Intelligent adjusting system for automatic door motor operation

Abstract

The invention discloses an intelligent adjusting system for automatic door motor operation, relates to the technical field of automatic control, and effectively improves the opening and closing efficiency of an automatic door. According to the invention, the assembly line image model is established according to the scene condition data, the assembly line monitoring network is established according to the assembly line image model, and the video data and the cargo information data of a plurality of cargoes are acquired through the Internet, so that the corresponding feature regions and the classification labels are generated. The feature areas and the classification labels are mapped to an assembly line monitoring network, cargo nodes are established according to the scene condition data, the cargo nodes are input into the assembly line monitoring network, and cargo filling nodes are positioned in the assembly line monitoring network according to the matching relation between the cargo nodes and the feature areas and the classification labels; and setting cargo transportation speed for each cargo node to carry out opening and closing simulation, and generating and executing a corresponding automatic door opening and closing instruction according to the opening and closing simulation.

Description

Technical Field

[0001] The invention relates to the technical field of automatic control, in particular to an intelligent regulating system for the operation of an automatic door motor. Background Art

[0002] Automatic doors for assembly lines are an automated door system used in factories and production environments. They are able to open and close automatically without human intervention. This automation can be achieved through a variety of sensors and control systems, such as infrared sensors, radar sensors or push button switches, effectively improving the efficiency of industrial assembly lines.

[0003] However, due to the work intensity and flow speed of the assembly line, it is easy for the automatic door to fail to open and close in time, resulting in reduced work efficiency and even damage to the goods by the automatic door.

[0004] Therefore, how to improve the opening and closing efficiency of the automatic door while ensuring that the goods are not automatically damaged is a difficulty in the prior art. For this purpose, an intelligent adjustment system for the operation of the automatic door motor is provided. Summary of the invention

[0005] In order to solve the above technical problems, the object of the present invention is to provide an intelligent adjustment system for the operation of an automatic door motor.

[0006] In order to achieve the above object, the present invention provides the following technical solutions: An intelligent adjustment system for the operation of an automatic door motor includes a cloud computing platform, wherein the cloud computing platform is communicatively connected with a scene data acquisition module, an assembly line monitoring network module, and an automatic door control module; The scene data acquisition module is used to set a data acquisition cycle, and collect scene optical video data and scene infrared video data of the handling line in each data acquisition cycle, and then integrate the scene optical video data and scene infrared video data of each data acquisition cycle to obtain scene status data, and send the scene status data to the assembly line monitoring network module and the automatic door control module; The assembly line monitoring network module is used to establish an assembly line image model according to the scene status data, and to establish an assembly line monitoring network according to the assembly line image model, and to obtain video data and cargo information data of several types of cargo through the Internet, thereby generating corresponding feature areas and classification labels, and mapping each feature area and classification label on the assembly line monitoring network; The automatic door control module is used to establish cargo nodes according to scene status data, and input the cargo nodes into the assembly line monitoring network. According to the matching relationship between the cargo nodes and the feature areas and classification labels, the cargo loading nodes are located in the assembly line monitoring network, and then the cargo transportation speed is set for each cargo node to perform opening and closing simulation, and the corresponding automatic door opening and closing instructions are generated and executed according to the opening and closing simulation.

[0007] Further, the transport assembly line is composed of Num automatic detection doors, conveyor belts, and multiple cargo loading areas, where Num is a natural number greater than 0; The conveyor belt is composed of several rolling pulleys connected together, and the conveyor belt is provided with several rolling forks, and each rolling fork is equipped with an automatic detection door; The automatic door detection device is provided with an infrared sensor, a control unit and a door leaf, wherein the door leaf is composed of a plurality of door leaves arranged in an "X" shape, each door leaf is connected to a corresponding section of a conveyor belt, and each door leaf is marked with a number; The cargo loading area is located at the edge of the transport line and is used to load the cargo sorted by the transport line or to transport the cargo to the transport line.

