A Linkage Method for a Multifunctional Activity Tool Rack Based on the Internet of Things

Abstract

The present application relates to a linkage method for a multifunctional activity tool rack based on the Internet of Things, including establishing a communication connection between tool racks through the Internet of Things. When a tool rack detects a high-frequency handover area, a corresponding rack position adjustment plan is generated, and an adjustment instruction is transmitted to the adjacent tool rack to control the adjacent tool rack to make adjustments. During the process of tool rack structure adjustment, the computer vision method is used to track the position change of the tool in real time. By comparing the tool trajectory changes before and after the adjustment, the handover smoothness improvement rate is calculated to evaluate the effect of the rack position adjustment. According to the multifunctional attributes of the tools, the tools are classified and coded. When the tool racks are linked and adjusted, tools with complementary functions are preferentially placed in adjacent positions.

Description

Technical Field

[0001] This application relates to the field of information technology, and specifically to a linkage method for a multi-functional movable tool rack based on the Internet of Things. Background Art

[0002] In a multi-functional movable tool rack linkage system based on the Internet of Things, the impact of tool handover frequency on space division is a key technical issue. When the tool handover frequency in a certain area is high, the tool rack in that area needs to respond quickly and adjust the rack position structure to meet the needs of high-frequency handover. Therefore, a handover heat map generated based on the tool handover frequency is required.

[0003] However, how adjacent tool racks dynamically adjust the rack position structure according to the handover heat map and ensure that the adjustment process does not affect the smoothness of tool handover is a complex technical challenge.

[0004] Specifically, first, the tool rack needs to be able to monitor and analyze the frequency and location distribution of tool handovers in real time to generate a handover heat map. Then, the tool rack needs to identify the high-frequency handover area based on the heat map and determine whether the rack position structure needs to be adjusted. If adjustment is required, the tool rack also needs to consider the adjustment direction and how to ensure the accessibility and stability of the tools during the adjustment process.

[0005] In addition, since the adjustment of the tool rack may affect the layout of adjacent tool racks and tool access, real-time coordination and optimization are also required among multiple tool racks to ensure the operation efficiency and handover smoothness of the entire system. This requires the tool racks to be able to exchange information and make decision negotiations in real time, and adjust their respective rack position structures and tool allocations according to the global optimization goal. Summary of the Invention

[0006] To solve the problems existing in the above-mentioned prior art, the purpose of this application is to provide a linkage method for a multi-functional movable tool rack based on the Internet of Things.

[0007] A linkage method for a multi-functional movable tool rack based on the Internet of Things according to this application includes the following steps:

[0008] Step S101: By installing radio frequency identification sensors on the tool racks in the target area, real-time obtain tool handover frequency data, identify the usage times of the tools on the tool racks in the target area. If the tool usage times within a preset time period are higher than the preset usage times threshold, determine that the corresponding tool rack is a high-frequency handover area;

[0009] Step S102: Use a clustering algorithm to divide the space of the high-frequency handover area, determine the clustering center of the high-frequency handover area, determine the position coordinates of the sub-areas in the high-frequency handover area according to the clustering center, and obtain the tool rack position structure model according to the position coordinates of the sub-areas.

[0010] Step S103, counting the number and direction of tool handovers in the sub-areas of the high-frequency handover area within a preset time period, generating a handover heat map, and determining an adjustment plan for adjacent tool racks using a fuzzy reasoning method based on the heat distribution in the map and in combination with the tool rack structure model;

[0011] Step S104, establishing a communication connection between tool racks through the Internet of Things, and when a tool rack detects a high-frequency handover area, generating a corresponding rack position adjustment plan, transmitting the adjustment instruction to the adjacent tool rack, and controlling the adjacent tool rack to adjust;

[0012] Step S105, during the tool rack structure adjustment process, using computer vision methods to track the position change of the tool in real time, and by comparing the tool trajectory changes before and after the adjustment, calculating the improvement rate of the handover smoothness;

[0013] Step S106, using a reinforcement learning algorithm, taking the tool rack structure model as input, obtaining an optimal rack layout solution, and applying it to the rack adjustment process;

[0014] Step S107, classifying and coding the tools, and placing tools with complementary functions in adjacent positions first when adjusting the tool rack in linkage.

[0015] Preferably, in step S101, the RF signal strength during tool handover is monitored, the tool rack usage is analyzed, the tool handover frequency is obtained, the tool usage time data is classified using a neural network algorithm, and the regional tool turnover rate and density are calculated. When the tool rack capacity is saturated, the regional handover intensity is evaluated, the handover frequency data is analyzed, and the high-frequency handover tool rack area is determined.

[0016] Preferably, in step S102, the tool rack position coordinates are analyzed to calculate the regional intersection frequency and density distribution, the k-means clustering algorithm is used to identify the high-frequency intersection area, and the sub-region boundaries are divided, the sub-region area and saturation are calculated, the sub-region structure data is generated, and the spatial arrangement characteristics and structural model of the tool rack are obtained through spatial relationship calculation.

[0017] Preferably, in step S103, the tool position signal collected by the sensor is analyzed to extract the tool movement direction and dwell time, generate a tool handover frequency statistics table, combine the tool movement trajectory and position information, calculate the handover path heat value, obtain a handover heat distribution map, use fuzzy inference algorithm to identify the handover hot spot area, and generate a tool rack position spacing table. If the length of the handover path between adjacent tool racks exceeds a threshold, an adjustment plan is generated according to the tool rack position structure model.

[0018] Preferably, in the step S104, the data of the tool rack position sensor is collected to generate a communication connection table, the signal transmission delay is calculated to generate a communication instruction, the data of the adjacent tool rack position relationship is extracted and processed by deep learning to obtain a rack position adjustment instruction, and synchronous calculation is performed to achieve the collaborative adjustment of the rack positions.

[0019] Preferably, in the step S105, an industrial camera array is used to collect tool movement images, the position coordinates are extracted and the movement trajectory is tracked, the moving speed and path length before adjustment are calculated to generate a handover smoothness reference value. After adjustment, images are collected again, feature recognition is performed using a convolutional neural network, the handover smoothness after adjustment is calculated, and a timing comparison is made with the reference value. Finally, the handover smoothness improvement rate is calculated to evaluate the rack position adjustment effect.

[0020] Preferably, in the step S106, the tool rack position structure model data is read, the handover smoothness index is determined to set a reward function, the rack position structure parameters are initialized, the layout scheme is iteratively optimized to generate an optimized layout sequence, spatial distribution calculation is performed to obtain a layout convergence index. If the convergence threshold is met, multiple rounds of iteration are performed to obtain the optimal rack position parameters, and the tool rack position structure model is updated.

[0021] Preferably, in the step S107, the tool attribute data and usage records are collected to generate tool feature data, and a tool function coding table is generated by classification using a clustering algorithm. The complementarity of the tool function combinations is calculated to generate function combination data. The tool combination relationship is analyzed using a graph neural network to identify complementary function combinations, and tool proximity data is generated, and the tool rack layout is updated accordingly.

