A refrigerated truck intelligent Internet of Things cloud monitoring system and monitoring method

Abstract

The invention discloses an intelligent Internet of Things cloud monitoring system and a monitoring method for a refrigerated truck, which relate to the field of intelligent monitoring technology, and include a contact simulation module, a hand motion capture module, a hand motion simulation module, a contact simulation module, a large-screen contact detection module, a result comparison module, a personnel operation automatic calibration module, a machine learning unit, and a result reporting unit; the intelligent Internet of Things cloud monitoring system and the monitoring method for a refrigerated truck can not only detect whether an error occurs in the operation of a person on a vehicle-mounted central control large screen by comparing the contact detection result with the contact simulation result, but also remind the operator when the operation occurs, and autonomously control the system to correct the erroneous operation; by performing contact detection on the vehicle-mounted central control large screen and setting the contact simulation of the operator, the screen can be detected while the operation detection is being performed, and problems with the screen can be discovered in time.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to an intelligent Internet of Things cloud monitoring system and a monitoring method for a refrigerated truck. Background Art

[0002] With the development of national productivity and consumption upgrade, the cold chain logistics industry has developed rapidly, and the cold chain transportation volume has increased year by year. As an important link in cold chain logistics, the demand for refrigerated trucks has also shown an increasing trend year by year. The compressor units of refrigerated trucks are divided into independent and non-independent types. The power source of the non-independent compressor unit is the car generator, and the fan is powered by the battery. The independent compressor unit relies on its own diesel engine for power, and the fan is driven by the generator. At present, the temperature control of refrigerated trucks is basically controlled by the on-board central control screen, and the on-board central control screen mostly uses a capacitive touch screen. The disadvantage of the capacitive touch screen is that it is easily affected by electromagnetic interference, resulting in drift. The electromagnetic interference voltage from the inside and outside will be coupled to the touch screen device through the capacitor. The electromagnetic interference will cause the potential movement in the touch screen, resulting in inaccurate contact positioning, which is usually manifested as screen jumping, inaccurate touch or complete failure.

[0003] At present, when the vehicle is in motion, the operator's fingers may break when operating the touch screen due to the bumps of the vehicle, resulting in a decrease in the accuracy of the operator's temperature control over the refrigerated truck. At the same time, since the vehicle-mounted central control screen cannot independently detect whether the screen is damaged during use, the operator cannot promptly discover whether the screen is damaged, resulting in the operator being unable to control the temperature of the refrigerated truck due to screen damage during transportation. Summary of the invention

[0004] The object of the present invention is to provide a refrigerated truck intelligent Internet of Things cloud monitoring system and monitoring method to solve the above-mentioned deficiencies in the prior art.

[0005] In order to achieve the above object, the present invention provides the following technical solution: an intelligent Internet of Things cloud monitoring system for refrigerated trucks, comprising:

[0006] A contact simulation module, which is used to load a contact simulation algorithm to perform contact simulation on the vehicle-mounted central control large screen;

[0007] A hand motion capture module, which is used to capture the finger motions of the operator;

[0008] A hand motion simulation module, which is used to simulate the finger motion of the operator based on the finger motion capture result of the operator;

[0009] A contact simulation module, the contact simulation module is used to combine the operator's finger movement simulation results with the contact simulation results;

[0010] A large screen touch point detection module, which is used to perform touch point detection on the vehicle-mounted central control large screen and obtain a touch point detection result;

[0011] A result comparison module, which is used to compare the contact simulation result of the current operator with the contact detection result;

[0012] A human-operated automatic calibration module, which is used to perform operation correction on an operator's misoperation;

[0013] A machine learning unit, wherein the machine learning unit is used to perform machine learning;

[0014] The result reporting unit is used to report the result and arrange corresponding maintenance personnel to perform maintenance on the central control large screen through the background.