[0008] Furthermore, the process of collecting the scene status data includes: The scene data acquisition module installs several cameras at fixed distances on the handling line, and sets numbers a1, a2, a3, ..., a for each camera and the automatic door detection. n , b1, b2, b3, ..., b m Where n and m are natural numbers greater than 0; The scene data acquisition module communicates with each camera and the infrared sensor for detecting the automatic door, and then sets the same data acquisition cycle for each camera and the infrared sensor; When the cargo loading device conveys the cargo to the conveyor belt, each camera and infrared sensor continuously captures the scene optical video data and scene infrared video data within its capture range; When the data collection cycle ends, each camera and infrared sensor sends the scene optical video data and scene infrared video data generated in its corresponding data collection cycle to the scene data collection module. At the same time, each camera and infrared sensor resets the current shooting behavior and proceeds to the next data collection cycle. When the scene data acquisition module receives the scene optical video data and the scene infrared video data, it marks the number of the corresponding camera for each scene optical video data, and marks the number of the corresponding detected automatic door for the scene infrared video data; Then, the scene data acquisition module integrates each data acquisition cycle to generate scene optical video data and scene infrared video data to obtain scene status data.

[0009] Furthermore, the process of establishing the pipeline image model includes: The pipeline monitoring network module extracts scene optical video data and scene infrared video data from the scene status data, establishes corresponding scene optical image models and scene infrared image models according to the scene optical video data and the scene infrared video data, and marks corresponding numbers on each scene optical image model and scene infrared image model; The optical image models and infrared image models of each scene are sequentially spliced ​​according to the actual positions of the corresponding cameras and infrared sensors on the handling line; Several overlapping blocks are set at the edge positions of each image model. Since the shooting ranges of cameras or infrared sensors at adjacent spatial positions are intersecting, the image models at adjacent splicing positions must have the same parts corresponding to the scene optical image model and the scene infrared image model. Then, the overlapping blocks at the edge positions of each image model are matched with each other. If the overlapping blocks from different image models are the same, the two overlapping blocks are merged. If they are not the same, no operation is performed. The above-mentioned matching and merging operations of the overlapping areas of the image models are repeated to obtain a pipeline image model.

[0010] Furthermore, the process of establishing a pipeline monitoring network according to the pipeline image model includes: The assembly line monitoring network module establishes corresponding monitoring nodes according to the corresponding automatic door detection part in the assembly line image model, and sets the same number of cargo loading nodes according to the image model positions corresponding to each cargo loading device in the assembly line image model, and sets connection lines between each monitoring node and the cargo loading node in proportion according to the corresponding conveyor belt part in the assembly line image model, and divides the connection lines into several connection sections according to the distribution of cameras on the conveyor belt; Each connection section and monitoring node is marked with the corresponding camera and infrared sensor number to obtain the assembly line monitoring network.

[0011] Furthermore, the process of generating the characteristic region and classification label of the goods includes: Obtain video data and cargo information data of several types of cargo through the Internet, and generate corresponding standard cargo image models according to the video data of each cargo; Establish a three-dimensional coordinate system, overlap and map the image models of different types of standard goods on the three-dimensional coordinate system, divide each standard goods image model into a number of image model areas, select the image model area of ​​each standard goods image model in turn as the feature detection area, and then compare the feature detection area with the image model areas of other standard goods image models. If there is a feature detection area that is not similar to the image models of other standard goods image models, then the image model area corresponding to the feature detection area is recorded as the feature area of ​​the corresponding type of goods, otherwise no operation is performed; At the same time, classification labels are set for each feature area according to the cargo information data. The staff assigns multiple classification labels to the cargo loading nodes in the assembly line monitoring network, and traverses the assembly line monitoring network starting from the connection section numbered a1. According to the traversal results, the cargo loading nodes associated with each monitoring node are obtained, and then the classification labels of each cargo loading node are marked on the corresponding monitoring nodes.