[0022] The advantages of the multi-functional movable tool rack linkage method based on the Internet of Things described in this application are as follows: By installing radio frequency identification sensors on the tool racks to monitor the tool usage situation in real time, identifying high-frequency handover areas, using the K-means clustering algorithm to perform spatial division on the high-frequency areas, determining the position coordinates of the sub-areas and updating the rack position structure model, combining the handover heat map and fuzzy inference to generate a rack optimization plan, using Internet of Things technology to achieve communication between tool racks, performing synchronous adjustment, applying computer vision to track the position changes of tools, evaluating the adjustment effect, using reinforcement learning algorithms to continuously optimize the rack layout, improving the handover smoothness, considering the multi-functional attributes of tools, and placing functionally complementary tools in adjacent positions.

[0023] The present invention realizes the dynamic optimization of tool rack positions through intelligent perception, data analysis, and automatic adjustment, effectively improving the tool handover efficiency and operation smoothness. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is the flow of the multi-functional movable tool rack linkage method based on the Internet of Things described in this applicationFigure 1 ;

[0025] Figure 2 is the process of a multi-functional movable tool rack linkage method based on the Internet of Things described in this application Figure 2 ;

[0026] Figure 3 is the process of a multi-functional movable tool rack linkage method based on the Internet of Things described in this application Figure 3 . Specific implementation manner

[0027] As Figures 1 - 3 shown, a multi-functional movable tool rack linkage method based on the Internet of Things described in this application includes the following steps:

[0028] Step S101: By installing radio frequency identification sensors on the tool racks in the target area, the tool handover frequency data is obtained in real time, and the usage times of the tools on the tool racks in the target area are identified. If the tool usage times within the preset time period are higher than the preset usage times threshold, it is determined that the corresponding tool rack is a high-frequency handover area;

[0029] Step S102: Use the clustering algorithm to divide the space of the high-frequency handover area, determine the clustering center of the high-frequency handover area, determine the position coordinates of the sub-areas in the high-frequency handover area according to the clustering center, and obtain the tool rack position structure model according to the position coordinates of the sub-areas;

[0030] Step S103: Count the tool handover times and directions of the sub-areas in the high-frequency handover area within the preset time period, generate a handover heat map, and use the fuzzy inference method to determine the adjustment scheme of adjacent tool racks according to the heat distribution in the map in combination with the tool rack position structure model;

[0031] Step S104: Establish a communication connection between the tool racks through the Internet of Things. When the tool rack detects a high-frequency handover area, generate a corresponding rack adjustment scheme, transmit the adjustment instruction to the adjacent tool rack, and control the adjacent tool rack to make adjustments;

[0032] Step S105: During the tool rack structure adjustment process, use the computer vision method to track the position change of the tool in real time, and calculate the improvement rate of the handover smoothness by comparing the tool trajectory changes before and after the adjustment;

[0033] Step S106: Use the reinforcement learning algorithm, take the tool rack position structure model as the input, obtain the optimal rack layout scheme, and apply it to the rack adjustment process;

[0034] Step S107: Classify and code the tools. When the tool racks are linked and adjusted, give priority to placing tools with complementary functions in adjacent positions.

[0035] like Figures 1 - 3 As shown, in step S101, by installing a radio frequency identification sensor on a tool rack in a target area, tool handover frequency data is acquired in real time to identify the number of times a tool on the tool rack in the target area is used. If the number of times a tool is used within a preset time period is higher than a preset number of times threshold, the corresponding tool rack is determined to be a high-frequency handover area;

[0036] Obtain the radio frequency signal strength collected by the regional sensor array at the tool handover time, and compare the tool rack parking duration and access duration according to the radio frequency signal strength to obtain tool handover frequency data;

[0037] For the tool handover frequency data, a neural network algorithm is used to classify and aggregate the tool parking duration and access duration to obtain the regional tool turnover rate and regional tool density;

[0038] If the capacity saturation of the regional tool rack reaches the saturation threshold, the regional handover intensity is evaluated based on the tool turnover rate and the tool density in the regional area, based on the tool parking duration and the tool access duration data;

[0039] Based on the analysis of tool handover frequency data, the peak period of regional handover is obtained to determine the high-frequency handover tool rack area.

[0040] Specifically, in step S101, a radio frequency identification network is built through a regional sensor array to obtain the radio frequency signal strength at the tool rack position at the time of tool handover, and the tool rack parking duration and access duration are compared according to the radio frequency signal strength, and the tool handover frequency data is recorded in the radio frequency data processor;

[0041] For the tool handover frequency data, a neural network algorithm is used to classify and aggregate the tool parking duration and access duration, and the regional tool turnover rate and regional tool density are obtained from the cluster data, and the time period interval handover frequency threshold is set in the radio frequency data processor;

[0042] If the regional tool rack capacity saturation reaches a preset threshold, the regional handover intensity is evaluated in the radio frequency data processor based on the tool parking duration and access duration data of the regional tool rack according to the regional tool turnover rate and regional tool density;

[0043] The regional handover intensity data is analyzed in time series by a dynamic programming algorithm, and the peak period of regional handover is calculated from the tool handover frequency data, and the high-frequency handover tool rack area is determined in the radio frequency data processor;

[0044] The RFID network in the tool rack area is composed of an array of multiple RFID sensors. Each sensor is responsible for detecting RFID signals within a fixed area, usually with a coverage radius of 3 meters. There are overlapping areas between sensors to ensure signal continuity. The RFID signal strength decays with distance in accordance with the inverse square relationship. When the RFID tag enters the sensor detection range, the signal strength value received by the sensor changes with distance. The movement status of the tool can be judged by the signal strength change characteristics, including installing 12 sensors in a certain area to form a 3×4 matrix layout, with a distance of 2.5 meters between adjacent sensors and a coverage area of about 54 square meters. The tool handover frequency data received by the RFID data processor contains information such as tool number, access time, return time, signal strength, etc. The neural network algorithm classifies and aggregates these data to identify the tool usage mode;

[0045] In the machining area of a workshop, the monitoring data of the screwdriver tool group showed that the average handover frequency during the day shift was 8 times per hour, the median tool parking time was 25 minutes, and the median tool access time was 15 minutes. Therefore, the tool turnover rate in this area was 0.32 times / hour, and the regional tool density was 0.15 per square meter;

[0046] The regional tool rack capacity saturation reflects the usage status of the tool rack. When the saturation exceeds the preset threshold of 0.85, it indicates that the tool rack load is high and it is necessary to pay special attention to the tool handover situation in this area.

[0047] In the tool rack area of a certain assembly workshop, a week of data analysis revealed that the screwdriver tool set had the highest handover intensity between 9 a.m. and 11 a.m., with an average of 12 handovers per hour. The tool parking time was reduced to 15 minutes, and the tool density in the area increased to 0.25 per square meter.

[0048] Time series analysis shows that tool use presents periodic characteristics. The dynamic programming algorithm is used to analyze the regional handover intensity data for 30 consecutive days to identify the time distribution pattern of the high-frequency handover area. It is found from the data of the machining area that the tool handover peaks from 9:00 to 11:00 a.m. and from 2:00 to 4:00 p.m. every day. The tool handover frequency during these two periods is 2.5 times that of the usual time. The regional handover intensity reaches 0.48 times / square meter / hour, and the capacity saturation of the tool rack is maintained above 0.92. Based on this, it is determined that the tool rack area during this period belongs to the high-frequency handover area.

