Fixed asset physical management method and system based on RFID

Abstract

The invention relates to an RFID-based fixed asset physical management method and system, and the method comprises the steps: firstly obtaining the signal intensity data and positioning data of an RFID tag corresponding to a fixed asset in a use state at a plurality of historical inventory time points, and then carrying out the data sample screening through the signal intensity data and positioning data at the plurality of historical inventory time points; and signal abnormal time points caused by external signal interference are removed. Then, the inertial dynamic feature of each RFID is constructed through accurate positioning, so that whether each RFID tag has the inertial dynamic feature or not is found out, and the inertial dynamic features are extracted; meanwhile, the synchronous use features among different RFID tags are found by utilizing accurate positioning, so that the synchronous inertia atlas is constructed. And performing early warning management and inventory management on the dynamic characteristics of the RFID tags under various conditions by using the inertia dynamic characteristics and the synchronous inertia atlas. The method is more flexible and fits the use habit of the fixed assets in the use state.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a fixed asset physical management method and system based on RFID. Background Art

[0002] RFID-based fixed asset management is crucial, enabling rapid asset inventory. Traditionally, fixed assets have been managed manually, leading to risks such as items not being cleaned and verified for years and a lack of visibility into their specific status. Business operations lack system support, have a low level of electronicization, and are not integrated with management systems. Fixed asset identification and counting are completely manual, resulting in low inventory efficiency. A count of 20,000 fixed assets would take two people 10 hours. RFID automates data collection, reducing manpower. Furthermore, RFID technology can cover a variety of locations where manual inventory is difficult, such as high locations, corners, or enclosed containers. RFID can also be read remotely, eliminating the tedious process of traditional manual inventory. Furthermore, RFID tags enable rapid compilation of full-process tracking information for physical fixed assets, facilitating asset status management.

[0003] However, RFID technology also presents several challenges when it comes to inventorying fixed assets. For example, fixed assets include not only equipment stored on shelves but also equipment in use. Therefore, for fixed assets in use (such as laptops and instruments), there is no fixed location. Therefore, using electronic fencing 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 technical problems.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The RFID-based fixed asset physical management method of the present invention comprises the following steps:

[0007] Obtaining signal strength data and positioning data of RFID tags of fixed assets in use at multiple historical inventory time points, and obtaining real-time positioning data of RFID tags of fixed assets in use, wherein the positioning data and the real-time positioning data are calculated by multiple RFID base stations based on a time difference of arrival method, and the signal strength data includes signal strengths 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 inertia database and a synchronized inertia map are constructed for the corresponding fixed assets' RFID tags. The inertia database includes the inertial dynamic characteristics of the signal strength of each fixed asset's RFID tag, and the synchronized inertia map represents the shared probability of multiple groups of shared inertial RFID tags.

[0009] Extracting dynamic features from real-time positioning data of the RFID tag of the fixed asset during a target time period;

[0010] Verifying the dynamic characteristics and real-time positioning data of the corresponding RFID tag based on the inertial dynamic characteristics to obtain a first verification result; and verifying the real-time positioning data of the shared inertial RFID tag based on the synchronized inertial spectrum 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 performed on the fixed assets in use.

[0012] In one embodiment of the present application, an inertial database and a synchronized inertial map of RFID tags corresponding to fixed assets are constructed based on signal strength data and positioning data of multiple RFID tags at multiple historical inventory time points, including:

[0013] For each RFID tag, the signal strength data at multiple historical inventory time points are located based on a pre-built signal strength heat map of each RFID base station, thereby obtaining a target grid corresponding to the signal strength data at the multiple historical inventory time points. The signal strength heat map includes multiple grids and the signal strength data value ranges corresponding to the multiple grids.

[0014] Comparing the target grid and positioning data at the same historical inventory time point, and when the positioning data falls into the target grid, determining that the signal strength data and positioning data at the corresponding historical inventory time point are normal; otherwise, determining that the signal strength data and positioning data at the corresponding historical inventory time point are abnormal;

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

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

[0017] In one embodiment of the present application, the inertial dynamic characteristics include a displacement reference period , displacement reference activity , Inertial activity position range and inertial activity time range The positioning data includes positioning coordinates and positioning time points. The inertial database of the RFID tags corresponding to the fixed assets is constructed based on the positioning data of 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; the positioning cluster with sample data greater than the preset number threshold is regarded as the target position cluster, and the calculation range ellipse of the positioning data within the target position is calculated, and the range ellipse is used as the inertial activity position range of the RFID tag. ;

[0019] For each RFID tag, the positioning data whose positioning coordinates do not fall within the inertial activity position range and the positioning data whose positioning coordinates are empty are marked as dynamic data as dynamic positioning data, and the dynamic positioning data are clustered based on the positioning time point to obtain multiple time clusters; the time cluster with sample data greater than the preset number threshold in the cluster is taken as the target time cluster; and the inertial activity time range is constructed based on the positioning time points of the dynamic positioning data in the target time cluster ;

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

[0021]