[0015] Furthermore, the hand motion capture module includes:

[0016] A front-end image data acquisition device, wherein the front-end image data acquisition device is a binocular stereo vision camera. When the front-end image data acquisition device is used, visual distortion correction and parameter calibration of the binocular stereo vision camera are required to make the optical axes of the left and right cameras parallel and the internal visual parameters of the left and right cameras the same;

[0017] The operation control unit module is mainly responsible for analyzing and processing the image data collected by the front-end visual device: first, the hand image collected by the binocular stereo camera is filtered and pre-processed to obtain a smooth hand image by eliminating noise interference; then, the finger joint point data is extracted through the gesture recognition detection algorithm and the three-dimensional measurement principle of binocular stereo vision, and then the three-dimensional world coordinates of all the finger joint points of both hands are calculated and restored;

[0018] A data storage and visualization effect display device is used to display the capture effect of hand movements and the storage of three-dimensional motion data.

[0019] Furthermore, the specific working steps of the hand motion capture module are:

[0020] A1, first collects image data of the human hand touching the screen;

[0021] A2, filter preprocessing of the hand image collected by the binocular stereo camera;

[0022] A3, eliminates noise interference and obtains a smooth hand image;

[0023] A4, extracts finger joint point data through gesture recognition detection algorithm and three-dimensional measurement principle of binocular stereo vision;

[0024] A5, calculate and restore the three-dimensional world coordinates of all finger joints of both hands;

[0025] A6, real-time display of hand gestures and movements; storage of 3D world coordinate data of all hand joints.

[0026] Furthermore, the specific method for the machine learning unit to perform machine learning is:

[0027] B1. Select data and randomly select a number of real-time data as sample data groups;

[0028] B2. Initialize each weight and bias to a random value close to zero, and set the adjustment parameter, accuracy requirement parameter and adjustment rate;

[0029] B3. For each sample in the training set, calculate the output of the measured data and the predicted result of the predicted data;

[0030] B4. Compare the elements in the predicted results with the elements in the sample results and calculate the error of the measured data;

[0031] B5. Calculate the increments of each weight and bias based on the result of step B4;

[0032] B6, update weights and biases;

[0033] B7, calculate the total error of the predicted data and compare it with the accuracy parameter to determine whether the total error of the predicted data is less than the accuracy control parameter. If not, return to step B3 to continue the operation;

[0034] B8. Adjustment is complete, and the current weights and biases are saved. At this point, it can be considered that all parameters have reached a stable state and machine learning is complete.

[0035] Furthermore, the specific working method of the personnel operation automatic calibration module is: when the result of the result comparison module outputs that the personnel operation is abnormal but the screen is normal, the personnel operation automatic calibration module is started, and the personnel operation automatic calibration module determines the current personnel operation abnormality type. When the judgment result is a false touch, the automatic calibration module automatically deletes the extra contacts during the personnel operation. When the judgment result is a broken touch, the automatic calibration module automatically adds the missing contacts during the personnel operation.

[0036] A refrigerated truck intelligent Internet of Things cloud monitoring method comprises the following steps:

[0037] S1, load the contact simulation algorithm to simulate the contact points of the vehicle's central control screen;

[0038] S2, the operator performs touch screen operation on the vehicle's central control screen;

[0039] S3, the vehicle-mounted central control screen performs a contact detection to obtain a contact detection result B, wherein the contact detection result B includes a contact detection result B1 and a contact detection result B0. The contact detection result B1 indicates that the current screen is not damaged, and the contact detection result B0 indicates that the current screen is damaged;

[0040] S4, capturing the operator's finger movements;

[0041] S5, simulating the operator's finger motion by capturing the operator's finger motion result;

[0042] S6, combining the operator's finger motion simulation result with the contact simulation result to obtain a contact simulation result A of the current operator, wherein the contact simulation result A includes a contact simulation result A1 and a contact simulation result A0, wherein the contact simulation result A0 indicates that the current operator has an error in operation, and the contact simulation result A1 indicates that the current operator has an error in not operating;

[0043] S7, compare the current operator's contact simulation result with the contact detection result. When the operator's contact simulation result A is A1 and the contact detection result B is B1, it is marked as the central control screen is in normal working state, and step S8 is executed. When the operator's contact simulation result A is A1 and the contact detection result B is B0, it is marked as the screen is damaged but the operator's operation is normal, and step S9 is executed. When the operator's contact simulation result A is A0 and the contact detection result B is B1, it is marked as the operator's operation is abnormal but the screen is normal, and step S10 is executed. When the operator's contact simulation result A is A0 and the contact detection result B is B0, step S11 is executed.

