An RFID-based anti-static turnover container intelligent positioning and management method

Abstract

This invention discloses an RFID-based intelligent positioning and management method for anti-static turnover containers, belonging to the field of intelligent positioning technology. The method includes the following steps: binding RFID tags to the anti-static turnover containers to obtain a unique identifier set; deploying access control based on the unique identifier set to obtain channel identification results; calculating the regional signal strength based on the channel identification results to obtain the regional attribution result; performing time-series correlation based on the regional attribution result to obtain a trajectory sequence; calculating the dwell time based on the trajectory sequence to obtain the dwell state result; and performing path evolution calculation based on the dwell state result to obtain the location prediction result. This invention, through regional signal strength calculation, avoids the bias caused by a single reader, achieves stable output of regional attribution, improves the accuracy and stability of positioning, and enhances adaptability in complex environments.

Description

Technical Field

[0001] This invention relates to the field of intelligent positioning technology, specifically to an RFID-based intelligent positioning and management method for anti-static turnover containers. Background Technology

[0002] In electronics manufacturing, semiconductor processing, and precision component assembly, anti-static containers are widely used for storing and transporting sensitive devices. These containers need to move frequently between production lines, storage areas, and aisles, and their management quality directly affects production efficiency and product safety. Due to stringent electrostatic protection requirements, these containers are typically made of specialized materials and must move within closed or semi-closed environments. Therefore, precise management of their location, status, and movement paths is of paramount importance.

[0003] In existing technologies, there are shortcomings in the location of the container's location: existing location methods usually determine the location of the area based on whether it is recognized by a certain reader or writer, or take the area corresponding to the reader with the strongest signal as the target area, ignoring the influence of the superposition of signals from multiple readers and writers, not considering signal fluctuations and environmental interference, and the instantaneous signal strength is unstable, which can easily lead to misjudgment and unclear location, and is not adaptable to the environment. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an RFID-based intelligent positioning and management method for anti-static turnover containers, thereby solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides an RFID-based intelligent positioning and management method for anti-static turnover containers, comprising the following steps:

[0007] S1. By binding RFID tags to antistatic turnover containers, a unique set of identifiers is obtained;

[0008] S2. Deploy access control identification based on the unique identifier set to obtain the channel identification result;

[0009] S3. Calculate the regional signal strength based on the channel identification results to obtain the regional attribution results;

[0010] S4. Perform temporal correlation based on the region attribution results to obtain the trajectory sequence;

[0011] S5. Calculate the dwell time based on the trajectory sequence to obtain the dwell status result;

[0012] S6. Based on the results of the stagnation status, perform path evolution calculations to obtain the location prediction results.

[0013] To further optimize this technical solution, the RFID tag binding in step S1 includes:

[0014] By numbering the antistatic turnover containers and installing uniquely identified RFID tags on the container surface at locations that do not affect the antistatic performance, a mapping relationship between the containers and the tags is established, resulting in a set of unique identifiers.

[0015] To further optimize this technical solution, the access control identification deployment in step S2 includes:

[0016] Select key logistics path nodes to install fixed RFID reader devices, set identification trigger conditions, and identify containers passing through based on a unique identifier set to obtain channel identification results.

[0017] To further optimize this technical solution, the regional signal strength calculation in step S3 includes:

[0018] Based on the obtained channel identification results, the entire working space is divided into multiple regions with clear boundaries. Multiple readers are deployed in each region to obtain the signal strength when the container is read and to perform weighted and comprehensive processing to obtain a comprehensive signal value. By comparing and selecting the magnitude of the comprehensive signal value, the region assignment result is obtained.

[0019] To further optimize this technical solution, the integrated signal value includes:

[0020]

[0021] in:

[0022] Container area The overall signal value within;

[0023] Reader In the region Signal strength in;

[0024] Reader In the region The weighting coefficients in the text;

[0025] The number of readers / writers in a given area;

[0026] The overall signal value is calculated by weighting and combining the signal strengths of all readers in the area.

[0027] To further optimize this technical solution, the timing correlation in step S4 includes:

[0028] Based on the obtained regional attribution results, an initial trajectory sequence is constructed by arranging them in chronological order, and time intervals are detected. By completing the time intervals and marking breakpoints, a trajectory sequence is formed.