[0022] Where, is the total number of positioning data, Indicates the sequence number of the dynamic positioning data, Indicates the number of dynamic positioning data, Indicates the time point corresponding to the positioning data at the current time point, Indicates the sequence number of the dynamic positioning data, Indicates the time point of the first positioning data. Indicates the unit quantity of activity;

[0023] For each RFID tag, the positioning data of multiple target time points are binarized based on the inertial activity position range to obtain binary positioning data. , calculate multiple binary positioning data The autocorrelation function is obtained to obtain an autocorrelation curve; the peak position of the autocorrelation curve is extracted, and when the peak value of the peak position is greater than the 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 the present application, a synchronized inertial map of the RFID tags corresponding to fixed assets is constructed based on positioning data of multiple RFID tags at multiple target time points, including:

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

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

[0027] Calculate the average of the absolute values ​​of multiple binary positioning data in the difference sequence , and the average value Comparing with a pre-established positive correlation determination threshold and a pre-established negative correlation determination threshold, wherein the positive correlation determination threshold is smaller than the negative correlation determination threshold;

[0028] In the average When the average value is greater than the negative correlation determination threshold, it is determined that the two corresponding RFID tags are negatively correlated; When the average value is less than the positive correlation determination threshold, it is determined that the two corresponding RFID tags are positively correlated; When the value is between the negative correlation determination threshold and the positive correlation determination threshold, it is determined that the corresponding two RFID tags have no correlation;

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

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

[0031] Extracting positioning data of a target time period from the real-time positioning data, wherein the target time period is a time period of a target duration before the current time point;

[0032] Extracting displacement activity from the positioning data of the RFID tag during the target time period And the current cumulative inactivity time , wherein the current accumulated static time It is the continuous duration of the RFID tag’s positioning data in a target time period;

[0033] A dynamic feature is constructed based on the displacement activity and the accumulated static time.

[0034] In one embodiment of the present application, the dynamic characteristics of the corresponding RFID tag and the 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 in the target time period Activeness with the displacement reference For comparison, When the RFID tag is judged to have abnormal activity risk; When the current accumulated static time is With the reference cycle For comparison, When the RFID tag is abnormally stationary, it is determined that there is a risk of abnormal stationary state;

[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. Compare and compare the real-time positioning data with the inertial activity position range By comparison, the positioning time of the real-time positioning data is not within the inertial activity time range. , and the real-time positioning data is not within the inertial activity position range When the RFID tag is within the specified range, it is determined that there is a risk of abnormal position of the RFID tag.

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

[0038] When the positioning state of the current RFID tag changes, the positioning change state of the current RFID tag is obtained, and the positioning change state of the related RFID tags of the current RFID tag between the current moment and the extended moment is obtained. When the positioning change state of the related RFID tags does not meet the corresponding correlation with the positioning change state of the current RFID tag, it is determined that the current RFID tag has a synchronization abnormality risk.

[0039] In one embodiment of the present application, performing early warning management and inventory management on fixed assets in use based on the first verification result or the second verification result includes:

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

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

[0042] Based on the inertial activity position range and inertial activity time range A dynamic electronic fence is constructed, and the activity range of the RFID tag is managed based on the dynamic electronic fence.

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

[0044] an acquisition module, configured to acquire signal strength data and positioning data of RFID tags of fixed assets in use at multiple historical inventory time points, and to acquire real-time positioning data of RFID tags of fixed assets in use, wherein the positioning data and the real-time positioning data are calculated by multiple RFID base stations based on a time difference of arrival method, and the signal strength data includes signal strengths measured by multiple RFID base stations;

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

[0046] A feature extraction module is used to extract dynamic features from the real-time positioning data of the RFID tag of the fixed asset in a target time period;

[0047] a verification module configured to verify the dynamic characteristics and real-time positioning data of the corresponding RFID tag based on the inertial dynamic characteristics to obtain a first verification result; and verify the real-time positioning data of the shared inertial RFID tag based on the synchronized inertial spectrum to obtain a second verification result;

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

[0049] The beneficial effects of the present invention are as follows: the RFID-based fixed asset physical management method and system of the present invention first obtains the signal strength data and positioning data of the RFID tags corresponding to the fixed assets in use at multiple historical inventory time points, and then uses the signal strength data and positioning data of the multiple historical inventory time points to screen data samples and remove abnormal signal time points caused by external signal interference. Then, the inertial dynamic characteristics of each RFID are constructed using precise positioning to find out whether each RFID tag has inertial dynamic characteristics and extract the inertial dynamic characteristics. At the same time, the present application also uses precise positioning to find the synchronous use characteristics between different RFID tags, thereby constructing a synchronous inertial map. The inertial dynamic characteristics and the synchronous inertial map are used to perform early warning management and inventory management on the dynamic characteristics of RFID tags in various situations. It is more flexible and fits the usage habits of fixed assets in use. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0051] Figure 1 is a flow chart of a fixed asset physical management method based on RFID shown in one embodiment of the present application;

[0052] Figure 2 Schematic diagram of the relative positions of an RFID base station and an RFID tag in one embodiment of the present application;

[0053] Figure 3 A schematic diagram of a signal strength heat map in an embodiment of the present application;

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

[0055] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0056] The following describes the embodiments of the present invention through specific examples. 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. The 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 the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0057] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations 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 number, shape and size ratio of the layers in actual implementation. In actual implementation, the type and number of each layer can be changed at will, and the layer layout may also be more complicated.

