Clearing robot remote monitoring method and system based on Internet of Things

Abstract

The invention is suitable for the technical field of remote monitoring, and particularly relates to a remote monitoring method and system for a clearance robot based on the Internet of Things, and the method comprises the steps: collecting video monitoring data in a cabin through preset Internet of Things video monitoring equipment, intercepting a snapshot, delimiting the application range of the clearance robot, and carrying out the remote monitoring of the clearance robot. The application range is segmented into a plurality of blocks, nodes in one-to-one correspondence with the blocks are created, the snapshots are synchronized to the corresponding nodes, foreground features in the snapshots are extracted, and the nodes are covered. According to the method, the task chain is generated, so that the smoothness of the clearance task can be greatly improved, the clearance efficiency is improved, the edge coordinates are calculated, the position of the residue can be identified, the clearance path can be optimized, the operation precision and the clearance efficiency are greatly improved, and the clearance effect can be further improved by verifying the residue; errors and repeated clearance are reduced, and it is ensured that the clearance process is more accurate and efficient.

Description

Technical Field

[0001] The present invention relates to the field of remote monitoring technology, and in particular to a remote monitoring method and system for a warehouse cleaning robot based on the Internet of Things. Background Art

[0002] Transport ships usually use bucket wheel reclaimers, grab cranes and other equipment to load and unload the transported goods; however, for some special types of goods, such as high-value rare earths, grains that are prone to mold, and coal with high pollution risks, these goods often leave residues during the loading and unloading process. If they are not cleared in time, it will not only cause economic losses, but also lead to cross-contamination between different goods, and pollute the hull, affecting navigation safety.

[0003] However, most of the existing warehouse cleaning robots use a predetermined path planning method and cannot dynamically respond to sudden environmental changes. When working in a large space, it is easy to cause problems such as repeated cleaning or missed cleaning. Therefore, "how to use video surveillance equipment to provide warehouse cleaning guidance for warehouse cleaning robots" is the technical problem that the present invention needs to solve. Summary of the invention

[0004] The purpose of the present invention is to provide a remote monitoring method and system for a warehouse cleaning robot based on the Internet of Things, so as to solve the problem of "how to use video monitoring equipment to provide warehouse cleaning guidance for the warehouse cleaning robot" raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A remote monitoring method for a warehouse cleaning robot based on the Internet of Things, the method comprising: Using the preset IoT video surveillance equipment, the video surveillance data in the cabin is collected, and a snapshot is taken to define the use range of the warehouse cleaning robot, the use range is divided into several blocks, nodes corresponding to the blocks are created, the snapshots are synchronized to the corresponding nodes, the foreground features in the snapshots are extracted, and the nodes are covered, all the nodes are integrated, a task chain is generated, a position coordinate system is constructed, and the edge coordinates of the foreground features in each node are calculated using the edge detection algorithm; Obtaining the use authority of the laser generating device pre-integrated on the warehouse cleaning robot, and reading the reflected light signal at the edge coordinates, and performing residue verification on all nodes in turn. If the verification fails, the corresponding node is deleted from the task chain; Locate the real-time position of the warehouse cleaning robot, use the real-time position as the starting point and the preset position as the end point, generate a moving path, use the edge coordinates to generate waypoints, insert the waypoints into the moving path, and send the moving path to the warehouse cleaning robot; Embed a monitoring mechanism into the task chain, update the snapshot, and adjust the task chain, wherein the adjustment at least includes: merging and splitting; Furthermore, the steps of using a preset IoT video surveillance device to collect video surveillance data in the cabin, taking out snapshots, defining the use range of the warehouse cleaning robot, and dividing the use range into a plurality of blocks include: Obtain the equipment distribution in the cabin and divide the prohibited areas; Based on the forbidden area, a virtual fence is generated, and the virtual fence is marked in the snapshot; Determine whether the real-time position exceeds the virtual fence, and if so, trigger a preset alarm mechanism.

