding control actions. In this invention, the control component does not need to first determine whether it needs to perform a control action. Instead, it combines the received output data from other node devices with the perception data collected by its own sensors, inputs this data into its own data processing model, and outputs the result data indicating whether each control component of the control component has acted and what action it has taken.

[0134] In this invention, for a car transporter, the result data calculated by its control components includes the optimal solution for all scenarios obtained through collaborative calculations between all car transporters and the external environment at the current moment. The control requirements of the car transporter are reflected by the control components executing the control actions corresponding to the optimal solution. In this invention, the results of calculations on various types of information by the peer-to-peer network are all represented as result data. All control components of all car transporters, as node devices, contribute the optimal solution for all scenarios when participating in the collaborative calculations of the peer-to-peer network. Furthermore, the control commands of all control components are the optimal solution commands output by the node devices connected to them after collaborative calculations. This invention does not include traditional generation and transmission commands, precisely to avoid security vulnerabilities that could make the control components a risk point.

[0135] In this embodiment, the control component is a node device that connects to a control component with a specific function. The execution feedback information of the control component is fed back to the control component and participates in the calculation of the subsequent result data of the control component.

[0136] In this invention, since the control component can be one of the node devices, its response execution is based on the calculation results obtained through collaborative computing, resulting in high response efficiency and avoiding illegal responses such as false execution or failure to execute when required due to network attacks. To prevent hijacking, this invention can also use multiple node devices to collaboratively control the control component, further improving its immunity to hijacking attacks.

[0137] In a peer-to-peer network, the result data calculated and output by node devices can be implemented as a representation of the state corresponding to the perceived data (i.e., the raw data). This state value can be used for representation, thus eliminating the need for node devices to store and transmit the raw data. In this embodiment, the data or elements in the multidimensional matrix are related to the installation location, attributes, etc., of each node device. Therefore, when transmitting the result data, what is actually transmitted is the transcoded result after transcoding multiple sets of parameters. A multidimensional matrix is ​​actually a combination of multiple sets of parameters. For example, if the path of a target is from abcd, and the physical locations of the abcd node devices are fixed, then the sequence abcd can be expressed by a single character or a similar concept during the transcoding and transmission of multiple sets of parameters.

[0138] Based on the technical characteristics of peer-to-peer networks, they can be applied to various use cases that provide targeted services or control for a specific target or event. Since the data transmitted between node devices is the result of information processing, rather than the information itself, the raw data collected (i.e., perceived data) does not need to be stored. Node devices only receive the calculation results output by other node devices and send out their own calculation results. The information contained in a single calculation result is insufficient to reconstruct any event or target information; a definite result can only be obtained through collaborative calculation using the calculation results across the entire peer-to-peer network, multi-dimensional data matrix elements, and the correspondence between physical space and facilities. Collaborative calculation has less dependence on the information transmitted by a few node devices, thus fundamentally changing the traditional single-point security sensitivity of information systems.

[0139] In this invention, since the output data of each node device reflects the state evolution of the output data of the preceding node devices, the behavior, attributes, state, or events of the target when it was perceived by the preceding node devices can be inferred based on the output data received by the current node device. For example, when it is necessary to find the location of target 'a' 15 minutes ago, the location of the node device that perceived target 'a' can be obtained at the current moment, thus inferring the location of target 'a'. Then, based on the transmission path of the output data, it can be inferred back to 15 minutes ago to estimate the location of target 'a' 15 minutes ago (determined by the node device that perceived target 'a'). Furthermore, the node device does not need to store the original data about target 'a'. That is, based on this invention, it is not necessary to identify the original data to find target 'a', but rather to first infer the node device that perceived target 'a', and if necessary, obtain the original data about target 'a' at the time when it needs to be found from the storage device connected to the node device.