[0029] To further optimize this technical solution, the dwell time calculation in step S5 includes:

[0030] Based on the trajectory sequence of each container, the entry and exit times of the containers are determined by identifying continuous area segments, and the dwell time in the area is calculated to determine the dwell status and obtain the dwell status result.

[0031] To further optimize this technical solution, the path evolution calculation in step S6 includes:

[0032] Based on the obtained stagnation status results, the current state of the container and the nearest trajectory point are obtained, the prediction time interval is determined, and the movement rate and position are calculated based on the container state to obtain the position prediction result.

[0033] To further optimize this technical solution, the movement rate calculation includes:

[0034]

[0035] in:

[0036] :container The rate of movement;

[0037] : Region distance function, used to calculate the topological distance between regions;

[0038] :container exist The region of time;

[0039] :container exist The region of time;

[0040] The container's movement rate is calculated by dividing the change in area between two time points by the time interval.

[0041] To further optimize this technical solution, the location calculation includes:

[0042]

[0043] in:

[0044] :container exist The predicted region at that time;

[0045] Predicted time interval;

[0046] The predicted position of the container is calculated by combining its current position with its movement speed and the predicted time interval.

[0047] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the RFID-based intelligent positioning and management method for anti-static turnover containers as described in the first aspect of the present invention.

[0048] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of an RFID-based intelligent positioning and management method for anti-static turnover containers as described in the first aspect of the present invention.

[0049] Compared with existing technologies, this invention provides an RFID-based intelligent positioning and management method for anti-static turnover containers, which has the following beneficial effects:

[0050] This RFID-based intelligent positioning and management method for anti-static turnover containers avoids the deviation caused by a single reader by calculating the regional signal strength, achieves stable output of regional ownership, improves the accuracy and stability of positioning, and enhances adaptability in complex environments. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart illustrating an RFID-based intelligent positioning and management method for antistatic turnover containers proposed in this invention. Detailed Implementation

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0054] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0056] Example 1:

[0057] Reference Figure 1 This is the first embodiment of the present invention, which provides an RFID-based intelligent positioning and management method for anti-static turnover containers, including the following steps:

[0058] S1. By binding RFID tags to the anti-static turnover containers, a unique set of identifiers is obtained.

[0059] In this embodiment, the RFID tag binding includes:

[0060] In practical applications of anti-static turnover containers, these containers typically exhibit characteristics such as large quantity, high appearance consistency, complex flow paths, and strict anti-static performance constraints. If containers lack unique identifiers, it becomes impossible to distinguish individual containers, leading to unclear management targets, an inability to establish a mapping relationship between containers and their location and status, and subsequent steps (such as channel identification and trajectory construction) failing to link to specific objects. Furthermore, issues such as lost, misused, or mixed-use containers are difficult to trace. Therefore, it is necessary to establish a unique, machine-identifiable identifier for each container.

[0061] By numbering the antistatic containers and installing uniquely identified RFID tags on the container surface at locations that do not affect the antistatic performance, a mapping relationship between the containers and the tags is established, resulting in a unique set of identifiers. This establishes a unique identity for each container, avoids data confusion between containers, preserves the antistatic performance, and provides a foundation for subsequent steps.

[0062] The RFID tag binding process includes:

[0063] Define container numbering rules: Plan the numbering of the container set, with each container corresponding to a unique number. The number length is fixed, such as 96 bits. The number consists of enterprise code, container type identifier, and serial number. The number must meet the requirements of global uniqueness and non-repeatability, making it scalable, distinguishable, and resolvable.

[0064] Select RFID tag type: Choose UHF RFID electronic tag (EPC Gen2 standard). This tag has a moderate reading distance (to meet logistics scenarios), can be read without contact, is powered by a passive power source, does not require batteries, is suitable for long-term use, and can read multiple tags in batches and identify multiple containers at the same time.

[0065] Write a unique identifier into the RFID tag: Use an RFID writing device to read the initial state of the tag to be written, write the number into the corresponding tag storage area, and verify whether the writing result is consistent. After writing, mark the tag as bound, so that each tag has a unique identification capability.