[0058] In the following description, numerous details are set forth to provide a more thorough explanation of the embodiments of the present invention; however, it is apparent to one skilled in the art that the embodiments of the present invention may be practiced without these specific details.

[0059] Figure 1 FIG. 1 is a flow chart of a method for physical management of fixed assets based on RFID according to an embodiment of the present application. Figure 1 The RFID-based fixed asset physical management method of this embodiment may include steps S110 to S150:

[0060] S110, obtaining signal strength data and positioning data of the RFID tags of the fixed assets in use at multiple historical inventory time points, and obtaining real-time positioning data of the RFID tags of the fixed assets in use, wherein the positioning data and the real-time positioning data are calculated by multiple RFID base stations based on a time difference of arrival method, the signal strength data includes signal strengths measured by the multiple RFID base stations, and the positioning data includes positioning coordinates and positioning 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 FIG. 1 is a schematic diagram of the relative positions of the RFID base station and the RFID tag in an embodiment of the present application, as shown in FIG. Figure 2 As shown, the present application includes at least three RFID base stations, thereby performing a Time Difference of Arrival (TDOA) positioning method to locate the RFID tag.

[0062] In this application, at least three RFID base stations are included, for example, BS-001, BS-002, and BS-003;

[0063] BS-001 is used as the base RFID base station. Therefore, it is necessary to calculate the arrival time of each return signal received by BS-002 and BS-003. 、 The arrival time of each return signal received by BS-001 The difference, that is and ;

[0064] Then, the distance difference equation is constructed based on the time difference. and The corresponding distance difference is and , is the electromagnetic wave speed; the corresponding distance difference equation is:

[0065]

[0066]

[0067] Where, is the coordinate of the RFID tag, is the positioning coordinate of the benchmark RFID base station BS-001, are the coordinates of base station BS-002, The coordinates of base station BS-003.

[0068] Finally, the multiple time differences calculated based on the multiple return signals obtained during the current inventory cycle and the known coordinates of the three RFID base stations are substituted into the above equation and fitted with the least squares method to obtain a preliminary positioning result. The least squares fitting process includes:

[0069] Construct the objective function. The goal in this embodiment is to minimize the residual sum of squares of all equations, that is:

[0070]

[0071] Where, represents the total number of equations (determined by the number of base station pairs), for example, there are two pairs in the above equation group. ; Indicates the Base stations within a base station pair , Indicates the Base stations within a base station pair ;

[0072] Then, choose the initial estimate , the target position can be roughly estimated by geometric methods (such as hyperbola intersection).

[0073] In the initial estimate Perform a first-order Taylor expansion at , and get the residual function :

[0074]

[0075] in,

[0076]

[0077] The residual Substituting in, we can get the linear equation system:

[0078]

[0079] Where, is the Jacobian matrix, , , , ;

[0080] Update the estimate by solving a system of linear equations, , , ;

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

[0082] S120, constructing an inertia database and a synchronized inertia map for the RFID tags of corresponding fixed assets based on signal strength data and positioning data of multiple RFID tags at multiple historical inventory time points, wherein the inertia database includes inertial dynamic characteristics of the signal strength of the RFID tag of each fixed asset, and the synchronized inertia map represents the shared probability of multiple groups of shared inertia RFID tags;

[0083] In this application, in order to judge whether the state of an RFID tag that is in use and prone to being in motion is normal or abnormal, an inertial database and a synchronous inertial map of the RFID tag are constructed through positioning data.

[0084] In the above process, accurate positioning of RFID tags requires no external signal interference. RFID test signals are susceptible to interference from other electromagnetic waves in the workplace, such as UHF RFID tags that are susceptible to interference from 2.5GHz Wi-Fi signals. Therefore, in order to extract relatively accurate positioning data, this application requires verification of positioning data based on RFID signal strength. The verification process includes:

[0085] S1201: For each RFID tag, locate the signal strength data of multiple historical inventory time points based on a pre-constructed signal strength heat map of each RFID base station, and obtain target grids corresponding to the signal strength data of the multiple historical inventory time points, wherein the signal strength heat map includes multiple grids and signal strength data value ranges corresponding to the multiple grids;

[0086] The signal strength (RSSI) of an RFID tag decays nonlinearly with distance from the reader (affected by environmental interference, multipath effects, etc.). Using a pre-built heat map, signal strength can be mapped to specific spatial locations (grids).

[0087] A heat map is a digital model of environmental signal characteristics. It reflects the distribution of base station signal strength at different locations, providing a reference for dynamic positioning. When constructing a heat map, the environment is divided into multiple grids (for example, a two-dimensional grid or a three-dimensional grid), and the signal strength range of the base station within each grid (such as the minimum and maximum RSSI values) is recorded.