[0140] In this invention, the control component receives result data output from other node devices. The principle is as follows: when a request command requires automatic control from a corresponding control component, if the result data calculated by one or more node devices can determine the control component requiring automatic control, then the corresponding control component is added to the node list for transmitting the current result data. The one or more node devices directly transmit the result data to the control component or the node device connected to the control component. This is done based on preset conditions, algorithm output, or model output, adding the corresponding control component to the node list for transmitting result data. Alternatively, the control component receives result data output from other node devices in a layer-by-layer transmission manner. During collaborative computing in the peer-to-peer network, each node device calculates a list of nodes that need to receive result data during each result data calculation. Based on the current result data, it clearly knows which one or more control components need to be added and adds them to the node list. The control component or the node device connected to the control component is then directly used as the next layer's subsequent node device to directly receive the current result data, achieving cross-layer transmission and transforming the peer-to-peer network into a three-dimensional architecture. For example, if the result data from the current node device clearly indicates that it needs to be submitted to the public security bureau as evidence, then according to the normal layer-by-layer transmission method, the result data from the current node device would require at least one or more layers of transmission to reach the corresponding node device in the public security bureau. However, if the node device in the public security bureau's network is added to the node list, then the corresponding node device in the public security bureau can directly receive the result data from the current node device when it is transmitted to the next layer, thereby greatly shortening the processing time and improving responsiveness. This invention uses a peer-to-peer network; therefore, this temporary construction is precisely the advantage of this invention. The traditional layer-by-layer aggregation architecture of information systems cannot withstand the complex computing requirements brought about by this temporary network construction.

[0141] In the event of unavoidable malfunctions, this invention utilizes the control components of the car transporter as node devices, integrated into a peer-to-peer network. Furthermore, if collaborative computation determines that the control components are malfunctioning (i.e., there are changes in factors within the parking area), the malfunctioning data is used as input to calculate the resulting data, leading to a processing solution. This processing solution can be based on collaborative computation: the peer-to-peer network detects a malfunction in a component of the car transporter (in the peer-to-peer network, numerous node devices perform collaborative computation to detect events; in many cases, the event detection process is unnecessary, as numerous features are incorporated into the network's computation, allowing the corresponding node device's connected control component to respond). A fault-free car transporter automatically moves to meet the malfunctioning one, intercepting or replacing it, thus achieving automatic alarm and automatic merging, significantly improving troubleshooting efficiency. If necessary, such as when collaborative computation detects that the malfunctioning car transporter needs to record raw data, the current node device receives the result data transmitted from other node devices and uses it as raw input, storing it on its connected raw data storage device.

[0142] For the car transporter itself, each car transporter is equipped with a safety mechanism in read-only storage mode. If all the node devices connected to the control component are damaged, and a safety accident is detected based on collaborative computing, the safety mechanism of the car transporter is activated. It takes over other control components according to the preset mechanism in read-only storage, and controls the car transporter to decelerate or stop. If some braking components fail, other braking components adjust the braking force to keep the car transporter in a stable posture until it stops.

[0143] The above embodiments are merely illustrative of the present invention and are not intended to limit the invention. Any changes or modifications to the above embodiments based on the technical essence of the present invention will fall within the scope of the claims of the present invention.

Claims

1. A monitoring and control method for a car transporter, characterized in that, The location of the vehicle to be transported and the location of the car transporter are determined. Based on the locations of the vehicle and the car transporter, the car transporter is controlled to move towards the vehicle. Each side of the car transporter is equipped with a lifting mechanism, which includes a support lifting arm and a rotating lifting arm. The car transporter moves along the length of the vehicle into the underside of the vehicle. Before reaching the vehicle's wheels, the support lifting arm is adjusted to its support lifting working position and remains in place. The car transporter continues to move until the support lifting arm touches the wheel, at which point the car transporter stops. The rotating lifting arm is adjusted to its rotating lifting working position, and the support lifting arm and rotating lifting arm approach each other, compressing and lifting the wheel from both sides. Peer-to-peer networks are used to perform non-specific feature recognition and location recognition of targets, including car transporters, vehicles, and other fixed or moving objects other than car transporters and vehicles. The peer-to-peer network includes multiple node devices, and there is no master-slave relationship among all node devices. Each node device is equipped with a data acquisition device and a computing module. The data acquisition device includes at least one type of sensor, including an image acquisition device, for collecting different corresponding types of sensing data. Node devices set at different acquisition locations collect at least one point sample of the target, and the point sample is sensing data of the corresponding sensor type. For a given node device, the collected sensing data is processed to obtain result data, which is then propagated to other node devices. Other node devices that receive the result data use it as one of the original data collected, and the result data influences the result data of other node devices. Based on this, without needing to obtain the target's identity information, multiple node devices in the peer-to-peer network perform collaborative computation to determine each unique target as itself, achieving non-specific feature recognition and target location recognition, including the distance between the two sides of the car transporter and the wheels on the same side during the process of the car transporter entering the bottom of the vehicle. In a peer-to-peer network, for a specific point sample of a target, the resulting data transmitted from the node device that collected the point sample to other node devices allows subsequent node devices to adjust their perceptual attention based on the features of that point sample, or report the features of that point sample for subsequent node devices to adjust their perceptual attention. If other subsequent node devices do not detect the features of that point sample, but can determine from the features of other point samples that the undetected features still belong to the target, then the undetected features of that point sample are continued to be represented in the result data of the current node device and transmitted to other node devices. The method for reporting the features of the point sample to subsequent node devices for adjusting the perceptual attention is as follows: based on the result data expressing the features of the point sample provided by the preceding node device, or the features of the point sample, adjust the parameters of the data processing model of the subsequent node device so that the subsequent node device can improve the computing power of the subsequent node device to identify the features of the point sample; or, the subsequent node device uses the perceptual attention model to match the features of the received point sample or the result data expressing the features of the point sample to adjust the computing power.

