RFID-based methods and systems for physical management of fixed assets

Abstract

This invention relates to a method and system for physical management of fixed assets based on RFID. The application first acquires signal strength and location data of RFID tags corresponding to fixed assets in use at multiple historical inventory points. Then, it uses this data to filter data samples, removing time points with abnormal signals due to external interference. Next, it uses precise positioning to construct the inertial dynamic characteristics of each RFID tag to determine whether each tag possesses inertial dynamic characteristics and to extract these characteristics. Simultaneously, the application also uses precise positioning to find the synchronous usage characteristics between different RFID tags, thereby constructing a synchronous inertial map. The inertial dynamic characteristics and synchronous inertial map are used for early warning management and inventory management of RFID tag dynamic characteristics under various conditions, resulting in greater flexibility and better alignment with the usage habits of fixed assets in use.

Description

Technical Field

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

[0002] RFID tag-based fixed asset management is of great significance, for example, enabling rapid asset inventory. Traditional fixed asset management relies on manual methods, which carries risks such as items not being checked and verified for years, and a lack of understanding of their specific conditions. Operational processes 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 inventory efficiency; counting 20,000 fixed assets requires two people working for 10 hours. RFID, through automated data collection, reduces manpower. Furthermore, RFID technology can cover various scenarios where manual inventory is difficult, such as high places, corners, or enclosed containers, allowing for long-distance reading and avoiding the cumbersome process of traditional manual inventory. Simultaneously, RFID tags can be used to quickly compile full-process tracking information for fixed assets, facilitating the management of their status.

[0003] However, RFID technology also presents several problems for the inventory of fixed assets. For example, fixed assets include not only equipment stored on shelves but also equipment in use. Therefore, fixed assets in use (such as laptops, instruments, etc.) do not have fixed locations, so using electronic fences for management would cause unnecessary trouble for users and lack flexibility. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a fixed asset physical management 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 present invention provides a fixed asset physical management method based on RFID, comprising the following steps:

[0007] The system acquires signal strength and location data of RFID tags of fixed assets in use at multiple historical inventory time points, and acquires real-time location data of RFID tags of fixed assets in use. The location data and the real-time location data are calculated by multiple RFID base stations based on the time difference of arrival method, and the signal strength data includes the signal strength measured by multiple RFID base stations.

[0008] Based on the signal strength data and positioning data of multiple RFID tags at multiple historical inventory time points, an inertial database and a synchronous inertial map of the corresponding fixed assets' RFID tags are constructed. The inertial database includes the inertial dynamic characteristics of the signal strength of the RFID tags of each fixed asset, and the synchronous inertial map represents the sharing probability of multiple sets of shared inertial RFID tags.

[0009] Dynamic features are extracted from the real-time location data of the RFID tags of the fixed assets during the target time period;

[0010] The dynamic characteristics and real-time positioning data of the corresponding RFID tags are verified based on the inertial dynamic characteristics to obtain a first verification result; and the real-time positioning data of shared inertial RFID tags are verified based on the synchronous inertial map to obtain a second verification result.

[0011] Based on the first verification result or the second verification result, early warning management and inventory management are carried out for fixed assets in use.

[0012] In one embodiment of this application, an inertial database and a synchronous inertial map of RFID tags for corresponding fixed assets are constructed based on signal strength data and location data of multiple RFID tags at multiple historical inventory time points, including:

[0013] For each RFID tag, the signal strength data of multiple historical inventory time points are located based on the pre-constructed signal strength heatmap of each RFID base station, and the target grid corresponding to the signal strength data of multiple historical inventory time points is obtained. The signal strength heatmap includes multiple grids and the signal strength data value range corresponding to the multiple grids.

[0014] The target grid and positioning data at the same historical inventory time point are compared. When the positioning data falls into the target grid, the signal strength data and positioning data at the corresponding historical inventory time point are determined to be normal. Otherwise, the signal strength data and positioning data at the corresponding historical inventory time point are determined to be abnormal.

[0015] Remove historical time points with abnormal signal strength and positioning data to obtain the target time point;

[0016] An inertial database of RFID tags for a fixed asset is constructed based on the positioning data of multiple RFID tags at multiple target time points; and a synchronous inertial map of RFID tags for a fixed asset is constructed based on the positioning data of multiple RFID tags at multiple target time points.

[0017] In one embodiment of this application, the inertial dynamic characteristic includes a displacement reference period. Displacement reference activity Inertial movement position range and inertial activity time range The location data includes location coordinates and location time points. Specifically, an inertial database of RFID tags for corresponding fixed assets is constructed based on location data from multiple RFID tags at multiple target time points, including:

[0018] For each RFID tag, density clustering is performed on the positioning data of multiple target time points based on the positioning coordinates to obtain multiple positioning clusters. Positioning clusters with more than a preset threshold of sample data within each cluster are designated as target location clusters. An ellipse representing the calculation range of the positioning data within the target location is calculated, and this ellipse is used as the inertial movement position range of the RFID tag. ;

[0019] For each RFID tag, positioning data whose coordinates do not fall within the inertial activity position range, as well as positioning data with empty coordinates, are marked as dynamic positioning data. These dynamic positioning data are then clustered based on their positioning time points to obtain multiple time clusters. Time clusters with more than a preset threshold of sample data within each cluster are designated as target time clusters. An inertial activity time range is then constructed based on the positioning time points of the dynamic positioning data within the target time clusters. ;

[0020] For each RFID tag, the displacement reference activity is calculated based on the dynamic positioning data and the total amount of positioning data. The displacement reference activity The mathematical expression is:

[0021]

[0022] In the formula, The total amount of location data. This indicates the sequence number of the dynamic positioning data. This indicates the quantity of the dynamic positioning data. This indicates the time point corresponding to the current location data. This indicates the sequence number of the dynamic positioning data. This indicates the time point of the first location data. This represents the unit of activity level.