[0012] Furthermore, the process of locating the cargo loading node in the assembly line monitoring network includes: The automatic door control module extracts scene optical video data and scene infrared video data from the scene status data, and then extracts image data of each cargo from the scene optical video data and scene infrared video data, and establishes a corresponding cargo image model; According to the collection time of the scene status data and the serial numbers of the scene optical video data and the scene infrared video data, a cargo node is synchronously generated and mapped on the assembly line monitoring network; Whenever a new cargo node appears in the assembly line monitoring network, a cargo number is set for the corresponding cargo, and the automatic door control module retrieves the corresponding cargo image model and matches the feature area of ​​each cargo with the cargo image model. According to the matching result, the classification label of the feature area is marked on the corresponding cargo node; Then, the automatic door control module matches the corresponding cargo loading node according to the classification label carried by the cargo node. When the cargo node matches multiple cargo loading nodes at the same time, the cargo loading node with the largest number of the same classification labels is selected as the target cargo loading node. If there are multiple cargo loading nodes with the largest number of the same classification labels at the same time, a cargo loading node is randomly selected as the target cargo loading node.

[0013] Furthermore, the generation and execution process of the automatic door opening and closing instruction includes: According to the target cargo loading node corresponding to the cargo node, the monitoring node with the corresponding number is found in the assembly line monitoring network, and then the corresponding cargo transportation path is generated; Set the same cargo transportation speed for all cargo nodes so that the cargo passes through just one connection section in one data collection cycle; Then, an opening and closing simulation is performed according to the cargo transportation path and cargo transportation speed of each cargo node. According to the opening and closing simulation results, an event timeline is set for each monitoring node. According to the simulation results of the opening and closing simulation, the estimated arrival time node of each cargo node is marked on the event timeline, and the corresponding cargo number and transportation direction are marked on each estimated arrival time node; The event timeline of each monitoring node is sent to the corresponding control unit for detecting the automatic door, and then the control unit sends automatic door opening and closing instructions to the door leaf according to the estimated time node on the event timeline and the transportation direction control. The automatic door opening and closing instructions include the opening time, closing time and the door page number that executes the closing instruction.

[0014] Furthermore, the opening and closing simulation process includes: All cargo transportation routes are input into the assembly line monitoring network, and according to the sequence of each cargo node on the assembly line monitoring network, each cargo node passes through each connecting section in sequence according to the cargo transportation speed, and then the estimated time for each cargo node to pass through the monitoring node according to the cargo transportation route is obtained.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention establishes an assembly line monitoring network according to an assembly line image model, and simultaneously obtains video data and cargo information data of several types of cargo through the Internet, thereby generating corresponding feature areas and classification labels, and mapping each feature area and classification label on the assembly line monitoring network, establishing cargo nodes according to scene status data, and inputting the cargo nodes into the assembly line monitoring network, locating cargo loading nodes in the assembly line monitoring network according to the matching relationship between cargo nodes and feature areas and classification labels, and then setting cargo transportation speeds for each cargo node to perform opening and closing simulations, generating corresponding automatic door opening and closing instructions according to the opening and closing simulations and executing them, thereby improving the opening and closing efficiency of automatic doors while ensuring that the cargo is not automatically damaged. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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.

[0017] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0018] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0019] like Figure 1 As shown, an intelligent adjustment system for the operation of an automatic door motor includes a cloud computing platform, wherein the cloud computing platform is communicatively connected with a scene data acquisition module, an assembly line monitoring network module, and an automatic door control module; The scene data acquisition module is used to set a data acquisition cycle, and collect scene optical video data and scene infrared video data of the handling line in each data acquisition cycle, and then integrate the scene optical video data and scene infrared video data of each data acquisition cycle to obtain scene status data, and send the scene status data to the assembly line monitoring network module and the automatic door control module; The assembly line monitoring network module is used to establish an assembly line image model according to the scene status data, and to establish an assembly line monitoring network according to the assembly line image model, and to obtain video data and cargo information data of several types of cargo through the Internet, thereby generating corresponding feature areas and classification labels, and mapping each feature area and classification label on the assembly line monitoring network; The automatic door control module is used to establish cargo nodes according to scene status data, and input the cargo nodes into the assembly line monitoring network. According to the matching relationship between the cargo nodes and the feature areas and classification labels, the cargo loading nodes are located in the assembly line monitoring network, and then the cargo transportation speed is set for each cargo node to perform opening and closing simulation, and the corresponding automatic door opening and closing instructions are generated and executed according to the opening and closing simulation.