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

[0089] Figure 3 This is a schematic diagram of a signal strength heat map in an embodiment of the present application. The constructed signal strength heat map is as follows: Figure 3 shown.

[0090] S1202, comparing the target grid and the positioning data at the same historical inventory time point, and if the positioning data falls into the target grid, determining that the signal strength data and positioning data at the corresponding historical inventory time point are normal; otherwise, determining that the signal strength data and positioning data at the corresponding historical inventory time point are abnormal;

[0091] In this application, the target grid (located by the 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 considered normal; otherwise, it is considered abnormal. By comparing multi-source data, abnormal data caused by environmental interference or equipment errors is filtered out.

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

[0093] If the RSSI corresponding to the positioning data is abnormal, the corresponding time point will be eliminated and only the reliable time point, that is, the target time point, will be retained.

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

[0095] After obtaining the target time point with relatively accurate positioning, the inertial characteristics of each RFID tag can be extracted based on accurate historical data. The inertial characteristics include the displacement reference period , displacement reference activity , Inertial activity position range and inertial activity time range , this application constructs an inertial database and a synchronized inertial map through the following methods, including:

[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 greater than a preset number threshold are regarded as target position clusters, and the calculation range ellipse of the positioning data within the target position is calculated, and the range ellipse is used as the inertial activity 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 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 cluster validity. Only clusters with a sample count greater than the threshold are retained as target location clusters, representing the tag's inertial activity range.

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

[0100] (1-1-1) First calculate the average coordinates of all positioning coordinates in the target location cluster ;

[0101] (1-1-2) Then calculate the horizontal coordinate variance of all positioning coordinates in the target location cluster , vertical axis variance and the horizontal and vertical covariance , to reflect the distribution characteristics of the positioning coordinates within the target location cluster, where

[0102]

[0103]

[0104]

[0105] in, is the total number of positioning points in the target cluster, The first The coordinates of the positioning points, For all the positioning points in the target cluster Coordinate mean, For all the positioning points in the target cluster Coordinate mean.

[0106] Based on the horizontal coordinate variance , the vertical coordinate variance and the horizontal and vertical covariance 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 , and get the first eigenvalue and the second eigenvalue ,in, represents the eigenvalue, is the identity matrix;

[0109] (12) Based on the first eigenvalue and the second eigenvalue Construct the long axis separately and short axis ,in, , , is the range adjustment parameter;

[0110] (13) Based on the long axis and the short axis A range ellipse is constructed and used as a reference range for inspection points.

[0111] By constructing an elliptical range, a continuous reference range is constructed for each target location cluster. This range reflects the fixed movement range of the RFID tag in the work scenario. For example, devices such as laptops and instruments have a relatively fixed range of movement. If this range is exceeded, it is considered that the fixed asset corresponding to the RFID tag has left its corresponding location or inertial movement range during use. For example, carrying a laptop on a business trip or carrying instruments on field work.

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

[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 activity position ellipse;

[0114] Data whose positioning coordinates are empty (such as signal loss or reading failure).

[0115] Cluster the positioning time points of dynamic positioning data to identify time periods with high temporal density (time clusters). Select time clusters with a sample count greater than a preset threshold as target time clusters. Based on the time points of the target time clusters, construct time ranges (such as continuous time periods or discrete time windows) to represent the dynamic activity periods of the tags.

[0116] When performing time clustering, we analyze the temporal distribution of dynamic data to identify the regular movement periods of tags (such as daily commuting hours). 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 , wherein the displacement reference activity The mathematical expression is:

[0118]

[0119] Where, is the total number of positioning data, Indicates the sequence number of the dynamic positioning data, Indicates the number of dynamic positioning data, Indicates the time point corresponding to the positioning data at the current time point, Indicates the sequence number of the dynamic positioning data, Indicates the time point of the first positioning data. Indicates the unit quantity of activity;

[0120] Activity is an indicator of the movement frequency of RFID tags. In this application, the dynamic positioning data is weighted, and its weight is , which is related to the time interval of the current time point. The longer the time interval, the lower the weight. Exponential convergence, increasing the weight ratio of time. At the same time, using To describe the activity of different RFID tags.

[0121] Assign activity scores to tags to assist with asset utilization analysis or abnormal behavior detection (such as high-frequency abnormal movement). Highly active 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 binary positioning data , calculate multiple binary positioning data The autocorrelation function is obtained to obtain an autocorrelation curve; the peak position of the autocorrelation curve is extracted, and when the peak value of the peak position is greater than the 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] Binarize the positioning data according to the inertial activity position range: if the coordinate is within the ellipse, it is marked as 1 (static), otherwise it is marked as 0 (dynamic). The complex spatial position information is converted into a simple existence mark through 0 / 1 encoding to reduce the computational complexity. Then calculate multiple binary positioning data The autocorrelation function of , the mathematical expression of the autocorrelation function is:

[0124]

[0125] Where, represents the autocorrelation value, is the average value of the binary positioning data, is the number of binary positioning data, is the time lag;

[0126] If the peak If the threshold is exceeded (such as 0.7), the tag is judged to be periodic and the time is delayed. This serves as a displacement reference period. By detecting the periodic repetitive patterns in the binary sequence, regular tag behavior can be identified. If there is no movement for a period exceeding a certain magnification, this may indicate an abnormal situation, such as a tag falling off.

