Fixed asset checking method and system based on RFID

Abstract

The invention relates to an RFID-based fixed asset checking method and system, and the method comprises the steps: collecting the historical read data of a current time period, and obtaining a reference checking period of the current time period through the analysis of the historical read data; according to the method, the RFID tags are stored in the storage units, the shelf images at multiple time points in the current time period are analyzed, the activeness of the RFID tags in the storage units is analyzed, the inventory cycle of the fixed assets with the high activeness is shortened, differences can be found and corrected in time through high-frequency inventory, it is ensured that inventory data of key assets are real-time and accurate, and the risk of information errors is reduced. And for fixed assets with relatively low activity, the checking period of the fixed assets is prolonged. Limited inventory resources (manpower and time) are concentrated on key fixed assets, resource waste on low-activity materials is avoided, and the overall inventory efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a fixed asset inventory method and system based on RFID. Background Technology

[0002] Traditional fixed asset management relies on manual methods, which carries risks such as items not being inventoried or verified for years, and a lack of understanding of their specific conditions. Operations lack system support, have low levels of digitalization, and are not integrated with management systems. The identification and counting of fixed assets are entirely manual, resulting in low efficiency; counting 20,000 fixed assets requires two people working for 10 hours. Therefore, current technology typically utilizes RFID (Radio Frequency Identification) tags to bind fixed assets for inventory purposes. During inventory, the radio frequency information from the RFID tags is read to update and record information.

[0003] Existing RFID tags are divided into active and passive tags. Due to cost limitations, most scenarios still use passive tags. Passive tags rely on the reader's energy for activation, which is low-cost but requires proximity to the reader. Therefore, even when using passive RFID tags, workers still need to carry reading devices close to the tags to read information, resulting in relatively low efficiency. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a fixed asset inventory method and system based on RFID to solve the above-mentioned technical problems.

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

[0006] The RFID-based fixed asset inventory method of the present invention includes the following steps:

[0007] The system acquires historical reading data of multiple RIFD tags at multiple time points within the current time period, and acquires shelf images at multiple time points within the current time period. The historical reading data includes reading time points and identification information. The RIFD tags are bound one-to-one with fixed assets, which are placed on shelves. The current time period is the time period of the target duration prior to the current time point.

[0008] The inventory cycle for each RFID tag is calculated based on historical reading data from multiple time points, thus obtaining the baseline inventory cycle for each RFID tag.

[0009] The RFID tags bound to the fixed assets in each storage unit in the shelf image are determined based on a pre-constructed RFID tag-storage unit mapping table for the current inventory period; the change status of the fixed assets in the storage unit is determined based on the shelf images at any adjacent time points to obtain the change determination result; and the activity level of each RFID tag is calculated based on the change determination result of each storage unit. The RFID tag-storage unit mapping table for the current inventory period is generated based on the inventory results of the previous inventory period.

[0010] The baseline inventory cycle for each RFID tag is adjusted based on the activity level to obtain the adjusted inventory cycle; and the inventory time point for the RFID tag is determined based on the adjusted inventory cycle.

[0011] In one embodiment of this application, the inventory cycle of each RFID tag is calculated based on historical reading data from multiple time points to obtain a baseline inventory cycle for each RFID tag, including:

[0012] Extract the reading time point of each RFID tag from the historical reading data;

[0013] For each RFID tag, calculate the time difference between any two adjacent time points to obtain multiple inventory cycles;

[0014] For each RFID tag, the average value of multiple inventory cycles is calculated to obtain the baseline inventory cycle.

[0015] In one embodiment of this application, the method for constructing the RFID tag-storage unit mapping table for the current inventory period includes:

[0016] Obtain the RFID tag-storage unit correspondence table of the previous inventory period and the change information of the previous judgment period. The change information includes the storage units that have been changed and the identification information of the changed RFID tags.

[0017] Based on the change information of the previous judgment period, the RFID tag-storage unit correspondence table of the previous inventory period is modified to obtain the RFID tag-storage unit correspondence table of the current inventory period.

[0018] In one embodiment of this application, the change status of fixed assets within a storage unit is determined based on a shelf image at any given time point, resulting in a change determination result, including:

[0019] For the shelf image Preprocessing is performed to obtain a preprocessed image. The preprocessing methods include grayscale conversion and high-pass filtering. Indicates a point in time;

[0020] Extract the preprocessed image The contours in the data are filtered to identify closed contours, and the size features of the closed contours are extracted. The size features include the length and width of the minimum bounding rectangle, the rectangularity, and the contour area.