2. The monitoring and control method for a car transporter according to claim 1, characterized in that, Each car transporter is matched with a pair of target wheels. Multiple car transporters drive under the vehicle from the same or different directions of travel to lift the matched target wheels. If the direction of travel of the car transporter is to pass the non-target wheels first and then reach the target wheels, the support lifting arm remains in the support lifting and storage position before passing the non-target wheels at the end, and is adjusted to the support lifting working position before reaching the target wheels and remains in position.

3. The monitoring and control method for a car transporter according to claim 1 or 2, characterized in that, The support lifting arm includes a rotatably mounted support base and a support rod connected to the support base. The support base is driven by a first rotation drive device to rotate about a vertical axis, thereby controlling the support lifting arm to swing back and forth between a retracted support lifting arm position and a working support lifting arm position. When the support lifting arm is in the working support lifting arm position, it has a certain rotation margin. When the support rod abuts against the wheel, the car transporter continues to move, and the support lifting arm continues to rotate by the rotation margin until the support base triggers a switch mechanism to control the car transporter to stop moving. Alternatively, the support rod is equipped with a touch sensor. When the support lifting arm is in the working position and is in contact with the tire, the touch sensor is triggered to control the car transporter to stop moving forward. Alternatively, the sensing data from the switching mechanism or touch sensor can be used as input to the node devices of a peer-to-peer network. The resulting data can be obtained through collaborative computation by the peer-to-peer network, and the stepper motor or braking component of the car transporter can then perform automatic control to stop the vehicle based on the resulting data.

4. The monitoring and control method for a car transporter according to claim 3, characterized in that, The rotating lifting arm includes a support frame and several rolling components that are rolled on the support frame; the rolling components are in one or more rows. When multiple rows of rolling components are provided, the rolling components form an arc profile on the upward side relative to the supporting lifting arm, from near to far.

5. The monitoring and control method for a car transporter according to claim 1 or 2, characterized in that, Visual markers are installed at the ends of both the support rod and the support frame. The visual markers are visually identified and positioned using image data. Based on the identification and positioning results of the visual markers of the support lifting arm and the rotating lifting arm, the working status of the support lifting arm and the rotating lifting arm is determined. Among them, the visual markers have direction specificity and are used to determine the running angle of the support rod or the support frame.

6. The monitoring and control method for a car transporter according to claim 1, characterized in that, Visual recognition and positioning of car transporters and vehicles are performed using image data taken from a downward angle. When the car transporter logo and the vehicle logo overlap in the image data, the positional relationship between the car transporter and the vehicle is calculated based on the amount of the car transporter logo that is exposed above the vehicle logo.

7. The monitoring and control method for a car transporter according to claim 6, characterized in that, Through visual recognition and visual positioning or collaborative computing based on peer-to-peer networks, the vehicle model, wheel appearance, and position of the car transporter under the vehicle are identified. The distances between the two sides of the car transporter and the wheels on both sides of the vehicle are determined. The support lifting arm and / or rotating lifting arm are pre-deployed at a certain preparatory angle. The distance that the support lifting arm and / or rotating lifting arm extends laterally after being deployed at the preparatory angle is less than the distance between the support lifting arm and / or rotating lifting arm and the wheel on the same side.

8. The monitoring and control method for a car transporter according to claim 6 or 7, characterized in that, The dimensions of the car transporter are such that when the support arm of the car transporter touches the top of the wheel, the car transporter is not completely under the vehicle; the part of the car transporter exposed above the vehicle is used for visual identification and visual positioning; or, when the car transporter is completely obscured by the vehicle, the part of the support arm exposed above the vehicle is used for visual identification and visual positioning.