[0066] Determine the tag installation location: Select a fixed installation area on the container surface. The installation area should meet the following requirements: it should not affect the structural strength of the container, it should not be in an area of ​​frequent friction (to prevent detachment or damage), it should be far away from a fully conductive area, and it should be close to an RF transparent area (to prevent reading failure or signal fluctuation), so that the tag remains identifiable throughout its entire life cycle.

[0067] Attaching RFID tags to containers: Depending on the container structure, methods such as surface bonding (suitable for flat surfaces), embedded installation (suitable for structures with reserved space) or slot fixing (suitable for detachable structures) can be used for fixing. During the fixing process, the tag surface needs to be in close contact with the container surface to avoid gaps that could cause signal reflection or tag movement that could cause position changes. The direction of the tag antenna should be as consistent as possible with the direction of the reader to prevent signal strength loss and shortened reading distance. The original conductive path distribution of the container should not be changed to reduce the risk of static electricity accumulation.

[0068] Detecting changes in antistatic performance: Perform electrical performance testing on containers after labeling. Measure the surface resistance before and after installation, calculate the change, and determine if the resistance change is within the allowable range. If not, adjust the label installation position or method to ensure functional safety and avoid potential hazards.

[0069] Generate a unique identifier set: After binding the container with the RFID tag, a unique identifier set is formed, which is used for all subsequent identification and calculation steps.

[0070] S2. Deploy access control identification based on the unique identifier set to obtain the channel identification result.

[0071] In this embodiment, the access control identification deployment includes:

[0072] In practical applications, anti-static turnover containers have continuous flow characteristics, that is, they move between warehouses, production lines, and buffer areas, and their positions change between different functional areas. The movement behavior has clear path nodes (such as doorways and interface points). If only static identification (such as reading from fixed areas) is relied upon, there may be problems such as not being able to determine whether the container has entered or left a certain area, not being able to determine the time node, not being able to determine whether the container is inside the area or has just entered, and not being able to read from multiple areas at the same time, which can easily lead to misjudgment. Therefore, it is necessary to establish strong constraint identification points at key passage locations to capture the container's passage behavior.

[0073] Select key logistics path nodes to install fixed RFID reader devices, set identification trigger conditions, identify containers through a unique identifier set, obtain channel identification results, thereby obtaining the key location nodes of the containers, providing boundary references for subsequent area division, avoiding location ambiguity, and providing a data foundation for subsequent steps.

[0074] The implementation steps for access control and identification system deployment include:

[0075] Determine the location of the channel: Identify key logistics path nodes as channel identification points, such as warehouse entrances and exits, production line entrances and exits, connecting channels between different functional areas, and the boundary between the buffer area and the main process. Containers must pass through these locations, and the spatial paths are relatively concentrated, which can form clear boundaries, making the reader coverage more stable, thereby ensuring that all important flow behaviors are recorded.

[0076] Install fixed RFID reader / writer equipment: Install fixed RFID readers / writers (such as UHF fixed readers / writers) at the above-mentioned channel locations. The reader / writer antenna should cover the entire channel cross-section, and the antenna direction should be as consistent as possible with the direction of the container tag. The installation height should match the height of the container, and avoid metal structures blocking the signal propagation path. Through reasonable deployment, the container will inevitably enter the effective identification range of the reader / writer when passing through the channel.

[0077] Set recognition trigger conditions: To ensure the validity of the recognition results, set trigger conditions, which mainly include signal strength reaching the set threshold, eliminating long-distance interference, the tag being read a certain number of times, confirming that the container has indeed passed through the channel, eliminating instantaneous signal fluctuations, and ensuring that the reading time is within a reasonable range to avoid the same container being recorded multiple times and to avoid duplicate counting due to excessively long reading processes.

[0078] Container identification: When a container carrying a tag enters the channel area, the reader emits an radio frequency signal, the tag receives the energy and returns identification information, the reader receives the signal and parses the tag to determine whether the triggering conditions are met, and confirms that the container has passed through the channel. This identification process is completed non-contactly and does not affect the normal flow of containers.

[0079] Generate channel recognition event logs: When recognition is successful, a channel event log is generated, which includes container identifier, recognition time and channel location information, thereby distinguishing different objects, realizing sorting and trajectory construction, and used for spatial analysis. Each time a container passes through, an independent record is generated.