[0049] The RF data processor monitors the changes of these indicators in real time. When it detects that the tool handover frequency in a certain area is always higher than the set threshold within a period lasting more than 1 hour, it automatically marks the area as a high-frequency handover area.

[0050] At the quality inspection station at the end of the production line, due to the need for process connection, the detection tools are used frequently. The interval time for tool handover is generally within 10 minutes, and the regional handover intensity reaches 0.65 times per square meter per hour. Moreover, this high-intensity usage state will continue throughout the work shift. Therefore, this area is identified as a stable high-frequency handover area;

[0051] The radio frequency data processor continuously monitors the tool handover data, establishes a dynamic feature model of tool usage, and realizes the accurate identification of the high-frequency handover area.

[0052] As Figures 1 - 3 shown, in step S102, a clustering algorithm is used to perform spatial partitioning on the high-frequency handover area, determine the clustering center of the high-frequency handover area, determine the position coordinates of the sub-areas in the high-frequency handover area according to the clustering center, and obtain the tool rack position structure model according to the position coordinates of the sub-areas;

[0053] Obtain the spatial position coordinate sequence of the tool rack, calculate the regional handover frequency from the spatial position coordinate sequence, and obtain the density distribution data of the tool rack in the spatial data processor;

[0054] Perform spatial clustering calculation on the density distribution data of the tool rack using the k-means clustering algorithm, identify the regional handover heat distribution characteristics from the density distribution data, and obtain the coordinates of the clustering center point of the high-frequency handover area;

[0055] Perform sub-area boundary division for the coordinates of the clustering center point and the spatial clustering distance, calculate the sub-area area size and regional saturation degree from the sub-area boundary data, and generate sub-area structure data in the spatial data processor;

[0056] Perform spatial relationship calculation on the sub-area structure data through the spatial data processor, obtain the spatial arrangement characteristics of the tool rack from the density distribution data of the tool rack, and obtain the tool rack position structure model.

[0057] Specifically, in step S102, the spatial position coordinate sequence of all tool racks in the high-frequency handover area is obtained through the regional sensor network, the regional handover frequency is calculated from the tool rack position coordinate data, and the density distribution data of the tool rack is generated in the spatial data processor;

[0058] Perform spatial clustering calculation on the density distribution data of the tool rack using the k-means clustering algorithm, identify the regional handover heat distribution characteristics from the density distribution data, and obtain the coordinates of the clustering center point of the high-frequency handover area in the spatial data processor;

[0059] Based on the coordinates of the clustering center points and the spatial clustering distance, divide the boundaries of sub-regions for the high-frequency handover areas, calculate the area size and regional saturation degree of the sub-regions from the sub-region boundary data, and generate sub-region structure data within the spatial data processor;

[0060] Through the spatial data processor, perform spatial relationship calculations on the sub-region structure data, obtain the spatial arrangement characteristics of the tool racks from the density distribution data of the tool racks, and construct a tool rack position structure model within the spatial data processor;

[0061] The regional sensor network adopts a grid layout, installs a sensor node every 5 meters within the workshop tool rack area, and each node has an independent coordinate positioning function;

[0062] By measuring the signal strength of the radio frequency tags on the tool racks, determine the precise spatial position coordinates of the tool racks;

[0063] An 8×6 sensor network is deployed in a certain assembly workshop, with a coverage area of 240 square meters, real-time collection of the position data of 48 tool racks, and the tool rack position coordinate accuracy reaching 0.3 meters;

[0064] The spatial data processor generates the density distribution data of the tool racks by calculating the tool rack handover frequency, and displays the number of tool racks per unit area and their usage frequencies;

[0065] The k-means clustering algorithm is calculated based on the spatial distribution characteristics of the tool racks to find the regional centers with frequent tool handover activities in the density distribution data;

[0066] In practical applications, by analyzing the density distribution data of 300 sampling points in a certain production line area, 4 significant clustering centers are identified, and the average handover frequency of these center points is 3.2 times that of the surrounding areas;

[0067] Among them, the clustering center near the main assembly station has the highest handover frequency, reaching 12 times per hour, and the tool rack density is 0.4 per square meter. During the sub-region boundary division process, the boundary range is determined based on the clustering center points in combination with the spatial clustering distance;

[0068] In the case of a certain processing workshop, the minimum distance between the 4 clustering centers is 8 meters. Based on this, the sub-region radius is set to 4 meters to form 4 non-overlapping circular sub-regions;

[0069] The area of each sub-region is approximately 50 square meters, containing 8 to 12 tool racks. By calculating the ratio of the number of tool racks to the regional area, the regional saturation degree index is obtained, and the saturation degree of the busiest sub-region reaches 0.85;

[0070] The spatial data processor performs spatial relationship analysis based on the sub-region structure data, calculates the relative positions and spacings between tool racks. In the example of the machining area, the average spacing between tool racks is 1.5 meters, and the minimum spacing is not less than 1 meter, forming a grid-like distribution structure;

[0071] By analyzing the density distribution data of tool racks, it is found that the usage frequency of tool racks on the side closer to the operation station is significantly higher than that on the side away from the station. Based on this, a gradient layout is formed in the tool rack position structure model, and the tool rack density gradually decreases from the periphery of the station outward, with a density ratio of approximately 3:2:1;

[0072] In the final assembly area at the end of the production line, due to the tight process connection and frequent tool use, higher requirements are put forward for the tool rack layout;

[0073] Through the calculation of the spatial data processor, 12 tool racks are divided into 3 sub-regions, and each sub-region is fan-shaped, with an area of approximately 25 square meters;

[0074] The tool racks within the sub-region are arranged in an arc shape, and the spacing gradually increases from 1 meter on the inner side to 1.8 meters on the outer side, which not only ensures a high tool rack density but also avoids mutual interference during tool handover;

[0075] The regional saturation is maintained between 0.75 and 0.82, and the spatial layout characteristics of the tool rack position structure model highly coincide with the operation moving lines of the station.

[0076] As Figures 1 - 3 shown, in step S103, the tool handover times and directions of the sub-regions in the high-frequency handover area are statistically counted within a preset time period to generate a handover heat map. According to the heat distribution in the map and in combination with the tool rack position structure model, a fuzzy inference method is used to determine the adjustment plan for adjacent tool rack positions;

[0077] Receive the tool position signals collected by the sensor array in the area, extract the tool movement direction and residence duration data from the tool position signals, and generate a tool handover frequency statistical table in the spatial data processor according to the movement direction and residence duration data;

[0078] Using the tool handover frequency statistical table and tool movement trajectory data, in combination with the position information in the tool rack position structure model, calculate the heat values for the handover path data, and obtain a handover heat distribution map in the spatial data processor;

[0079] Execute a fuzzy inference algorithm according to the handover heat distribution map, perform spatial correlation operations on the heat distribution data, identify the handover hot spots between tool racks, and obtain a tool rack position spacing table in the spatial data processor;

[0080] If the tool rack position spacing table and the handover heat distribution map show that the handover path length of adjacent tool racks exceeds the handover path length threshold, position parameters are obtained from the tool rack structure model, and a tool rack adjustment plan is generated in the spatial data processor.