[0023] For each RFID tag, the positioning data from multiple target time points are binarized based on the inertial activity position range to obtain binarized positioning data. Calculate multiple binarized positioning data The autocorrelation function is calculated to obtain an autocorrelation curve. The peak positions of the autocorrelation curve are extracted. When the peak value at a given position exceeds a set threshold, the RFID tag is determined to have periodicity, and the corresponding period is extracted as the displacement reference period of the RFID tag. .

[0024] In one embodiment of this application, a synchronous inertial map of RFID tags for a corresponding fixed asset is constructed based on the positioning data of multiple RFID tags at multiple target time points, including:

[0025] Based on the binary positioning data of each RFID tag Constructing a binarized localization data sequence ,in, Indicates the serial number of the RFID tag;

[0026] Binarized positioning data sequences of any two RFID tags After removing non-common time points, the data is aligned, and then the difference is calculated to obtain the difference sequence. ;

[0027] Calculate the average of the absolute values ​​of multiple binarized localization data in the difference sequence. and the average value The comparison is made with pre-built positive correlation and negative correlation thresholds, wherein the positive correlation threshold is less than the negative correlation threshold.

[0028] In the average value When the correlation is greater than the negative correlation threshold, the two corresponding RFID tags are determined to be negatively correlated; when the average value is greater than the threshold value, the two corresponding RFID tags are determined to be negatively correlated. When the value is less than the positive correlation threshold, the two corresponding RFID tags are determined to be positively correlated; when the average value is less than the threshold value, the two corresponding RFID tags are determined to be positively correlated. When the two RFID tags are between the negative correlation threshold and the positive correlation threshold, they are determined to be uncorrelated.

[0029] Construct a synchronous inertial map based on the correlation between any two RFID tags.

[0030] In one embodiment of this application, extracting dynamic features from the real-time location data of the RFID tag of the fixed asset within a target time period includes:

[0031] Location data for a target time period is extracted from real-time location data, wherein the target time period is the time period of the target duration prior to the current time point;

[0032] Extract displacement activity from the location data of the RFID tag during the target time period. and the current cumulative static duration The current cumulative static duration The continuous duration of RFID tag location data within a target time period;

[0033] Dynamic features are constructed based on the displacement activity and the cumulative static duration.

[0034] In one embodiment of this application, the dynamic characteristics of the corresponding RFID tag and real-time positioning data are verified based on the inertial dynamic characteristics to obtain a first verification result, including:

[0035] The displacement activity of RFID tags during the target time period With the displacement reference activity To make a comparison, in When it is determined that the RFID tag has an abnormal activity risk; At that time, the current cumulative static duration will be... With the reference period To make a comparison, in At that time, it was determined that the RFID tag was at risk of abnormal stillness;

[0036] Obtain the positioning time of the real-time positioning data, and compare the positioning time of the real-time positioning data with the inertial activity time range. The real-time positioning data is compared with the inertial activity position range. For comparison, the positioning time of the real-time positioning data is not within the range of the inertial activity time. Furthermore, the real-time positioning data is not within the range of the inertial movement position. Within a short period of time, it was determined that the RFID tag had a risk of abnormal location.

[0037] In one embodiment of this application, real-time positioning data with shared inertial RFID tags are verified based on the synchronized inertial map to obtain a second verification result, including:

[0038] When the current RFID tag undergoes a location change, the location change status of the current RFID tag is obtained, and the location change status of related RFID tags between the current time and the extended time is obtained. If the location change status of the related RFID tags does not match the corresponding correlation with the location change status of the current RFID tag, it is determined that the current RFID tag has a synchronization anomaly risk.

[0039] In one embodiment of this application, early warning management and inventory management of fixed assets in use are performed based on the first verification result or the second verification result, including:

[0040] When any RFID tag has the risk of abnormal activity, abnormal stillness, abnormal location, or abnormal synchronization, an early warning signal is output to the target object, and the status of the corresponding RFID tag is set to await inventory.

[0041] In one embodiment of this application, it further includes:

[0042] Based on the inertial activity location range and inertial activity time range Construct a dynamic electronic fence and manage the activity range of RFID tags based on the dynamic electronic fence.

[0043] This application also provides an RFID-based fixed asset physical management system, including:

[0044] The acquisition module is used to acquire signal strength data and location data of RFID tags of fixed assets in use at multiple historical inventory time points, and to acquire real-time location data of RFID tags of fixed assets in use. The location data and the real-time location data are calculated by multiple RFID base stations based on the time difference of arrival method, and the signal strength data includes the signal strength measured by multiple RFID base stations.

[0045] The prior data construction module is used to construct an inertial database and a synchronous inertial map of RFID tags for corresponding fixed assets based on the signal strength data and positioning data of multiple RFID tags at multiple historical inventory time points. The inertial database includes the inertial dynamic characteristics of the signal strength of RFID tags for each fixed asset, and the synchronous inertial map represents the sharing probability of multiple sets of shared inertial RFID tags.

[0046] The feature extraction module is used to extract dynamic features from the real-time location data of the RFID tags of the fixed assets during the target time period.

[0047] The verification module is used to verify the dynamic features of the corresponding RFID tag and the real-time positioning data based on the inertial dynamic features to obtain a first verification result; and to verify the real-time positioning data of shared inertial RFID tags based on the synchronous inertial map to obtain a second verification result.

[0048] The management module is used to perform early warning management and inventory management of fixed assets in use based on the first verification result or the second verification result.