[0080] Create a channel identification result set: Summarize all identification events to form a channel identification result set. This set is arranged in chronological order, with each record corresponding to one passage behavior, providing input for subsequent steps.

[0081] S3. Calculate the regional signal strength based on the channel identification results to obtain the regional attribution results.

[0082] In this embodiment, the calculation of the regional signal strength includes:

[0083] In actual identification, relying solely on channel identification will lead to all containers in the area being regarded as the same location, making it impossible to distinguish the area and support fine management. For example, it is impossible to determine whether a container has reached the designated workstation, or to calculate the distribution of stay within the area. Furthermore, since the container material is conductive, it will absorb and reflect RF signals. If only a single reader signal is relied upon, misjudgments are likely to occur.

[0084] Based on the obtained channel identification results, the entire working space is divided into multiple regions with clear boundaries. Multiple readers are deployed in each region to obtain the signal strength when the container is read and perform weighted comprehensive processing to obtain a comprehensive signal value. By comparing and selecting the magnitude of the comprehensive signal value, the region assignment result is obtained, thereby avoiding the bias caused by the judgment of a single reader and achieving stable output of region assignment. This expands discrete channel data into spatial region information, provides a spatial basis for trajectory construction, improves the accuracy and stability of positioning, enhances adaptability in complex environments, and reduces the impact of single-point errors.

[0085] Methods for calculating regional signal strength include:

[0086] Space division: Based on the actual scenario, the entire work space is divided into multiple areas, each with clear boundaries, such as storage area, production line area, buffer zone, and passageway area. Each area corresponds to a set of reader / writer coverage areas, thereby ensuring that there are clear boundaries when containers move from one area to another, providing candidate ranges for subsequent attribution determination.

[0087] Deploy multiple readers: Deploy multiple fixed RFID readers (UHF fixed readers) within each area. The coverage areas of different readers overlap to some extent. The readers are distributed in different locations (such as entrance, center, and exit) to ensure that the coverage is as uniform as possible. By deploying multiple points, the same container may be received by multiple readers at the same time, thereby achieving spatial redundancy coverage and improving identification stability.

[0088] Obtain the signal strength of the container under each reader: When the container enters the area, its tag is read by multiple readers, and each reader generates a signal strength value. For the same container, within the same time window, the signal strength data of all readers are collected and normalized to provide the raw input for subsequent fusion calculation.

[0089] Weighted processing of signals from different readers: For the same container, the signal strength obtained from multiple readers is not the same. By assigning a weight to each reader, which reflects the importance of the reader's location in the area, signal stability, and coverage reliability, multiple normalized signals are combined according to the weight to obtain a comprehensive signal value, thereby reducing the impact of single-point anomalies on the results and improving the overall judgment stability.

[0090] Compare the comprehensive signal values ​​of each region: For each container, calculate its comprehensive signal value in different regions and compare them. Select the region with the largest signal value as the region to which the container belongs, thereby reducing misjudgments and determining the most likely region to which it belongs.

[0091] Generate region attribution results: Generate a region attribution result for each container, including container identifier, region, and determination time, so that each container has a clear spatial attribution at any point in time, which serves as input for subsequent trajectory construction.

[0092] Furthermore, the synthesized signal value includes:

[0093]

[0094] in:

[0095] Container area The comprehensive signal value within the area is the direct basis for determining the area's affiliation; the larger the value, the stronger the signal.

[0096] Reader In the region The signal strength in the data reflects the wireless signal strength between the container and the reader. It is obtained by measuring and normalizing the data using a fixed RFID reader. A larger value indicates a closer distance or a smoother path, while a smaller value indicates a farther distance or a more obstructed signal.

[0097] Reader In the region The weighting coefficients in the model are used to adjust the degree of influence of different reader signals on the overall result. They can be set according to the reader installation location (high weight for the central location), historical stability (high weight for small fluctuations), or coverage range (high weight for large effective coverage area). They are calibrated and determined during deployment, and range from 0 to 1. The sum of the weights of all readers in a region is 1.

[0098] The number of readers in a region, typically 2 to 3 for small regions and 4 to 6 for large regions;

[0099] The overall signal value is calculated by weighting and combining the signal strengths of all readers in the area.