[0020] Further, the working principle of the present invention is described below by way of examples: The transport assembly line is composed of Num automatic detection doors, conveyor belts and multiple cargo loading areas, where Num is a natural number greater than 0; The conveyor belt is composed of several rolling pulleys connected together, and the conveyor belt is provided with several rolling forks, and each rolling fork is equipped with an automatic detection door; The automatic door detection device is provided with an infrared sensor, a control unit and a door leaf, wherein the door leaf is composed of a plurality of door leaves arranged in an "X" shape, each door leaf is connected to a corresponding section of a conveyor belt, and each door leaf is marked with a number; The cargo loading area is located at the edge of the transport line and is used to load the cargo sorted by the transport line or to transport the cargo to the transport line.

[0021] Furthermore, the scene data acquisition module installs several cameras at fixed distances on the handling line, and sets numbers a1, a2, a3, ..., a for each camera and the detection automatic door. n , b1, b2, b3, ..., b m Where n and m are natural numbers greater than 0; The scene data acquisition module communicates with each camera and the infrared sensor for detecting the automatic door, and then sets the same data acquisition cycle for each camera and the infrared sensor; It should be noted that the shooting ranges of cameras or infrared sensors at adjacent spatial positions have overlapping parts; When the cargo loading device conveys the cargo to the conveyor belt, each camera and infrared sensor continuously captures the scene optical video data and scene infrared video data within its capture range; When the data collection cycle ends, each camera and infrared sensor sends the scene optical video data and scene infrared video data generated in its corresponding data collection cycle to the scene data collection module. At the same time, each camera and infrared sensor resets the current shooting behavior and proceeds to the next data collection cycle. When the scene data acquisition module receives the scene optical video data and the scene infrared video data, it marks the number of the corresponding camera for each scene optical video data, and marks the number of the corresponding detected automatic door for the scene infrared video data; Then, the scene data acquisition module integrates each data acquisition cycle to generate scene optical video data and scene infrared video data, obtains scene status data, and sends the scene status data to the assembly line monitoring network module and the automatic door control module.

[0022] Further, the pipeline monitoring network module extracts scene optical video data and scene infrared video data from the scene status data, and establishes corresponding scene optical image models and scene infrared image models according to the scene optical video data and the scene infrared video data, and marks corresponding numbers on each scene optical image model and scene infrared image model; The optical image models and infrared image models of each scene are sequentially spliced ​​according to the actual positions of the corresponding cameras and infrared sensors on the handling line; Several overlapping blocks are set at the edge positions of each image model. Since the shooting ranges of cameras or infrared sensors at adjacent spatial positions are intersecting, the image models at adjacent splicing positions must have the same parts corresponding to the scene optical image model and the scene infrared image model. Then, the overlapping blocks at the edge positions of each image model are matched with each other. If the overlapping blocks from different image models are the same, the two overlapping blocks are merged. If they are not the same, no operation is performed. Repeat the above-mentioned matching and merging operation of the overlapping areas of the image models to obtain a pipeline image model.