[0049] The beneficial effects of this invention are as follows: The RFID-based fixed asset physical management method and system of this invention first acquires signal strength data and location data of RFID tags corresponding to fixed assets in use at multiple historical inventory time points. Then, it uses this signal strength data and location data from multiple historical inventory time points to filter data samples, removing time points with abnormal signals caused by external signal interference. Next, it uses precise positioning to construct the inertial dynamic characteristics of each RFID tag to determine whether each RFID tag possesses inertial dynamic characteristics and to extract these characteristics. Simultaneously, this application also uses precise positioning to find the synchronous usage characteristics between different RFID tags, thereby constructing a synchronous inertial map. The inertial dynamic characteristics and synchronous inertial map are used to perform early warning management and inventory management of RFID tag dynamic characteristics under various conditions. This approach is more flexible and better suited to the usage habits of fixed assets in use. Attached Figure Description

[0050] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0051] Figure 1 This is a flowchart illustrating a fixed asset physical management method based on RFID in one embodiment of this application;

[0052] Figure 2 This is a schematic diagram showing the relative positions of an RFID base station and an RFID tag in one embodiment of this application;

[0053] Figure 3 This is a schematic diagram of a signal strength heatmap in one embodiment of this application;

[0054] Figure 4 This is a structural diagram of an RFID-based fixed asset physical management system shown in one embodiment of this application;

[0055] 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

[0056] 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.

[0057] 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.

[0058] 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.

[0059] Figure 1 This is a flowchart illustrating an RFID-based fixed asset physical management method in one embodiment of this application, as shown below. Figure 1 Note: The RFID-based fixed asset physical management method of this embodiment may include steps S110 to S150:

[0060] S110, acquire signal strength data and location data of RFID tags of fixed assets in use at multiple historical inventory time points, and acquire real-time location data of RFID tags of fixed assets in use, wherein the location data and the real-time location data are calculated by multiple RFID base stations based on the time difference of arrival method, the signal strength data includes the signal strength measured by multiple RFID base stations; the location data includes location coordinates and location time points.

[0061] In this embodiment, the positioning data and the real-time positioning data are calculated by multiple RFID base stations based on the time difference of arrival method. Figure 2 This is a schematic diagram illustrating the relative positions of an RFID base station and an RFID tag in one embodiment of this application, as shown below. Figure 2 As shown, this application includes at least three RFID base stations to perform Time Difference of Arrival (TDOA) positioning to locate RFID tags.

[0062] This application includes at least three RFID base stations, such as BS-001, BS-002, and BS-003;

[0063] Using BS-001 as the baseline RFID base station, it is therefore necessary to calculate the arrival time of each received return signal for BS-002 and BS-003. , The arrival time of each return signal received by BS-001 The difference, i.e. and ;

[0064] Then, a distance difference equation is constructed based on the time difference. and The corresponding distance difference is and , The velocity of the electromagnetic wave is given by the equation for the corresponding distance difference:

[0065]

[0066]

[0067] In the formula, The coordinates of the RFID tag. The positioning coordinates of the reference RFID base station BS-001 The coordinates of base station BS-002 These are the coordinates of base station BS-003.

[0068] Finally, by substituting the multiple time differences calculated from the multiple return signals acquired during the current inventory cycle, along with the known coordinates of the three RFID base stations, into the above equations and fitting the data using the least squares method, a preliminary location can be obtained. The least squares fitting process includes:

[0069] Construct the objective function. In this embodiment, the objective is to minimize the sum of squared residuals of all equations, i.e.:

[0070]

[0071] In the formula, This represents the total number of equations (determined by the number of base station pairs). For example, the above system of equations contains two pairs. ; Indicates the first Each base station to the internal base station , Indicates the first Each base station to the internal base station ;

[0072] Then, select the initial estimate. The target position can be roughly estimated using geometric methods (such as the intersection of hyperbolas).

[0073] In the initial estimate Performing a first-order Taylor expansion at the point yields the residual function. :

[0074]

[0075] in,

[0076]

[0077] residual Substituting the values, we obtain the system of linear equations:

[0078]

[0079] In the formula, For Jacobian matrices, , , , ;

[0080] The estimated value is updated by solving a system of linear equations. , , ;

[0081] Repeat the above process until the residual converges to less than the threshold, and the location of the RFID tag can be obtained.

[0082] S120, construct an inertial database and a synchronous inertial map of RFID tags for the corresponding fixed assets based on the signal strength data and positioning data of multiple RFID tags at multiple historical inventory time points. The inertial database includes the inertial dynamic characteristics of the signal strength of the RFID tags of each fixed asset, and the synchronous inertial map represents the sharing probability of multiple sets of shared inertial RFID tags.

[0083] In this application, in order to determine whether an RFID tag is in normal or abnormal condition when it is in use and is prone to movement, an inertial database and a synchronous inertial map of the RFID tag are constructed using positioning data.

[0084] In the above process, accurate positioning of RFID tags requires the absence of external signal interference. RFID test signals are highly susceptible to interference from other electromagnetic waves in the workplace; for example, UHF RFID is easily interfered with by 2.5GHz Wi-Fi signals. Therefore, in order to extract relatively accurate positioning data, this application requires verification of the positioning data based on the RFID signal strength. The verification process includes:

[0085] S1201, for each RFID tag, the signal strength data of multiple historical inventory time points are located based on the pre-constructed signal strength heat map of each RFID base station, and the target grid corresponding to the signal strength data of multiple historical inventory time points is obtained. The signal strength heat map includes multiple grids and the signal strength data value range corresponding to the multiple grids.

[0086] The signal strength (RSSI) of an RFID tag decreases non-linearly with distance from the reader (affected by environmental interference, multipath effects, etc.). By using a pre-constructed heatmap, the signal strength can be mapped to a specific spatial location (grid).

[0087] A heatmap is a digital model of environmental signal characteristics that reflects the intensity distribution of base station signals at different locations, thus providing a reference for dynamic positioning. When constructing a heatmap, the environment is divided into multiple grids (e.g., two-dimensional or three-dimensional spatial grids), and the signal strength range of the base station in that grid (e.g., the minimum and maximum RSSI values) is recorded for each grid.

[0088] A signal strength heatmap (i.e., the range of signal strength values ​​corresponding to each grid) for each base station is created using machine learning or statistical methods (such as the K-nearest neighbor algorithm, interpolation, etc.). This pre-calibrated heatmap can partially compensate for the interference of environmental noise (such as obstacles and multipath effects) on signal strength.

