An intelligent fixture monitoring method and system for automobile production lines based on the Internet of Things

Abstract

The present invention relates to the fields of intelligent manufacturing and Internet of Things (IoT) monitoring technology, and discloses an IoT-based intelligent fixture monitoring method and system for automobile production lines. The method comprises: partitioning the computational area based on a Voronoi diagram; real-time monitoring of fixture status; filtering key data based on a data importance index; constructing a triangulated network in the cloud; and using a long-short-term memory (LSTM) network for position offset prediction and monitoring. Compared to the prior art, where data processing relies on cloud computing, resulting in high network bandwidth usage and large data transmission delays, and the inability to achieve low-latency fixture status monitoring, particularly under conditions of high-speed production line operation and large amounts of monitoring data, the present invention introduces edge computing and intelligent filtering mechanisms to achieve collaborative processing between local computing devices and the cloud, avoiding the problems of excessive bandwidth usage and untimely data processing, and improving the real-time nature of data processing and overall system efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent manufacturing and Internet of Things monitoring technology, and in particular relates to an Internet of Things-based intelligent fixture monitoring method and system for an automobile production line. Background Art

[0002] Currently, in intelligent manufacturing on automotive production lines, fixture status monitoring is crucial for ensuring production accuracy and process stability. However, existing intelligent fixture monitoring systems still have many shortcomings. For example, traditional monitoring systems typically rely on centralized cloud computing for data analysis and processing. However, in high-speed production lines, this architecture presents the following issues: Excessive network bandwidth usage: All monitoring data is uploaded to the cloud for processing, resulting in huge data traffic, increased communication costs, and potentially causing data transmission delays under high load; Large data processing latency: Because cloud computing involves remote data transmission and centralized processing, the system cannot respond in real time to critical events such as fixture misalignment and vibration anomalies on the production line, resulting in delayed detection and adjustment; Lack of intelligent data filtering mechanisms: Current systems often upload all data, even if some data is not of practical analytical value, still occupying cloud storage and computing resources, resulting in reduced computing efficiency; Inability to achieve accurate predictions: Existing monitoring systems typically use fixed thresholds for anomaly detection and lack intelligent predictive models, resulting in the inability to intervene before failures occur, affecting production continuity. Therefore, there is an urgent need for an intelligent fixture monitoring method that combines edge computing, intelligent data screening, geometric optimization calculation and machine learning prediction, so that efficient and low-latency fixture status monitoring can be achieved in a high data flow environment, and the fault prediction capability and the overall operation efficiency of the production line can be improved. Summary of the Invention

[0003] In response to the above-mentioned technical deficiencies, the purpose of the present invention is to propose an intelligent fixture monitoring method for automobile production lines based on the Internet of Things, aiming to solve the technical problem that data processing in the existing technology relies on cloud computing, resulting in high network bandwidth occupancy and large data transmission delay, especially under the conditions of high-speed operation of the production line and large amount of monitoring data, and low-latency fixture status monitoring cannot be achieved.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides an intelligent fixture monitoring method for automobile production lines based on the Internet of Things.

[0005] The IoT-based intelligent fixture monitoring method for automobile production lines includes:

[0006] Step S10: Obtain the basic load information of the monitoring node i of the intelligent fixture of the automobile production line, and calculate the real-time load balancing score L according to the basic load information. i , when L iWhen the real-time load balancing threshold is greater than the preset threshold, the current computing task is transferred to the adjacent monitoring node, the computing task transfer flow is recorded in real time, and the Voronoi diagram is used to set the computing area E to which the monitoring node i belongs;

[0007] Step S20: obtaining a set of monitoring points within the calculation area E, collecting the real-time status information of the intelligent fixture at time t within the set of monitoring points, and calculating the data importance index;

[0008] Step S30: Determine whether to upload the real-time status information of the intelligent fixture in the calculation area E to the cloud for processing based on the data importance index;

[0009] Step S40: receiving the real-time status information of the intelligent fixture in the cloud and constructing a triangulated data network using a triangulation algorithm accelerated by a K-dimensional binary tree, and outputting a fused data set;

[0010] Step S50: Taking the fused data set as input, the long short-term memory network is used to predict the predicted value P of the intelligent fixture position offset. fault , if P fault If the deviation is greater than the preset threshold, the monitoring alarm instruction is triggered.