[0006] Furthermore, the steps of creating nodes corresponding to the blocks one by one, synchronizing the snapshots to the corresponding nodes, extracting foreground features in the snapshots, covering the nodes, integrating all the nodes, and generating a task chain include: Collect historical data of residues in the cabin, annotate and preprocess them, and generate a training set; Creating a target detection model and optimizing it using the training set; The snapshot is input into the target detection model, and the foreground features and background features are output.

[0007] Furthermore, the step of constructing a position coordinate system and calculating edge coordinates of foreground features in each node using an edge detection algorithm includes: Determine a reference point, define an origin and an axis direction, correct the snapshot, integrate the origin and axis coordinates, and construct a coordinate system; The edge detection algorithm is used to delineate the boundaries of the foreground features in each block, and the edge coordinates of the boundaries are read out.

[0008] Furthermore, the step of obtaining the use authority of the laser generating device pre-integrated on the warehouse cleaning robot, reading the reflected light signal at the edge coordinates, and sequentially verifying the residues on all nodes includes: Configure the scanning priority of each block and set the scanning frequency to cluster the reflected light signal into a smooth part and a rough part; It is determined whether the reflected light signal at the edge coordinate is a rough portion, and if so, the verification is passed.

[0009] Further, the steps of locating the real-time position of the warehouse cleaning robot, taking the real-time position as the starting point and the preset position as the end point, generating a moving path, using the edge coordinates to generate waypoints, and inserting the waypoints into the moving path, and sending the moving path to the warehouse cleaning robot include: Integrate the starting point, the waypoint and the end point to generate a moving path, and determine deviation factors, wherein the deviation factors at least include: power, progress and obstacles; The deviation value between the real-time position and the moving path is calculated. If the deviation value is greater than a preset threshold, an alarm message is generated and sent to a preset terminal.

[0010] Furthermore, the steps of embedding a monitoring mechanism into the task chain, updating the snapshot, and adjusting the task chain include: Collect fusion data from multi-level IoT sensors and integrate them into the task chain, activate the monitoring mechanism, and monitor the status of the warehouse cleaning robot; The generation time of the snapshot is recorded, the snapshot is updated based on a preset frequency, and the status monitoring results and the snapshot are integrated to adjust the task chain.

[0011] Furthermore, the system comprises: A calculation module is used to collect video surveillance data in the cabin using a preset IoT video surveillance device, and to capture a snapshot, delineate the use range of the warehouse clearance robot, divide the use range into a number of blocks, create nodes corresponding to the blocks one by one, synchronize the snapshot to the corresponding node, extract the foreground feature in the snapshot, and cover the node, integrate all the nodes, generate a task chain, construct a position coordinate system, and use an edge detection algorithm to calculate the edge coordinates of the foreground feature in each node; A deletion module is used to obtain the use permission of the laser generating device pre-integrated on the warehouse cleaning robot, read the reflected light signal at the edge coordinates, and perform residue verification on all nodes in turn. If the verification fails, the corresponding node is deleted from the task chain; A sending module is used to locate the real-time position of the warehouse cleaning robot, generate a moving path with the real-time position as the starting point and the preset position as the end point, generate waypoints using the edge coordinates, insert the waypoints into the moving path, and send the moving path to the warehouse cleaning robot; The adjustment module is used to embed a monitoring mechanism into the task chain, update the snapshot, and adjust the task chain, wherein the adjustment at least includes: merging and splitting.

[0012] Furthermore, the calculation module includes: A marking unit, used to obtain the equipment distribution in the cabin, divide the forbidden area, generate a virtual fence based on the forbidden area, and mark the virtual fence in the snapshot; A judging unit, used to judge whether the real-time position exceeds the virtual fence, and if so, trigger a preset alarm mechanism; An output unit is used to collect historical data of residues in the cabin, annotate and preprocess them, generate a training set, create a target detection model, optimize using the training set, input the snapshot into the target detection model, and output foreground features and background features; The reading unit is used to determine the reference point, define the origin and the axis direction, and correct the snapshot, integrate the origin and the axis coordinates, construct a coordinate system, use the edge detection algorithm to delineate the boundary of the foreground feature in each block, and read the edge coordinates of the boundary.

