165 results about "Logistic management" patented technology

The invention relates to the technical field of logistics management, and discloses a logistics distribution path intelligent management method and system based on the Internet of Things, and the method comprises the steps: firstly carrying out the static path planning, then collecting the vehicle, cargo, traffic and order data in real time through an Internet of Things sensing layer in the distribution process, and carrying out the real-time management of a logistics distribution path. And dynamically judging whether to trigger path re-planning or not after edge computing node cleaning, fusion and standardization processing. The triggering conditions comprise road section state sudden change, new order insertion and carrying abnormity. Once triggered, the system quickly regenerates a global optimal path by taking the current position of the vehicle as a new starting point and taking the remaining nodes as a new task set. For large-range distribution, a three-level radiation area layered progressive planning mechanism is introduced, layer-by-layer independent planning is carried out, relay is carried out at an actual terminal point, and self-adaption, efficient optimization and reliable execution of a path in a complex environment are achieved.

The invention relates to the technical field of logistics information management, and discloses a logistics information management platform, which comprises a data acquisition module, a data processing module, a business logic module, an interface adaptation module, a data storage module and an information output module, wherein the data acquisition module acquires logistics related data, the data processing module performs standardization processing on the logistics related data, the business logic module executes a logistics management business process, the interface adaptation module realizes adaptation interaction with an external logistics service system, and the data storage module stores the related data. And the information output module outputs logistics key information. According to the invention, unified management and automatic processing of logistics information are realized, the logistics business processing efficiency and accuracy are improved, the multi-system docking cost is reduced, the business continuity is ensured, and the method is suitable for a cross-regional multi-service provider logistics management scene.

The invention relates to the technical field of logistics management, and particularly discloses a modern medicine logistics management operation system. The method is used for solving the problems of high cost, low utilization rate and difficulty in achieving SLA caused by separation of a box and a path, imperceptible and non-quantifiable temperature control risk, slow border-crossing disposal, insufficient loading stability and difficulty in balancing cost and carbon emission in medicine cold chain distribution. Comprising a customer order management module, a vehicle-mounted monitoring module, a boxing path optimization module, an execution scheduling module, an accounting module and a tracing alarm module. According to the method, an encasement-path integrated optimization system is driven by an equivalent stability index generated by multi-source monitoring, path feasibility and loading feasibility are unified through early warning trigger constraint injection and local recalculation, dynamic rescheduling and linkage accounting, meanwhile, carbon emission, time window, stability and compliance event monetization are merged into a target function, and the load performance is improved. The cooperation of a temperature control early warning closed loop and an encasement path is realized, the cost and carbon emission are reduced, and the SLA achievement rate and the transportation efficiency are improved.

The invention discloses an intelligent bulk commodity freight logistics management method and system, and belongs to the technical field of logistics management, and the method comprises the steps: obtaining the multi-dimensional information of a transportation task, and generating a task semantic tag set; collecting state data and performance behaviors of the transportation units to construct a resource feature vector set; constructing a multi-model scheduling system including transportation risks, node passing trends and loading and unloading waiting prediction, and generating candidate scheduling strategies; performing game optimization on the strategy set in a dynamic environment through a reinforcement learning optimizer, and outputting an optimal scheduling strategy; deploying a continuous monitoring mechanism at edge nodes, and realizing adaptive reconfiguration of paths and resources according to abnormal scores; the intelligent level and the response capability of transportation scheduling can be remarkably improved, the delay risk is reduced, the resource utilization rate is improved, and the method is suitable for a bulk commodity multi-path multi-node combined transportation scene.

The invention relates to the technical field of logistics management, in particular to a dynamic monitoring method and system for logistics waybills, computer equipment and a storage medium. The dynamic monitoring method comprises the steps of obtaining logistics data of a plurality of stages of goods in the whole transportation process, starting to judge the logistics data of the stage after obtaining the logistics data of the first stage, if the logistics data of the stage is consistent with the logistics data set by the system, marking the logistics data of the stage as valid data, and if not, marking the logistics data of the stage as invalid data; and a manual verification program is started. And comparing the logistics data of the next stage. Obtaining comparison results of the four stages in sequence and performing judgment; if the logistics data of the four stages are all valid data, marking the logistics waybill as a real waybill; and if the logistics data of any stage is invalid data, marking the logistics waybill as an abnormal waybill. The authenticity of the transportation task is dynamically monitored by collecting information data of key nodes in the transportation process.