[0011] Preferably, in step S10, the basic load information includes the initial position (x i ,y i ), CPU utilization C i , memory usage M i and network bandwidth utilization B i ; Real-time load balancing i The calculation formula is Among them, β is the preset monitoring point emergency adjustment coefficient.

[0012] Preferably, in step S20, the real-time status information of the intelligent fixture includes position data P t , alignment error A t and vibration value V t , the calculation formula of data importance index DII is Among them, P ref The standard position of the preset fixture, A ref V is the maximum alignment error allowed by the preset fixture. ref is the preset normal range of fixture vibration, w1, w2, w3 are data weight factors.

[0013] Preferably, in step S30, the step of determining whether to upload the real-time status information of the intelligent fixture in the calculation area E to the cloud for processing according to the data importance index specifically includes: presetting a data importance index threshold θ, and if DII>θ, marking the real-time status information of the intelligent fixture as key data D key If DII≤θ, the real-time status information of the smart fixture is stored in the local edge computing device. The local edge computing device includes an embedded development board inside the smart fixture of the production line, which uses a hierarchical storage architecture to store JSON format data.

[0014] Preferably, in step S40, the steps of receiving the real-time status information of the intelligent fixture in the cloud and constructing a triangulated data network using a triangulation algorithm accelerated by a K-dimensional binary tree include: introducing a K-dimensional binary tree data structure, using the K-dimensional binary tree data structure to triangulate the key data D key Accelerate the processing and find the nearest neighbor of each point in O(logn) time complexity to build a triangulated data network.

[0015] Preferably, in step S10, in the step of using the Voronoi diagram to set the calculation area E to which the monitoring node i belongs, the calculation area E satisfies the condition d(x i ,y i ,E)≤d(x i ,y i ,E * ), where E * is any calculation area outside E, d(x,y,E) is the location of the monitoring node (x i ,y i ) to the center of region E, d(x i ,y i ,E * ) is the location of the monitoring node (x i ,y i ) to the calculation area E * Euclidean distance to the center.

[0016] Preferably, in step S50, the long short-term memory network is used to predict the predicted value P of the intelligent fixture position offset. fault The steps include:

[0017] The key data D after fusion from step S40 mergef Clean and get the standardized data set D norm , construct a long short-term memory network, the input of the long short-term memory network is the standardized data set D norm , the output is the fixture position offset value at a certain moment in the future;

[0018] Use the preset historical data to train the long short-term memory network. The training goal is to optimize the parameters of the long short-term memory network by minimizing the mean square error loss function.

[0019] The trained long short-term memory network is applied to real-time data to predict the position offset of the intelligent fixture and obtain the predicted value P of the intelligent fixture position offset. fault .

[0020] The present invention also provides an Internet of Things-based intelligent fixture monitoring system for automobile production lines, comprising:

[0021] The task allocation module is used to obtain the basic load information of the monitoring node i of the intelligent fixture of the automobile production line and calculate the real-time load balancing distribution L according to the basic load information. i , when L i When the real-time load balancing threshold is greater than the preset threshold, the current computing task is transferred to the adjacent monitoring node, the computing task transfer flow is recorded in real time, and the Voronoi diagram is used to set the computing area E to which the monitoring node i belongs;

[0022] The data importance calculation module is used to obtain the set of monitoring points in the calculation area E, collect the real-time status information of the intelligent fixture at time t in the set of monitoring points, and calculate the data importance index;

[0023] The data upload decision module is used to decide whether to upload the real-time status information of the intelligent fixture in the calculation area E to the cloud for processing based on the data importance index;

[0024] The cloud data processing module is used to receive the real-time status information of the intelligent fixture in the cloud and use the triangulation algorithm accelerated by the K-dimensional binary tree to construct a triangulated data network and output a fused data set;

[0025] The intelligent monitoring and early warning module is used to take the fusion data set as input and use the long short-term memory network to predict the predicted value P of the intelligent fixture position offset. fault , if P fault If the deviation is greater than the preset threshold, the monitoring alarm instruction is triggered.