[0100] S4. Perform temporal correlation based on the regional attribution results to obtain the trajectory sequence.

[0101] In this embodiment, the timing association includes:

[0102] In actual management, retaining only the area attribution data will result in the inability to express the container movement process, such as which area it moves from to which area, the order of the movement path, etc. It will also be impossible to calculate time-related characteristics, such as dwell time, movement speed, and circulation cycle. Furthermore, since RFID reading has the characteristics of non-continuous reading and irregular reading intervals, the area attribution data is discontinuous in time and needs to be supplemented and correlated.

[0103] Based on the obtained regional attribution results, an initial trajectory sequence is constructed by arranging them in chronological order, and the time intervals are detected. By completing the time intervals and marking breakpoints, a trajectory sequence is formed, thereby transforming discrete data into continuous trajectories, providing behavioral analysis capabilities, and providing a basic structure for subsequent calculations.

[0104] Methods for implementing temporal correlation include:

[0105] Extracting region attribution data by container identifier: For each container, extract all its region attribution records from the result of step S3, thereby separating the data of different containers and constructing a trajectory for each container separately.

[0106] Sort the data by time: Sort the extracted data in ascending order of time, with adjacent data having a chronological relationship, providing a basis for trajectory construction and subsequent interval determination.

[0107] Constructing the initial trajectory sequence: According to the sorting results, the regions are connected in order to form the initial trajectory sequence. This sequence represents the spatial changes of the container at different times. Each time point corresponds to a region, and path connections are formed between adjacent time points.

[0108] Detecting time intervals and determining continuity: Comparing two adjacent time points and calculating the time interval, if the time interval is small, the trajectory is considered continuous; if the time interval exceeds a set threshold, data loss or trajectory interruption is considered. The threshold can be set according to positioning needs and actual conditions, thereby identifying RFID missed readings and distinguishing between normal movement and abnormal interruptions.

[0109] Time interval completion processing: When a time interval is detected to be within an acceptable range (such as exceeding the threshold but less than the maximum allowed interval) but there is a gap, the intermediate time interval is completed by setting the region of the intermediate time interval to the region of the previous moment to maintain the continuity of the region status, thereby making up for the RFID reading interval, ensuring the continuity of the trajectory, and improving the stability of subsequent calculations.

[0110] Marking trajectory breakpoints: When the time interval exceeds the maximum allowable value, it is marked as a trajectory breakpoint, dividing the trajectory into multiple subsequences. Breakpoints indicate that the container may have left the turnover range, or that there is a large data gap, or that abnormal behavior has occurred. This is used to distinguish different trajectory segments in subsequent analysis, and each trajectory segment is processed separately to prevent it from being mistaken for continuous movement.

[0111] Output trajectory sequence results: Form a trajectory sequence for each container, including time series (representing the state changes of the container at different time points), corresponding region sequence (representing the spatial location of the corresponding time point), and continuous segment and breakpoint information (used to describe the trajectory structure), as structured data output for subsequent processing steps.

[0112] S5. Calculate the dwell time based on the trajectory sequence to obtain the dwell status result.

[0113] In this embodiment, the calculation of the dwell time includes:

[0114] In practical applications, trajectory information alone is still insufficient to support management needs. This is because containers may stay in a certain area for too long, such as accumulating in front of the production line without being processed, remaining in the storage area for a long time, or not continuing to flow according to the process. If the dwell time is not calculated, these anomalies cannot be identified. Moreover, the trajectory only provides the path, not the time distribution, and cannot measure the flow efficiency. Therefore, the dwell status must be clearly defined.

[0115] Based on the trajectory sequence of each container, the entry and exit times of the containers are determined by identifying continuous regional segments. The dwell time in the region is calculated to determine the dwell state, thereby obtaining the dwell state result. In this way, time dimension features are extracted to identify the container state and provide data support for subsequent prediction and analysis.

[0116] The steps for calculating the stay time include:

[0117] Obtain the trajectory sequence of a single container: From the output of step S4, extract the complete trajectory sequence for each container. This sequence contains information on the region and continuous segments corresponding to each time point, providing data input for dwell time calculation.