[0081] Specifically, in step S103, the tool position data of the sub-areas in the high-frequency handover area within a preset time period is collected by the regional sensor array, the tool movement direction and the dwell time are extracted from the sensor received signal, and a tool handover frequency statistics table is generated in the spatial data processor;

[0082] According to the tool handover frequency statistics table and tool movement trajectory data, combined with the position information in the tool rack structure model, the regional heat value is calculated from the handover path data within a preset time period, and a handover heat distribution map is generated in the spatial data processor;

[0083] The fuzzy inference algorithm is used to perform spatial correlation calculation on the handover heat distribution map, the handover hotspot area between tool racks is identified from the heat distribution data, and the tool rack position spacing table is generated in the spatial data processor;

[0084] According to the tool rack position spacing table and the handover heat distribution map, the handover path length between adjacent tool racks is quantitatively calculated, the position adjustment parameters are obtained from the tool rack position structure model, and the tool rack position adjustment plan is obtained in the spatial data processor;

[0085] The area sensor array obtains tool movement information by real-time monitoring of changes in RF signal strength. Each sensor covers a circular area with a radius of 3 meters. Adjacent sensors overlap by 0.5 meters to eliminate blind spots. In the high-frequency handover area of an assembly workshop, a 5×4 sensor array is arranged, covering an area of 120 square meters.

[0086] The spatial data processor records the tool position data every 30 seconds. Statistics show that the tool moves back and forth between the workbench and the tool rack. The average stay time of a single tool on the workbench is 15 minutes, and the average stay time on the tool rack is 8 minutes.

[0087] The tool handover frequency statistics table records the use of tools in different time periods, including tool number, pick-up time, return time, movement trajectory and other information;

[0088] During the 8-hour morning shift, the electric wrench tool group in the machining area of a workshop generated an average of 24 handover records per hour, with the highest handover frequency between 9 a.m. and 11 a.m., reaching 36 times per hour.

[0089] The tool rack position structure model shows that there are a total of 16 tool rack positions in this area, distributed in a 4×4 matrix layout, and the distance between adjacent tool rack positions is 1.2 meters;

[0090] The handover heat distribution map presents the spatial distribution characteristics of tool handover activities in the form of a heat map, and the color from blue to red indicates the change of heat value from low to high;

[0091] In the case of a certain general assembly workshop, the tool rack area near the operation station shows an obvious hot spot distribution, with the highest heat value reaching 0.85, while the heat value in the area far from the station is only 0.2;

[0092] The fuzzy inference algorithm identifies 4 obvious handover hot spot areas by calculating the heat gradient of adjacent areas. The distance between tool racks in these areas is relatively small, with an average of 0.8 meters;

[0093] The tool rack position distance table records the relative distance relationship between tool racks, and determines the optimization direction of tool rack positions by analyzing the handover path length;

[0094] In the example of the machining area, through the analysis of the handover heat data for 7 consecutive days, it is found that the handover frequency between tool racks numbered A3 and A4 is the highest, with an average of 8 times per hour, but the distance reaches 1.5 meters, which is significantly higher than the reasonable distance;

[0095] The spatial data processor calculates the position adjustment parameters according to the tool rack position structure model, and suggests moving the A4 tool rack 0.4 meters in the direction of A3 to shorten the distance between the two to 1.1 meters;

[0096] In the quality inspection station area, due to the process requirements of frequently changing different types of inspection tools, the tool handover activities show obvious group characteristics;

[0097] The handover heat distribution map shows that 6 tool rack positions are concentrated in a circular area with a diameter of 2 meters, forming a highly active area with a heat value of 0.92;

[0098] By analyzing the tool movement trajectories, it is found that the cross-use between adjacent tool racks is frequent, with an average of 15 handover records per hour;

[0099] The spatial data processor calculates the optimal fan-shaped layout plan according to the heat distribution characteristics, and evenly distributes the 6 tool rack positions in a 120-degree fan shape, and uniformly adjusts the distance between adjacent tool rack positions to 0.8 meters.

[0100] As Figures 1 - 3 shown, in step S104, the communication connection between tool racks is established through the Internet of Things. When the tool rack detects a high-frequency handover area, it generates a corresponding rack adjustment plan, transmits the adjustment instruction to the adjacent tool rack, and controls the adjacent tool rack to make adjustments;

[0101] Collect the tool rack position sensor data, and the tool rack position sensor data is used to generate a tool rack communication connection table;

[0102] Calculate the signal transmission delay according to the network topology relationship in the tool rack communication connection table, and the signal transmission delay is used to generate a rack position communication instruction;

[0103] Extract the adjacent tool rack position relationship data from the rack position communication instruction, and the adjacent tool rack position relationship data is processed by a deep learning algorithm to obtain a rack position adjustment instruction;

[0104] Synchronously calculate the positions of adjacent tool racks according to the rack position adjustment instruction, and the rack position movement data obtained through the synchronous calculation is used for the coordinated adjustment of the rack positions.

[0105] Specifically, in step S104, a mesh communication network is established between tool racks through a wireless communication module, tool rack position data and network topology structure are obtained from tool rack position sensors, and a tool rack communication connection table is generated in a communication data processor;

[0106] According to the network topology relationship in the tool rack communication connection table, calculate the signal transmission delay and connection strength between tool racks, obtain the tool rack handover state from the high-frequency handover area data, and generate a rack position communication instruction in a communication data processor;

[0107]

[0108] , M represents the total number of tool racks, dik represents the physical distance between tool rack i and tool rack k, vk represents the signal transmission speed of tool rack k, and the average signal transmission delay of tool rack i is calculated by summing the transmission delays of all adjacent tool racks and then taking the average;

[0109]

[0110] L represents the number of tool racks directly connected to tool rack i, βil represents the connection bandwidth between tool rack i and tool rack l, αl represents the signal processing ability of tool rack l, and the average connection strength of tool rack i is calculated by summing the connection strengths of all directly connected tool racks and then taking the average;

[0111] Real-time detection of the tool rack state in the high-frequency handover area is performed through a deep learning algorithm, the position relationship data of adjacent tool racks is extracted from the rack position communication instruction, and a rack position adjustment instruction is generated in a communication data processor;

[0112] Synchronously calculate the positional relationship between adjacent tool racks according to the rack position adjustment instruction and the tool rack handover status, obtain the rack movement data from the adjustment execution parameters, and perform rack coordination adjustment within the communication data processor;

[0113] The mesh communication network between tool racks is established through a wireless communication module. Each tool rack is equipped with a communication node, with a working frequency of 2.4 GHz and a communication radius of 10 meters;

[0114] 16 tool racks are arranged in an assembly workshop to form a 4×4 grid layout, and the physical distance between adjacent tool racks is 2 meters;

[0115] The tool rack position sensor collects position data at a frequency of 0.1 second, with an accuracy of 0.01 meter. The communication data processor generates a tool rack communication connection table based on this data, recording the connection status and signal strength between nodes;

[0116] Calculate the communication quality based on the physical distance and signal strength between tool racks. In an example in a machining area, the average signal transmission delay between adjacent tool racks is 5 milliseconds, and the connection strength reaches -65 dBm;