[0026] The present invention also provides a computer program product, including an Internet of Things-based automobile production line intelligent fixture monitoring program, which, when executed by a processor, implements the Internet of Things-based automobile production line intelligent fixture monitoring method.

[0027] The beneficial effects of the present invention are: compared with the existing technology in which data processing relies on cloud computing, resulting in high network bandwidth occupancy and large data transmission delay, especially under the conditions of high-speed operation of the production line and large amount of monitoring data, the technical problem of being unable to achieve low-latency fixture status monitoring, the present invention introduces edge computing and intelligent screening mechanisms to achieve collaborative processing between local computing devices and the cloud, avoiding the problems of excessive bandwidth occupancy and untimely data processing, and improving the real-time performance of data processing and the overall efficiency of the system.

[0028] The present invention uses an LSTM network model to predict future fixture position offsets based on historical data, enabling real-time monitoring and adjustment. During actual production, if the predicted value exceeds a set threshold, an alarm mechanism can be triggered to ensure the accuracy and stability of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 This is a flow chart of a first embodiment of an intelligent fixture monitoring method for an automobile production line based on the Internet of Things according to the present invention.

[0031] Figure 2 Schematic diagram of the relationship between load balancing points and resource utilization in Example 1.

[0032] Figure 3 Schematic diagram of a heat map showing the correlation between resource usage and load balancing score in Example 1.

[0033] Figure 4 Schematic diagram of node calculation area division in Example 1.

[0034] Figure 5 Schematic diagram of triangulation and nearest neighbor analysis in Example 1.

[0035] Figure 6 Schematic diagram of the LSTM prediction fixture position offset and alarm mechanism in Example 1.

[0036] Figure 7 This is a schematic diagram of an equipment method for monitoring intelligent fixtures in an automobile production line based on the Internet of Things according to the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] Example 1: Figure 1 2 is a flow chart of the first embodiment of the intelligent fixture monitoring method for an automobile production line based on the Internet of Things of the present invention, and proposes the first embodiment of the intelligent fixture monitoring method for an automobile production line based on the Internet of Things of the present invention.

[0039] In a first embodiment, the method for monitoring intelligent fixtures in an automobile production line based on the Internet of Things includes:

[0040] Step S10: Obtain the basic load information of the monitoring node i of the intelligent fixture of the automobile production line, and calculate the real-time load balancing score L according to the basic load information. i , when L i When the real-time load balancing threshold is greater than the preset threshold, the current computing task is transferred to the adjacent monitoring node, the computing task transfer flow is recorded in real time, and the Voronoi diagram is used to set the computing area E to which the monitoring node i belongs;

[0041] It should be noted that in step S10, the basic load information includes the initial position (x i ,y i ), CPU utilization C i , memory usage M i and network bandwidth utilization B i ; Real-time load balancing i The calculation formula is Wherein, β is the preset monitoring point emergency adjustment coefficient. In step S10, in the step of using the Voronoi diagram to set the calculation area E to which the monitoring node i belongs, the calculation area E satisfies the condition d(x i ,y i ,E)≤d(x i ,y i ,E * ), where E * is any calculation area outside E, d(x,y,E) is the location of the monitoring node (x i ,y i ) to the center of region E, d(x i ,y i ,E * ) is the location of the monitoring node (x i ,y i ) to the calculation area E *Euclidean distance to the center.

[0042] It should be understood that by introducing a load balancing calculation formula, the load of each monitoring node can be accurately assessed. In the event of excessive load, tasks are transferred to less loaded nodes, achieving more efficient allocation of computing resources. Using a Voronoi diagram to partition the computational area ensures that each monitoring node is allocated the most appropriate computational area based on its physical location and load status. This approach effectively avoids task conflicts between nodes and improves the efficiency of computing task allocation.