[0013] Furthermore, the deletion module includes: A clustering unit, configured to configure a scanning priority of each block and set a scanning frequency, and cluster the reflected light signal into a smooth part and a rough part; The verification unit is used to determine whether the reflected light signal at the edge coordinate is a rough part, and if so, the verification is passed.

[0014] Compared with the prior art, the present invention has the following beneficial effects: By collecting video surveillance data and dividing it into several blocks, the scope of use can be managed in a refined manner, greatly improving the accuracy of clearing warehouses. By taking snapshots, the data basis and clearing guidance can be provided for the clearing robots. At the same time, the clearing robots can be monitored in real time and emergencies can be handled in a timely manner. By generating task chains, the smoothness of clearing tasks can be greatly improved, and the clearing efficiency can be improved. By calculating edge coordinates, the location of residues can be identified, and the clearing path can be optimized, which greatly improves the operation accuracy and clearing efficiency. By verifying the residues, the clearing effect can be further improved, errors and repeated clearing can be reduced, ensuring that the clearing process is more accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flowchart of a remote monitoring method for a warehouse cleaning robot based on the Internet of Things provided in an embodiment of the present invention; Figure 2 A first sub-flow diagram of a remote monitoring method for a warehouse cleaning robot based on the Internet of Things provided in an embodiment of the present invention; Figure 3 A second sub-flow diagram of the remote monitoring method of a warehouse cleaning robot based on the Internet of Things provided by an embodiment of the present invention; Figure 4 A third sub-flow diagram of the remote monitoring method of a warehouse cleaning robot based on the Internet of Things provided by an embodiment of the present invention; Figure 5 A fourth sub-flow chart of the remote monitoring method of a warehouse cleaning robot based on the Internet of Things provided by an embodiment of the present invention; Figure 6A block diagram of the composition of a warehouse cleaning robot remote monitoring system based on the Internet of Things provided by an embodiment of the present invention; Figure 7 A block diagram of the composition of a computing module in a warehouse cleaning robot remote monitoring system based on the Internet of Things provided by an embodiment of the present invention; Figure 8 A block diagram of the composition of a deletion module in a warehouse cleaning robot remote monitoring system based on the Internet of Things provided by an embodiment of the present invention; Fig. 9 A block diagram of the composition of a sending module in a warehouse cleaning robot remote monitoring system based on the Internet of Things provided by an embodiment of the present invention; Fig.10 A block diagram of the composition of the adjustment module in the remote monitoring system of the warehouse cleaning robot based on the Internet of Things provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In Example 1, Figure 1 The implementation process of the remote monitoring method of the warehouse cleaning robot based on the Internet of Things provided by the embodiment of the present invention is shown and described in detail as follows: S100: Using the preset IoT video surveillance equipment, collect the video surveillance data in the cabin, and take out snapshots, define the use range of the clearance robot, divide the use range into several blocks, create nodes corresponding to the blocks one by one, synchronize the snapshots to the corresponding nodes, extract the foreground features in the snapshots, and cover the nodes, integrate all the nodes, generate a task chain, construct a position coordinate system, and use the edge detection algorithm to calculate the edge coordinates of the foreground features in each node.

[0018] The IoT video surveillance equipment installed on the deck of the transport ship is used to collect video surveillance data in the cabin, extract key frames, and generate snapshots, where the snapshots are state diagrams after cargo loading and unloading. The scope of use of the clearance robot in the cabin is determined, and the scope of use is the area that needs to be cleared. The scope of use is divided into several blocks, a node is created for each block, and the snapshot part corresponding to the block is synchronized to the node. The foreground features in the snapshot are extracted using image processing technology, where the foreground features are the foreground parts in the snapshot, and the foreground parts are used to cover the snapshots in the nodes. All nodes are integrated to generate a task chain to ensure that each block can be cleared in turn. A global position coordinate system is constructed to determine the spatial relationship of each block, and an edge detection algorithm (such as the Canny or Sobel algorithm) is used to process the foreground features contained in each node, and the edge coordinates of the foreground features are calculated and extracted.