[0089] Figure 3 This is a schematic diagram of a signal strength heatmap in one embodiment of this application. The constructed signal strength heatmap is as follows: Figure 3 As shown.

[0090] S1202, compare the target grid and positioning data at the same historical inventory time point, and when the positioning data falls into the target grid, determine that the signal strength data and positioning data of the corresponding historical inventory time point are normal; otherwise, determine that the signal strength data and positioning data of the corresponding historical inventory time point are abnormal.

[0091] In this application, the target grid (located by signal strength heatmap) at the same time point is compared with the positioning data. If the positioning data falls within the target grid, it is determined to be normal; otherwise, it is determined to be abnormal. By comparing multi-source data, abnormal data caused by environmental interference or equipment errors is filtered out.

[0092] S1203, remove the historical inventory time points with abnormal signal strength data and positioning data to obtain the target time point;

[0093] If the RSSI corresponding to the location data is abnormal, then the corresponding time point will be removed, and only the reliable time point, i.e., the target time point, will be retained.

[0094] S1204, constructing an inertial database of RFID tags for a corresponding fixed asset based on the positioning data of multiple RFID tags at multiple target time points; and constructing a synchronous inertial map of RFID tags for a corresponding fixed asset based on the positioning data of multiple RFID tags at multiple target time points.

[0095] Once the target time point is obtained with relatively accurate positioning, the inertial characteristics of each RFID tag can be extracted based on accurate historical data. These inertial characteristics include the displacement reference period. Displacement reference activity Inertial movement position range and inertial activity time range This application constructs an inertial database and a synchronous inertial map using the following methods:

[0096] (1) Inertial database

[0097] (1-1) For each RFID tag, density clustering is performed on the positioning data of multiple target time points based on the positioning coordinates to obtain multiple positioning clusters; the positioning clusters with sample data within the cluster exceeding a preset threshold are taken as target location clusters, and the calculation range ellipse of the positioning data within the target location is calculated, and the range ellipse is taken as the inertial movement position range of the RFID tag. ;

[0098] Density clustering is performed on the location data (coordinates) of each RFID tag at multiple target time points to identify spatially densely distributed areas (location clusters). The number of samples within a cluster must meet a preset threshold (e.g., a minimum of 10 samples) to ensure the validity of the cluster. Only clusters with a number of samples greater than the threshold are retained as target location clusters, representing the range of the tag's inertial activity location.

[0099] The calculation method for the range ellipse is as follows:

[0100] (1-1-1) First, calculate the average coordinate of all positioning coordinates within the target location cluster. ;

[0101] (1-1-2) Then calculate the variance of the x-coordinates of all positioning coordinates within the target location cluster. ordinate variance and covariance of x and y coordinates This reflects the distribution characteristics of positioning coordinates within the target location cluster, where...

[0102]

[0103]

[0104]

[0105] in, This represents the total number of location points within the target cluster. For the first in the target cluster The coordinates of each positioning point For all positioning points within the target cluster Coordinate mean For all positioning points within the target cluster Coordinate mean.

[0106] Based on the variance of the abscissa The variance of the ordinate and the covariance of the horizontal and vertical coordinates Construct the covariance matrix of the target cluster The covariance matrix The mathematical expression is:

[0107]

[0108] Constructing the covariance matrix The calculation formula is: The first eigenvalue is obtained. Second eigenvalue ,in, Represents eigenvalues. It is the identity matrix;

[0109] (12) Based on the first feature value Second eigenvalue Construct the major axis separately and short axis ,in, , , For range adjustment parameters;

[0110] (13) Based on the long axis and the short axis Construct a range ellipse and use the range ellipse as the reference range for the inspection points.

[0111] By constructing an elliptical range, a continuous reference range is established for each target location cluster. This range reflects the fixed movement range of the RFID tag in the working scenario. For example, laptops, instruments, and other equipment all have relatively fixed ranges of movement. If the tag exceeds this range, it can be considered that the fixed asset corresponding to the RFID tag has left its corresponding location or inertial range of movement during use. For example, carrying a laptop when traveling or carrying instruments when working outdoors.

[0112] (1-2) For each RFID tag, positioning data whose positioning coordinates do not fall within the inertial activity position range and positioning data with empty positioning coordinates are marked as dynamic positioning data. The dynamic positioning data are clustered based on the positioning time point to obtain multiple time clusters. The time clusters with more than a preset number of sample data within the cluster are designated as target time clusters. The inertial activity time range is constructed based on the positioning time points of the dynamic positioning data within the target time clusters. ;

[0113] Among them, the following two types of data are marked as dynamic positioning data: data whose positioning coordinates do not fall within the inertial active position ellipse;

[0114] Data with empty location coordinates (e.g., signal loss or read failure).

[0115] Clustering is performed on the location time points of dynamic location data to identify time-dense periods (time clusters). Time clusters with more than a preset threshold of samples are selected as target time clusters. Based on the time points of the target time clusters, a time range (such as continuous time periods or discrete time windows) is constructed to represent the dynamic activity periods of the tags.

[0116] In temporal clustering, the regular movement periods of tags (such as daily commuting hours) are identified by analyzing the temporal distribution of dynamic data. This clarifies the regular locations and temporary movement periods of tags, providing a basis for anomaly detection (such as unplanned movement).

[0117] (1-3) For each RFID tag, calculate the displacement reference activity based on the dynamic positioning data and the total number of positioning data. The displacement reference activity The mathematical expression is:

[0118]

[0119] In the formula, The total amount of location data. This indicates the sequence number of the dynamic positioning data. This indicates the quantity of the dynamic positioning data. This indicates the time point corresponding to the current location data. This indicates the sequence number of the dynamic positioning data. This indicates the time point of the first location data. This represents the unit of activity level.