[0043] For example, there are 5 monitoring nodes, labeled A, B, C, D and E. The CPU utilization, memory usage, bandwidth utilization and load balancing score of each node are recorded as follows: {"node":"node A","CPU utilization (%)":80,"memory usage (%)":70,"bandwidth utilization (%)":85,"load balancing score":0.75}, {"node":"node B","CPU utilization (%)":90,"memory usage (%)":80,"bandwidth utilization (%)":95,"load balancing score":0.80}, {"node":"node C","CPU utilization (%)":90,"memory usage (%)":80,"bandwidth utilization (%)":95,"load balancing score":0.80} "CPU Utilization (%)": 60, "Memory Utilization (%)": 50, "Bandwidth Utilization (%)": 70, "Load Balancing Score": 0.60}, {"Node": "Node D", "CPU Utilization (%)": 65, "Memory Utilization (%)": 55, "Bandwidth Utilization (%)": 80, "Load Balancing Score": 0.70}, {"Node": "Node E", "CPU Utilization (%)": 85, "Memory Utilization (%)": 75, "Bandwidth Utilization (%)": 90, "Load Balancing Score": 0.77}, such as Figure 2 As shown in the scatter plot, we can intuitively see that the data is concentrated around 0.775, and the real-time load balancing threshold is based on this value; Figure 3 As shown in the figure, the heat map shows the correlation between CPU utilization, memory usage, bandwidth utilization and load balancing score. By looking at the depth of the color, you can intuitively understand that the correlation between resource usage is strong; Figure 4 As shown in FIG, different calculation areas can be obtained by using the Voronoi diagram to divide the calculation area.

[0044] Compared with traditional technologies, the solution of the present invention has higher dynamic scheduling capabilities. It can not only respond to resource usage in real time, but also perform intelligent resource adjustment and task migration when the load is too heavy.

[0045] Step S20: obtaining a set of monitoring points within the calculation area E, collecting the real-time status information of the intelligent fixture at time t within the set of monitoring points, and calculating the data importance index;

[0046] It should be noted that in step S20, the real-time status information of the intelligent fixture includes the position data P t , alignment error A t and vibration value V t , the calculation formula of data importance index DII is Among them, P ref The standard position of the preset fixture, A ref V is the maximum alignment error allowed by the preset fixture. ref is the preset normal range of fixture vibration, w1, w2, w3 are data weight factors.

[0047] It should be understood that the design concept of this formula is derived from a multi-factor comprehensive evaluation method. It can simultaneously consider the position error, position deviation, and vibration error of the fixture, and derive the Data Importance Index (DII) through weighted calculation, thus comprehensively reflecting the working status of the fixture. This formula evaluates the current status of the smart fixture by calculating the relative error between its current position, position deviation, and vibration values ​​and the preset state. The larger the calculated DII value, the more the real-time status of the fixture deviates from the preset standard state, indicating that more resources or adjustments are required.

[0048] Step S30: Determine whether to upload the real-time status information of the intelligent fixture in the calculation area E to the cloud for processing based on the data importance index;

[0049] It should be noted that in step S30, the step of determining whether to upload the real-time status information of the intelligent fixture in the calculation area E to the cloud for processing according to the data importance index specifically includes: presetting the data importance index threshold θ, and if DII>θ, marking the real-time status information of the intelligent fixture as key data D key If DII≤θ, the real-time status information of the smart fixture is stored in the local edge computing device. The local edge computing device includes an embedded development board inside the smart fixture of the production line, which uses a hierarchical storage architecture to store JSON format data.

[0050] It should be understood that the DII value can be used to determine in real time whether to upload data or save it locally. This dynamic decision-making mechanism can effectively reduce bandwidth pressure and optimize network resource utilization. Real-time status information that does not require immediate processing is stored on edge computing devices, reducing the burden on the cloud and improving response speed.