[0127] (2) Synchronous inertial spectrum

[0128] (2-1) Based on the binary positioning data of each RFID tag Constructing binary positioning data sequence ,in, Indicates the serial number of the RFID tag;

[0129] Binarize the positioning data of each RFID tag (as described in steps 1-4) to generate a binary sequence. For example, if the positioning coordinates are within the inertial motion range, mark it as 1 (static); otherwise, mark it as 0 (dynamic).

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

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

[0132] (2-3) Calculate the average of the absolute values ​​of multiple binary positioning data in the difference sequence , and the average value Comparing with a pre-established positive correlation determination threshold and a pre-established negative correlation determination threshold, wherein the positive correlation determination threshold is smaller than the negative correlation determination threshold;

[0133] (2-4) in the average When the average value is greater than the negative correlation determination threshold, it is determined that the two corresponding RFID tags are negatively correlated; When the average value is less than the positive correlation determination threshold, it is determined that the two corresponding RFID tags are positively correlated; When the value is between the negative correlation determination threshold and the positive correlation determination threshold, it is determined that the corresponding two RFID tags have no correlation;

[0134] Positive correlation: Tags 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: Tags may be in mutually exclusive locations (e.g., equipment used interchangeably) or have interfering behaviors (e.g., blocking each other).

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

[0136] Specifically, each RFID tag is regarded 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 the adjacency matrix G is constructed to represent negative correlation, no correlation, and positive correlation respectively.

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

[0138] Graph structures can be used to predict tag behavior (e.g., dynamic migration based on adjacent nodes). Isolated nodes or sudden changes in connectivity may indicate unusual events (e.g., device loss or illegal movement). By comparing binary sequences and combining the inertial activity range of RFID tags with collaborative behavior analysis, hidden group patterns can be revealed.

[0139] S130, extracting dynamic features from the real-time positioning data of the RFID tag of the fixed asset during a target time period;

[0140] The process specifically includes:

[0141] S131, extracting positioning data of a target time period from the real-time positioning data, wherein the target time period is a time period of a target duration before the current time point;

[0142] S132, extracting the displacement activity from the positioning data of the RFID tag in the target time period And the current cumulative inactivity time , wherein the current accumulated static time It is the continuous duration of the RFID tag’s positioning data in a target time period;

[0143] The calculation of activity is consistent with the above description and will not be repeated here. In this embodiment, the positioning data of the five days before the current time point is obtained to calculate the activity of the current time period.

[0144] S133: Construct a dynamic feature based on the displacement activity and the accumulated static time.

[0145] S140, verifying the dynamic characteristics and real-time positioning data of the corresponding RFID tag based on the inertial dynamic characteristics to obtain a first verification result; and verifying the real-time positioning data of the shared inertial RFID tag based on the synchronized inertial spectrum to obtain a second verification result;

[0146] In one embodiment of the present application, the dynamic characteristics of the corresponding RFID tag and the 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 Activeness with the displacement reference For comparison, When the RFID tag is judged to have abnormal activity risk; When the current accumulated static time is With the reference cycle For comparison, When the RFID tag is abnormally stationary, it is determined that there is a risk of abnormal stationary state;

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

[0149] Human anomaly: tags are frequently moved (such as illegal disassembly or misoperation);

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

[0151] Under normal circumstances, the tag's inactivity time should be less than its periodic activity interval (such as returning to inactivity after daily inventory). If the inactivity time is much longer than the period, it may indicate:

[0152] Equipment failure: the tag is not read correctly (e.g. battery exhaustion, signal obstruction);

[0153] Label drop: the label falls in 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 Compare and compare the real-time positioning data with the inertial activity position range By comparison, the positioning time of the real-time positioning data is not within the inertial activity time range. , and the real-time positioning data is not within the inertial activity position range When the RFID tag is within the specified range, it is determined that there is a risk of abnormal position of the RFID tag.

[0156] Normal activities must meet both time windows (such as working hours) and spatial scope (such as designated shelves). Deviations from either may indicate:

[0157] Illegal movement: the tag is taken out of the authorized area (such as outside the warehouse);

[0158] Device drift: The tag is misread to the wrong location due to environmental interference, etc.

[0159] The anomaly detection solution in this embodiment builds a comprehensive monitoring system covering the asset lifecycle through activity comparison, static duration and periodic verification, and dual time-space constraints. Its core advantages are: (1) reducing false alarms through multi-indicator linkage; (2) automatic parameter updates based on business needs; and (3) a real-time early warning mechanism to facilitate timely risk management.