[0118] Identifying continuous area segments: In the trajectory sequence, find continuous time periods in which the area remains unchanged, that is, within a certain period of time, the area identifier is the same and the area does not change at adjacent time points. This divides the trajectory into multiple stop segments, clarifies the boundary of each area stop behavior, and each segment corresponds to one stop behavior.

[0119] Determine entry and exit times: For each stay segment, determine the entry and exit times, which are the times when the area first appears and the times when the area last appears within the time period, to calculate the stay duration.

[0120] Calculate the dwell time in the area: For each dwell segment, the dwell time is calculated by subtracting the entry time from the departure time. This represents the duration for which the container stays in the area, reflecting the actual occupancy of the container in the area and whether there is any delay or waiting.

[0121] Set a dwell time threshold: Different areas have different functions and their normal dwell time is different. For example, the dwell time in the processing area is longer and the dwell time in the passage area is shorter. In order to determine whether the dwell time is abnormal, a time threshold is set. This threshold is set based on normal flow time, process processing time and historical statistical data, etc., and serves as a criterion for distinguishing between normal and abnormal states.

[0122] Determine the state of stay: Compare the stay time of each stay segment with a threshold. If the stay time exceeds the threshold, it is determined to be a stay; if it does not exceed the threshold, it is determined to be normal. This converts time information into state information and provides a judgment result that can be used directly.

[0123] Output the status results of the container: The status results of each container are output in a structured manner, including the container identifier, the area where it is located, the time of stay and the status identifier (staying or normal) of each stay segment, as input for subsequent steps.

[0124] S6. Based on the results of the stagnation status, perform path evolution calculations to obtain the location prediction results.

[0125] In this embodiment, the path evolution calculation includes:

[0126] Because RFID reading is intermittent and the identified data is discontinuous (e.g., it cannot be read when the container is outside the reader's coverage area), and the reading intervals are uneven, leading to missed reads, it cannot support real-time positioning requirements. Furthermore, the trajectory only reflects historical paths and lacks the ability to determine trends or predict future locations. However, practical applications require real-time monitoring of the container's location, including the ability to estimate its position even when no reading is performed. Therefore, path evolution calculation is necessary to predict the container's future location.

[0127] Based on the obtained stagnation status results, the current state of the container and the nearest trajectory point are obtained, the prediction time interval is determined, and the movement rate and position are calculated based on the container state to obtain the position prediction result, thereby improving the prediction rationality, filling the gap in RFID data, providing continuous position expression, supporting real-time scheduling and management, and improving practicality and completeness.

[0128] Methods for path evolution estimation include:

[0129] Obtain the current state and most recent trajectory point of the container: For each container, obtain the current state (staying or moving), the current area and the location records of the two most recent time points from step S5, thereby providing the initial conditions for prediction and determining the prediction starting point.

[0130] Determine the prediction time interval: Set the prediction time interval, which represents the length of time to extrapolate from the current time to the future. If the time interval is too short, the predicted changes will not be obvious. If the time interval is too long, errors will accumulate, causing deviations from the actual path. The time interval can be set according to application requirements or data update frequency to control the balance between prediction accuracy and stability.

[0131] Calculate the container's movement rate: When the container is in motion, the movement rate is calculated based on the two most recent trajectory points. By taking the two most recent time points, the distance change corresponding to the change in area is calculated, and the rate is obtained by dividing by the time interval. This rate reflects the container's movement trend in space.

[0132] Execution of position prediction calculation: Different prediction methods are used depending on the state. If the container is in a stationary state, the area is stable, and there is no obvious trend of movement, it is assumed that the position will not change within the prediction time, and the predicted position is equal to the current position in the area. If the container is in a moving state and there is a clear direction of movement, the future position is estimated based on the movement rate. Starting from the current area, the future position is estimated by extending the path according to the direction of change of the most recent trajectory and the movement rate.

[0133] Perform region mapping correction: Since the prediction results may fall between region boundaries or not belong to any defined region, region mapping is required to map the predicted location to the nearest region, maintain data structure consistency, avoid invalid locations, and improve stability.

[0134] Output location prediction results: Output prediction results for each container, including container identifier, prediction time (the future time point corresponding to the prediction result), and prediction area or location (the spatial location of the container at the prediction time point).