[0019] S200: Obtain the use authority of the laser generating device pre-integrated on the warehouse cleaning robot, read the reflected light signal at the edge coordinates, and perform residue verification on all nodes in turn. If the verification fails, delete the corresponding node from the task chain.

[0020] Deploy a laser generating device in the clearance machine, obtain the use permission of the laser generating device, activate the laser generating device with the use permission, emit the laser, emit the laser to the edge coordinate point by point, record the intensity and pattern of the reflected light signal, and combine the preset signal threshold to determine whether there is any residue. If there is no residue in the block, delete the corresponding node from the task chain.

[0021] S300: Locate the real-time position of the warehouse-clearing robot, use the real-time position as the starting point and the preset position as the end point, generate a moving path, use the edge coordinates to generate waypoints, insert the waypoints into the moving path, and send the moving path to the warehouse-clearing robot.

[0022] Using video surveillance equipment, the real-time position of the warehouse-clearing robot in the position coordinate system is determined, and a moving path is generated with the real-time position as the starting point and the preset position as the end point, where the preset position can be the starting point, the charging position, or the end position, etc. On this basis, the edge coordinates are determined as the waypoints, the moving path is adjusted, and the adjusted moving path is sent to the warehouse-clearing robot.

[0023] S400: embedding a monitoring mechanism into the task chain, updating the snapshot, and adjusting the task chain, wherein the adjustment at least includes: merging and splitting.

[0024] By calling the IoT video surveillance equipment in real time, the task area is dynamically monitored. The monitoring mechanism is as follows: real-time monitoring of the clearance robot to determine whether it has deviated, been interfered by obstacles, or suffered mechanical failures; snapshots are updated at a preset frequency, and the task chain is adjusted. The specific adjustment methods include: deleting processed blocks, merging blocks, and reordering them.

[0025] In Example 2, Figure 2 The implementation process of the remote monitoring method of the warehouse cleaning robot based on the Internet of Things provided by an embodiment of the present invention is shown. The following is a detailed description of the steps of using the preset Internet of Things video monitoring equipment to collect video monitoring data in the cabin, and taking snapshots, defining the use range of the warehouse cleaning robot, and dividing the use range into several blocks, as follows: S101: Obtain equipment distribution in the cabin, divide a restricted area, generate a virtual fence based on the restricted area, and mark the virtual fence in a snapshot.

[0026] Use IoT video surveillance equipment to determine the distribution of equipment in the cabin and the restricted areas for the clearance robot in the cabin; divide the restricted areas into dynamic restricted areas (such as areas blocked due to temporary operations) and static restricted areas (such as fixed dangerous equipment areas); generate virtual fences based on the restricted areas to prevent the clearance robot from entering the virtual fences.

[0027] S102: Determine whether the real-time position exceeds the virtual fence, and if so, trigger a preset alarm mechanism.

[0028] If the real-time position of the warehouse cleaning robot exceeds the virtual fence, a preset alarm mechanism is triggered, wherein the alarm mechanism sends an alarm message to a preset terminal, wherein the preset terminal is the terminal of the person in charge of loading and unloading.

[0029] In Example 3, Figure 2 The implementation process of the remote monitoring method for a warehouse cleaning robot based on the Internet of Things provided by an embodiment of the present invention is shown. The steps of creating nodes corresponding to the blocks one by one, synchronizing the snapshots to the corresponding nodes, extracting the foreground features in the snapshots, covering the nodes, integrating all the nodes, and generating a task chain are described in detail as follows: S103: Collect historical data of residues in the cabin, annotate and preprocess, generate a training set, create a target detection model, optimize using the training set, input the snapshot into the target detection model, and output foreground features and background features.

[0030] Collect snapshots of the residues in the cabin at historical moments, manually annotate the location and size of the residues, strengthen the snapshots, perform data enhancement, etc., integrate all the snapshots to generate a training set.