[0120] Activity level is an indicator of the frequency of RFID tag movement. In this application, the dynamic positioning data is weighted, and the weight is... In other words, it is related to the time interval at the current point in time; the longer the time interval, the lower the weight, and it adopts... Exponential convergence is used, with an increased weighting of time. Simultaneously, [the process is] utilized... To describe the activity level of different RFID tags.

[0121] Assign activity scores to tags to aid in asset utilization analysis or anomaly detection (such as high-frequency abnormal movement). High-activity tags may require more frequent monitoring or priority maintenance.

[0122] (1-4) For each RFID tag, the positioning data of multiple target time points are binarized based on the inertial activity position range to obtain binarized positioning data. Calculate multiple binarized positioning data The autocorrelation function is calculated to obtain an autocorrelation curve. The peak positions of the autocorrelation curve are extracted. When the peak value at a given position exceeds a set threshold, the RFID tag is determined to have periodicity, and the corresponding period is extracted as the displacement reference period of the RFID tag. .

[0123] The positioning data is binarized according to the inertial activity range: if the coordinates are within an ellipse, they are marked as 1 (static); otherwise, they are marked as 0 (dynamic). This 0 / 1 encoding transforms complex spatial location information into simple existence identifiers, reducing computational complexity. Then, multiple binarized positioning data are calculated. The autocorrelation function, the mathematical expression of the autocorrelation function is:

[0124]

[0125] In the formula, Indicates the autocorrelation value. This is the average value of the binarized positioning data. The amount of binarized positioning data, Due to time lag;

[0126] If peak If the value exceeds a threshold (e.g., 0.7), the label is determined to have periodicity, and the time is delayed. The period serves as a displacement reference period. By detecting the periodic repetition pattern of the binarized sequence, the regular behavior of the tag can be identified. If there is no movement for a period exceeding a certain multiple, it may indicate an abnormal situation such as the tag falling off.

[0127] (2) Synchronous inertial map

[0128] (2-1) Binarized positioning data based on each RFID tag Constructing a binarized localization data sequence ,in, Indicates the serial number of the RFID tag;

[0129] The positioning data of each RFID tag is binarized (as described in steps 1-4) to generate a binarized sequence. For example, if the positioning coordinates are within the range of inertial movement, they are marked as 1 (static); otherwise, they are marked as 0 (dynamic).

[0130] (2-2) Binarize the positioning data sequences of any two RFID tags After removing non-common time points, the data is aligned, and then the difference is calculated to obtain the difference sequence. ;

[0131] Comparisons are performed only at common points in time to avoid errors caused by timestamp mismatches. A difference of 1 indicates that the tag states are opposite (one active and one passive), while a difference of 0 indicates that the states are the same.

[0132] (2-3) Calculate the average of the absolute values ​​of multiple binarized localization data in the difference sequence. and the average value The comparison is made with pre-built positive correlation and negative correlation thresholds, wherein the positive correlation threshold is less than the negative correlation threshold.

[0133] (2-4) In the average value When the correlation is greater than the negative correlation threshold, the two corresponding RFID tags are determined to be negatively correlated; when the average value is greater than the threshold value, the two corresponding RFID tags are determined to be negatively correlated. When the value is less than the positive correlation threshold, the two corresponding RFID tags are determined to be positively correlated; when the average value is less than the threshold value, the two corresponding RFID tags are determined to be positively correlated. When the two RFID tags are between the negative correlation threshold and the positive correlation threshold, they are determined to be uncorrelated.

[0134] Positive correlation: Labels may be located in the same area (e.g., shared shelves) or subject to the same process (e.g., complete sets of equipment). Negative correlation: Labels may be in mutually exclusive locations (e.g., equipment used alternately) or exhibit interfering behavior (e.g., mutual obstruction).

[0135] (2-5) Construct a synchronous inertial map based on the correlation between any two RFID tags.

[0136] Specifically, each RFID tag is treated as a node in the graph; the correlation between tags (positive correlation, negative correlation, no correlation) is used as the weight or type of the edge; and an adjacency matrix G is constructed to represent negative correlation, no correlation, and positive correlation, respectively.

[0137] Identify groups of labeled individuals with similar behaviors through graph clustering (such as community detection algorithms); analyze the topological characteristics of the graph (such as central nodes and isolated nodes) to assist in decision-making.

[0138] Graph structures can be used to predict tag behavior (such as dynamic migration based on neighboring nodes). Isolated nodes or abrupt connections may indicate anomalous events (such as device loss or unauthorized movement). Hidden group patterns can be revealed by comparing binarized sequences and combining the inertial range of RFID tags with cooperative behavior analysis.

[0139] S130, extract dynamic features from the real-time location data of the RFID tags of the fixed assets during the target time period;

[0140] The process specifically includes:

[0141] S131, Extract location data for a target time period from real-time location data, wherein the target time period is a time period of a target duration prior to the current time point;

[0142] S132, Extract displacement activity from the positioning data of the RFID tag during the target time period. and the current cumulative static duration The current cumulative static duration The continuous duration of RFID tag location data within a target time period;

[0143] The calculation of activity level is the same as described above and will not be repeated here. In this embodiment, location data from the five days prior to the current time point is obtained to calculate the activity level for the current time period.

[0144] S133, construct dynamic features based on the displacement activity and the cumulative static duration.

[0145] S140, based on the inertial dynamic characteristics, the dynamic characteristics of the corresponding RFID tag and the real-time positioning data are verified to obtain a first verification result; and based on the synchronous inertial map, the real-time positioning data of shared inertial RFID tags are verified to obtain a second verification result.

[0146] In one embodiment of this application, the dynamic characteristics of the corresponding RFID tag and real-time positioning data are verified based on the inertial dynamic characteristics to obtain a first verification result, including:

[0147] (1) The displacement activity of RFID tags in the target time period With the displacement reference activity To make a comparison, in When it is determined that the RFID tag has an abnormal activity risk; At that time, the current cumulative static duration will be... With the reference period To make a comparison, in At that time, it was determined that the RFID tag was at risk of abnormal stillness;

[0148] Normal activity level reflects the regular frequency of tag movement (such as daily inventory checks or transportation). If the current activity level is significantly higher than the reference value, it may indicate:

[0149] Human error: The label is frequently moved (e.g., unauthorized removal, misoperation);

[0150] Device malfunction: False movement caused by reader misreading or tag signal interference.