[0044] S8, return to step S2 and wait for the next operation of the operator;

[0045] S9, reporting that the current central control screen is damaged, and returning to step S8 after the maintenance personnel repair the central control screen;

[0046] S10, reminding the operator of an operation error, correcting the operator's mis-touch operation, and returning to step S8, the operation error includes mis-touch and broken touch, the mis-touch identification condition is that the contact detection result B is greater than the contact simulation result A, and the broken touch identification condition is that the contact detection result B is less than the contact simulation result A;

[0047] S11, perform machine learning and return to step S1.

[0048] Furthermore, if the output result after step S11 returns to step S1 still outputs screen damage and abnormal operation by personnel, the screen damage and abnormal operation by personnel are determined, the damage of the current central control screen is reported, and maintenance personnel are arranged to repair the central control screen.

[0049] Compared with the prior art, the present invention provides an intelligent IoT cloud monitoring system and monitoring method for refrigerated trucks. By comparing the contact detection results with the contact simulation results, it can not only detect whether there are errors in the operation of the on-board central control screen by the personnel, but also remind the operator when the operation occurs, and autonomously control the system to correct the erroneous operation. By performing contact detection on the on-board central control screen and setting the operator's contact simulation, the screen can be detected while the operation is being detected, so that problems with the screen can be discovered in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0051] Figure 1 A schematic diagram of the overall method flow provided in Embodiment 1 of the present invention;

[0052] Figure 2 A schematic diagram of the workflow of a hand motion capture module provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0054] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0055] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0056] Example embodiments will be described more fully below with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, the purpose of providing these embodiments is to make the present disclosure thorough and complete and to enable those skilled in the art to fully understand the scope of the present disclosure.

[0057] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0058] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0059] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded.

[0060] The embodiments described herein may be described with reference to plan views and / or cross-sectional views by means of idealized schematic diagrams of the present disclosure. Therefore, the example illustrations may be modified according to manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to the embodiments shown in the accompanying drawings, but include modifications of the configurations formed based on the manufacturing process. Therefore, the regions illustrated in the accompanying drawings have schematic properties, and the shapes of the regions shown in the figures illustrate the specific shapes of the regions of the elements, but are not intended to be limiting.

[0061] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined as such herein.

[0062] See also Figure 2 , an intelligent Internet of Things cloud monitoring system for refrigerated trucks, comprising:

[0063] The contact simulation module is used to load the contact simulation algorithm and perform contact simulation on the vehicle's central control screen;

[0064] Hand motion capture module, which is used to capture the finger motions of operators;

[0065] A hand motion simulation module, which is used to simulate the operator's finger motion based on the operator's finger motion capture results;

[0066] A contact simulation module, which is used to combine the operator's finger motion simulation results with the contact simulation results;

[0067] Large screen touch point detection module: The large screen touch point detection module is used to perform touch point detection on the vehicle-mounted central control large screen and obtain the touch point detection result;

[0068] A result comparison module, which is used to compare the contact simulation result of the current operator with the contact detection result;

[0069] The personnel-operated automatic calibration module is used to correct the operator's misoperation;

[0070] A machine learning unit, which is used to perform machine learning;

[0071] The result reporting unit is used to report the results and arrange corresponding maintenance personnel to repair the central control large screen through the background.

[0072] In this way, the contact simulation algorithm is loaded through the contact simulation module to perform contact simulation of the vehicle-mounted central control large screen, the contact simulation result of the current operator is obtained through the hand motion capture module, the hand motion simulation module and the contact simulation module, the contact detection result of the vehicle-mounted central control large screen is performed through the large-screen contact detection module, and the contact detection result is obtained, the contact simulation result of the current operator is compared with the contact detection result through the result comparison module, the operator's mis-touch is corrected through the personnel operation automatic calibration module, and machine learning is performed through the machine learning unit to increase the accuracy of the system's work. At the same time, by performing machine learning on the comparison result of the operator's contact simulation result A being A0 and the contact detection result B being B0, re-running the program can avoid incorrect inspection results due to system reasons, the result is reported through the result reporting unit, and the corresponding maintenance personnel are arranged through the background to repair the central control large screen.