[0051] Step S40: receiving the real-time status information of the intelligent fixture in the cloud and constructing a triangulated data network using a triangulation algorithm accelerated by a K-dimensional binary tree, and outputting a fused data set;

[0052] It should be noted that in step S40, the steps of receiving the real-time status information of the intelligent fixture in the cloud and constructing the triangulated data network using the triangulation algorithm accelerated by the K-dimensional binary tree include: introducing the K-dimensional binary tree data structure, using the K-dimensional binary tree data structure to triangulate the key data D key Accelerate the processing and find the nearest neighbor of each point in O(logn) time complexity to build a triangulated data network.

[0053] As you can understand, a K-dimensional binary tree is a data structure used in multidimensional space. It is particularly well-suited for solving the "nearest neighbor search" problem, quickly finding the closest node in a triangulated network. It recursively partitions data points into several regions, forming a binary tree structure. When searching, only the relevant portion of the Kd tree needs to be traversed, allowing the target data to be found within O(logn) time complexity.

[0054] It should be understood that by using the Kd Tree, the system can query the nearest neighbors of a data point with a low time complexity (O(logn)), thereby accelerating the construction of the triangulated network. This technology can significantly improve the efficiency of real-time data processing and is particularly suitable for production line environments with dynamic changes and large amounts of data.

[0055] For example, if Figure 5 As shown in the figure, it shows how to divide the point set into several triangular areas through triangulation. By using the K-dimensional binary tree and Delaunay triangulation, we can not only efficiently query the nearest neighbor data points, but also build a clear data network, which plays an important optimization role in the real-time monitoring and data processing of intelligent fixtures on the production line.

[0056] Step S50: Taking the fused data set as input, the long short-term memory network is used to predict the predicted value P of the intelligent fixture position offset. fault , if P fault If the deviation is greater than the preset threshold, the monitoring alarm instruction is triggered.

[0057] It should be noted that in step S50, the long short-term memory network is used to predict the predicted value P of the intelligent fixture position offset. fault The steps include:

[0058] The key data D after fusion from step S40 merged Clean and get the standardized data set D norm , construct a long short-term memory network, the input of the long short-term memory network is the standardized data set D norm , the output is the fixture position offset value at a certain moment in the future;

[0059] Use the preset historical data to train the long short-term memory network. The training goal is to optimize the parameters of the long short-term memory network by minimizing the mean square error loss function.

[0060] The trained long short-term memory network is applied to real-time data to predict the position offset of the intelligent fixture and obtain the predicted value P of the intelligent fixture position offset. fault .

[0061] It should be understood that the LSTM network model can predict future fixture position offsets based on historical data, allowing for real-time monitoring and adjustment. During actual production, if the predicted value exceeds a set threshold, an alarm mechanism can be triggered to ensure the accuracy and stability of the production line.

[0062] For example, the incoming fusion dataset contains the following data:

[0063]

[0064] like Figure 6 As shown in the figure, through the LSTM network model, we can intuitively see the prediction effect of the LSTM model on the fixture position offset. The display of the alarm point can clearly indicate the situation when the predicted value exceeds the set threshold, thereby prompting the system to trigger the alarm mechanism to ensure the accuracy and stability of the production line.

[0065] Embodiment 2: In addition, the present invention provides an IoT-based intelligent fixture monitoring system for automobile production lines. This system employs the IoT-based intelligent fixture monitoring method for automobile production lines described in the aforementioned embodiment, thereby resolving the technical issues surrounding IoT-based intelligent fixture monitoring for automobile production lines. Compared to the prior art, the IoT-based intelligent fixture monitoring system for automobile production lines provided by the present invention achieves the same beneficial effects as the IoT-based intelligent fixture monitoring method for automobile production lines provided in the aforementioned embodiment. Other technical features of the IoT-based intelligent fixture monitoring system for automobile production lines are the same as those disclosed in the aforementioned embodiment and are not further elaborated upon here.