[0151] Under normal circumstances, the static period of the tag should be less than its periodic activity interval (e.g., returning to static status after daily inventory). If the static period significantly exceeds the cycle, it may indicate:

[0152] Equipment malfunction: Tag not read correctly (e.g., battery depleted, signal blocked);

[0153] Tag Drop: The tag falls into a corner and remains stationary;

[0154] Process interruption: Assets are illegally detained or the process is stuck, etc.

[0155] (2) Obtain the positioning time of the real-time positioning data, and compare the positioning time of the real-time positioning data with the inertial activity time range. The real-time positioning data is compared with the inertial activity position range. For comparison, the positioning time of the real-time positioning data is not within the range of the inertial activity time. Furthermore, the real-time positioning data is not within the range of the inertial movement position. Within a short period of time, it was determined that the RFID tag had a risk of abnormal location.

[0156] Normal activities must simultaneously meet both a time window (e.g., working hours) and a spatial scope (e.g., designated shelving). Deviations from both may indicate:

[0157] Unauthorized movement: The tag was taken out of the authorized area (e.g., outside the warehouse);

[0158] Equipment drift: Tags are misread to the wrong location due to environmental interference, etc.

[0159] The anomaly detection scheme in this embodiment constructs a comprehensive monitoring system covering the asset lifecycle through activity comparison, inactivity duration and periodic verification, and time-space dual constraints. Its core advantages are: (2) reducing false alarms through multi-indicator linkage; (2) parameters are automatically updated according to business needs; and (3) a real-time early warning mechanism helps to deal with risks in a timely manner.

[0160] The above process can be widely applied to fields such as smart warehousing, industrial IoT, and logistics tracking, improving the security and efficiency of asset management through automated anomaly detection.

[0161] In one embodiment of this application, real-time positioning data with shared inertial RFID tags are verified based on the synchronized inertial map to obtain a second verification result, including:

[0162] When the current RFID tag undergoes a location change, the location change status of the current RFID tag is obtained, and the location change status of related RFID tags between the current time and the extended time is obtained. If the location change status of the related RFID tags does not match the corresponding correlation with the location change status of the current RFID tag, it is determined that the current RFID tag has a synchronization anomaly risk.

[0163] Finally, based on the first or second verification result, early warning management and inventory management are performed on fixed assets in use, including:

[0164] When any RFID tag has the risk of abnormal activity, abnormal stillness, abnormal location, or abnormal synchronization, an early warning signal is output to the target object, and the status of the corresponding RFID tag is set to await inventory.

[0165] Based on the inertial activity location range and inertial activity time range Construct a dynamic electronic fence and manage the activity range of RFID tags based on the dynamic electronic fence.

[0166] When an RFID tag triggers any of the following risks: abnormal activity risk (activity exceeding the threshold), abnormal inactivity risk (inactivity duration exceeding the cycle), abnormal inactivity risk (inactivity duration exceeding the cycle), or synchronous anomaly risk (contradictory behavior of associated tags in the synchronous inertial map), the system automatically outputs an early warning signal. At the same time, the system status of the abnormal tag is marked as "awaiting inventory count," and its regular business processes (such as inventory updates and transportation scheduling) are suspended to ensure the safety of fixed assets.

[0167] The electronic fence data source defines the fence boundary from a spatial dimension based on the range of inertial activity locations (the ellipse in step 1-1); and from a temporal dimension based on the range of inertial activity time (the time cluster in step 1-2).

[0168] The following are specific implementation scenarios based on electronic fences and anomaly detection in this application:

[0169] Scenario 1: Warehouse Management

[0170] Problem: A warehouse has been found to have tags that move abnormally frequently (such as frequently entering and leaving high-value areas).

[0171] Solution:

[0172] An abnormal activity risk warning was triggered, the tag was locked and set to "awaiting inventory".

[0173] Dynamic electronic fences restrict the tag's movement within designated areas;

[0174] After verification, security personnel found that it was an employee's mistake, updated the employee's permissions, and removed the status.

[0175] Scenario 2: Safety monitoring in a chemical plant

[0176] Problem: Labels in the hazardous materials storage area suddenly stopped working and timed out.

[0177] Solution:

[0178] Triggering an abnormal stationary risk warning, the system is activated to monitor the scene.

[0179] The dynamic electronic fence detected that the tag position was normal but the timing was abnormal, which was determined to be a device malfunction.

[0180] Technicians replaced the tag battery and restored the system to normal operation.

[0181] This invention discloses an RFID-based fixed asset physical management method. First, it acquires signal strength and location data of RFID tags corresponding to fixed assets in use at multiple historical inventory points. Then, it uses this data to filter data samples, removing time points with abnormal signals due to external interference. Next, it uses precise positioning to construct the inertial dynamic characteristics of each RFID tag to determine whether and extract these characteristics. Simultaneously, it uses precise positioning to find the synchronous usage characteristics between different RFID tags, thereby constructing a synchronous inertial map. The inertial dynamic characteristics and synchronous inertial map are used for early warning management and inventory management of RFID tag dynamic characteristics under various conditions, resulting in greater flexibility and better alignment with the usage habits of fixed assets in use.

[0182] like Figure 4 As shown, this application also provides an RFID-based fixed asset physical management system, including:

[0183] The acquisition module is used to acquire signal strength data and location data of RFID tags of fixed assets in use at multiple historical inventory time points, and to acquire real-time location data of RFID tags of fixed assets in use. The location data and the real-time location data are calculated by multiple RFID base stations based on the time difference of arrival method, and the signal strength data includes the signal strength measured by multiple RFID base stations.