[0073] The hand motion capture module includes:

[0074] The front-end image data acquisition device is a binocular stereo vision camera. When the front-end image data acquisition device is used, the binocular stereo vision camera needs to be corrected for visual distortion and calibrated for parameters, so that the optical axes of the left and right cameras are parallel and the internal visual parameters of the left and right cameras are the same;

[0075] The operation control unit module is mainly responsible for analyzing and processing the image data collected by the front-end visual device: first, the hand image collected by the binocular stereo camera is filtered and pre-processed to obtain a smooth hand image by eliminating noise interference; then, the finger joint point data is extracted through the gesture recognition detection algorithm and the three-dimensional measurement principle of binocular stereo vision, and then the three-dimensional world coordinates of all the finger joint points of both hands are calculated and restored;

[0076] Data storage and visualization effect display equipment, data storage and visualization effect display equipment is used to display the capture effect of hand movements and the storage of three-dimensional motion data.

[0077] The specific working steps of the hand motion capture module are:

[0078] A1, first collects image data of the human hand touching the screen;

[0079] A2, filter preprocessing of the hand image collected by the binocular stereo camera;

[0080] A3, eliminates noise interference and obtains a smooth hand image;

[0081] A4, extracts finger joint point data through gesture recognition detection algorithm and three-dimensional measurement principle of binocular stereo vision;

[0082] A5, calculate and restore the three-dimensional world coordinates of all finger joints of both hands;

[0083] A6, real-time display of hand gestures and movements; storage of 3D world coordinate data of all hand joints.

[0084] The specific method of the machine learning unit for machine learning is:

[0085] B1. Select data and randomly select a number of real-time data as sample data groups;

[0086] B2. Initialize each weight and bias to a random value close to zero, and set the adjustment parameter, accuracy requirement parameter and adjustment rate;

[0087] B3. For each sample in the training set, calculate the output of the measured data and the predicted result of the predicted data;

[0088] B4. Compare the elements in the predicted results with the elements in the sample results and calculate the error of the measured data;

[0089] B5. Calculate the increments of each weight and bias based on the result of step B4;

[0090] B6, update weights and biases;

[0091] B7, calculate the total error of the predicted data and compare it with the accuracy parameter to determine whether the total error of the predicted data is less than the accuracy control parameter. If not, return to step B3 to continue the operation;

[0092] B8. Adjustment is complete, and the current weights and biases are saved. At this point, it can be considered that all parameters have reached a stable state and machine learning is complete.

[0093] The specific working method of the personnel operation automatic calibration module is: when the result comparison module outputs that the personnel operation is abnormal but the screen is normal, the personnel operation automatic calibration module is started, and the personnel operation automatic calibration module determines the current type of personnel operation abnormality. When the judgment result is a false touch, the automatic calibration module automatically deletes the extra contacts during the personnel operation. When the judgment result is a broken touch, the automatic calibration module automatically adds the missing contacts during the personnel operation.

[0094] See also Figure 1 , a refrigerated truck intelligent Internet of Things cloud monitoring method, comprising the following steps:

[0095] S1, load the contact simulation algorithm to simulate the contact points of the vehicle's central control screen;

[0096] S2, the operator performs touch screen operation on the vehicle's central control screen;

[0097] S3, the vehicle-mounted central control screen performs a contact detection and obtains a contact detection result B, which includes a contact detection result B1 and a contact detection result B0. The contact detection result B1 indicates that the current screen is not damaged, and the contact detection result B0 indicates that the current screen is damaged;

[0098] S4, capturing the operator's finger movements;

[0099] S5, simulating the operator's finger motion by capturing the operator's finger motion result;

[0100] S6, combining the operator's finger motion simulation result with the contact simulation result to obtain a contact simulation result A of the current operator, wherein the contact simulation result A includes a contact simulation result A1 and a contact simulation result A0, wherein the contact simulation result A0 indicates that the current operator has an error in operation, and the contact simulation result A1 indicates that the current operator has an error in not operating;