The invention relates to the technical field of logistics management systems, and particularly discloses an artificial intelligence-based supply chain logistics information management system, which comprises a data acquisition module, a demand analysis module, a logistics decision module, a dynamic sensing module and a supply chain monitoring module. Through multi-source data acquisition and standardized preprocessing, the problem of traditional system data is solved, and a foundation is laid for analysis; on the basis of an improved LSTM model and a dynamic threshold value, demands are accurately predicted, risks are early warned, and inventory and stockout losses are reduced; a hybrid algorithm is adopted to collaboratively optimize paths, assess inventory and dynamically adjust environmental interference, and balance logistics efficiency and cost; a toughness index is generated by means of an Attention-LSTM model, and risk quantitative traceability and rapid disposal are realized in combination with hierarchical response and visualization; the modules are in real-time cooperative closed loop, cooperate with parameter self-optimization and threshold updating, adapt to industry and environment changes, and provide stable and intelligent support for supply chain management.

The invention relates to the technical field of logistics management, in particular to an intelligent supervision control system for operation and maintenance of a cold chain system, which comprises the steps of collecting path environment data to screen an operation and maintenance optimal section, then identifying an energy consumption over-limit risk in the section, and executing main-standby switching on equipment with temperature rise exceeding the standard, meanwhile, power supply sufficiency is judged according to load requirements so as to start a redundant power supply, finally, a priority scheduling object is determined according to resource and task urgency by integrating a power supply state and energy consumption data, and a cold chain operation and maintenance centralized supervision control set is generated. According to the method, path selection is optimized by monitoring path environment fluctuation in real time to filter unstable road sections, energy consumption data and load standards are compared to identify and early warn overload risks, equipment switching and power supply scheduling are automatically executed to avoid operation and maintenance interruption when equipment temperature rise is too high or power supply is insufficient, and finally each link is intelligently scheduled through linkage management and control. The system operates stably and efficiently, the energy use efficiency is improved, and the sudden failure influence is reduced.

The invention discloses a production and logistics management method and system based on digital twinning, and the method comprises the steps: obtaining a real-time data flow from a production equipment sensor and a logistics node, carrying out the cleaning and formatting of the real-time data flow through a streaming processing technology, and obtaining a structured real-time data set; aiming at the structured real-time data set, detecting equipment abnormity and order fluctuation by applying a time sequence analysis algorithm; if equipment abnormity or order fluctuation is detected, carrying out real-time simulation on production and logistics links through a pre-established digital twinborn model to obtain a dynamic change trend of event influence; and according to the dynamic change trend, a reinforcement learning algorithm is adopted to optimize and adjust the production scheduling and logistics path, and an optimized production and transportation plan is determined. According to the invention, intelligent cooperation of production and logistics links is realized, abnormal conditions can be quickly responded, scheduling decisions are dynamically optimized, and the overall operation efficiency is improved.

The invention discloses a general-purpose general cargo wharf typical operation scene stockpiling strategy optimization method, and relates to the technical field of port logistics management, and the method comprises the steps: generating a storage yard global state true phase source data pool through multi-source data perception and fusion processing; carrying out storage yard state entropy calculation and chaos degree quantitative evaluation based on the storage yard global state true phase source data pool to obtain global and partitioned real-time entropy monitoring reports; according to global and partitioned real-time entropy monitoring reports, carrying out adaptive switching decision making to obtain a strategy switching instruction and a new strategy parameter; and according to the strategy switching instruction and the new strategy parameter, carrying out dynamic reconstruction and elastic resource allocation on the virtual stockpiling container to obtain a latest virtual stockpiling container distribution diagram. According to the method, collaborative stockpiling plan generation and operation instruction issuing are performed based on the newest distribution diagram to obtain a plan diagram and an instruction list, decision rules are optimized and updated through closed-loop feedback and entropy learning after execution and monitoring, and self-adaptive optimization of a stockpiling strategy is realized.