[0160] The above process can be widely used in smart warehousing, industrial Internet of Things, logistics tracking and other fields, and can improve the security and efficiency of asset management through automated anomaly detection.

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

[0162] When the positioning state of the current RFID tag changes, the positioning change state of the current RFID tag is obtained, and the positioning change state of the related RFID tags of the current RFID tag between the current moment and the extended moment is obtained. When the positioning change state of the related RFID tags does not meet the corresponding correlation with the positioning change state of the current RFID tag, it is determined that the current RFID tag has a synchronization abnormality risk.

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

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

[0165] Based on the inertial activity position range and inertial activity time range A dynamic electronic fence is constructed, and the activity range of the RFID tag is managed 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 stillness risk (stillness duration exceeding the period), abnormal stillness risk (stillness duration exceeding the period), and synchronous abnormality risk (inconsistency with the behavior of the associated tag in the synchronous inertia map), the system automatically outputs an early warning signal; at the same time, the system status of the abnormal tag is marked as "waiting for inventory", 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 based on the inertial activity position range (the ellipse in step 1-1) in the spatial dimension; and defines the fence effective period based on the inertial activity time range (the time cluster in step 1-2) in the temporal dimension.

[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 discovered tags with high-frequency abnormal movements (such as frequent entry and exit of high-value areas).

[0171] Solution:

[0172] Triggering an abnormally active risk warning, locking the label and setting it to "waiting for inventory";

[0173] Dynamic electronic fence restricts the tag to move in a designated area;

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

[0175] Scenario 2: Chemical Plant Safety Monitoring

[0176] Issue: Tags in hazardous material storage areas are experiencing sudden inactivity and timing out.

[0177] Solution:

[0178] Trigger an abnormal stationary risk warning and link the monitoring system to check the scene;

[0179] The dynamic electronic fence detects that the tag position is normal but the time is abnormal, and it is determined to be a device failure;

[0180] The technician replaced the tag battery and restored the system status.

[0181] The present invention's RFID-based fixed asset physical management method first obtains the signal strength data and positioning data of the RFID tags corresponding to the fixed assets in use at multiple historical inventory time points, and then uses the signal strength data and positioning data of the multiple historical inventory time points to screen data samples and remove abnormal signal time points caused by external signal interference. Then, precise positioning is used to construct the inertial dynamic characteristics of each RFID to find out whether each RFID tag has inertial dynamic characteristics and extract the inertial dynamic characteristics. At the same time, the present application also uses precise positioning to find the synchronous use characteristics between different RFID tags, thereby constructing a synchronous inertial spectrum. Inertial dynamic characteristics and synchronous inertial spectrum are used to perform early warning management and inventory management of RFID tag dynamic characteristics in various situations. It is more flexible and fits 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] an acquisition module, configured to acquire signal strength data and positioning data of RFID tags of fixed assets in use at multiple historical inventory time points, and to acquire real-time positioning data of RFID tags of fixed assets in use, wherein the positioning data and the real-time positioning data are calculated by multiple RFID base stations based on a time difference of arrival method, and the signal strength data includes signal strengths measured by multiple RFID base stations;

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

[0185] A feature extraction module is used to extract dynamic features from the real-time positioning data of the RFID tag of the fixed asset in a target time period;

[0186] a verification module configured to verify the dynamic characteristics and real-time positioning data of the corresponding RFID tag based on the inertial dynamic characteristics to obtain a first verification result; and verify the real-time positioning data of the shared inertial RFID tag based on the synchronized inertial spectrum to obtain a second verification result;

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

[0188] The RFID-based fixed asset physical management system of the present invention first obtains the signal strength data and positioning data of the RFID tags corresponding to the fixed assets in use at multiple historical inventory time points, and then uses the signal strength data and positioning data of the multiple historical inventory time points to screen data samples and remove abnormal signal time points caused by external signal interference. Then, the inertial dynamic characteristics of each RFID are constructed using precise positioning to find out whether each RFID tag has inertial dynamic characteristics and extract the inertial dynamic characteristics. At the same time, the present application also uses precise positioning to find the synchronous use characteristics between different RFID tags, thereby constructing a synchronous inertial spectrum. The inertial dynamic characteristics and the synchronous inertial spectrum are used to perform early warning management and inventory management of the dynamic characteristics of RFID tags in various situations. It is more flexible and fits the usage habits of fixed assets in use.

[0189] Figure 5 The following is a schematic diagram showing the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application. Figure 5The computer system of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present 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 a read-only memory (ROM) 502 or programs loaded from a storage unit 508 into a random access memory (RAM) 503. RAM 503 also stores various programs and data required for system operation. CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0191] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, mouse, and the like; an output section 507 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 508 including devices such as a hard disk; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. Removable media 511, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 510 as needed, so that computer programs read from the media can be installed in the storage section 508 as needed.

[0192] In particular, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 509 and / or installed from removable media 511. When executed by the central processing unit (CPU) 501, the computer program performs the various functions defined in the system of the present application.