[0031] From the existing data set, a target detection model architecture is selected and initialized. The target detection model architecture is trained and optimized using the training set to obtain a target detection model, wherein the target detection model is mainly used to separate foreground features and background features in a snapshot.

[0032] In Example 4, Figure 2 The implementation process of the remote monitoring method of the warehouse cleaning robot based on the Internet of Things provided by the embodiment of the present invention is shown. The steps of constructing the position coordinate system and calculating the edge coordinates of the foreground features in each node by using the edge detection algorithm are described in detail as follows: S104: Determine the reference point, define the origin and axis direction, correct the snapshot, integrate the origin and axis coordinates, construct a coordinate system, use the edge detection algorithm to delineate the boundary of the foreground feature in each block, and read the edge coordinates of the boundary.

[0033] Determine the reference point and select a fixed point as the origin of the coordinate system, where the reference point and the origin are both devices in the cabin, determine the direction of the coordinate axis, and construct the coordinate system; use edge detection algorithms (such as Canny or Sobel, etc.) to process the snapshots in each block, extract the foreground features in the snapshots, and define the boundaries of the foreground features; read the edge coordinates of the boundary from the coordinate system, where all the edge coordinates constitute the specific location of the residue.

[0034] In Example 5, Figure 3 The implementation process of the remote monitoring method of the warehouse cleaning robot based on the Internet of Things provided by an embodiment of the present invention is shown. The steps of obtaining the use authority of the laser generating device pre-integrated on the warehouse cleaning robot, reading the reflected light signal at the edge coordinates, and sequentially verifying the residues of all nodes are described in detail as follows: S201: configuring a scanning priority for each block and setting a scanning frequency, and clustering the reflected light signal into a smooth part and a rough part.

[0035] Determine the scanning priority of each block. If the area composed of edge coordinates in the block accounts for a large proportion, a larger scanning frequency should be set.

[0036] S202: Determine whether the reflected light signal at the edge coordinate is a rough portion, and if so, the verification is successful.

[0037] The reflected light signal in each block is read, and the reflected light signal is divided into a smooth part and a rough part, wherein the smooth part is an area without residue, and the rough part is an area with residue.

[0038] In Example 6, Figure 4 The implementation process of the remote monitoring method of the warehouse cleaning robot based on the Internet of Things provided by an embodiment of the present invention is shown. The following is a detailed description of the steps of locating the real-time position of the warehouse cleaning robot, taking the real-time position as the starting point and the preset position as the end point, generating a moving path, using the edge coordinates to generate waypoints, inserting the waypoints into the moving path, and sending the moving path to the warehouse cleaning robot, as follows: S301: Integrate the starting point, the waypoints and the end point to generate a moving path, and determine deviation factors, wherein the deviation factors at least include: power, progress and obstacles.

[0039] Integrate the starting point, passing points and end point to generate a moving path; use the Internet of Things video monitoring equipment to read out the deviation factors in the moving path.

[0040] S302: Calculate the deviation value between the real-time position and the moving path. If the deviation value is greater than a preset threshold, generate an alarm message and send the alarm message to a preset terminal.

[0041] The point where the warehouse clearance robot starts to deviate from the moving path is marked, and the distance between the real-time position and this point is calculated, defined as the deviation value, and the deviation value is monitored in real time. If the deviation value is greater than the preset threshold, an alarm message is sent to the preset terminal, where the preset terminal is the terminal of the person responsible for loading and unloading.

[0042] In Example 7, Figure 5 The implementation process of the remote monitoring method for a warehouse cleaning robot based on the Internet of Things provided by an embodiment of the present invention is shown. The steps of embedding a monitoring mechanism into the task chain, updating the snapshot, and adjusting the task chain are described in detail as follows: S401: Collect fusion data of multi-level IoT sensors and integrate them into the task chain, activate the monitoring mechanism, and monitor the status of the warehouse cleaning robot.

[0043] Various IoT sensors (such as temperature and humidity sensors, infrared sensors, etc.) installed in the cabin collect data in real time and fuse the collected data to determine the operating status of the clearance robot and judge whether there are any faults such as route deviation and mistaken entry into prohibited areas.