The invention discloses a WMS inventory optimization method and system based on digital twinning, and relates to the technical field of warehouse logistics management, and the method comprises the steps: collecting cargo inventory state data, refrigerated transport resource data and seasonal operation progress data from each cold chain warehouse management system, converting the data into different data formats through employing a standardized protocol, and carrying out the optimization of the data formats; the method comprises the following steps: acquiring a real-time inventory data set in a unified format, synchronizing the real-time inventory data set to a central processing node by adopting a distributed database mechanism according to the acquired real-time inventory data set, determining current cargo inventory levels and available refrigerated transport capacities of all cold-chain warehouse nodes, and if the cargo inventory level of a certain cold-chain warehouse node is determined to be lower than a preset threshold value, sending the cold-chain warehouse node to the central processing node; if yes, comparing the cargo inventory level and the transportation capacity available evaluation of adjacent warehouse nodes through an inventory level threshold; according to the WMS inventory optimization method and system based on digital twinning, efficient circulation of cold chain products such as goods is guaranteed.

The invention discloses an intelligent warehouse logistics management system and method based on a mobile robot, and relates to the technical field of warehouse logistics, and the system comprises a warehouse-in management module, a warehouse-out management module, an operation management module, an inventory management module, a report management module, a task management module and a log management module. The warehousing management module comprises an arrival list sub-module, a production plan list sub-module, a red character material warehouse-out sub-module and a manual warehousing list sub-module; the warehouse-out management module comprises a material picking application list sub-module, a delivery list sub-module, a manual warehouse-out list sub-module and a sales order sub-module; and the operation management module comprises a spot receiving operation sub-module, a sampling inspection confirmation sub-module, an inspection submodule, a quality inspection operation sub-module, a warehouse returning operation sub-module, a racking operation sub-module, a goods picking operation sub-module and a warehouse moving operation sub-module. Automatic storage and taking of the robot are achieved, the material carrying accuracy and efficiency are improved, and line side warehouses and three-dimensional warehouses are dynamically managed. And cost reduction and benefit increase of enterprises are realized.

The invention discloses a comprehensive logistics management method and system based on an AI algorithm and big data analysis, and relates to the technical field of intelligent logistics management.The system comprises the steps that sensing terminals are deployed at key nodes of an e-commerce logistics park, and resources, equipment and path data are collected and summarized into a multi-dimensional data set; constructing and training a multilayer neural network model, extracting task scheduling and equipment state features, and realizing scheduling analysis and strategy prediction; calculating a resource scheduling adaptation coefficient and comparing the resource scheduling adaptation coefficient with a threshold value to judge a scheduling state; aGV operation stability is monitored, and a state fluctuation coefficient is calculated; identifying an abnormal operation behavior and calculating an abnormal behavior coefficient; finally, multiple evaluation results are fed back to the AI model, model parameters are dynamically optimized, and the scheduling intelligence and stability of the system are improved.

The invention relates to the technical field of warehouse logistics management and artificial intelligence, in particular to a warehouse logistics dynamic scheduling optimization method and system based on deep learning, and the method comprises the steps: collecting static map data and dynamic sensor data of a warehouse environment; constructing a dynamic space-time diagram state at the current moment; in response to a business logic rule of the warehouse management system, triggering a scheduling optimization process, which comprises the following steps: generating a preliminary scheduling intention set by a constraint reinforcement learning scheduler; predicting to obtain a future space-time congestion thermodynamic diagram through a space-time diagram attention network predictor; quantitative processing is carried out through a self-defined aggregation function, and the future congestion cost of the scalar is determined; correcting the preliminary scheduling intention set by constraining a Lagrange solving framework of a reinforcement learning scheduler, and generating a final scheduling intention; outputting the final scheduling intention to a warehousing control system for execution; according to the method, the problem that decision making and prediction are disjointed in traditional scheduling is solved, and conversion from lagging response to active avoidance is achieved.

The application relates to the technical field of warehouse logistics management, in particular to a dynamic picking path optimization method and system based on deep reinforcement learning, which comprises the following steps: obtaining a picker state vector based on warehouse structured coding data analysis; calculating the moving value density of orders in each channel according to order queue data to be processed and the picker state vector; defining a state space according to the picker state vector and the order queue data to be processed; modeling a warehouse order picking problem as a Markov decision process based on the state space and a discrete action space; solving the Markov decision process by using a deep reinforcement learning neural network model based on the real-time state of the picker and the moving value density, so that an optimal picking path decision is obtained; and controlling the picker to perform a picking operation according to the optimal picking path decision. The application combines the deep reinforcement learning neural network model to solve the Markov decision process, realizes accurate optimization of the picking path, and effectively improves the efficiency of warehouse order picking.