[0023] Furthermore, the assembly line monitoring network module establishes corresponding monitoring nodes according to the portion of the assembly line image model corresponding to the detection of the automatic door, and sets the same number of cargo loading nodes according to the image model positions corresponding to each cargo loading device in the assembly line image model, and sets connecting lines in proportion between each monitoring node and the cargo loading node according to the corresponding conveyor belt portion in the assembly line image model, and divides the connecting lines into a number of connecting sections according to the distribution of cameras on the conveyor belt; Each connection section and monitoring node is marked with the corresponding camera and infrared sensor number, thereby obtaining the pipeline monitoring network; The assembly line monitoring network module obtains video data and cargo information data of several types of cargo through the Internet, and generates corresponding standard cargo image models according to the video data of each cargo; Establish a three-dimensional coordinate system, overlap and map the image models of different types of standard goods on the three-dimensional coordinate system, divide each standard goods image model into a number of image model areas, select the image model area of ​​each standard goods image model in turn as the feature detection area, and then compare the feature detection area with the image model areas of other standard goods image models. If there is a feature detection area that is not similar to the image models of other standard goods image models, then the image model area corresponding to the feature detection area is recorded as the feature area of ​​the corresponding type of goods, otherwise no operation is performed; At the same time, a classification label is set for each characteristic area according to the cargo information data, wherein the cargo information data includes the raw materials for the cargo, the manufacturer of the cargo, the function, etc., and the classification label may be, for example, the function of the cargo, the manufacturer of the cargo, etc.; The staff assigns multiple classification labels to the cargo loading nodes in the assembly line monitoring network, and traverses the assembly line monitoring network starting from the connection section numbered a1. According to the traversal results, the cargo loading nodes associated with each monitoring node are obtained, and then the classification labels of each cargo loading node are marked on the corresponding monitoring nodes. It should be noted that for any monitoring node, it can carry multiple classification labels at the same time, and the total classification labels carried by any cargo loading node and the monitoring node associated with it are the same.

[0024] Further, the assembly line monitoring network module sends the assembly line monitoring network to the automatic door control module; The automatic door control module extracts scene optical video data and scene infrared video data from the scene status data, and then extracts image data of each cargo from the scene optical video data and scene infrared video data, and establishes a corresponding cargo image model; According to the collection time of the scene status data and the serial numbers of the scene optical video data and the scene infrared video data, a cargo node is synchronously generated and mapped on the assembly line monitoring network; It should be noted that whenever the automatic door control module receives new scene status data, it updates the real-time position of the corresponding cargo node on the assembly line monitoring network in real time; Whenever a new cargo node appears in the assembly line monitoring network, a cargo number is set for the corresponding cargo, and the automatic door control module retrieves the corresponding cargo image model and matches the feature area of ​​each cargo with the cargo image model. According to the matching result, the classification label of the feature area is marked on the corresponding cargo node; Then, the automatic door control module matches the corresponding cargo loading node according to the classification label of the cargo node. If the classification label of the cargo node is more than 90% identical with the classification label of a cargo loading node, the cargo node is judged to match the cargo loading node, otherwise it is judged not to match. When a cargo node matches multiple cargo loading nodes at the same time, the cargo loading node with the largest number of identical classification labels is selected as the target cargo loading node. If there are multiple cargo loading nodes with the largest number of identical classification labels at the same time, a cargo loading node is randomly selected as the target cargo loading node.

[0025] Furthermore, according to the target cargo loading node corresponding to the cargo node, the monitoring node with the corresponding number is found in the assembly line monitoring network, and then the corresponding cargo transportation path is generated; Set the same cargo transportation speed for all cargo nodes so that the cargo passes through just one connection section in one data collection cycle; Then, an opening and closing simulation is performed according to the cargo transportation path and cargo transportation speed of each cargo node. The opening and closing simulation process includes: All cargo transportation routes are input into the assembly line monitoring network, and according to the order of each cargo node on the assembly line monitoring network, each cargo node is made to pass through each connection section in turn according to the cargo transportation speed, and then the estimated time taken for each cargo node to pass through the monitoring node according to the cargo transportation route is obtained; An event timeline is set for each monitoring node. According to the simulation results of the opening and closing simulation, the estimated arrival time node of each cargo node is marked on the event timeline. At the same time, the corresponding cargo number and transportation direction are marked on each estimated arrival time node.