[0021] The system matches the pre-configured size screening parameters with the size features of the closed contour, and uses the closed contour that matches the size screening parameters as the overall outer contour of the shelf. The size filtering parameters include length range, width range, rectangularity range, and contour area range.

[0022] Based on the overall outer contour of the shelf Construct a mask image, and extract data from the preprocessed image based on the mask image. Extracting shelf area images ;

[0023] Obtain a pre-constructed affine transformation matrix, and apply the pre-constructed affine transformation matrix to the image of the shelf area. Perform an affine transformation to obtain the front view image of the shelving area. ;

[0024] Front view of the shelving area Extract multiple storage cell images and extract the contour distribution features of the multiple storage cell images. and grayscale distribution characteristics ,in, Indicates the sequence number of the storage unit;

[0025] For any two adjacent time points, the contour distribution characteristics of storage cells in the same location will be... and grayscale distribution characteristics By comparing the results, we can obtain the determination of the changes.

[0026] In one embodiment of this application, a front view image of the shelf area is shown. Extract multiple storage cell images and extract the contour distribution features of the multiple storage cell images. And, including:

[0027] Pre-built shelf mask Front view image of the shelving area Alignment is performed, and the alignment is based on the aligned shelf mask. Front view of the shelving area Extract storage unit image Among them, the storage unit image The mathematical expression is:

[0028]

[0029] For the image of the storage unit Morphological processing is performed to obtain a morphologically processed image;

[0030] Extract the contours from the morphologically processed image; and remove contours with an area smaller than a preset screening threshold to obtain candidate contours of fixed assets.

[0031] The morphologically processed image is meshed to obtain a mesh image; each mesh in the mesh image is calculated. Percentage of outline pixels in candidate outlines of internal fixed assets , ,in, For grid The number of outline pixels in the image. This represents the total number of grid pixels.

[0032] Extract the average gray value of each grid cell The average gray value is then normalized to obtain a normalized gray value. ;

[0033] Contour pixel percentage based on multiple grids Constructing vectorized contour distribution features And based on the normalized gray values ​​of multiple grids. Constructing vectorized grayscale distribution features .

[0034] In one embodiment of this application, the contour distribution features of storage cells with the same location are... and grayscale distribution characteristics The comparison yields the change determination results, including:

[0035] The contour distribution characteristics of each storage cell and grayscale distribution characteristics Combined into feature vectors ;

[0036] Calculate the cosine similarity of the feature vectors of storage units at any two adjacent time points. Wherein, the cosine similarity The mathematical expression is:

[0037]

[0038] When the cosine similarity is greater than or equal to a set similarity threshold, it is determined that the storage unit is at time point [time missing]. Time If the time interval remains unchanged, otherwise, determine that the storage unit is at the specified time point. Time The time periods between them vary.

[0039] In one embodiment of this application, the activity level of each RFID tag is calculated based on the change determination result of each storage unit, including:

[0040] For each storage unit, calculate the number of time points in the current time period that have changed. ;

[0041] Based on the total number of time points in the time period and the number of time points in time when changes occurred Calculate activity , .

[0042] In one embodiment of this application, the adjusted inventory cycle The mathematical expression is:

[0043]

[0044] In the formula, Indicates the baseline activity level. For storage units The baseline inventory cycle for internal RFID tags. Represents storage unit Activity level.

[0045] In one embodiment of this application, determining the inventory time point of RFID tags based on the adjusted inventory cycle includes:

[0046] Based on the adjusted cycle and the latest reading time, the theoretical inventory time for each RFID tag is determined;

[0047] Based on the theoretical inventory time points, multiple RFID tags are density-clustered to obtain multiple clusters; and the average of the theoretical inventory time points of multiple RFID tags in each cluster is calculated to obtain the inventory time points of the RFID tags.

[0048] This application also provides an RFID-based fixed asset inventory system, including:

[0049] The acquisition module is used to acquire historical reading data of multiple RIFD tags at multiple time points within the current time period, and to acquire shelf images at multiple time points within the current time period. The historical reading data includes reading time points and identification information. The RIFD tags are bound one-to-one with fixed assets, which are placed on shelves. The current time period is the time period of the target duration before the current time point.

[0050] The cycle calculation module is used to calculate the inventory cycle of each RFID tag based on historical reading data from multiple time points, and obtain the baseline inventory cycle of each RFID tag.

[0051] The activity calculation module is used to determine the bound RFID tags of fixed assets in each storage unit in the shelf image based on a pre-built RFID tag-storage unit correspondence table for the current inventory period; to determine the change status of fixed assets in the storage unit based on shelf images at any adjacent time points, and to obtain the change determination result; and to calculate the activity of each RFID tag based on the change determination result of each storage unit, wherein the RFID tag-storage unit correspondence table for the current inventory period is generated based on the inventory results of the previous inventory period;

[0052] The inventory management module is used to adjust the baseline inventory cycle of each RFID tag based on the activity level to obtain the adjusted inventory cycle; and to perform inventory based on the adjusted inventory cycle.