[0193] It should be noted that the computer-readable medium described in the embodiments of this application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may, for example, be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media 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, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, 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. This propagated data signal may take a variety of forms, including, but not limited to, an electromagnetic signal, an optical signal, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0194] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the above-mentioned module, program segment or part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart and the combination of boxes in the block diagram or flowchart can be implemented using a dedicated hardware-based system that performs the specified functions or operations, or can be implemented using a combination of dedicated hardware and computer instructions.

[0195] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0196] Another aspect of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a computer processor, the computer executes the aforementioned method. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device.

[0197] Another aspect of the present application provides a computer program product or computer program, which includes 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 above embodiments.

[0198] The above embodiments are only preferred embodiments for fully illustrating the present application, and the protection scope of the present application is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art based on the present application are within the protection scope of the present application.

Claims

1. The method for physical management of fixed assets based on RFID is characterized by: Including steps: Obtaining signal strength data and positioning data of RFID tags of fixed assets in use at multiple historical inventory time points, and obtaining real-time positioning data of RFID tags of fixed assets in use, wherein the positioning data and the real-time positioning data are calculated by multiple RFID base stations based on a time difference of arrival method, and the signal strength data includes signal strengths measured by multiple RFID base stations; Based on the signal strength data and positioning data of multiple RFID tags at multiple historical inventory time points, an inertia database and a synchronized inertia map are constructed for the corresponding fixed assets' RFID tags. The inertia database includes the inertial dynamic characteristics of the signal strength of each fixed asset's RFID tag, and the synchronized inertia map represents the shared probability of multiple groups of shared inertial RFID tags. Extracting dynamic features from real-time positioning data of the RFID tag of the fixed asset during a target time period; Verifying the dynamic characteristics and real-time positioning data of the corresponding RFID tag based on the inertial dynamic characteristics to obtain a first verification result; and verifying the real-time positioning data of the shared inertial RFID tag based on the synchronized inertial spectrum to obtain a second verification result; Based on the first verification result or the second verification result, early warning management and inventory management are performed on the fixed assets in use.

2. The RFID-based fixed asset physical management method according to claim 1, characterized in that: Based on the signal strength data and positioning data of multiple RFID tags at multiple historical inventory time points, an inertial database and a synchronized inertial map of the corresponding RFID tags of fixed assets are constructed, including: For each RFID tag, the signal strength data at multiple historical inventory time points are located based on a pre-built signal strength heat map of each RFID base station, thereby obtaining a target grid corresponding to the signal strength data at the multiple historical inventory time points. The signal strength heat map includes multiple grids and the signal strength data value ranges corresponding to the multiple grids. Comparing the target grid and positioning data at the same historical inventory time point, and when the positioning data falls into the target grid, determining that the signal strength data and positioning data at the corresponding historical inventory time point are normal; otherwise, determining that the signal strength data and positioning data at the corresponding historical inventory time point are abnormal; Remove the historical inventory time points with abnormal signal strength data and positioning data to obtain the target time point; An inertial database of RFID tags corresponding to fixed assets is constructed based on the positioning data of multiple RFID tags at multiple target time points; and a synchronous inertial map of RFID tags corresponding to fixed assets is constructed based on the positioning data of multiple RFID tags at multiple target time points.

3. The RFID-based fixed asset physical management method according to claim 2, characterized in that: The inertial dynamic characteristics include the displacement reference period , displacement reference activity , Inertial activity position range and inertial activity time range The positioning data includes positioning coordinates and positioning time points. The inertial database of the RFID tags corresponding to the fixed assets is constructed based on the positioning data of 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; the positioning cluster with sample data greater than the preset number threshold is regarded as the target position cluster, and the calculation range ellipse of the positioning data within the target position is calculated, and the range ellipse is used as the inertial activity position range of the RFID tag. ; For each RFID tag, the positioning data whose positioning coordinates do not fall within the inertial activity position range and the positioning data whose positioning coordinates are empty are marked as dynamic data as dynamic positioning data, and the dynamic positioning data are clustered based on the positioning time point to obtain multiple time clusters; the time cluster with sample data greater than the preset number threshold in the cluster is taken as the target time cluster; and the inertial activity time range is constructed based on the positioning time points of the dynamic positioning data in the target time cluster ; For each RFID tag, the displacement reference activity is calculated based on the dynamic positioning data and the total number of positioning data. , wherein the displacement reference activity The mathematical expression is: Where, is the total number of positioning data, Indicates the sequence number of the dynamic positioning data, Indicates the number of dynamic positioning data, Indicates the time point corresponding to the positioning data at the current time point, Indicates the sequence number of the dynamic positioning data, Indicates the time point of the first positioning data. Indicates the unit quantity of activity; For each RFID tag, the positioning data of multiple target time points are binarized based on the inertial activity position range to obtain binary positioning data. , calculate multiple binary positioning data The autocorrelation function is obtained to obtain an autocorrelation curve; the peak position of the autocorrelation curve is extracted, and when the peak value of the peak position is greater than the 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 .