[0026] Furthermore, the automatic door control module sends the event timeline of each monitoring node to the corresponding control unit for detecting the automatic door, and then the control unit sends an automatic door opening and closing instruction to the door leaf according to the estimated time node on the event timeline and the transportation direction control, and the automatic door opening and closing instruction includes the opening time, closing time and the door leaf number for executing the closing instruction; When the goods arrive at the corresponding cargo loading area, the corresponding cargo number is removed.

[0027] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent regulation system for the operation of an automatic door motor, comprising a cloud computing platform, characterized in that: The cloud computing platform is communicatively connected with a scene data acquisition module, an assembly line monitoring network module and an automatic door control module; The scene data acquisition module is used to set a data acquisition cycle, and collect scene optical video data and scene infrared video data of the handling line in each data acquisition cycle, and then integrate the scene optical video data and scene infrared video data of each data acquisition cycle to obtain scene status data, and send the scene status data to the assembly line monitoring network module and the automatic door control module; The assembly line monitoring network module is used to establish an assembly line image model according to the scene status data, and to establish an assembly line monitoring network according to the assembly line image model, and to obtain video data and cargo information data of several types of cargo through the Internet, thereby generating corresponding feature areas and classification labels, and mapping each feature area and classification label on the assembly line monitoring network; The automatic door control module is used to establish cargo nodes according to scene status data, and input the cargo nodes into the assembly line monitoring network. According to the matching relationship between the cargo nodes and the feature areas and classification labels, the cargo loading nodes are located in the assembly line monitoring network, and then the cargo transportation speed is set for each cargo node to perform opening and closing simulation, and the corresponding automatic door opening and closing instructions are generated and executed according to the opening and closing simulation.

2. An intelligent adjustment system for automatic door motor operation according to claim 1, characterized in that: The transport assembly line is composed of Num automatic detection doors, conveyor belts and multiple cargo loading areas, where Num is a natural number greater than 0; The conveyor belt is composed of several rolling pulleys connected together, and the conveyor belt is provided with several rolling forks, and each rolling fork is equipped with an automatic detection door; The automatic door detection device is provided with an infrared sensor, a control unit and a door leaf, wherein the door leaf is composed of a plurality of door leaves arranged in an "X" shape, each door leaf is connected to a corresponding section of a conveyor belt, and each door leaf is marked with a number; The cargo loading area is located at the edge of the transport line and is used to load the cargo sorted by the transport line or to transport the cargo to the transport line.

3. The intelligent adjustment system for automatic door motor operation according to claim 2, characterized in that: The process of collecting the scene status data includes: The scene data acquisition module installs several cameras at fixed distances on the handling line, and sets numbers a1, a2, a3, ..., a for each camera and the automatic door detection. n , b1, b2, b3, ..., b m Where n and m are natural numbers greater than 0; The same data collection cycle is set for each camera and infrared sensor. When the cargo loading device conveys the cargo to the conveyor belt, each camera and infrared sensor continuously captures the scene optical video data and scene infrared video data within its capture range. The optical video data of each scene is labeled with the number of the corresponding camera, and the infrared video data of the scene is labeled with the number of the corresponding automatic door detection, and then the scene optical video data and scene infrared video data generated by each data collection cycle are integrated to obtain the scene status data.

4. The intelligent adjustment system for automatic door motor operation according to claim 3, characterized in that: The process of establishing the pipeline image model includes: Establishing corresponding scene optical image models and scene infrared image models according to the scene optical video data and the scene infrared video data, and marking corresponding numbers on each scene optical image model and scene infrared image model; Each scene optical image model and scene infrared image model are spliced ​​in sequence, and several overlapping blocks are set at the edge position of each image model. Then, the overlapping blocks at the edge position of each image model are matched with each other. If the overlapping blocks from different image models are the same, the two overlapping blocks are merged. If they are not the same, no operation is performed. Repeat the above-mentioned matching and merging operation of the overlapping areas of the image models to obtain the pipeline image model.