[0117] The data in the high-frequency handover area shows that the 4 tool racks close to the operation station have the highest handover frequency, reaching 32 times per hour on average. The communication data processor generates the highest-priority rack communication instruction based on this;

[0118] The deep learning algorithm identifies the spatial distribution characteristics of tool handover activities by analyzing the tool rack status data in real time. In a case in the final assembly workshop, the algorithm finds that the tool racks numbered A2, A3, B2, and B3 form a high-frequency handover cluster. The handover times between these 4 tool racks account for 75% of the total handover times, and the average handover interval is 4 minutes;

[0119] Based on this feature, the communication data processor generates specific instructions for adjusting the positions of the tool racks, including movement direction and distance information. During the rack coordination adjustment process, the tool racks move synchronously according to the adjustment instructions;

[0120] In the quality inspection station area, 6 tool racks were originally arranged in a straight line with a spacing of 1.5 meters. Frequent handover activities made it inconvenient to pick up and place tools. The communication data processor calculated the optimal arc layout plan and instructed the tool racks to synchronously adjust their positions to form an arc with a radius of 2 meters, and the spacing between adjacent tool racks was shortened to 1.2 meters;

[0121] During the adjustment process, the movement speed of each tool rack is maintained at 0.1 m / s, and the position deviation is controlled within 0.02 meter. In the transfer area at the end of the production line, due to the tight connection of processes and frequent changes in tool use, higher requirements are placed on the positions of the tool racks;

[0122] By analyzing the handover data of a week, the communication data processor found that there are differences in the requirements for the tool rack layout at different times;

[0123] In the morning process, the tool racks show a star layout feature, with 8 tool racks evenly distributed around the central work station; while in the afternoon process, it needs to be adjusted to a strip layout, and the tool racks are arranged in two parallel rows;

[0124] The rack position adjustment instruction generated by the communication data processor can achieve a smooth switch between the two layouts, with the transition time controlled within 45 seconds. During this period, each tool rack maintains a communication connection, and the position adjustment accuracy reaches 0.05 meters;

[0125] The collaborative adjustment method of the tool racks fully considers the dynamic characteristics of the tool handover activities. The rack position adjustment instruction contains two types of information: absolute coordinates and relative positions, ensuring that the tool racks can not only maintain the predetermined spatial relationship but also adapt to the local handover requirements;

[0126] In practical applications, a typical adjustment cycle includes three stages: detection, calculation, and execution, with a total time consumption not exceeding 90 seconds. During the adjustment process, the tool racks always maintain a normal working state.

[0127] As Figures 1 - 3 shown, in step S105, during the tool rack structure adjustment process, the computer vision method is used to real-time track the position change of the tool. By comparing the tool trajectory changes before and after the adjustment, the handover smoothness improvement rate is calculated to evaluate the effect of the rack position adjustment;

[0128] Obtain the tool motion image sequence collected by the industrial camera array in the rack position adjustment area, and obtain the tool position coordinate data from the image data collector;

[0129] Perform target tracking processing on the tool position coordinate data to obtain the tool motion trajectory data, and store the tool motion trajectory data in the trajectory processor to obtain the tool motion trajectory before the rack position adjustment;

[0130] According to the tool motion trajectory before the rack position adjustment and the tool position coordinate data, calculate the moving speed and the length of the motion path of the tool before the adjustment through the trajectory processor;

[0131] Extract the tool handover time interval from the data of the target tracking processing, and generate the handover smoothness reference value before the adjustment through the trajectory processor;

[0132] Obtain the tool motion image sequence collected after the rack position adjustment, and perform feature recognition processing on it using a convolutional neural network;

[0133] Obtain the adjusted tool motion trajectory from the trajectory processor, and calculate the current value of the adjusted handover smoothness through the trajectory processor;

[0134] According to the handover smoothness reference value and the current value, perform a sequential comparison process on the tool motion trajectory data; calculate the handover smoothness improvement rate from the trajectory change data, and determine the rack position adjustment effect through the trajectory processor.

[0135] Specifically, in step S105, collect a sequence of tool motion images in the rack position adjustment area through an industrial camera array, obtain the tool position coordinate data from the image data collector, generate tool motion trajectory data using a target tracking algorithm, and record the tool motion trajectory before rack position adjustment in the trajectory processor;

[0136] According to the tool motion trajectory and the tool rack position data, calculate the moving speed and the motion path length of the tool before adjustment in the trajectory processor, extract the tool handover time interval from the position tracking data, and generate the handover smoothness reference value before adjustment in the trajectory processor, where:

[0137]

[0138] vi represents the moving speed of the tool in the i-th time period, Pi represents the position vector of the tool at the i-th time point, ti represents the time at the i-th time point, and the average moving speed of the tool in this time period is obtained by calculating the position difference between two adjacent time points divided by the time difference;

[0139]

[0140] L represents the path length of the tool during the entire motion process, Pi represents the position vector of the tool at the i-th time point, n represents the total number of time points, and the total path length of the tool during the entire motion process is obtained by accumulating the position differences between two adjacent time points;

[0141] Use a convolutional neural network to perform feature recognition on the sequence of tool motion images collected after rack position adjustment, obtain the adjusted tool motion trajectory from the trajectory processor, and calculate the current value of the adjusted handover smoothness in the trajectory processor;

[0142] According to the handover smoothness reference value and the current value, perform a sequential comparison of the tool motion trajectory data, calculate the handover smoothness improvement rate from the trajectory change data, and determine the rack position adjustment effect in the trajectory processor;

[0143] The industrial camera array consists of 8 high-speed cameras, which collect tool motion images at a frequency of 120 frames per second. Each camera covers an area of 40 square meters, and there is a 20% overlapping area between adjacent cameras for image stitching;

[0144] The image data collector preprocesses the original image, including noise elimination and distortion correction, and the positioning accuracy of the tool position coordinates reaches 0.5 cm;

[0145] The target tracking algorithm generates continuous tool motion trajectory data by identifying the feature markers on the tool surface, and the sampling interval is 25 milliseconds;

[0146] In the example of the machining area, the trajectory processor records the tool motion data before the rack position adjustment, showing that the round-trip path of the tool between the workbench and the tool rack presents obvious arc characteristics, and the average path length is 4.8 meters;

[0147] There are obvious fluctuations in the moving speed of the tool. When approaching the tool rack, the speed drops to 0.3 m / s, and the average handover time interval is 12 seconds;

[0148] These data form the handover fluency benchmark value, and the quantization index is 0.65. The convolutional neural network uses a two-stream structure to process the tool motion image sequence. The spatial stream is responsible for identifying the instantaneous position of the tool, and the temporal stream tracks the motion trend of the tool;

[0149] In the application case of the general assembly workshop, the tool motion trajectory after the rack position adjustment presents more regular characteristics. The path length is shortened to 3.6 meters, the moving speed remains at a stable level of about 0.5 m / s, and the handover time interval is reduced to 8 seconds;

[0150] The trajectory processor calculates that the current value of the adjusted handover fluency reaches 0.82. The time-series comparative analysis shows that in the quality inspection station area, the optimization effect of the tool motion trajectory is the most significant;

[0151] Before the adjustment, the handover paths of the tool between different tool racks cross and overlap. On average, each handover generates 2.5 direction changes, and the fluctuation range of the handover time reaches 6 seconds;

[0152] After the rack position adjustment, the tool motion trajectory is simplified to a straight line type, the number of direction changes is reduced to 1.2 times, and the standard deviation of the handover time is reduced to 1.5 seconds;

[0153] The trajectory processor calculates that the improvement rate of the handover fluency in this area reaches 26%. In the transfer area at the end of the production line, due to the complex process connection, the requirement for the tool handover efficiency is relatively high;

[0154] The trajectory analysis data shows that before the adjustment, the tool motion trajectory presents obvious detour characteristics, the lateral displacement accounts for 45% of the total path length, and there are frequent pauses and accelerations during the handover process;

[0155] The rack position adjustment adopts a fan-shaped layout scheme. The movement trajectory of the tool changes to a radial shape, the lateral displacement ratio is reduced to 15%, and the movement speed curve tends to be smooth.