[0053] The beneficial effects of this invention are as follows: The RFID-based fixed asset inventory method and system of this invention collects historical reading data for the current time period. By analyzing the historical reading data, a baseline inventory cycle for the current time period is obtained. Then, shelf images at multiple time points within the current time period are analyzed to determine the activity level of RFID tags in each storage unit. For fixed assets with high activity levels, their inventory cycle is shortened. Through high-frequency inventory counting, discrepancies can be detected and corrected in a timely manner, ensuring the real-time accuracy of inventory data for key assets and reducing the risk of information errors. For fixed assets with low activity levels, their inventory cycle is extended. This application concentrates limited inventory resources (manpower and time) on key fixed assets, avoiding waste of resources on low-activity materials and improving overall inventory efficiency. Attached Figure Description

[0054] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0055] Figure 1 This is a diagram illustrating an application scenario of an RFID-based fixed asset inventory method in one embodiment of this application.

[0056] Figure 2 This is a flowchart illustrating an RFID-based fixed asset inventory method in one embodiment of this application;

[0057] Figure 3 This is a schematic diagram of the entire process of activity generation in one embodiment of this application;

[0058] Figure 4This is a structural diagram of an RFID-based fixed asset inventory system shown in one embodiment of this application;

[0059] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0060] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0061] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the layers related to the present invention and are not drawn according to the actual number, shape and size ratio of the layers in the actual implementation. In the actual implementation, the form and number of each layer can be arbitrarily changed, and the layer layout may also be more complex.

[0062] Numerous details are explored in the following description to provide a more thorough explanation of embodiments of the invention; however, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details.

[0063] Figure 1 This is a diagram illustrating an application scenario of an RFID-based fixed asset inventory method in one embodiment of this application. Figure 1 As shown, some fixed assets (such as industrial equipment, instruments, etc.) in this application are placed in the storage units of the shelf 110, and the fixed assets are bound with RFID tags 120. In addition, the scenario also includes a surveillance camera 130, which is tilted towards the shelf 110 and uploads the collected images to the server 140. After analysis, the server 140 sends the analysis results to the terminal 150, and the inventory personnel can perform asset inventory according to the prompts on the terminal 150.

[0064] Figure 2 This is a flowchart illustrating an RFID-based fixed asset inventory method in one embodiment of this application, as shown below. Figure 2 Note: The RFID-based fixed asset inventory method of this embodiment may include steps S210 to S250:

[0065] S210, acquire historical reading data of multiple RIFD tags at multiple time points within the current time period, and acquire shelf images at multiple time points within the current time period. The historical reading data includes reading time points and identification information. The RIFD tags are bound one-to-one with fixed assets, which are placed on shelves. The current time period is the time period before the target duration of the current time point.

[0066] This application uses data from the week preceding the current point in time as the basis for the baseline inventory cycle.

[0067] The shelf images in this application are acquired either by timed acquisition or by pressure change triggering acquisition. Among them, pressure change triggering acquisition is more accurate. A pressure sensor is set under the storage unit to trigger the shooting when the pressure data changes.

[0068] S220: Calculate the inventory cycle of each RFID tag based on historical reading data from multiple time points to obtain the baseline inventory cycle of each RFID tag;

[0069] In this application, the inventory data from the previous week is used as the base data, and analysis is performed based on the base data to obtain the baseline inventory cycle, specifically including:

[0070] S221, Extract the reading time point of each RFID tag from the historical reading data;

[0071] During operation, RFID systems continuously record the reading events of each tag (such as timestamps and locations). Historical reading data is typically stored in a database or log file, containing the tag's unique identifier (EPC code) and the time of each reading.

[0072] By extracting the reading time point of each tag, its time series data can be constructed. For example, the reading time point of tag A is... .

[0073] S222, for each RFID tag, calculate the time difference between any two adjacent time points to obtain multiple inventory cycles;

[0074] Inventory cycle ;

[0075] S223, for each RFID tag, calculate the average value of multiple inventory cycles to obtain the baseline inventory cycle.

[0076] Finally, the average value of multiple inventory cycles for each RFID tag is calculated, i.e. This yields the baseline inventory cycle.