The invention relates to the technical field of industrial cloud platforms, in particular to a retail system and method for a decentralized supply chain of a community store, and the method comprises the steps: receiving a user order through a data interaction module; the store management module starts a collaborative bidding process, converts an order into a demand packet, broadcasts the demand packet to a supplier, intelligently selects the supplier by using a built-in deep game theory algorithm, and automatically generates a purchase order; after receiving the purchase order, the supplier management module constructs an enhanced mixed integer programming model to perform optimal path planning so as to minimize the total driving distance and the total delivery time; the operator management module compiles the profit sharing rule into an intelligent contract, verifies the authenticity of cost data by using a zero-knowledge proof technology, and realizes decentralized profit sharing calculation and settlement; and the logistics management module completes delivery to the store according to the planned optimal path. According to the invention, retail management is realized through the decentralized supply chain.

The invention discloses a cloud chain information platform construction system and method for the flour industry. The cloud chain information platform construction system comprises a basic data management module, a supply chain cooperation module, a production management module, a quality tracing module, a sales management module, a warehouse logistics management module, a data analysis and decision support module, a user management module and a system management module. Through cooperative work of a plurality of functional modules, information sharing and cooperation of links of a supply chain in the flour industry are realized, the efficiency of the supply chain is improved, and the cost is reduced; the whole-course quality tracing of the product is realized, and the safety management level of the product quality is improved; and through the data analysis and decision support module, a scientific basis is provided for decision making of an enterprise, and the competitiveness of the enterprise is improved.

This invention discloses a collaborative management method and system for the entire warehousing and logistics process, belonging to the field of warehousing and logistics information technology. Addressing the technical shortcomings of existing warehousing and logistics management, such as low inventory accuracy, poor scheduling efficiency, lagging information collaboration, and insufficient cost control, this invention integrates the entire process of inbound, storage, picking, outbound, transportation, and traceability, constructing a closed-loop management system of "data perception - algorithm decision-making - process collaboration - dynamic optimization." Through multi-dimensional data collection, optimized algorithm scheduling, cross-system data collaboration, and full-process traceability technology, this invention achieves precise inventory control, optimized logistics route planning, and efficient resource allocation, significantly improving warehousing and logistics operational efficiency, reducing overall costs, and enhancing management transparency. It is applicable to the digital management needs of warehousing and logistics in various industries, including manufacturing, retail, and e-commerce.

This invention provides a method for allocating storage locations in an automated three-dimensional warehouse using digital twin technology. The method constructs a warehouse management system through five dimensions: a physical layer, a twin layer, a data layer, a service layer, and a connectivity layer. The physical layer represents the actual warehousing operation scenario; the twin layer maps the equipment and environment of the physical layer to a virtual space, constructing a digital model of the warehouse; the data layer is responsible for the collection, processing, and storage of data from the physical layer, providing accurate input for the service and twin layers; the service layer provides optimization decision-making and human-computer interaction functions for users and system management; and the connectivity layer is responsible for the data and control transmission between the layers, ensuring the system's synergy and real-time performance. By supporting multi-objective optimization algorithms through digital twin technology, intelligent and precise storage location allocation is achieved, providing a novel solution for digital logistics management.

The present development relates to a method for managing the logistics of a customer service area, such as an operating room, which improves the process of scheduling procedures and the management of resources assigned to the allocation of a customer service area (0). This is achieved through the use of technological surveillance tools. In particular, the disclosed method takes into consideration the use of electronic identification elements, such as, for example, a radio frequency system (RFID tag), a real-time location system (RTLS tag), or a Bluetooth beacon system, linked to a user or assistant, a supply, or equipment. This information allows to establish a status of the service provision and to carry out a statistical analysis that allows to obtain patterns in terms of to the interactions between staff, supplies and equipment in relation to a service area, historically.