[0135] Furthermore, the movement rate calculation includes:

[0136]

[0137] in:

[0138] :container The rate of movement, i.e. the number of areas traversed per unit time, is used to describe the degree of spatial change of a container per unit time.

[0139] : The region distance function is used to calculate the topological distance between regions. The distance between adjacent regions is 1, the distance between non-adjacent regions is the path length, and the distance within the same region is 0.

[0140] :container exist The region at that time point represents the spatial location of the container at that point in time, which is obtained through the region assignment result of step S3;

[0141] :container exist The region of time;

[0142] The container's movement rate is calculated by dividing the change in area between two time points by the time interval.

[0143] Furthermore, the location estimation includes:

[0144]

[0145] in:

[0146] :container exist The predicted region at that time represents the predicted location of the container at a future time.

[0147] The prediction time interval represents the length of time from the current time to the future.

[0148] The predicted position of the container is calculated by combining its current position with its movement speed and the predicted time interval.

[0149] Example 2:

[0150] In practical applications, this invention can be applied to the management of anti-static turnover containers in semiconductor packaging production lines, enabling precise positioning, trajectory tracking, and retention identification of containers across multiple processes. The invention is illustrated using the typical scenario of "container cross-process flow in integrated circuit packaging and testing workshops".

[0151] In this scenario, the packaging and testing workshop includes a material loading area, a bonding area, a packaging area, a testing area, and a finished product buffer area, all connected by fixed channels. First, an UHF RFID electronic tag is attached to each anti-static turnover container, and each tag is assigned a unique identifier. The tags employ a high-temperature resistant, anti-static packaging structure, operate in the 920 to 925 MHz frequency band, and have a read / write distance of 2.5 meters to ensure stable identification performance even in metal equipment environments.

[0152] Fixed RFID readers are installed at the entrances of each area's passageways. These readers periodically transmit interrogation signals. When a container passes through the passageway, its tag is simultaneously identified by multiple readers, recording the following data: tag identifier, reader number, received signal strength, and timestamp. The identification results from multiple readers are then aggregated to form a set of passageway identification results within the same time window.

[0153] Subsequently, the signal strength of the same container on multiple readers is calculated. The signal strength value of each reader is normalized, and the signals of all readers within the same area are weighted and summed to obtain the comprehensive signal strength of each area. By comparing the signal strength of each area, the area corresponding to the maximum value is selected as the area to which the container belongs at the corresponding time, thus obtaining the area sequence of the container at each time point.

[0154] After the region sequence is generated, the data for each container is arranged in ascending order of time, and data that remain unchanged within a continuous time period are divided into the same segment. When the time interval between adjacent intervals is less than 5 seconds, the intermediate time point is completed using a state continuation method; when the time interval is greater than 30 seconds, it is marked as a trajectory breakpoint and no completion is performed. Through this process, a continuous trajectory sequence is formed for each container, and the trajectory includes the time sequence, region sequence, and breakpoint information.

[0155] Based on the trajectory sequence, the dwell time of each region is analyzed. The time when the container enters a region and the time when it leaves the region are recorded, and the dwell time is calculated. When the dwell time exceeds a preset threshold (e.g., 600 seconds for the bonding region and 900 seconds for the testing region), the container is marked as being in a stagnant state; otherwise, it is marked as being in a normal flow state. This state is used to identify production line bottlenecks. For example, if a batch of containers lingers in the testing region for an extended period, it can be determined that there is a processing delay in the testing equipment.

[0156] During location prediction, the current time point is used as a baseline, and a prediction time interval (e.g., 10 seconds) is set. For containers in a stationary state, their predicted location is directly set as the current region, assuming they will not move in the short term. For containers in a moving state, the regional changes at the two most recent time points are selected, their regional movement rate is calculated, and the future location is estimated by extending along the existing path according to this rate. If the calculated result lies between region boundaries, it is mapped to the nearest actual region, ensuring that the prediction result always belongs to the region set.

[0157] Through the above implementation methods, continuous positioning, trajectory reconstruction and status identification of antistatic turnover containers in the packaging and testing workshop can be achieved without human intervention. Even when RFID is not read in real time, the predicted position can still be output, thereby supporting practical needs such as production line scheduling, anomaly detection and logistics optimization.