[0077] S230: Based on a pre-built RFID tag-storage unit mapping table for the current inventory period, determine the RFID tags bound to fixed assets in each storage unit in the shelf image; determine the change status of fixed assets in the storage unit based on shelf images at any adjacent time points, and obtain change determination results; and calculate the activity level of each RFID tag based on the change determination results of each storage unit, wherein the RFID tag-storage unit mapping table for the current inventory period is generated based on the inventory results of the previous inventory period;

[0078] Since inventory data for fixed assets is obtained through reading RFID tags, an RFID tag-storage unit mapping table for the current inventory period is needed to determine the location of different RFID tags, thereby determining the location of the fixed assets. The RFID tag-storage unit mapping table for the current inventory period is constructed using a rolling information update mechanism. Figure 3 This is a schematic diagram of the entire process of activity generation in one embodiment of this application, as shown below. Figure 3 As shown, the entire process includes:

[0079] S2301, Obtain the RFID tag-storage unit correspondence table of the previous inventory cycle and the change information of the previous judgment cycle, wherein the change information includes the storage units that have been changed and the identification information of the changed RFID tags;

[0080] The RFID tag-storage unit mapping table records the correspondence between each RFID tag and a storage unit (such as a shelf, pallet, or storage location). For example, tag A is located in unit A1 of shelf 1, and tag B is located in unit B3 of shelf 2.

[0081] S2302, Based on the change information of the previous judgment period, modify the RFID tag-storage unit correspondence table of the previous inventory period to obtain the RFID tag-storage unit correspondence table of the current inventory period.

[0082] Based on the comparison table of the previous inventory cycle, incremental updates are performed in conjunction with change information, including:

[0083] Add: Add any new RFID tags or storage units to the table.

[0084] Delete: Remove expired or invalid tags or storage units.

[0085] Modification: Adjust the correspondence between tags and storage units based on the change information (e.g., move tags).

[0086] Secondly, based on shelf images at any adjacent time points, the change status of fixed assets within the storage unit is determined, and the change determination result is obtained based on machine vision processing technology, specifically including:

[0087] S2311, regarding the shelf image Preprocessing is performed to obtain a preprocessed image. The preprocessing methods include grayscale conversion and high-pass filtering. Indicates a point in time;

[0088] First, the color image is converted to grayscale to reduce computational complexity and remove color interference. Then, a high-pass filter is used to enhance high-frequency information (such as edges and contours) in the image and suppress low-frequency background noise, resulting in a preprocessed image. The metal frame of the shelf and the boundaries of the items are more clearly defined after high-pass filtering, which facilitates subsequent contour extraction.

[0089] S2312, Extract the preprocessed image The contours in the data are filtered to identify closed contours, and the size features of the closed contours are extracted. The size features include the length and width of the minimum bounding rectangle, the rectangularity, and the contour area.

[0090] Based on edge detection (such as the Canny operator) or region growing, continuous boundaries in the image are identified. Broken or open contours are excluded by judging the closure of the contours (e.g., the start and end points coincide) or by using connected component analysis. Then, the length of the minimum bounding rectangle of the contour is calculated. Hekuan And calculate its rectangularity. and outline area The formula for calculating rectangularity is:

[0091]

[0092] S2313, Match the pre-configured size screening parameters with the size features of the closed contour, and use the closed contour that matches the size screening parameters as the overall outer contour of the shelf. The size filtering parameters include length range, width range, rectangularity range, and contour area range.

[0093] Size filtering parameters include length range Width range Rectangularity range and the range of the outline area A match is indicated when the following conditions are met simultaneously.

[0094]

[0095]

[0096]

[0097]

[0098] Set a threshold range (e.g., length range [200, 300] pixels) based on the actual dimensions of the shelf (e.g., length, width, rectangularity, area). Compare the dimensional features of each closed contour with the preset parameters item by item, and retain only contours that meet all conditions.

[0099] S2314, based on the overall outer contour of the shelf Construct a mask image, and extract data from the preprocessed image based on the mask image. Extracting shelf area images ;

[0100] Using the outer contour of the shelf as a mask, the non-shelf areas in the image are set to zero, leaving only the shelf areas.

[0101] S2315, Obtain the pre-constructed affine transformation matrix, and apply the pre-constructed affine transformation matrix to the shelf area image. Perform an affine transformation to obtain the front view image of the shelving area. ;

[0102] The image of the shelving area and a pre-defined affine matrix (e.g., obtained through calibration). Affine transformation matrices (e.g., rotation, translation, scaling) are used to correct for perspective distortion (e.g., view distortion) in the shelving area. Affine transformations eliminate differences in shooting angles, ensuring the accuracy of subsequent storage unit extraction. Masked images reduce background interference and improve processing efficiency.