The invention discloses a warehouse logistics scheduling method and system based on cloud computing, and relates to the field of warehouse logistics management, and the method comprises the steps: collecting to-be-processed cargo information and available robot state information in a warehouse management system in real time based on a cloud scheduling platform; carrying out the clustering of the to-be-processed goods, carrying out the calculation and evaluation of a clustering result, and obtaining an optimal task partition division scheme; obtaining the shortest collision-free path from each available robot to each task partition, and calculating the path length of the shortest collision-free path; calculating path suitability, load suitability and electric quantity sufficiency between each available robot and each task partition; determining an optimal allocation scheme between the available robots and the task partitions based on the three features; and scheduling the available robots based on the optimal allocation scheme and monitoring the operation states of the robots in real time. According to the invention, real-time, efficient and balanced global integrated scheduling can be carried out on logistics robots and order tasks, and the collaborative operation capability of a robot cluster is improved.

The invention relates to the technical field of data processing and logistics management, and discloses a cross-border e-commerce logistics order analysis and management system based on big data, and the system comprises a data obtaining module which is used for calculating and forming real-time state data; the potential energy calculation module is used for quantifying the state data into a regional logistics potential energy value; the risk identification module is used for monitoring the potential energy value change to identify a potential risk area; the risk matching module is used for comparing the current risk feature with a preset historical interrupt event feature fingerprint; and the strategy generation module is used for generating a self-adaptive intervention strategy accordingly. According to the method, the potential energy field model and the fingerprint database are constructed, so that the logistics risk is pre-judged in advance and accurately determined, invalid distribution caused by traffic control can be effectively avoided, the decision-making efficiency is improved, the customer recognition degree is guaranteed by generating an intervention strategy, and the analysis and management level of cross-border e-commerce logistics is systematically improved.

The application provides a logistics management device for monitoring the position of goods in real time and a management system thereof, and relates to the technical field of logistics management. The logistics management device for monitoring the position of goods in real time and the management system thereof comprise a protective shell made of a compression-resistant and wear-resistant material, both sides of the protective shell are provided with fixing holes for fixed installation, a control module is arranged inside the protective shell, and a buffer heat-conducting pad is filled between the control module and the inner wall of the protective shell; a water tank is fixedly arranged inside the protective shell near the lower end, the water tank is provided with cooling liquid and a built-in water pump, and the buffer heat-conducting pad is partially embedded in the water tank; an antenna is arranged; and a signal line is fixedly connected to the lower end of the antenna. The sealed cooling liquid is circulated and linked to heat dissipation and antenna adjustment, the buffer heat-conducting function is integrated, no additional power is needed, the operation stability is significantly optimized, the energy consumption cost is reduced, and the signal transmission continuity is strengthened.

The invention discloses a lottery logistics accurate inventory and distribution optimization method. The method comprises the following steps: S1, data acquisition and input; s2, inventory management and analysis: predicting the sales volume of each lottery ticket type in a specified period in the future based on historical sales data, external influence factors and a real-time inventory state, and dynamically adjusting the safety inventory level and replenishment threshold value of each warehouse / sales point according to the sales volume; s3, distribution path optimization: based on the input related information, automatically calculating a vehicle distribution path set which meets the constraint condition and has the minimum total cost; and S4, delivery execution and feedback. According to the lottery logistics precise inventory and distribution optimization method disclosed by the invention, the problems of inaccurate inventory, large demand prediction deviation, low distribution efficiency and the like in traditional lottery logistics management are solved, precise management of lottery logistics is realized, the operation cost is reduced, and the service quality is improved.

The invention relates to the technical field of logistics intelligent management, and particularly discloses a large logistics cognitive collaborative management system which is constructed based on AI and an industrial internet platform and is provided with an edge layer, a platform layer, an engine layer and an application layer. The edge layer is constructed based on an IT system, OT equipment and manual filling of the logistics transportation industry, and corresponding data acquisition capability is activated according to a received data request; the platform layer starts a corresponding data processing flow according to the received task instruction; the engine layer performs analysis, identification and task planning processing on the received request, and performs intelligent analysis processing on the received data; and the application layer analyzes the received user request and outputs the received data result. According to the invention, the IT system and the OT equipment in the logistics transportation industry are integrated, intuitive and efficient cognitive collaborative management can be formed for large logistics, and a timely, comprehensive and accurate data support basis is provided for business and technical decisions of enterprises.