[0044] S402: Record the generation time of the snapshot, update the snapshot based on a preset frequency, integrate the status monitoring result and the snapshot, and adjust the task chain.

[0045] The snapshot is updated at a preset frequency and the task chain is adjusted. The specific adjustment methods include: deleting processed blocks, merging and reordering blocks, etc.

[0046] Figure 6 The structure diagram of the warehouse cleaning robot remote monitoring system based on the Internet of Things provided by an embodiment of the present invention is shown. The warehouse cleaning robot remote monitoring system based on the Internet of Things 1 includes: The computing module 11 is used to collect video surveillance data in the cabin using a preset IoT video surveillance device, and to capture a snapshot, delineate the use range of the warehouse cleaning robot, divide the use range into a number of blocks, create nodes corresponding to the blocks one by one, synchronize the snapshot to the corresponding node, extract the foreground feature in the snapshot, and cover the node, integrate all the nodes, generate a task chain, construct a position coordinate system, and use an edge detection algorithm to calculate the edge coordinates of the foreground feature in each node; The deletion module 12 is used to obtain the use authority of the laser generating device pre-integrated on the warehouse cleaning robot, read the reflected light signal at the edge coordinates, and perform residue verification on all nodes in turn. If the verification fails, the corresponding node is deleted from the task chain; The sending module 13 is used to locate the real-time position of the warehouse cleaning robot, generate a moving path with the real-time position as the starting point and the preset position as the end point, generate waypoints using the edge coordinates, insert the waypoints into the moving path, and send the moving path to the warehouse cleaning robot; The adjustment module 14 is used to embed a monitoring mechanism into the task chain, update the snapshot, and adjust the task chain, wherein the adjustment at least includes: merging and splitting.

[0047] Figure 7 The structure diagram of the warehouse cleaning robot remote monitoring system based on the Internet of Things provided by an embodiment of the present invention is shown, and the computing module 11 includes: The marking unit 111 is used to obtain the equipment distribution in the cabin, divide the forbidden area, generate a virtual fence based on the forbidden area, and mark the virtual fence in the snapshot; A determination unit 112, configured to determine whether the real-time position exceeds the virtual fence, and if so, trigger a preset alarm mechanism; The output unit 113 is used to collect historical data of the residues in the cabin, and to perform annotation and preprocessing, generate a training set, create a target detection model, and optimize using the training set, input the snapshot into the target detection model, and output foreground features and background features; The reading unit 114 is used to determine the reference point, define the origin and the axis direction, and correct the snapshot, integrate the origin and the axis coordinates, construct a coordinate system, use the edge detection algorithm to delineate the boundary of the foreground feature in each block, and read the edge coordinates of the boundary.

[0048] Figure 8 The structure diagram of the warehouse cleaning robot remote monitoring system based on the Internet of Things provided by an embodiment of the present invention is shown, and the deletion module 12 includes: A clustering unit 121, configured to configure a scanning priority of each block and set a scanning frequency, and cluster the reflected light signal into a smooth part and a rough part; The verification unit 122 is used to determine whether the reflected light signal at the edge coordinate is a rough part, and if so, the verification is passed.

[0049] Fig. 9 The structure diagram of the warehouse cleaning robot remote monitoring system based on the Internet of Things provided by an embodiment of the present invention is shown, and the sending module 13 includes: The deviation unit 131 is used to integrate the starting point, the waypoint and the end point to generate a moving path and determine deviation factors, wherein the deviation factors at least include: power, progress and obstacles; The sending unit 132 is used to calculate the deviation value between the real-time position and the moving path. If the deviation value is greater than a preset threshold, an alarm message is generated and sent to a preset terminal.