[0184] The prior data construction module is used to construct an inertial database and a synchronous inertial map of RFID tags for corresponding fixed assets based on the signal strength data and positioning data of multiple RFID tags at multiple historical inventory time points. The inertial database includes the inertial dynamic characteristics of the signal strength of RFID tags for each fixed asset, and the synchronous inertial map represents the sharing probability of multiple sets of shared inertial RFID tags.

[0185] The feature extraction module is used to extract dynamic features from the real-time location data of the RFID tags of the fixed assets during the target time period.

[0186] The verification module is used to verify the dynamic features of the corresponding RFID tag and the real-time positioning data based on the inertial dynamic features to obtain a first verification result; and to verify the real-time positioning data of shared inertial RFID tags based on the synchronous inertial map to obtain a second verification result.

[0187] The management module is used to perform early warning management and inventory management of fixed assets in use based on the first verification result or the second verification result.

[0188] This invention relates to an RFID-based fixed asset physical management system. First, it acquires signal strength and location data of RFID tags corresponding to fixed assets in use at multiple historical inventory points. Then, it uses this data to filter data samples, removing time points with abnormal signals due to external interference. Next, it uses precise positioning to construct the inertial dynamic characteristics of each RFID tag to determine whether and extract these characteristics. Simultaneously, it uses precise positioning to find the synchronous usage characteristics between different RFID tags, thereby constructing a synchronous inertial map. The inertial dynamic characteristics and synchronous inertial map are used for early warning management and inventory management of RFID tag dynamic characteristics under various conditions, resulting in greater flexibility and better alignment with the usage habits of fixed assets in use.

[0189] 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 5The 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.

[0190] like Figure 5 As 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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 physical management method based on RFID, characterized in that, Including the following steps: The system acquires signal strength and location data of RFID tags of fixed assets in use at multiple historical inventory time points, and acquires real-time location data of RFID tags of fixed assets in use. The location data and the real-time location data are calculated by multiple RFID base stations based on the time difference of arrival method, and the signal strength data includes the signal strength measured by multiple RFID base stations. Based on signal strength and location data of multiple RFID tags at multiple historical inventory time points, an inertial database and synchronous inertial map of RFID tags for corresponding fixed assets are constructed. The inertial database includes the inertial dynamic characteristics of the signal strength of each fixed asset's RFID tags, and the synchronous inertial map represents the sharing probability of multiple sets of RFID tags sharing common inertial data. The construction of the inertial database and synchronous inertial map of RFID tags for corresponding fixed assets based on signal strength and location data of multiple RFID tags at multiple historical inventory time points includes: for each RFID tag, locating the signal strength data at multiple historical inventory time points based on a pre-constructed signal strength heatmap of each RFID base station, obtaining the signal strength data corresponding to the historical inventory time points. The target grid is defined as follows: the signal strength heatmap includes multiple grids and the corresponding signal strength data ranges for each grid; the target grids and positioning data at the same historical inventory time point are compared, and when the positioning data falls into the target grid, the signal strength data and positioning data at the corresponding historical inventory time point are determined to be normal; otherwise, the signal strength data and positioning data at the corresponding historical inventory time point are determined to be abnormal; historical inventory time points with abnormal signal strength data and positioning data are removed to obtain the target time point; an inertial database of RFID tags for the corresponding fixed asset is constructed based on the positioning data of multiple RFID tags at multiple target time points; and a synchronous inertial map of RFID tags for the corresponding fixed asset is constructed based on the positioning data of multiple RFID tags at multiple target time points. Dynamic features are extracted from the real-time location data of the RFID tags of the fixed assets during the target time period; The dynamic characteristics and real-time positioning data of the corresponding RFID tags are verified based on the inertial dynamic characteristics to obtain a first verification result; and the real-time positioning data of shared inertial RFID tags are verified based on the synchronous inertial map to obtain a second verification result. Based on the first verification result or the second verification result, early warning management and inventory management are carried out for fixed assets in use.

2. The fixed asset physical management method based on RFID according to claim 1, characterized in that, The inertial dynamic characteristics include the displacement reference period. Displacement reference activity Inertial movement position range and inertial activity time range The location data includes location coordinates and location time points. Specifically, an inertial database of RFID tags for corresponding fixed assets is constructed based on location data from multiple RFID tags at multiple target time points, including: For each RFID tag, density clustering is performed on the positioning data of multiple target time points based on the positioning coordinates to obtain multiple positioning clusters. Positioning clusters with more than a preset threshold of sample data within each cluster are designated as target location clusters. An ellipse representing the calculation range of the positioning data within the target location is calculated, and this ellipse is used as the inertial movement position range of the RFID tag. ; For each RFID tag, positioning data whose coordinates do not fall within the inertial activity position range, as well as positioning data with empty coordinates, are marked as dynamic positioning data. These dynamic positioning data are then clustered based on their positioning time points to obtain multiple time clusters. Time clusters with more than a preset threshold of sample data within each cluster are designated as target time clusters. An inertial activity time range is then constructed based on the positioning time points of the dynamic positioning data within the target time clusters. ; For each RFID tag, the displacement reference activity is calculated based on the dynamic positioning data and the total amount of positioning data. The displacement reference activity The mathematical expression is: In the formula, The total amount of location data. This indicates the sequence number of the dynamic positioning data. This indicates the quantity of the dynamic positioning data. This indicates the time point corresponding to the current location data. This indicates the sequence number of the dynamic positioning data. This indicates the time point of the first location data. This represents the unit of activity level. For each RFID tag, the positioning data from multiple target time points are binarized based on the inertial activity position range to obtain binarized positioning data. Calculate multiple binarized positioning data The autocorrelation function is calculated to obtain an autocorrelation curve. The peak positions of the autocorrelation curve are extracted. When the peak value at a given position exceeds a set threshold, the RFID tag is determined to have periodicity, and the corresponding period is extracted as the displacement reference period of the RFID tag. .