[0066] Example 3: The present invention provides an intelligent fixture monitoring device for automobile production lines based on the Internet of Things. Figure 7An intelligent fixture monitoring device for an automobile production line based on the Internet of Things includes: at least one processor; and a memory in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the intelligent fixture monitoring method for an automobile production line based on the Internet of Things in the first embodiment described above. An intelligent fixture monitoring device for an automobile production line based on the Internet of Things in an embodiment of the present invention may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. An intelligent fixture monitoring device for an automobile production line based on the Internet of Things is merely an example and should not limit the functions and scope of use of the embodiments of the present invention. An IoT-based intelligent fixture monitoring device for an automobile production line may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the IoT-based intelligent fixture monitoring device for an automobile production line. Processing device 1001, ROM 1002, and RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 can allow an IoT-based intelligent fixture monitoring device for an automobile production line to communicate wirelessly or wired with other devices to exchange data. Although the figure shows an IoT-based intelligent fixture monitoring device for an automobile production line with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have instead.

[0067] Example 4: The present invention also provides a computer program product, comprising a computer program. When executed by a processor, the computer program implements the steps of the aforementioned method for monitoring intelligent fixtures in an automotive production line based on the Internet of Things. The computer program product provided by the present invention can solve the technical problem of monitoring intelligent fixtures in an automotive production line based on the Internet of Things. Compared to the prior art, the beneficial effects of the computer program product provided by the present invention are similar to those of the method for monitoring intelligent fixtures in an automotive production line based on the Internet of Things provided in the aforementioned embodiment, and are not further elaborated here.

[0068] In particular, according to the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are performed.

[0069] It should be understood that the various parts disclosed in the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0070] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A smart fixture monitoring method for automobile production lines based on the Internet of Things, characterized in that: Methods include: Step S10: Obtain the basic load information of the monitoring node i of the intelligent fixture of the automobile production line, and calculate the real-time load balancing distribution according to the basic load information. ,when When the real-time load balancing threshold is greater than the preset value, the current computing task is transferred to the adjacent monitoring node, the computing task transfer flow is recorded in real time, and the Voronoi diagram is used to set the computing area to which the monitoring node i belongs. ; Among them, the basic load information includes the initial position , CPU utilization , memory usage and network bandwidth utilization ; Real-time load balancing The calculation formula is ,in, It is the emergency adjustment coefficient of the preset monitoring point; Step S20: Obtain calculation area The monitoring point set within the monitoring point set is collected to collect the real-time status information of the intelligent fixture at time t within the monitoring point set, and the data importance index is calculated; Among them, the real-time status information of the intelligent fixture includes position data , alignment error and vibration values , data importance index The calculation formula is ,in, The standard position of the preset fixture, is the maximum alignment error allowed by the preset fixture, The normal range of the preset fixture vibration is is the data weight factor; Step S30: Determine whether to upload the calculation area based on the data importance index The real-time status information of the intelligent fixture is uploaded to the cloud for processing; Step S40: receiving the real-time status information of the intelligent fixture in the cloud and constructing a triangulated data network using a triangulation algorithm accelerated by a K-dimensional binary tree, and outputting a fused data set; Step S50: Take the fused dataset as input and use the long short-term memory network to predict the predicted value of the intelligent fixture position offset ,like If the deviation is greater than the preset threshold, the monitoring alarm instruction is triggered.

2. The method for monitoring intelligent fixtures in an automobile production line based on the Internet of Things according to claim 1, wherein: In step S30, the calculation area is determined based on the data importance index. The steps of uploading the real-time status information of the intelligent fixture to the cloud for processing include: presetting the data importance index threshold ,like , then mark the real-time status information of the intelligent fixture as key data ;like , the real-time status information of the smart fixture is stored in the local edge computing device; among them, the local edge computing device includes an embedded development board inside the smart fixture of the production line, which uses a hierarchical storage architecture to store JSON format data.