9. The monitoring and control method for a car transporter according to claim 6 or 7, characterized in that, The car transporter moves by being driven by a stepper motor. The stepper motor's step data is acquired, and a pre-trained position estimation model is created using machine learning to convert the step data into estimated position information. If the estimated position information is inconsistent with the actual position information obtained through visual positioning, the actual position information obtained through visual positioning is taken as the standard. The step data, the operating parameters of the sensors that acquire the step data, the ground friction coefficient, the estimated position information, and the actual position information are used as training samples and added to a sample library for further training and adjustment of the position estimation model.

10. The monitoring and control method for a car handling machine according to claim 9, characterized in that, As all the truck transporters move forward, a dynamic dataset of friction coefficients is generated for each location on the ground. The latest friction coefficients for each location in the dynamic dataset are then incorporated into a location estimation model for calculating the conversion of location information.

11. The monitoring and control method for a car transporter according to claim 1, characterized in that, The current node device receives the result data output by other node devices; for the current node device, it combines the collected sensing data with the result data from other node devices to calculate the result data of the current node device, and then sends it to other node devices; In a peer-to-peer network, node devices perform collaborative computation as they collect sensing data and calculate result data.

12. The monitoring and control method for a car transporter according to claim 1, characterized in that, When a node device processes the output data of several preceding node devices, based on the data processing model, if the target described by several preceding node devices can be identified as the same target through certain common point sample features, the point sample features and other information described by each node device are merged into the same target.

13. The monitoring and control method for a car transporter according to claim 12, characterized in that, If the result data received by a node device indicates that the flag used by the current node device to identify the target before the current receipt of result data is different from the flag used by other node devices to identify the target, and the flags assigned to the target by other node devices have been updated, then the flag used by the current node device to identify the target before the current receipt of result data is converted.

14. The monitoring and control method for a car transporter according to claim 12, characterized in that, The method for converting the flag used to identify the target by the current node device before the current reception of result data is as follows: Replace the flag used by the current node device to identify the target before the current reception of result data with the latest flag assigned to the target by other node devices; Alternatively, record the conversion relationship between the flag used by the current node device to identify the target before the current reception of result data and the updated flags assigned to the target by other node devices, and perform the conversion when it is necessary to reference the result data received by the current node device in the current reception. Alternatively, node devices can deploy transformation models to perform corresponding transformations on the labels of multiple targets based on the input raw data or result data.

15. The monitoring and control method for a car transporter according to claim 1, characterized in that, For one or more point samples collected sequentially by node devices at different collection locations, if the feature values ​​of one or more point samples at different collection locations meet the preset similarity conditions or are determined by a specific model to have a correlation threshold, and are unique at each collection location, then it is determined that the point samples at different collection locations are correlated.

16. The monitoring and control method for a car transporter according to claim 1, characterized in that, If node devices at different acquisition locations collect one or more point samples simultaneously, and if the node devices at different acquisition locations collect samples from the same spatial field, and there is only one target in the spatial field, or the collected point sample can correctly point to one of the multiple targets, then for a certain target, the one or more point samples collected by node devices at different acquisition locations are correlated.

17. The monitoring and control method for a car transporter according to claim 16, characterized in that, The data acquisition device of the node equipment includes one or more of the following: image acquisition device, electromagnetic induction device, temperature measurement device, vibration frequency sensing device, and lidar. It performs joint calculations on the data acquired by the above devices and the 3D point cloud acquired by the lidar, or on the point cloud generated from images acquired by multiple image acquisition devices, to obtain 3D points with data. It uses image color, contour, lines, reflectivity, motion trend, electromagnetic characteristics, temperature, temperature change trend, vibration frequency, and vibration frequency change trend based on 2D perception as additional attributes of the corresponding 3D points, forming an attributed 3D point cloud. Combining electromagnetic induction, temperature patterns, vibration frequency change characteristics, motion correlation, and reflectivity, it determines the correspondence between each region of the attributed 3D point cloud and each or related part of the consumer's 3D appearance.

18. The monitoring and control method for a car transporter according to claim 1, characterized in that, When it is necessary to obtain the target's identity information, an identity information acquisition command is triggered. The identity information acquisition command is used as one of the inputs to participate in the calculation of the result data of the node device. By driving the node device in the peer-to-peer network that is connected to the barrier-free data collection conditions that can obtain the target's identity information, the corresponding result data is responded to, thereby realizing the acquisition of the target's identity information.