3. The fixed asset physical management method based on RFID according to claim 2, characterized in that, Based on the location data of multiple RFID tags at multiple target time points, a synchronous inertial map of RFID tags for corresponding fixed assets is constructed, including: Based on the binary positioning data of each RFID tag Constructing a binarized localization data sequence ,in, Indicates the serial number of the RFID tag; Binarized positioning data sequences of any two RFID tags After removing non-common time points, the data is aligned, and then the difference is calculated to obtain the difference sequence. ; Calculate the average of the absolute values ​​of multiple binarized localization data in the difference sequence. and the average value The comparison is made with pre-built positive correlation and negative correlation thresholds, wherein the positive correlation threshold is less than the negative correlation threshold. In the average value When the correlation is greater than the negative correlation threshold, the two corresponding RFID tags are determined to be negatively correlated; when the average value is greater than the threshold value, the two corresponding RFID tags are determined to be negatively correlated. When the value is less than the positive correlation threshold, the two corresponding RFID tags are determined to be positively correlated; when the average value is less than the threshold value, the two corresponding RFID tags are determined to be positively correlated. When the two RFID tags are between the negative correlation threshold and the positive correlation threshold, they are determined to be uncorrelated. Construct a synchronous inertial map based on the correlation between any two RFID tags.

4. The fixed asset physical management method based on RFID according to claim 3, characterized in that, Dynamic features are extracted from the real-time location data of the RFID tags of the fixed assets during the target time period, including: Location data for a target time period is extracted from real-time location data, wherein the target time period is the time period of the target duration prior to the current time point; Extract displacement activity from the location data of the RFID tag during the target time period. and the current cumulative static duration The current cumulative static duration The continuous duration of RFID tag location data within a target time period; Dynamic features are constructed based on the displacement activity and the cumulative static duration.

5. The fixed asset physical management method based on RFID according to claim 4, characterized in that, Based on the inertial dynamic characteristics, the dynamic characteristics of the corresponding RFID tag and real-time positioning data are verified to obtain a first verification result, including: The displacement activity of RFID tags during the target time period With the displacement reference activity To make a comparison, in When it is determined that the RFID tag has an abnormal activity risk; At that time, the current cumulative static duration will be... With the reference period To make a comparison, in At that time, it was determined that the RFID tag was at risk of abnormal stillness; Obtain the positioning time of the real-time positioning data, and compare the positioning time of the real-time positioning data with the inertial activity time range. The real-time positioning data is compared with the inertial activity position range. For comparison, the positioning time of the real-time positioning data is not within the range of the inertial activity time. Furthermore, the real-time positioning data is not within the range of the inertial movement position. Within a short period of time, it was determined that the RFID tag had a risk of abnormal location.

6. The fixed asset physical management method based on RFID according to claim 5, characterized in that, Based on the synchronized inertial map, the real-time positioning data of shared inertial RFID tags are verified to obtain a second verification result, including: When the current RFID tag undergoes a location change, the location change status of the current RFID tag is obtained, and the location change status of related RFID tags between the current time and the extended time is obtained. If the location change status of the related RFID tags does not match the corresponding correlation with the location change status of the current RFID tag, it is determined that the current RFID tag has a synchronization anomaly risk.

7. The fixed asset physical management method based on RFID according to claim 6, characterized in that, Based on the first verification result or the second verification result, early warning management and inventory management are performed on fixed assets in use, including: When any RFID tag has the risk of abnormal activity, abnormal stillness, abnormal location, or abnormal synchronization, an early warning signal is output to the target object, and the status of the corresponding RFID tag is set to await inventory.

8. The fixed asset physical management method based on RFID according to claim 2, characterized in that, Also includes: Based on the inertial activity location range and inertial activity time range Construct a dynamic electronic fence and manage the activity range of RFID tags based on the dynamic electronic fence.

9. A fixed asset physical management system based on RFID, characterized in that, include: The acquisition module is used to acquire signal strength data and location data of RFID tags of fixed assets in use at multiple historical inventory time points, and to acquire real-time location data of RFID tags of fixed assets in use. The location data and the real-time location data are calculated by multiple RFID base stations based on the time difference of arrival method, and the signal strength data includes the signal strength measured by multiple RFID base stations. The prior data construction module is used to construct an inertial database and a synchronous inertial map of RFID tags for a fixed asset based on signal strength data and location data of multiple RFID tags at multiple historical inventory time points. The inertial database includes the inertial dynamic characteristics of the signal strength of each RFID tag for the fixed asset, and the synchronous inertial map represents the sharing probability of multiple sets of RFID tags sharing common inertial data. Constructing the inertial database and synchronous inertial map of RFID tags for a fixed asset based on signal strength data and location data of multiple RFID tags at multiple historical inventory time points includes: for each RFID tag, locating the signal strength data of multiple historical inventory time points based on a pre-constructed signal strength heatmap of each RFID base station, obtaining the signal strength data of multiple historical inventory time points. The signal strength heatmap includes multiple grids and corresponding signal strength data ranges for each grid. The target grids and positioning data at the same historical inventory time point are compared. If the positioning data falls into a target grid, the signal strength and positioning data at the corresponding historical inventory time point are determined to be normal; otherwise, they are determined to be abnormal. Historical inventory time points with abnormal signal strength and positioning data are removed to obtain target time points. An inertial database of RFID tags for the corresponding fixed assets is constructed based on the positioning data of multiple RFID tags at multiple target time points. A synchronous inertial map of RFID tags for the corresponding fixed assets is constructed based on the positioning data of multiple RFID tags at multiple target time points. The feature extraction module is used to extract dynamic features from the real-time location data of the RFID tags of the fixed assets during the target time period. The verification module is used to verify the dynamic features of the corresponding RFID tag and the real-time positioning data based on the inertial dynamic features to obtain a first verification result; and to verify the real-time positioning data of shared inertial RFID tags based on the synchronous inertial map to obtain a second verification result. The management module is used to perform early warning management and inventory management of fixed assets in use based on the first verification result or the second verification result.

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