4. The RFID-based fixed asset physical management method according to claim 3, characterized in that: Based on the positioning data of multiple RFID tags at multiple target time points, a synchronized inertial map of the RFID tags corresponding to the fixed assets is constructed, including: Based on the binary positioning data of each RFID tag Constructing binary positioning data sequence ,in, Indicates the serial number of the RFID tag; Binarized positioning data sequence of any two RFID tags After removing the non-common time points in the , the difference is calculated to obtain the difference sequence ; Calculate the average of the absolute values ​​of multiple binary positioning data in the difference sequence , and the average value Comparing with a pre-established positive correlation determination threshold and a pre-established negative correlation determination threshold, wherein the positive correlation determination threshold is smaller than the negative correlation determination threshold; In the average When the average value is greater than the negative correlation determination threshold, it is determined that the two corresponding RFID tags are negatively correlated; When the average value is less than the positive correlation determination threshold, it is determined that the two corresponding RFID tags are positively correlated; When the value is between the negative correlation determination threshold and the positive correlation determination threshold, it is determined that the corresponding two RFID tags have no correlation; Construct a synchronized inertial map based on the correlation between any two RFID tags.

5. The RFID-based fixed asset physical management method according to claim 4, characterized in that: Extracting dynamic features from the real-time positioning data of the RFID tag of the fixed asset during a target time period includes: Extracting positioning data of a target time period from the real-time positioning data, wherein the target time period is a time period of a target duration before the current time point; Extracting displacement activity from the positioning data of the RFID tag during the target time period And the current cumulative inactivity time , wherein the current accumulated static time It is the continuous duration of the RFID tag’s positioning data in a target time period; A dynamic feature is constructed based on the displacement activity and the accumulated static time.

6. The RFID-based fixed asset physical management method according to claim 5, characterized in that: Verifying the dynamic characteristics of the corresponding RFID tag and the real-time positioning data based on the inertial dynamic characteristics to obtain a first verification result includes: The displacement activity of RFID tags in the target time period Activeness with the displacement reference For comparison, When the RFID tag is judged to have abnormal activity risk; When the current accumulated static time is With the reference cycle For comparison, When the RFID tag is abnormally stationary, it is determined that there is a risk of abnormal stationary state; 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. Compare and compare the real-time positioning data with the inertial activity position range By comparison, the positioning time of the real-time positioning data is not within the inertial activity time range. , and the real-time positioning data is not within the inertial activity position range When the RFID tag is within the specified range, it is determined that there is a risk of abnormal position of the RFID tag.

7. The RFID-based fixed asset physical management method according to claim 6, characterized in that: Verifying the real-time positioning data of the shared inertial RFID tag based on the synchronized inertial map to obtain a second verification result includes: When the positioning state of the current RFID tag changes, the positioning change state of the current RFID tag is obtained, and the positioning change state of the related RFID tags of the current RFID tag between the current moment and the extended moment is obtained. When the positioning change state of the related RFID tags does not meet the corresponding correlation with the positioning change state of the current RFID tag, it is determined that the current RFID tag has a synchronization abnormality risk.

8. The RFID-based fixed asset physical management method according to claim 7, characterized in that: Performing early warning management and inventory management on the fixed assets in use based on the first verification result or the second verification result includes: When any RFID tag has the risk of abnormal activity, abnormal stillness, abnormal position or abnormal synchronization, an early warning signal is output to the target object and the status of the corresponding RFID tag is set to waiting for inventory.

9. The RFID-based fixed asset physical management method according to claim 3, characterized in that: Also includes: Based on the inertial activity position range and inertial activity time range A dynamic electronic fence is constructed, and the activity range of the RFID tag is managed based on the dynamic electronic fence.

10. The RFID-based fixed asset physical management system is characterized by: include: an acquisition module, configured to acquire signal strength data and positioning data of RFID tags of fixed assets in use at multiple historical inventory time points, and to acquire real-time positioning data of RFID tags of fixed assets in use, wherein the positioning data and the real-time positioning data are calculated by multiple RFID base stations based on a time difference of arrival method, and the signal strength data includes signal strengths measured by multiple RFID base stations; A priori data construction module is used to construct an inertia database and a synchronized inertia map for the RFID tags of corresponding fixed assets based on the signal strength data and positioning data of multiple RFID tags at multiple historical inventory time points. The inertia database includes the inertial dynamic characteristics of the signal strength of the RFID tag of each fixed asset, and the synchronized inertia map represents the shared probability of multiple groups of shared inertial RFID tags. A feature extraction module is used to extract dynamic features from the real-time positioning data of the RFID tag of the fixed asset in a target time period; a verification module configured to verify the dynamic characteristics and real-time positioning data of the corresponding RFID tag based on the inertial dynamic characteristics to obtain a first verification result; and verify the real-time positioning data of the shared inertial RFID tag based on the synchronized inertial spectrum to obtain a second verification result; A management module is used to perform early warning management and inventory management on the fixed assets in use based on the first verification result or the second verification result.

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