19. The monitoring and control method for a car transporter according to claim 18, characterized in that, Peer-to-peer networks determine a target's permissions by verifying the authenticity of the target's identity information. In this process, the node devices in the peer-to-peer network that can obtain identity information do not provide the identity information itself, but only express the verification results in the result data of the node device based on the verification requirements for the authenticity of the identity information in the received result data.

20. The monitoring and control method for a car transporter according to claim 19, characterized in that, In a peer-to-peer network, the node device capable of obtaining identity information does not provide the identity information itself. Instead, the information source device that drives the provision of identity information establishes an encrypted information transmission channel with the node device input terminal that needs to obtain the identity information, or establishes an encrypted information transmission channel using other network communication modes, and uses the identity information as one of the inputs to the node device.

21. The monitoring and control method for a car transporter according to claim 1, characterized in that, The data acquisition device includes one or more of the following: image acquisition device, audio acquisition device, temperature measurement device, vibration frequency sensing device, lidar, chemical sensor, and electromagnetic induction device.

22. The monitoring and control method for a car transporter according to claim 1, characterized in that, The system acquires the component parameters of each control component of the car transporter; each control component of the car transporter, or a combination of multiple control components forming the same function, joins the peer-to-peer network through one or more node devices; if, based on collaborative calculation, it is determined that a certain control component or a combination of multiple control components of the car transporter needs to be automatically controlled according to the corresponding control requirements, the current node device will send control commands to the control components connected to the current node device according to the calculated result data, and control the control components to complete the control actions, including controlling the travel distance of the car transporter through the stepping distance of the stepper motor, and controlling the corresponding lateral extension distance through the preparatory angles of the unfolding of the support lifting arms and the rotating lifting arms on both sides.

23. The monitoring and control method for a car transporter according to claim 22, characterized in that, Control requirements are represented by result data; the control unit receives the result data output by the connected node device. If a specific element in the result data indicates that the control unit needs to perform automatic control, or if the result data is used as one of the inputs to the data processing model of the node device, and it is calculated that the corresponding control unit needs to perform automatic control, then the control unit executes the corresponding control action.

24. The monitoring and control method for a car transporter according to claim 23, characterized in that, When automatic control of the control component is required, the control component combines the result data received from other node devices to calculate its own result data, and controls the control component on the control component to perform the corresponding control action based on the obtained result data.

25. The monitoring and control method for a car transporter according to claim 24, characterized in that, The control unit receives the result data output by other node devices. The principle is: when the control command requires the corresponding control unit to operate automatically, if the result data calculated by one or more node devices can determine the control unit that needs to be operated automatically, then the corresponding control unit is added to the node list for transmitting the current result data. The one or more node devices directly transmit the result data to the control unit or the node device connected to the control unit. Alternatively, the control unit receives the result data output by other node devices in a layer-by-layer transmission manner.

26. The monitoring and control method for a car transporter according to claim 25, characterized in that, Based on preset conditions or algorithm output and model output, the corresponding control components are added to the node list for transmitting result data.

27. The monitoring and control method for a car transporter according to claim 23, characterized in that, The control unit is a node device that connects to the execution unit for a specific function. The execution feedback information of the control unit is fed back to the control unit and participates in the calculation of the subsequent result data of the control unit.

28. The monitoring and control method for a car transporter according to claim 1, characterized in that, The control components of the car transporter are added to the peer-to-peer network as node devices. If an anomaly is determined to exist in the control components based on collaborative computing, the anomaly data is used as one of the inputs to participate in the calculation of the result data, and a processing solution is obtained through collaborative computing.

29. The monitoring and control method for a car transporter according to claim 28, characterized in that, Each car transporter is configured with a safety mechanism in read-only storage mode. If all the node devices connected to the control components fail, and a safety accident is detected based on collaborative computing, the safety mechanism of the car transporter is activated. It takes over other control components according to the preset mechanism in read-only storage, and controls the car transporter to decelerate or stop. If some braking components fail, other braking components adjust the braking force to keep the car transporter in a stable position until it stops.

30. The monitoring and control method for a car transporter according to claim 22, characterized in that, The control components of a car transporter include, but are not limited to, a switch mechanism, a touch sensor, a first rotary drive device for driving the support base to rotate, a second rotary drive device or push drive device for driving the lifting arm to rotate, a stepper motor, a braking component, a steering component, and a power component.

Images