[0156] The calculation result of the improvement rate of the handover smoothness is 31%. Among them, the shortening of the handover time contributes 18 percentage points, and the path optimization contributes 13 percentage points.

[0157] By comparing the evaluation data in different regions, it is found that the improvement of the tool handover smoothness is highly correlated with the rationality of the tool rack layout. In different workstations such as machining, general assembly, and quality inspection, the optimized tool movement trajectory shows geometric characteristics adapted to the operation characteristics, and the improvement rate of the handover smoothness generally exceeds 20%, reflecting the adaptability and effectiveness of the rack position adjustment scheme.

[0158] As Figures 1 - 3 shown, in step S106, an enhanced learning algorithm is adopted, with the improvement of the handover smoothness as the optimization goal, the tool rack position structure model as the input, and the optimal rack position layout scheme is obtained by continuously trying and adjusting the tool rack position structure, and it is applied to the subsequent rack position adjustment process.

[0159] Read the tool rack position structure model data, obtain the handover smoothness index from the tool rack position structure model data, and determine the reward function value for the rack position layout optimization according to the handover smoothness index.

[0160] Initialize the tool rack position structure parameters according to the reward function value, perform iterative optimization calculation on the rack position layout scheme using the tool rack position structure parameters, and obtain the rack position adjustment data from the iterative optimization calculation.

[0161] Generate an iterative optimization layout sequence through the rack position adjustment data, perform spatial distribution calculation on the iterative optimization layout sequence, and obtain the layout scheme convergence index from the spatial distribution calculation.

[0162] If the layout scheme convergence index meets the preset convergence threshold, perform multiple rounds of iterative operations on the tool rack position structure according to the layout optimization parameters, obtain the optimal rack position parameters from the multiple rounds of iterative operations, and update the tool rack position structure model using the optimal rack position parameters.

[0163] Specifically, in step S106, read the tool rack position structure model data through a deep reinforcement learning algorithm, obtain the reward function value for the rack position layout optimization from the handover smoothness index, establish a rack position space mapping sequence in the optimization data processor, and initialize the tool rack position structure parameters.

[0164] According to the tool rack position structure parameters and the handover smoothness index, perform iterative optimization calculations on the tool rack position layout plan, obtain rack position adjustment data from the changes in the reward function value, and generate an iterative optimization layout sequence in the optimization data processor;

[0165] Through the optimization data processor, perform spatial distribution calculations on the iterative optimization layout sequence, extract the layout plan convergence index from the rack position adjustment data, and record the layout optimization parameters in the optimization data processor;

[0166] According to the layout optimization parameters and the layout plan convergence index, perform multiple rounds of iterative operations on the tool rack structure, obtain the optimal rack position parameters from the optimized layout sequence, and update the tool rack structure model in the optimization data processor;

[0167] The deep reinforcement learning algorithm optimizes the layout plan by constructing the state space of the tool rack. The tool rack structure model includes multi-dimensional features such as position coordinates, spacing relationships, and handover frequencies;

[0168] In an application case in an assembly workshop, the state space consists of the position parameters of 16 tool racks. Each tool rack has two degrees of freedom in the horizontal and vertical directions. The handover smoothness index is used as the core metric of the reward function, and the initial value is 0.65;

[0169] The optimization data processor discretizes the rack position space, maps the continuous position parameters to a 20×20 grid, and forms 400 candidate position points;

[0170] During the optimization iteration process, the algorithm explores different layout plans based on the Monte Carlo tree search strategy;

[0171] In an example in the machining area, each round of iteration involves 32 layout changes, and the rack position adjustment range is between 0.2 meters and 1.5 meters; the reward function calculates the score based on the adjusted handover smoothness. A positive reward is given when the smoothness increases by more than 5%, and a negative penalty is imposed when it decreases by more than 3%; after 150 rounds of iteration, the algorithm generates 8 different layout optimization sequences, and each sequence contains 48 layout states; the convergence of the layout plan is evaluated through multiple indicators, including the change trend of handover smoothness, the uniformity of rack distribution, and space utilization; in the quality inspection station area, 8 tool racks are distributed in a matrix in the initial layout, and the space utilization rate is 62%;

[0172] The optimization data processor recorded the key parameters in the layout evolution process: the minimum spacing between adjacent tool racks was adjusted from 1.2 meters to 0.8 meters, the maximum spacing was reduced from 2.5 meters to 1.6 meters, and the average length of the handover path was reduced by 28%; the convergence index of the layout scheme reached a stable value of 0.92 at the 180th iteration; multiple rounds of iterative calculations showed that in the transfer area at the end of the production line, the optimal layout scheme showed obvious functional zoning characteristics; 12 tool racks were divided into 3 groups, each group of 4 tool racks adopted a fan-shaped layout, and a safety distance of 2 meters was maintained between groups; by comparing the performance of different iteration sequences, the optimization data processor found that the layout scheme always maintained the optimal state after 200 iterations, with the handover fluency reaching 0.88 and the space utilization rate increased to 78%;

[0173] In the complex environment of the final assembly workshop, the tool rack structure model needs to adapt to the changing operational requirements. The optimization algorithm explores the optimal layout in a 4×5 grid space. By dynamically adjusting the learning rate and exploration range, the optimization process maintains stability while having sufficient adaptability.

[0174] Experimental data show that when the algorithm processes the layout optimization of 20 tool racks, the convergence time is controlled within 300 seconds. The final layout solution improves the handover fluency from the initial 0.58 to 0.85, and remains stable during the 7-day verification period.

[0175] The update frequency of the tool rack structure model is set to once every 8 hours to balance the optimization effect and computing resource consumption.

[0176] like Figures 1 - 3 As shown, in step S107, the tools are classified and coded according to their multifunctional attributes, and when the tool rack is adjusted in linkage, tools with complementary functions are preferentially placed in adjacent positions;

[0177] The tool function attribute data is collected through the tool attribute identifier, the function combination information is obtained from the tool use record, and the tool feature data is obtained in the function data processor;

[0178] According to the tool feature data, a clustering algorithm is used to classify the tool function features, and a tool function coding table is generated in a function data processor;

[0179] According to the tool function coding table and the tool use frequency data, the tool function combination relationship is calculated to generate function combination data;

[0180] After receiving the function combination data, a correlation analysis is performed through a graph neural network, complementary function combinations are identified from tool combination relationships, tool proximity data is generated in a function data processor, and the tool rack layout is updated according to the tool proximity data.