[0050] Fig.10 The structure diagram of the warehouse cleaning robot remote monitoring system based on the Internet of Things provided by an embodiment of the present invention is shown, and the adjustment module 14 includes: An activation unit 141 is used to collect fusion data of multi-level IoT sensors and integrate it into the task chain, activate the monitoring mechanism, and monitor the status of the warehouse cleaning robot; The updating unit 142 is used to record the generation time of the snapshot, update the snapshot based on a preset frequency, and integrate the status monitoring result and the snapshot to adjust the task chain.

[0051] The calculation module 11 is mainly used to complete step S100, the deletion module 12 is mainly used to complete step S200, the sending module 13 is mainly used to complete step S300, and the adjustment module 14 is mainly used to complete step S400; The marking unit 111 is mainly used to complete step S101, the judging unit 112 is mainly used to complete step S102, the output unit 113 is mainly used to complete step S103, and the reading unit 114 is mainly used to complete step S104; The clustering unit 121 is mainly used to complete step S201, and the verification unit 122 is mainly used to complete step S202; The deviation unit 131 is mainly used to complete step S301, and the sending unit 132 is mainly used to complete step S302; The activation unit 141 is mainly used to complete step S401, and the updating unit 142 is mainly used to complete step S402.

[0052] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0053] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0054] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A remote monitoring method for a warehouse cleaning robot based on the Internet of Things, characterized in that: The method comprises: Using the preset IoT video surveillance equipment, the video surveillance data in the cabin is collected, and a snapshot is taken to define the use range of the warehouse cleaning robot, the use range is divided into several blocks, nodes corresponding to the blocks are created, the snapshots are synchronized to the corresponding nodes, the foreground features in the snapshots are extracted, and the nodes are covered, all the nodes are integrated, a task chain is generated, a position coordinate system is constructed, and the edge coordinates of the foreground features in each node are calculated using the edge detection algorithm; Obtain the use permission of the laser generating device pre-integrated on the warehouse cleaning robot, read the reflected light signal at the edge coordinates, and perform residue verification on all nodes in turn. If the verification fails, the corresponding node is deleted from the task chain; Locate the real-time position of the warehouse cleaning robot, use the real-time position as the starting point and the preset position as the end point, generate a moving path, use the edge coordinates to generate waypoints, insert the waypoints into the moving path, and send the moving path to the warehouse cleaning robot; A monitoring mechanism is embedded into the task chain, the snapshot is updated, and the task chain is adjusted, wherein the adjustment at least includes: merging and splitting.

2. The remote monitoring method of a warehouse cleaning robot based on the Internet of Things according to claim 1 is characterized in that: The steps of using the preset Internet of Things video surveillance equipment to collect video surveillance data in the cabin, taking snapshots, defining the use range of the warehouse cleaning robot, and dividing the use range into a plurality of blocks include: Obtain the equipment distribution in the cabin and divide the prohibited areas; Based on the prohibited area, a virtual fence is generated, and the virtual fence is marked in the snapshot; It is determined whether the real-time position exceeds the virtual fence, and if so, a preset alarm mechanism is triggered.

3. The remote monitoring method of a warehouse cleaning robot based on the Internet of Things according to claim 2 is characterized in that: The steps of creating nodes corresponding to the blocks one by one, synchronizing the snapshots to the corresponding nodes, extracting the foreground features in the snapshots, covering the nodes, integrating all the nodes, and generating a task chain include: Collect historical data of residues in the cabin, annotate and preprocess them, and generate a training set; Creating a target detection model and optimizing it using the training set; The snapshot is input into the target detection model, and the foreground features and background features are output.

4. The remote monitoring method of a warehouse cleaning robot based on the Internet of Things according to claim 1 is characterized in that: The step of constructing a position coordinate system and calculating edge coordinates of foreground features in each node using an edge detection algorithm comprises: Determine a reference point, define an origin and an axis direction, correct the snapshot, integrate the origin and axis coordinates, and construct a coordinate system; The edge detection algorithm is used to delineate the boundaries of the foreground features in each block, and the edge coordinates of the boundaries are read out.