3. The method for monitoring intelligent fixtures in an automobile production line based on the Internet of Things according to claim 2, characterized in that: In step S40, the steps of receiving the real-time status information of the intelligent fixture in the cloud and constructing the triangulated data network by using the triangulation algorithm accelerated by the K-dimensional binary tree are as follows: introducing the K-dimensional binary tree data structure, using the K-dimensional binary tree data structure to triangulate the key data Accelerate the processing and find the nearest neighbor of each point in O(logn) time complexity to build a triangulated data network.

4. The method for monitoring intelligent fixtures in an automobile production line based on the Internet of Things according to claim 1, wherein: In step S10, the Voronoi diagram is used to set the computational area to which the monitoring node i belongs. In the step of Meet the conditions ,in, for Any calculation area outside is the location of the monitoring node ( ) to the area Euclidean distance to the center, is the location of the monitoring node ( ) to the calculation area Euclidean distance to the center.

5. The method for monitoring intelligent fixtures in automobile production lines based on the Internet of Things according to claim 1, characterized in that: In step S50, the long short-term memory network is used to predict the predicted value of the intelligent fixture position offset The steps include: The key data after fusion from step S40 Clean and obtain a standardized data set , build a long short-term memory network, the input of the long short-term memory network is a standardized data set , the output is the fixture position offset value at a certain moment in the future; Use the preset historical data to train the long short-term memory network. The training goal is to optimize the parameters of the long short-term memory network by minimizing the mean square error loss function. Apply the trained long short-term memory network to real-time data to predict the position offset of the intelligent fixture and obtain the predicted value of the intelligent fixture position offset. .

6. An intelligent fixture monitoring system for automobile production lines based on the Internet of Things, characterized in that: The IoT-based intelligent fixture monitoring system for automobile production lines includes: The task allocation module is used to obtain the basic load information of the monitoring node i of the intelligent fixture of the automobile production line, and calculate the real-time load balancing distribution based on the basic load information. ,when When the real-time load balancing threshold is greater than the preset value, the current computing task is transferred to the adjacent monitoring node, the computing task transfer flow is recorded in real time, and the Voronoi diagram is used to set the computing area to which the monitoring node i belongs. ; Among them, the basic load information includes the initial position , CPU utilization , memory usage and network bandwidth utilization ; Real-time load balancing The calculation formula is ,in, It is the emergency adjustment coefficient of the preset monitoring point; Data importance calculation module, used to obtain the calculation area The monitoring point set within the monitoring point set is collected to collect the real-time status information of the intelligent fixture at time t within the monitoring point set, and the data importance index is calculated; Among them, the real-time status information of the intelligent fixture includes position data , alignment error and vibration values , data importance index The calculation formula is ,in, The standard position of the preset fixture, is the maximum alignment error allowed by the preset fixture, The normal range of the preset fixture vibration is is the data weight factor; Data upload decision module, used to decide whether to upload the calculation area based on the data importance index The real-time status information of the intelligent fixture is uploaded to the cloud for processing; The cloud data processing module is used to receive the real-time status information of the intelligent fixture in the cloud and use the triangulation algorithm accelerated by the K-dimensional binary tree to construct a triangulated data network and output a fused data set; The intelligent monitoring and early warning module is used to take the fused data set as input and use the long short-term memory network to predict the predicted value of the intelligent fixture position offset ,like If the deviation is greater than the preset threshold, the monitoring alarm instruction is triggered.

7. An intelligent fixture monitoring device for automobile production lines based on the Internet of Things, characterized in that: The IoT-based intelligent fixture monitoring device for an automobile production line includes: a memory, a processor, and an IoT-based intelligent fixture monitoring program for an automobile production line stored in the memory and executable on the processor. When the IoT-based intelligent fixture monitoring program for an automobile production line is executed by the processor, the IoT-based intelligent fixture monitoring method for an automobile production line according to any one of claims 1 to 5 is implemented.

8. A computer program product, characterized in that The computer program product includes an Internet of Things-based intelligent fixture monitoring program for automobile production lines. When the Internet of Things-based intelligent fixture monitoring program for automobile production lines is executed by a processor, it implements the Internet of Things-based intelligent fixture monitoring method for automobile production lines described in any one of claims 1 to 5.

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