[0103] S2316, Front view image of the shelf area Extract multiple storage cell images and extract the contour distribution features of the multiple storage cell images. and grayscale distribution characteristics ,in, Indicates the sequence number of the storage unit;

[0104] Based on the grid structure of the shelf front view image (such as fixed-interval storage locations), a mask extraction method is used to extract multiple storage cell images, and then the grid features (contour distribution features) of the fixed assets within the storage cell are extracted. and grayscale distribution characteristics The specific process is as follows:

[0105] S23161, pre-built shelf mask Front view image of the shelving area Alignment is performed, and the alignment is based on the aligned shelf mask. Front view of the shelving area Extract storage unit image ;

[0106] In this process, affine transformations or feature matching algorithms (such as SIFT and ORB) are used to precisely align the shelf mask with the front view image, eliminating visual deviations. For example, if the shelf mask is a template based on a standard shelf design, it needs to be aligned with the current front view image through translation, rotation, or scaling to ensure that the mask covers the shelf area.

[0107] Using the aligned mask as a binary mask, and combining it with the grid structure of the shelf front view image (such as a preset row and column division), the image region of each storage unit is segmented. The storage unit image... The mathematical expression is:

[0108]

[0109] In the above process, mask alignment is used to ensure that the shelf area is not offset, thus avoiding misalignment of storage units due to differences in viewing angle.

[0110] S23162, regarding the image of the storage unit Morphological processing is performed to obtain a morphologically processed image;

[0111] Morphological processing includes opening and closing operations. Opening involves erosion followed by dilation, eliminating small noises while preserving the contour shape. Closing involves dilation followed by erosion, filling small gaps. This eliminates noise and artifacts in the stored image, improving the accuracy of contour detection.

[0112] S23163, Extract the contours from the morphologically processed image; and remove contours with an area smaller than a preset screening threshold to obtain candidate contours of fixed assets;

[0113] Based on the morphologically processed image, contours are extracted using edge detection algorithms (such as Canny) or connected component analysis. A threshold is then set based on the minimum size of the fixed asset (such as label size or item volume). For example, contours with an area less than 100 pixels may be noise or irrelevant objects. Noise or irrelevant objects are removed, retaining only the contour features of the fixed assets. Area filtering reduces false detections, ensuring that subsequent analysis focuses on valid targets.

[0114] S23164, The morphologically processed image is meshed to obtain a mesh image; each mesh in the mesh image is calculated. Percentage of outline pixels in candidate outlines of internal fixed assets , ,in, For grid The number of outline pixels in the image. This represents the total number of grid pixels.

[0115] The storage unit image is divided into a uniform grid (such as a 4×4 or 8×8 grid), with each grid corresponding to a local region. Specifically, gridding is achieved through a sliding window or a fixed row and column division method.

[0116] By dividing the storage unit into sub-regions through gridding, local contour distribution patterns (such as item offset or tilt) are captured. Even if an item is partially occluded or its position is offset, the local proportion can still reflect its existence.

[0117] S23165, Extract the average gray value of each grid cell. The average gray value is then normalized to obtain a normalized gray value. ;

[0118] The mathematical expression for normalization is:

[0119]

[0120] Normalization eliminates the impact of ambient lighting differences on grayscale values, ensuring consistent contrast across different time points. The average grayscale value of the grid reflects the grid fill level of the storage cell (e.g., high grayscale in empty spaces and low grayscale when fully occupied).

[0121] S23166, Contour pixel percentage based on multiple grids Constructing vectorized contour distribution features And based on the normalized gray values ​​of multiple grids. Constructing vectorized grayscale distribution features .

[0122] Arrange the outline pixels of all grids into a vector in order of percentage, where:

[0123] ; .

[0124] S2317, for any two adjacent time points, the contour distribution characteristics of storage cells with the same location. and grayscale distribution characteristics By comparing the results, we can obtain the determination of the changes.

[0125] Since the vectorized contour distribution features were extracted in the previous text... and grayscale distribution characteristics Therefore, vector similarity calculation is used for comparison, and the specific process is as follows:

[0126] S23171, the contour distribution characteristics of each storage cell and grayscale distribution characteristics Combined into feature vectors ;

[0127]

[0128] By combining contour and grayscale information, the limitations of single features are overcome (e.g., contours cannot reflect the material of an object, and grayscale cannot reflect the shape). For example, if a storage cell has a high proportion of contour pixels but a low grayscale value, it may indicate that the object is made of metal; conversely, if the grayscale value is high but the proportion of contour pixels is low, it may indicate that the object is made of a semi-transparent material. Feature vectors integrate shape and grayscale information, improving the robustness of change detection. Even if a single feature has noise (e.g., changes in lighting affect grayscale), another feature can still provide supplementary information.