5. The intelligent adjustment system for automatic door motor operation according to claim 4, characterized in that: The process of establishing a pipeline monitoring network according to the pipeline image model includes: The assembly line monitoring network module establishes corresponding monitoring nodes according to the corresponding automatic door detection part in the assembly line image model, and sets the same number of cargo loading nodes according to the image model positions corresponding to each cargo loading device in the assembly line image model, and sets connection lines between each monitoring node and the cargo loading node in proportion according to the corresponding conveyor belt part in the assembly line image model, and divides the connection lines into several connection sections according to the distribution of cameras on the conveyor belt; Each connection section and monitoring node is marked with the corresponding camera and infrared sensor number to obtain the assembly line monitoring network.

6. The intelligent adjustment system for automatic door motor operation according to claim 5, characterized in that: The process of generating the characteristic regions and classification labels of the goods includes: Obtain video data and cargo information data of several types of cargo through the Internet, and generate corresponding standard cargo image models according to the video data of each cargo; Establishing a three-dimensional coordinate system, overlapping and mapping the image models of different types of standard goods on the three-dimensional coordinate system, dividing each standard goods image model into a plurality of image model regions, selecting the image model region of each standard goods image model in turn as a feature detection region, and then comparing the feature detection region with the image model regions of other standard goods image models, and recording the corresponding image model region as the feature region of the corresponding type of goods according to the comparison result; Classification labels are set for each feature area based on the cargo information data. The staff assigns multiple classification labels to the cargo loading nodes in the assembly line monitoring network. At the same time, the assembly line monitoring network is traversed from the connection section numbered a1 as the starting point. The cargo loading nodes associated with each monitoring node are obtained based on the traversal results, and the classification labels of each cargo loading node are then marked on the corresponding monitoring nodes.

7. An intelligent adjustment system for automatic door motor operation according to claim 6, characterized in that: The process of locating cargo loading nodes in the assembly line monitoring network includes: Extracting image data of each cargo from the scene optical video data and the scene infrared video data, and establishing a corresponding cargo image model; According to the collection time of the scene status data and the serial numbers of the scene optical video data and the scene infrared video data, a cargo node is synchronously generated and mapped on the assembly line monitoring network; A cargo number is set for each cargo, the automatic door control module retrieves the corresponding cargo image model, and matches the feature area of ​​each cargo with the cargo image model. According to the matching result, the classification label of the feature area is marked on the corresponding cargo node, and then the automatic door control module matches the corresponding cargo loading node according to the classification label of the cargo node.

8. The intelligent adjustment system for automatic door motor operation according to claim 7, characterized in that: The generation and execution process of the automatic door opening and closing instruction includes: According to the target cargo loading node corresponding to the cargo node, the monitoring node with the corresponding number is found in the assembly line monitoring network, and then the corresponding cargo transportation path is generated, and the same cargo transportation speed is set for all cargo nodes; Then, an opening and closing simulation is performed according to the cargo transportation path and cargo transportation speed of each cargo node. According to the opening and closing simulation results, an event timeline is set for each monitoring node. According to the simulation results of the opening and closing simulation, the estimated arrival time node of each cargo node is marked on the event timeline, and the corresponding cargo number and transportation direction are marked on each estimated arrival time node; The event timeline of each monitoring node is sent to the corresponding control unit for detecting the automatic door, and then the control unit sends automatic door opening and closing instructions to the door leaf according to the estimated time node on the event timeline and the transportation direction control. The automatic door opening and closing instructions include the opening time, closing time and the door page number that executes the closing instruction.

9. The intelligent adjustment system for automatic door motor operation according to claim 8, characterized in that: The process of the opening and closing simulation includes: All cargo transportation routes are input into the assembly line monitoring network, and according to the sequence of each cargo node on the assembly line monitoring network, each cargo node passes through each connecting section in sequence according to the cargo transportation speed, and then the estimated time for each cargo node to pass through the monitoring node according to the cargo transportation route is obtained.

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