[0158] Example 3:

[0159] This embodiment also provides a computer device applicable to an RFID-based intelligent positioning and management method for antistatic containers, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the RFID-based intelligent positioning and management method for antistatic containers as proposed in the above embodiment.

[0160] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements an RFID-based intelligent positioning and management method for anti-static turnover containers as proposed in the above embodiments.

[0161] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0162] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0163] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0164] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0165] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0166] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An RFID-based intelligent positioning and management method for anti-static turnover containers, characterized in that, Includes the following steps: S1. By binding RFID tags to antistatic turnover containers, a unique set of identifiers is obtained; S2. Deploy access control identification based on the unique identifier set to obtain the channel identification result; S3. Calculate the regional signal strength based on the channel identification results to obtain the regional attribution results; S4. Perform temporal correlation based on the region attribution results to obtain the trajectory sequence; S5. Calculate the dwell time based on the trajectory sequence to obtain the dwell status result; S6. Based on the results of the stagnation status, perform path evolution calculations to obtain the location prediction results.

2. The intelligent positioning and management method for anti-static turnover containers based on RFID according to claim 1, characterized in that, The RFID tag binding in step S1 includes: By numbering the antistatic turnover containers and installing uniquely identified RFID tags on the container surface at locations that do not affect the antistatic performance, a mapping relationship between the containers and the tags is established, resulting in a set of unique identifiers.

3. The intelligent positioning and management method for anti-static turnover containers based on RFID according to claim 1, characterized in that, The access control identification deployment in step S2 includes: Select key logistics path nodes to install fixed RFID reader devices, set identification trigger conditions, and identify containers passing through based on a unique identifier set to obtain channel identification results.

4. The intelligent positioning and management method for anti-static turnover containers based on RFID according to claim 1, characterized in that, The regional signal strength calculation in step S3 includes: Based on the obtained channel identification results, the entire working space is divided into multiple regions with clear boundaries. Multiple readers are deployed in each region to obtain the signal strength when the container is read and to perform weighted and comprehensive processing to obtain a comprehensive signal value. By comparing and selecting the magnitude of the comprehensive signal value, the region assignment result is obtained.

5. The intelligent positioning and management method for anti-static turnover containers based on RFID according to claim 4, characterized in that, The combined signal value includes: in: Container area The overall signal value within; Reader In the region Signal strength in; Reader In the region The weighting coefficients in the text; The number of readers / writers in a given area; The overall signal value is calculated by weighting and combining the signal strengths of all readers in the area.

6. The intelligent positioning and management method for anti-static turnover containers based on RFID according to claim 1, characterized in that, The timing correlation in step S4 includes: Based on the obtained regional attribution results, an initial trajectory sequence is constructed by arranging them in chronological order, and time intervals are detected. By completing the time intervals and marking breakpoints, a trajectory sequence is formed.

7. The intelligent positioning and management method for anti-static turnover containers based on RFID according to claim 1, characterized in that, The dwell time calculation in step S5 includes: Based on the trajectory sequence of each container, the entry and exit times of the containers are determined by identifying continuous area segments, and the dwell time in the area is calculated to determine the dwell status and obtain the dwell status result.

8. The intelligent positioning and management method for anti-static turnover containers based on RFID according to claim 1, characterized in that, The path evolution calculation in step S6 includes: Based on the obtained stagnation status results, the current state of the container and the nearest trajectory point are obtained, the prediction time interval is determined, and the movement rate and position are calculated based on the container state to obtain the position prediction result.

9. The intelligent positioning and management method for anti-static turnover containers based on RFID according to claim 8, characterized in that, The movement rate calculation includes: in: :container The rate of movement; : Region distance function, used to calculate the topological distance between regions; :container exist The region of time; :container exist The region of time; The container's movement rate is calculated by dividing the change in area between two time points by the time interval.

10. The intelligent positioning and management method for anti-static turnover containers based on RFID according to claim 8, characterized in that, The location estimation includes: in: :container exist The predicted region at that time; Predicted time interval; The predicted position of the container is calculated by combining its current position with its movement speed and the predicted time interval.

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