[0129] S23172, Calculate the cosine similarity of the feature vectors of the storage unit at any two adjacent time points. Wherein, the cosine similarity The mathematical expression is:

[0130]

[0131] The cosine similarity ranges from [-1, 1]. The closer the value is to 1, the more similar the directions of the two vectors are (i.e., the more stable the storage unit state).

[0132] S23173, when the cosine similarity is greater than or equal to a set similarity threshold, determine that the storage unit is at time point... Time If the time interval remains unchanged, otherwise, determine that the storage unit is at the specified time point. Time The time periods between them vary.

[0133] Cosine similarity A value greater than 0.8 indicates that the storage cell state has not changed significantly (e.g., the item has not been moved or replaced). Otherwise, it indicates that the storage cell state has changed significantly (e.g., the item has been moved in / out or its position has been adjusted).

[0134] After analyzing the changes in the status of fixed assets at multiple time points using machine vision technology, the activity level of each type of fixed asset (bound to RFID tags and storage units) can be further calculated. The specific process is as follows:

[0135] S2321, For each storage unit, calculate the number of time points in the current time period that have changed. ;

[0136] S2322, based on the total number of time points in the time period and the number of time points in time when changes occurred Calculate activity , .

[0137] In this application, the activity level is represented by the ratio of active time points to all time points. The activity level ranges from [0,1]. In addition, the activity level calculation can be further optimized by combining weighted time points (such as giving higher weight to high-frequency monitoring periods).

[0138] S240, adjust the baseline inventory cycle of each RFID tag based on the activity level to obtain the adjusted inventory cycle; and determine the inventory time point of the RFID tag based on the adjusted inventory cycle.

[0139] Specifically, the adjusted inventory cycle The mathematical expression is:

[0140]

[0141] In the formula, Indicates the baseline activity level. For storage units The baseline inventory cycle for internal RFID tags.

[0142] In the above calculation formula, a pre-built benchmark activity level is used for proportional calculation. If it is greater than the benchmark activity level, it indicates that the activity level is high, and the corresponding adjusted inventory period is determined. Increase, anyway, the adjusted inventory cycle Shrink.

[0143] Finally, since the adjustment may result in multiple storage units having multiple different inventory count times, in order to save inventory count costs, a clustering approach is used to unify the inventory count time. Specifically, the inventory count time for RFID tags is determined based on the adjusted inventory count cycle, including:

[0144] S241, Based on the adjusted cycle and the latest reading time, determine the theoretical inventory time for each RFID tag;

[0145] S242, perform density clustering on multiple RFID tags based on the theoretical inventory time points to obtain multiple clusters; and calculate the average of the theoretical inventory time points of multiple RFID tags in each cluster to obtain the inventory time points of the RFID tags.

[0146] In the above process, the density-based spatial clustering (DBSCAN) algorithm is used to divide multiple RFID tags into multiple clusters. The inventory time points of RFID tags within a cluster are similar; therefore, the average value within the cluster is further calculated to obtain a typical inventory time point. Inventory personnel can refer to this time point to perform the inventory for the next inventory cycle. The inventory location can be determined by referring to the RFID tag-storage unit mapping table described earlier.

[0147] This invention discloses an RFID-based fixed asset inventory method. The method collects historical data for the current time period and analyzes this data to obtain a baseline inventory cycle for the current time period. Then, it analyzes shelf images at multiple time points within the current time period to determine the activity level of RFID tags in each storage unit. For fixed assets with high activity levels, the inventory cycle is shortened. High-frequency inventory checks allow for timely detection and correction of discrepancies, ensuring real-time accuracy of key asset inventory data and reducing the risk of information errors. For fixed assets with low activity levels, the inventory cycle is extended. This method concentrates limited inventory resources (manpower and time) on key fixed assets, avoiding waste on low-activity items and improving overall inventory efficiency.

[0148] like Figure 4 As shown, this application also provides an RFID-based fixed asset inventory system, including:

[0149] The acquisition module is used to acquire historical reading data of multiple RIFD tags at multiple time points within the current time period, and to acquire shelf images at multiple time points within the current time period. The historical reading data includes reading time points and identification information. The RIFD tags are bound one-to-one with fixed assets, which are placed on shelves. The current time period is the time period of the target duration before the current time point.

[0150] The cycle calculation module is used to calculate the inventory cycle of each RFID tag based on historical reading data from multiple time points, and obtain the baseline inventory cycle of each RFID tag.