[0101] S7, compare the current operator's contact simulation result with the contact detection result. When the operator's contact simulation result A is A1 and the contact detection result B is B1, it is marked as the central control screen is in normal working state, and step S8 is executed. When the operator's contact simulation result A is A1 and the contact detection result B is B0, it is marked as the screen is damaged but the operator's operation is normal, and step S9 is executed. When the operator's contact simulation result A is A0 and the contact detection result B is B1, it is marked as the operator's operation is abnormal but the screen is normal, and step S10 is executed. When the operator's contact simulation result A is A0 and the contact detection result B is B0, step S11 is executed.

[0102] S8, return to step S2 and wait for the next operation of the operator;

[0103] S9, reporting that the current central control screen is damaged, and returning to step S8 after the maintenance personnel repair the central control screen;

[0104] S10, reminding the operator of the operation error, correcting the operator's accidental touch operation, and returning to step S8, the operation error includes accidental touch and broken touch, the determination condition of the accidental touch is that the contact detection result B is greater than the contact simulation result A, and the determination condition of the broken touch is that the contact detection result B is less than the contact simulation result A;

[0105] S11, perform machine learning and return to step S1.

[0106] If the output result after step S11 returns to step S1 still outputs screen damage and abnormal operation by personnel, the screen damage and abnormal operation by personnel are determined, the damage of the current central control screen is reported, and maintenance personnel are arranged to repair the central control screen.

[0107] By comparing the contact detection results with the contact simulation results, it is not only possible to detect whether there are any errors in the operation of the on-board central control screen, but also to remind the operator when an operation occurs, and to autonomously control the system to correct the erroneous operation. By performing contact detection on the on-board central control screen and setting up contact simulation of the operator, the screen can be tested while the operation is being tested, and problems with the screen can be discovered in time.

[0108] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An intelligent IoT cloud monitoring system for refrigerated trucks, characterized by: include: A contact simulation module, which is used to load a contact simulation algorithm to perform contact simulation on the vehicle-mounted central control large screen; A hand motion capture module, which is used to capture the finger motions of the operator; A hand motion simulation module, which is used to simulate the finger motion of the operator based on the finger motion capture result of the operator; A contact simulation module, the contact simulation module is used to combine the operator's finger movement simulation results with the contact simulation results; A large screen touch point detection module, which is used to perform touch point detection on the vehicle-mounted central control large screen and obtain a touch point detection result; A result comparison module, which is used to compare the contact simulation result of the current operator with the contact detection result; A human-operated automatic calibration module, which is used to perform operation correction on an operator's misoperation; A machine learning unit, wherein the machine learning unit is used to perform machine learning; The result reporting unit is used to report the result and arrange corresponding maintenance personnel to perform maintenance on the central control large screen through the background.

2. According to claim 1, a refrigerated truck intelligent Internet of Things cloud monitoring system is characterized by: The hand motion capture module comprises: A front-end image data acquisition device, wherein the front-end image data acquisition device is a binocular stereo vision camera. When the front-end image data acquisition device is used, visual distortion correction and parameter calibration of the binocular stereo vision camera are required to make the optical axes of the left and right cameras parallel and the internal visual parameters of the left and right cameras the same; The operation control unit module is mainly responsible for analyzing and processing the image data collected by the front-end visual device: first, the hand image collected by the binocular stereo camera is filtered and pre-processed to obtain a smooth hand image by eliminating noise interference; then, the finger joint point data is extracted through the gesture recognition detection algorithm and the three-dimensional measurement principle of binocular stereo vision, and then the three-dimensional world coordinates of all the finger joint points of both hands are calculated and restored; A data storage and visualization effect display device is used to display the capture effect of hand movements and the storage of three-dimensional motion data.

3. According to claim 1, a refrigerated truck intelligent Internet of Things cloud monitoring system is characterized by: The specific working steps of the hand motion capture module are: A1, first collects image data of the human hand touching the screen; A2, filter preprocessing of the hand image collected by the binocular stereo camera; A3, eliminates noise interference and obtains a smooth hand image; A4, extracts finger joint point data through gesture recognition detection algorithm and three-dimensional measurement principle of binocular stereo vision; A5, calculate and restore the three-dimensional world coordinates of all finger joints of both hands; A6, real-time display of hand gestures and movements; storage of 3D world coordinate data of all hand joints.