5. The remote monitoring method of a warehouse cleaning robot based on the Internet of Things according to claim 1 is characterized in that: The steps of obtaining the use authority of the laser generating device pre-integrated on the warehouse cleaning robot, reading the reflected light signal at the edge coordinates, and sequentially verifying the residues on all nodes include: Configure the scanning priority of each block and set the scanning frequency to cluster the reflected light signal into a smooth part and a rough part; It is determined whether the reflected light signal at the edge coordinate is a rough portion, and if so, the verification is passed.

6. The remote monitoring method of a warehouse cleaning robot based on the Internet of Things according to claim 1 is characterized in that: The steps of locating the real-time position of the warehouse cleaning robot, taking the real-time position as the starting point and the preset position as the end point, generating a moving path, using the edge coordinates to generate waypoints, and inserting the waypoints into the moving path, and sending the moving path to the warehouse cleaning robot include: Integrate the starting point, the waypoint and the end point to generate a moving path, and determine deviation factors, wherein the deviation factors at least include: power, progress and obstacles; The deviation value between the real-time position and the moving path is calculated. If the deviation value is greater than a preset threshold, an alarm message is generated and sent to a preset terminal.

7. The remote monitoring method of a warehouse cleaning robot based on the Internet of Things according to claim 1 is characterized in that: The steps of embedding a monitoring mechanism into the task chain, updating the snapshot, and adjusting the task chain include: Collect fusion data from multi-level IoT sensors and integrate them into the task chain, activate the monitoring mechanism, and monitor the status of the warehouse cleaning robot; The generation time of the snapshot is recorded, the snapshot is updated based on a preset frequency, and the status monitoring results and the snapshot are integrated to adjust the task chain.

8. A remote monitoring system for warehouse cleaning robots based on the Internet of Things, characterized in that: The system comprises: A calculation module is used to collect video surveillance data in the cabin using a preset IoT video surveillance device, and to capture a snapshot, delineate the use range of the warehouse clearance robot, divide the use range into a number of blocks, create nodes corresponding to the blocks, synchronize the snapshots to the corresponding nodes, extract foreground features in the snapshots, and cover the nodes, integrate all nodes, generate a task chain, construct a position coordinate system, and use an edge detection algorithm to calculate the edge coordinates of the foreground features in each node; A deletion module is used to obtain the use permission of the laser generating device pre-integrated on the warehouse cleaning robot, read the reflected light signal at the edge coordinates, and perform residue verification on all nodes in turn. If the verification fails, the corresponding node is deleted from the task chain; A sending module is used to locate the real-time position of the warehouse cleaning robot, generate a moving path with the real-time position as the starting point and the preset position as the end point, generate waypoints using the edge coordinates, insert the waypoints into the moving path, and send the moving path to the warehouse cleaning robot; The adjustment module is used to embed a monitoring mechanism into the task chain, update the snapshot, and adjust the task chain, wherein the adjustment at least includes: merging and splitting.

9. The remote monitoring system for warehouse cleaning robots based on the Internet of Things according to claim 8 is characterized in that: The calculation module comprises: A marking unit, used to obtain the equipment distribution in the cabin, divide the forbidden area, generate a virtual fence based on the forbidden area, and mark the virtual fence in the snapshot; A judging unit, used to judge whether the real-time position exceeds the virtual fence, and if so, trigger a preset alarm mechanism; An output unit is used to collect historical data of residues in the cabin, annotate and preprocess them, generate a training set, create a target detection model, optimize using the training set, input the snapshot into the target detection model, and output foreground features and background features; The reading unit is used to determine the reference point, define the origin and the axis direction, and correct the snapshot, integrate the origin and the axis coordinates, construct a coordinate system, use the edge detection algorithm to delineate the boundary of the foreground feature in each block, and read the edge coordinates of the boundary.

10. The remote monitoring system for warehouse cleaning robots based on the Internet of Things according to claim 9 is characterized in that: The deletion module comprises: A clustering unit, configured to configure a scanning priority of each block and set a scanning frequency, and cluster the reflected light signal into a smooth part and a rough part; The verification unit is used to determine whether the reflected light signal at the edge coordinate is a rough part, and if so, the verification is passed.

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