[0151] The activity calculation module is used to determine the bound RFID tags of fixed assets in each storage unit in the shelf image based on a pre-built RFID tag-storage unit correspondence table for the current inventory period; to determine the change status of fixed assets in the storage unit based on shelf images at any adjacent time points, and to obtain the change determination result; and to calculate the activity of each RFID tag based on the change determination result of each storage unit, wherein the RFID tag-storage unit correspondence table for the current inventory period is generated based on the inventory results of the previous inventory period;

[0152] The inventory management module is used to adjust the baseline inventory cycle of each RFID tag based on the activity level to obtain the adjusted inventory cycle; and to perform inventory based on the adjusted inventory cycle.

[0153] This invention relates to an RFID-based fixed asset inventory system. The system collects historical data for the current time period and analyzes this data to determine the baseline inventory cycle for that period. Then, it analyzes shelf images at multiple time points within the current time period to assess the activity level of RFID tags in each storage unit. For fixed assets with high activity levels, the inventory cycle is shortened. High-frequency inventory checks allow for timely detection and correction of discrepancies, ensuring real-time accuracy of key asset inventory data and reducing the risk of information errors. For fixed assets with low activity levels, the inventory cycle is extended. This system concentrates limited inventory resources (manpower and time) on key fixed assets, avoiding waste on low-activity items and improving overall inventory efficiency.

[0154] Figure 5 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 5 The computer system of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0155] like Figure 5As shown, the computer system includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 502 or programs loaded from storage portion 508 into Random Access Memory (RAM) 503. The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An Input / Output (I / O) interface 505 is also connected to the bus 504.

[0156] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0157] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs various functions defined in the system of this application.

[0158] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0159] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0160] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0161] Another aspect of this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0162] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various embodiments described above.

[0163] The above embodiments are merely preferred embodiments provided to fully illustrate this application, and the scope of protection of this application is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on this application are all within the scope of protection of this application.

Claims

1. A fixed asset inventory method based on RFID, characterized in that, Including the following steps: The system acquires historical reading data of multiple RIFD tags at multiple time points within the current time period, and acquires shelf images at multiple time points within the current time period. The historical reading data includes reading time points and identification information. The RIFD tags are bound one-to-one with fixed assets, which are placed on shelves. The current time period is the time period of the target duration prior to the current time point. The inventory cycle for each RFID tag is calculated based on historical reading data from multiple time points, thus obtaining the baseline inventory cycle for each RFID tag. The RFID tags bound to the fixed assets in each storage unit in the shelf image are determined based on a pre-constructed RFID tag-storage unit mapping table for the current inventory period; the change status of the fixed assets in the storage unit is determined based on the shelf images at any adjacent time points to obtain the change determination result; and the activity level of each RFID tag is calculated based on the change determination result of each storage unit. The RFID tag-storage unit mapping table for the current inventory period is generated based on the inventory results of the previous inventory period. The baseline inventory cycle for each RFID tag is adjusted based on the activity level to obtain the adjusted inventory cycle; and the inventory time point for the RFID tag is determined based on the adjusted inventory cycle.

2. The RFID-based fixed asset inventory method according to claim 1, characterized in that, The inventory cycle for each RFID tag is calculated based on historical read data from multiple time points, resulting in a baseline inventory cycle for each RFID tag, including: Extract the reading time point of each RFID tag from the historical reading data; For each RFID tag, calculate the time difference between any two adjacent time points to obtain multiple inventory cycles; For each RFID tag, the average value of multiple inventory cycles is calculated to obtain the baseline inventory cycle.

3. The RFID-based fixed asset inventory method according to claim 1, characterized in that, The method for constructing the RFID tag-storage unit mapping table for the current inventory period includes: Obtain the RFID tag-storage unit correspondence table of the previous inventory period and the change information of the previous judgment period. The change information includes the storage units that have been changed and the identification information of the changed RFID tags. Based on the change information of the previous judgment period, the RFID tag-storage unit correspondence table of the previous inventory period is modified to obtain the RFID tag-storage unit correspondence table of the current inventory period.

4. The RFID-based fixed asset inventory method according to claim 1, characterized in that, The changes in the status of fixed assets within the storage unit are determined based on shelf images at any adjacent time points, yielding the following results: For the shelf image Preprocessing is performed to obtain a preprocessed image. The preprocessing methods include grayscale conversion and high-pass filtering. Indicates a point in time; Extract the preprocessed image The contours in the data are filtered to identify closed contours, and the size features of the closed contours are extracted. The size features include the length and width of the minimum bounding rectangle, the rectangularity, and the contour area. The system matches the pre-configured size screening parameters with the size features of the closed contour, and uses the closed contour that matches the size screening parameters as the overall outer contour of the shelf. The size filtering parameters include length range, width range, rectangularity range, and contour area range. Based on the overall outer contour of the shelf Construct a mask image, and extract data from the preprocessed image based on the mask image. Extracting shelf area images ; Obtain a pre-constructed affine transformation matrix, and apply the pre-constructed affine transformation matrix to the image of the shelf area. Perform an affine transformation to obtain the front view image of the shelving area. ; Front view of the shelving area Extract multiple storage cell images and extract the contour distribution features of the multiple storage cell images. and grayscale distribution characteristics ,in, Indicates the sequence number of the storage unit; For any two adjacent time points, the contour distribution characteristics of storage cells with the same location will be... and grayscale distribution characteristics By comparing the results, we can obtain the determination of the changes.