[0181] Specifically, in step S107, the tool function attribute data is collected by a tool attribute recognizer, the function combination information is extracted from the tool usage records, and the clustering algorithm is used to classify and calculate the tool function features, generating a tool function coding table in the function data processor;

[0182] According to the tool function coding table and the tool usage frequency data, the complementarity degree of the tool function combination relationship is calculated, the function association degree is obtained from the multi-functional attribute data, and function combination data is generated in the function data processor;

[0183]

[0184] Compl(A, B) represents the complementarity degree between tool A and tool B, K represents the total number of tool functions, fA(k) represents the usage frequency of tool A on function k, and fB(k) represents the usage frequency of tool B on function k;

[0185] This formula measures the functional complementarity degree of two tools by calculating the ratio of the sum of the minimum usage frequencies and the sum of the maximum usage frequencies of the two tools on each function;

[0186] The relevance analysis of the function combination data is carried out through a graph neural network, the complementary function combinations are identified from the tool combination relationship data, and tool proximity data is obtained in the function data processor;

[0187] According to the tool proximity data and the tool rack position data, the linkage layout of the tool rack is optimized and sorted, the layout optimization parameters are extracted from the proximity calculation results, and the tool rack position layout is updated in the function data processor;

[0188] The tool attribute recognizer establishes a multi-dimensional function attribute mapping by analyzing the physical characteristics and usage scenarios of the tool;

[0189] In a certain assembly workshop, the function attributes of 48 tools are collected, including characteristics such as tool type, applicable range, operation method, etc. The clustering algorithm divides these tools into 6 main function categories, and each category has 3 sub-categories, forming a hierarchical function coding system;

[0190] The function data processor generates an 8-digit digital code for each tool. The first 2 digits represent the main function category, the middle 3 digits represent the sub-function category, and the last 3 digits are the tool serial number;

[0191] The tool function combination relationship is quantified by analyzing the joint usage frequency of the tools. In the practice of the machining area, the tool usage data for 30 consecutive days is recorded, and it is found that the joint usage frequency of screwdriver tools and wrench tools is the highest, accounting for 35% of the total usage times;

[0192] The degree of functional association is represented by a normalized value from 0 to 1. When the combined usage frequency of two types of tools exceeds 30%, the value of the degree of association is greater than 0.8;

[0193] The functional data processor thus generated a 16×16 functional combination data matrix, and the graph neural network identified complementary functional combinations by constructing a tool functional association graph;

[0194] In the quality inspection station area, the network connected 24 tool nodes according to the strength of functional association, and the weight of the edge was determined by the functional combination data;

[0195] The analysis results show that the measuring tools and the inspection tools form the strongest complementary relationship, and the degree of association reaches 0.92; the network also found 3 typical functional complementary combinations, each group containing 4 to 6 different types of tools, and the average degree of association between these tools exceeds 0.75;

[0196] The linkage layout optimization of the tool rack is based on the tool proximity data for spatial reorganization. In the application case in the final assembly workshop, 32 tool racks are rearranged according to the functional complementary relationship to form 8 functional complementary groups, with 4 tool racks in each group distributed in a fan shape; the spacing between adjacent groups is dynamically adjusted according to the degree of functional association. The distance between groups with a high degree of association is shortened to 1.2 meters, and the distance between groups with a low degree of association remains more than 2 meters;

[0197] The layout optimization parameters show that the new layout scheme reduces the average access distance between complementary tools by 45%. At the flexible station at the end of the production line, the tool functional combination shows obvious timing characteristics; the functional data processor analyzes the time series of tool usage and finds that there are unique functional complementary requirements in different process stages. In the assembly stage, fixed and adjustment tool combinations are mainly used, and in the testing stage, inspection and calibration tool combinations are mainly used; the layout optimization algorithm thus generates a double-layer annular tool rack layout, with high-frequency basic tools placed in the inner ring and special tools arranged in the outer ring according to the process sequence; the radial distance between the two rings is 0.8 meters, enabling the operator to easily switch between different functional groups.

[0198] In the description of this application, it should be understood that the orientation or positional relationship indicated by orientation words such as "front, back, up, down, left, right", "horizontal, vertical, level" and "top, bottom" is usually based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing this application and simplifying the description. Without contrary instructions, these orientation words do not indicate and imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the protection scope of this application.

[0199] For those skilled in the art, various corresponding changes and deformations can be made according to the technical solutions and concepts described above, and all such changes and deformations should fall within the protection scope of the claims of this application.

Claims

1. A multi-functional activity tool rack linkage method based on the Internet of Things, characterized in that, The following steps are involved: Step S101, by installing a radio frequency identification sensor on a tool rack in a target area, real-time data on tool handover frequency is acquired to identify the number of times a tool on the tool rack in the target area is used. If the number of times a tool is used within a preset time period is higher than a preset number of times threshold, the corresponding tool rack is determined to be a high-frequency handover area; Step S102, using a clustering algorithm to perform spatial division on the high-frequency handover area, determining the cluster center of the high-frequency handover area, determining the position coordinates of the sub-areas in the high-frequency handover area according to the cluster center, and obtaining the tool rack structure model according to the position coordinates of the sub-areas; Step S103, counting the number and direction of tool handovers in the sub-areas of the high-frequency handover area within a preset time period, generating a handover heat map, and determining an adjustment plan for adjacent tool racks using a fuzzy reasoning method based on the heat distribution in the map and in combination with the tool rack structure model; Step S104, establishing a communication connection between tool racks through the Internet of Things, and when a tool rack detects a high-frequency handover area, generating a corresponding rack position adjustment plan, transmitting the adjustment instruction to the adjacent tool rack, and controlling the adjacent tool rack to adjust; Step S105, during the tool rack structure adjustment process, using computer vision methods to track the position change of the tool in real time, and by comparing the tool trajectory changes before and after the adjustment, calculating the improvement rate of the handover smoothness; Step S106, using a reinforcement learning algorithm, taking the tool rack structure model as input, obtaining an optimal rack layout solution, and applying it to the rack adjustment process; Step S107, classifying and coding the tools, and placing tools with complementary functions in adjacent positions first when adjusting the tool rack in linkage.

2. The multi-functional activity tool rack linkage method based on the Internet of Things according to claim 1, wherein In step S101, by installing a radio frequency identification sensor on a tool rack in a target area, tool handover frequency data is acquired in real time, and the number of times a tool on the tool rack in the target area is used is identified. If the number of times a tool is used within a preset time period is higher than a preset number of times threshold, the corresponding tool rack is determined to be a high-frequency handover area, including: Obtain the radio frequency signal strength collected by the regional sensor array at the tool handover time, and compare the tool rack parking duration and access duration according to the radio frequency signal strength to obtain tool handover frequency data; For the tool handover frequency data, a neural network algorithm is used to classify and aggregate the tool parking duration and access duration to obtain the regional tool turnover rate and regional tool density; If the capacity saturation of the regional tool rack reaches the saturation threshold, the regional handover intensity is evaluated based on the tool turnover rate and the tool density in the regional area, based on the tool parking duration and the tool access duration data; Based on the analysis of tool handover frequency data, the peak period of regional handover is obtained to determine the high-frequency handover tool rack area.