The invention relates to the field of logistics, and discloses a logistics order dispatch monitoring method, device and equipment and a storage medium, and the method is used for carrying out dispatch monitoring on a logistics order to improve logistics dispatch efficiency and satisfaction. The method comprises the following steps: acquiring basic information, dynamic signing data and external environment data of a logistics order, and converting the basic information, the dynamic signing data and the external environment data into structured data; calculating the contribution degree score of each feature in the structured data to delay prediction, and screening out the features with the top 80% of the contribution degree scores as prediction features; inputting the prediction features into a pre-trained delay rate prediction model, calculating the delay rate of the logistics order, and matching the early warning level of the logistics order according to the delay rate; a receiver demand is obtained, and a processing scheme is generated in combination with the receiver demand, the early warning level and the structured data; and generating a visual early warning prompt according to the processing scheme, and synchronizing the visual early warning prompt to the logistics management platform, the courier mobile terminal and the receiver applet.

The invention relates to the technical field of e-commerce logistics management, and discloses an e-commerce logistics tracking system based on a block chain. The system obtains an order identifier through a distributed query module, retrieves logistics records in a block chain historical database, and assembles a historical logistics record set. And the event distinguishing module carries out event type distinguishing processing on the record set to generate a heterogeneous logistics event set. The feature extraction module executes feature extraction for each event set to obtain a logistics feature core value set and an associated time window set. And the multi-scale analysis module performs multi-level analysis on the logistics data sequence through the block chain smart contract by using the associated time window set as an analysis reference, and generates a plurality of logistics feature groups. And the feature fusion module performs inter-scale fusion processing on the feature group, and outputs a final fused logistics feature group as a logistics tracking result. According to the system, the transparency and credibility of e-commerce logistics tracking are improved, and multi-dimensional and multi-scale analysis of the whole logistics process is realized.

The invention relates to a production management system oriented to automatic control. The production management system comprises a production planning module, a production resource scheduling module, a production control module, a quality detection module and a logistics management module. The production planning module is used for generating a production plan according to production requirements; the production resource scheduling module is used for raw material scheduling, equipment scheduling and personnel scheduling; the production control module is used for controlling production process parameters according to collected real-time production data; the quality detection module is used for detecting the quality of the produced product; and the logistics module is used for managing raw material and product distribution.

The application relates to the technical field of warehouse logistics management, and discloses a warehouse container automation operation method and system based on a three-dimensional mechanism. The method comprises the following steps: counting operation scene characteristic parameters, calculating a target weight when the characteristic parameters continuously exceed a threshold value, and obtaining a daily weight through five-day linear interpolation smooth transition, scoring candidate storage locations according to the daily weight to select an optimal storage location, and selecting an optimal crane by comprehensively optimizing scheduling according to the moving distance, operation time and queue length. The application improves the self-adaptive capability of the storage location allocation decision to the scene change, the smoothness of the weight adjustment process and the strategy continuity, and the weight configuration convergence and operation efficiency optimization effect based on feedback correction.

The invention provides an intelligent generation method and system for a two-in-one label of a full-trusteeship order, and relates to the technical field of logistics management, and the method comprises the steps: obtaining order information; determining multi-dimensional information data of a shop side according to the order information; designing a factory end template according to the order information; performing data synchronization on the multi-dimensional information data and factory end information data; inputting the synchronized data and the order information into a factory end template to generate a label prototype; according to a verification condition, verifying the tag prototype to obtain a verification result; judging whether the verification result passes or not; and generating a two-in-one label according to the verified label prototype. According to the invention, centralized configuration and one-time multiplexing of multi-dimensional information are realized, and the problem of dispersed input of pain points is solved; the requirements of multiple platforms are met through standardized and self-defined template design, and the template adaptation cost is reduced; and the two-in-one label is generated, so that the full-hosting order delivery efficiency is greatly improved, and the manpower and compliance cost is reduced.

1. Name of the product in this design: RFID tag inlay (1). 2. Purpose of this design: This product is used for the identification and tracking of items, such as logistics management, inventory control, or anti-counterfeiting verification. 3. The key design features of this product are its pattern, which consists of two symmetrical vertical stripes, a central rectangular element, a serpentine line pattern connecting the rectangular elements, and two dots (one below the rectangular element and one in the serpentine line). 4. The image or photograph that best illustrates the design's key features: the front view. 5. This design is a two-dimensional product, so the left, right, top, and bottom views are omitted; the back of this design is a part that is not easily seen or cannot be seen during use, so the rear view is omitted. 6. Other situations requiring explanation: In the design product (RFID tag inlay) of this application, the tag body and the circle are an integral part of the product and will be attached together to the surface of the target item.