4. The intelligent Internet of Things cloud monitoring system for refrigerated trucks according to claim 1 is characterized in that: The specific method for the machine learning unit to perform machine learning is: B1. Select data and randomly select a number of real-time data as sample data groups; B2. Initialize each weight and bias to a random value close to zero, and set the adjustment parameter, accuracy requirement parameter and adjustment rate; B3. For each sample in the training set, calculate the output of the measured data and the predicted result of the predicted data; B4. Compare the elements in the predicted results with the elements in the sample results and calculate the error of the measured data; B5. Calculate the increments of each weight and bias based on the result of step B4; B6, update weights and biases; B7, calculate the total error of the predicted data and compare it with the accuracy parameter to determine whether the total error of the predicted data is less than the accuracy control parameter. If not, return to step B3 to continue the operation; B8. Adjustment is completed and the current weights and biases are saved.

5. The intelligent Internet of Things cloud monitoring system for refrigerated trucks according to claim 4 is characterized in that: The specific working method of the personnel operation automatic calibration module is: when the result of the result comparison module outputs that the personnel operation is abnormal but the screen is normal, the personnel operation automatic calibration module is started, and the personnel operation automatic calibration module determines the current personnel operation abnormality type. When the judgment result is a false touch, the automatic calibration module automatically deletes the extra contacts during the personnel operation. When the judgment result is a broken touch, the automatic calibration module automatically adds the missing contacts during the personnel operation.

6. A refrigerated truck intelligent Internet of Things cloud monitoring method, which is applicable to a refrigerated truck intelligent Internet of Things cloud monitoring system according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1, load the contact simulation algorithm to simulate the contact points of the vehicle's central control screen; S2, the operator performs touch screen operation on the vehicle's central control screen; S3, the vehicle-mounted central control screen performs a contact detection to obtain a contact detection result B, wherein the contact detection result B includes a contact detection result B1 and a contact detection result B0. The contact detection result B1 indicates that the current screen is not damaged, and the contact detection result B0 indicates that the current screen is damaged; S4, capturing the operator's finger movements; S5, simulating the operator's finger motion by capturing the operator's finger motion result; S6, combining the operator's finger motion simulation result with the contact simulation result to obtain a contact simulation result A of the current operator, wherein the contact simulation result A includes a contact simulation result A1 and a contact simulation result A0, wherein the contact simulation result A0 indicates that the current operator has an error in operation, and the contact simulation result A1 indicates that the current operator has an error in not operating; S7, compare the current operator's contact simulation result with the contact detection result. When the operator's contact simulation result A is A1 and the contact detection result B is B1, it is marked as the central control screen is in normal working state, and step S8 is executed. When the operator's contact simulation result A is A1 and the contact detection result B is B0, it is marked as the screen is damaged but the operator's operation is normal, and step S9 is executed. When the operator's contact simulation result A is A0 and the contact detection result B is B1, it is marked as the operator's operation is abnormal but the screen is normal, and step S10 is executed. When the operator's contact simulation result A is A0 and the contact detection result B is B0, step S11 is executed. S8, return to step S2 and wait for the next operation of the operator; S9, reporting that the current central control screen is damaged, and returning to step S8 after the maintenance personnel repair the central control screen; S10, reminding the operator of an operation error, correcting the operator's mis-touch operation, and returning to step S8, the operation error includes mis-touch and broken touch, the mis-touch identification condition is that the contact detection result B is greater than the contact simulation result A, and the broken touch identification condition is that the contact detection result B is less than the contact simulation result A; S11, perform machine learning and return to step S1.

7. A refrigerated truck intelligent Internet of Things cloud monitoring method according to claim 6, characterized in that: If the output result after step S11 returns to step S1 still outputs screen damage and abnormal operation by personnel, it is determined that the screen is damaged and the operation by personnel is abnormal, and the damage of the current central control screen is reported, and maintenance personnel are arranged to repair the central control screen.

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