5. The RFID-based fixed asset inventory method according to claim 4, characterized in that, Front view of the shelving area Extract multiple storage cell images and extract the contour distribution features of the multiple storage cell images. And, including: Pre-built shelf mask Front view image of the shelving area Alignment is performed, and the alignment is based on the aligned shelf mask. Front view of the shelving area Extract storage unit image Among them, the storage unit image The mathematical expression is: For the image of the storage unit Morphological processing is performed to obtain a morphologically processed image; Extract the contours from the morphologically processed image; and remove contours with an area smaller than a preset screening threshold to obtain candidate contours of fixed assets. The morphologically processed image is meshed to obtain a mesh image; each mesh in the mesh image is calculated. Percentage of outline pixels in candidate outlines of internal fixed assets , ,in, For grid The number of outline pixels in the image. This represents the total number of grid pixels. Extract the average gray value of each grid cell The average gray value is then normalized to obtain a normalized gray value. ; Contour pixel percentage based on multiple grids Constructing vectorized contour distribution features And based on the normalized gray values ​​of multiple grids. Constructing vectorized grayscale distribution features .

6. The RFID-based fixed asset inventory method according to claim 5, characterized in that, The contour distribution characteristics of storage cells in the same location and grayscale distribution characteristics The comparison yields the change determination results, including: The contour distribution characteristics of each storage cell and grayscale distribution characteristics Combined into feature vectors ; Calculate the cosine similarity of the feature vectors of storage units at any two adjacent time points. Wherein, the cosine similarity The mathematical expression is: When the cosine similarity is greater than or equal to a set similarity threshold, it is determined that the storage unit is at time point [time missing]. Time If the time interval remains unchanged, otherwise, determine that the storage unit is at the specified time point. Time The time periods between them vary.

7. The RFID-based fixed asset inventory method according to claim 1, characterized in that, The activity level of each RFID tag is calculated based on the change determination results of each storage unit, including: For each storage unit, calculate the number of time points in the current time period that have changed. ; Based on the total number of time points in the time period and the number of time points in time when changes occurred Calculate activity , .

8. The RFID-based fixed asset inventory method according to claim 1, characterized in that, The adjusted inventory cycle The mathematical expression is: In the formula, Indicates the baseline activity level. For storage units The baseline inventory cycle for internal RFID tags. Represents storage unit Activity level.

9. The RFID-based fixed asset inventory method according to claim 1, characterized in that, The inventory time point for RFID tags is determined based on the adjusted inventory cycle, including: Based on the adjusted cycle and the latest reading time, the theoretical inventory time for each RFID tag is determined; Based on the theoretical inventory time points, multiple RFID tags are density-clustered to obtain multiple clusters; and the average of the theoretical inventory time points of multiple RFID tags in each cluster is calculated to obtain the inventory time points of the RFID tags.

10. An RFID-based fixed asset inventory system, characterized in that, include: The acquisition module is used to acquire historical reading data of multiple RIFD tags at multiple time points within the current time period, and to acquire shelf images at multiple time points within the current time period. The historical reading data includes reading time points and identification information. The RIFD tags are bound one-to-one with fixed assets, which are placed on shelves. The current time period is the time period of the target duration before the current time point. The cycle calculation module is used to calculate the inventory cycle of each RFID tag based on historical reading data from multiple time points, and obtain the baseline inventory cycle of each RFID tag. The activity calculation module is used to determine the bound RFID tags of fixed assets in each storage unit in the shelf image based on a pre-built RFID tag-storage unit correspondence table for the current inventory period; to determine the change status of fixed assets in the storage unit based on shelf images at any adjacent time points, and to obtain the change determination result; and to calculate the activity of each RFID tag based on the change determination result of each storage unit, wherein the RFID tag-storage unit correspondence table for the current inventory period is generated based on the inventory results of the previous inventory period; The inventory management module is used to adjust the baseline inventory cycle of each RFID tag based on the activity level to obtain the adjusted inventory cycle; and to perform inventory based on the adjusted inventory cycle.

Images