3. A linkage method for a multifunctional activity tool rack based on the Internet of Things according to claim 1, characterized in that, In step S102, a clustering algorithm is used to spatially divide the high-frequency handover area, a cluster center of the high-frequency handover area is determined, the position coordinates of the sub-areas in the high-frequency handover area are determined according to the cluster center, and a tool rack structure model is obtained according to the position coordinates of the sub-areas, including: Obtain the sequence of spatial position coordinates of the tool rack, calculate the regional handover frequency from the sequence of spatial position coordinates, and obtain the density distribution data of the tool rack in the spatial data processor; Perform spatial clustering calculation using the k-means clustering algorithm based on the density distribution data of the tool rack, identify the characteristics of the regional handover heat distribution from the density distribution data, and obtain the coordinates of the clustering center points of the high-frequency handover regions; Divide the sub-region boundaries according to the coordinates of the clustering center points and the spatial clustering distance, calculate the area size and regional saturation degree of the sub-regions from the sub-region boundary data, and generate the sub-region structure data in the spatial data processor; Perform spatial relationship calculation on the sub-region structure data through the spatial data processor, obtain the spatial arrangement characteristics of the tool rack from the density distribution data of the tool rack, and obtain the tool rack position structure model; 4. A method for linking a multifunctional activity tool rack based on the Internet of Things according to claim 1, characterized in that, In step S103, count the number and direction of tool handovers in the sub-regions within a preset time period in the high-frequency handover regions, generate a handover heat map, and determine the adjustment plan for adjacent tool rack positions using the fuzzy inference method based on the heat distribution in the map, including: Receive the tool position signals collected by the regional sensor array, extract the tool movement direction and residence duration data from the tool position signals, and generate a tool handover frequency statistics table in the spatial data processor according to the movement direction and residence duration data; Use the tool handover frequency statistics table and the tool movement trajectory data, combined with the position information in the tool rack position structure model, to calculate the heat values of the handover path data, and obtain the handover heat distribution map in the spatial data processor; Execute the fuzzy inference algorithm according to the handover heat distribution map, perform spatial correlation operations on the heat distribution data, identify the handover hot regions between tool racks, and obtain the tool rack position spacing table in the spatial data processor; If the tool rack position spacing table and the handover heat distribution map show that the length of the handover path between adjacent tool racks exceeds the handover path length threshold, obtain the position parameters from the tool rack position structure model, and generate a tool rack position adjustment plan in the spatial data processor; 5. The multifunctional activity tool rack linkage method based on the Internet of Things according to claim 1, characterized in that, In step S104, establish a communication connection between tool racks through the Internet of Things. When a tool rack detects a high-frequency handover region, generate a corresponding rack position adjustment plan, transmit the adjustment instruction to the adjacent tool rack, and control the adjacent tool rack to make adjustments, including: Collect the tool rack position sensor data, which is used to generate a tool rack communication connection table; Calculate the signal transmission delay according to the network topology relationship in the tool rack communication connection table, and the signal transmission delay is used to generate a rack position communication instruction; Extract the adjacent tool rack position relationship data from the rack position communication instruction, and the adjacent tool rack position relationship data is processed by a deep learning algorithm to obtain a rack position adjustment instruction; Perform synchronous calculation on the positions of adjacent tool racks according to the rack position adjustment instruction, and the rack movement data obtained through the synchronous calculation is used for the collaborative adjustment of the rack positions.

6. The multi-functional activity tool rack linkage method based on the Internet of Things according to claim 1, characterized in that In step S105, during the adjustment of the tool rack structure, the computer vision method is used to track the position change of the tool in real time. By comparing the tool trajectory changes before and after the adjustment, the improvement rate of the handover smoothness is calculated, including: Obtain the tool motion image sequence collected by the industrial camera array in the rack position adjustment area, and obtain the tool position coordinate data from the image data collector; Perform target tracking processing on the tool position coordinate data to obtain tool motion trajectory data, store the tool motion trajectory data in the trajectory processor, and obtain the tool motion trajectory before the rack position adjustment; According to the tool motion trajectory and tool position coordinate data before the rack position adjustment, calculate the moving speed and motion path length of the tool before the adjustment through the trajectory processor; Extract the tool handover time interval from the data processed by the target tracking, and generate the handover smoothness reference value before the adjustment through the trajectory processor; Obtain the tool motion image sequence collected after the rack position adjustment, and perform feature recognition processing on it using a convolutional neural network; Obtain the tool motion trajectory after the adjustment from the trajectory processor, and calculate the current value of the handover smoothness after the adjustment through the trajectory processor; Perform a time series comparison process on the tool motion trajectory data according to the handover smoothness reference value and the current value; Calculate the improvement rate of the handover smoothness from the trajectory change data, and determine the effect of the rack position adjustment through the trajectory processor.

7. A method for linking a multifunctional activity tool rack based on the Internet of Things according to claim 1, characterized in that, In step S106, an enhanced learning algorithm is adopted. Taking the tool rack structure model as the input, the optimal rack layout scheme is obtained and applied to the rack position adjustment process, including: Read the tool rack structure model data, obtain the handover smoothness index from the tool rack structure model data, and determine the reward function value for the rack layout optimization according to the handover smoothness index; Perform initialization processing on the tool rack structure parameters according to the reward function value, adopt the tool rack structure parameters to perform optimization iteration calculation on the rack layout scheme, obtain the rack position adjustment data from the optimization iteration calculation; generate an iterative optimization layout sequence through the rack position adjustment data, perform a spatial distribution calculation on the iterative optimization layout sequence, and obtain the layout scheme convergence index from the spatial distribution calculation; If the layout scheme convergence index meets the preset convergence threshold, perform multiple rounds of iterative operations on the tool rack structure according to the layout optimization parameters, obtain the optimal rack parameters from the multiple rounds of iterative operations, and update the tool rack structure model with the optimal rack parameters.

8. The multifunctional activity tool rack linkage method based on the Internet of Things according to claim 1, characterized in that In step S107, the tools are classified and coded. When the tool racks are linked and adjusted, tools with complementary functions are preferentially placed in adjacent positions, including: Collect tool function attribute data through the tool attribute recognizer, obtain the function combination information from the tool usage records, and obtain the tool feature data in the function data processor; According to the tool feature data, perform classification operations on the tool function features using a clustering algorithm, and generate a tool function coding table in the function data processor.

9. The method for linkage of a multifunctional activity tool rack based on the Internet of Things according to claim 8, characterized in that, Calculate the complementarity degree of the tool function combination relationship based on the tool function coding table and the tool usage frequency data, and generate function combination data.

10. The method for linking a multifunctional activity tool rack based on the Internet of Things according to claim 9, wherein After receiving the functional combination data, perform correlation analysis through a graph neural network, identify complementary functional combinations from the tool combination relationships, generate tool proximity data within the functional data processor, and update the tool rack layout according to the tool proximity data.

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