Measurement asset warehouse management method and system based on RFID and Internet of Things light module

Abstract

The invention discloses a measurement asset warehouse management method and system based on RFID and an Internet of Things light module, and relates to the technical field of asset measurement, a multi-dimensional dynamic collaborative sensing network is formed by deploying a fixed first RFID antenna array, a fixed second RFID antenna array and a movable third RFID antenna array deployed on a mobile terminal, and the management of the asset warehouse is realized through the multi-dimensional dynamic collaborative sensing network. According to the method, observation signals from M effective antennas are fused, estimation parameters of independent estimation parameters and related estimation parameters are calculated, and a high-precision observation model function is constructed, so that the defects of unstable single signal strength, multipath interference and the like can be effectively overcome, specific three-dimensional coordinates or accurate storage positions of target measurement assets on a goods shelf are solved, and the measurement accuracy of the target measurement assets is improved. Therefore, the management personnel can directly and quickly position the target individuals stored in a mixed manner, the limitation that the traditional RFID technology can only identify regions is changed, and the core pain point that the accurate position is difficult to position is solved.

Description

Technical Field

[0001] This invention relates to the field of asset management technology, specifically to a method and system for managing metered assets warehouses based on RFID and IoT lighting modules. Background Technology

[0002] In the management of metering assets in the power metering equipment warehouse, meticulous control over metering assets such as electricity meters is crucial. Taking electricity meters as an example, electricity meters mainly include meters returned from the operating site and qualified meters in the warehouse that have been inspected and are ready for distribution. In large warehouses, for the sake of storage efficiency and convenient circulation, metering assets such as electricity meters are usually stored in batches and mixed in a centralized manner on bins or pallets. That is, multiple electricity meters of different batches, models, or conditions may be placed in the same storage unit.

[0003] Currently, the industry widely uses Radio Frequency Identification (RFID) technology for asset identification and management of electricity meters. Specifically, each electricity meter is bound to or embedded with an RFID electronic tag, and contactless batch reading is performed using fixed or handheld RFID readers, thereby quickly completing operations such as inventory checks upon entry and verification upon exit, significantly improving the efficiency of identity information collection.

[0004] However, traditional RFID technology reveals a significant technical deficiency in such applications: a lack of precise spatial positioning capabilities for RFID tags. While RFID readers primarily work by identifying the encoded information within tags through radio frequency signals and potentially receiving signal strength indicators (RSSI), in actual warehousing environments, when large numbers of electricity meters are densely and haphazardly stacked, the following problems arise:

[0005] Signal interference and attenuation: When multiple tags within the reader's antenna beam coverage area respond simultaneously, their signals interfere with and overlap, resulting in extremely unstable RSSI values. Furthermore, these values ​​are severely affected by factors such as the meter's metal casing, the surrounding environment, and multipath effects, making it impossible to reliably reflect the accurate distance between the tag and the reader.

[0006] Unable to distinguish relative position: Traditional systems can only determine that a tag exists within the reading and writing area of ​​a reader (usually a range of several meters to tens of meters), but cannot further determine the specific three-dimensional coordinates of the tag within the area. That is, it cannot distinguish whether it is a meter on the upper or lower layer of the bin, or a meter on the left or right side of the same bin.

[0007] Lack of proximity location: For managers, even if the system knows that the target meter is located on a certain bin or pallet, in the actual physical search, it is still necessary to manually identify or scan each meter in the batch one by one by visual inspection or close-range scanning. This is time-consuming, labor-intensive, and prone to errors. Especially when the warehouse area is large, the number of meters is large, and the storage location is dynamically updated, this problem seriously restricts the efficiency and accuracy of warehousing operations.

[0008] In summary, while existing RFID-based warehouse management systems have solved the problem of batch identification of metered assets, they cannot achieve precise location of individual metered assets in typical warehouse conditions characterized by batch and mixed storage. This has become a key bottleneck in achieving full-process, visualized, and intelligent management of power assets. Therefore, there is an urgent need for a technical solution that can achieve rapid and accurate location of individual metered assets in this complex environment. Summary of the Invention

[0009] To address the problem that existing RFID-based warehouse management systems cannot accurately locate individual assets in typical warehouse conditions involving batch and mixed storage, this invention aims to provide a warehouse management method and system for measurable assets based on RFID and IoT lighting modules. By deploying fixed first and second RFID antenna arrays, and a movable third RFID antenna array deployed on a mobile terminal, a multi-dimensional dynamic collaborative sensing network is formed. This network integrates observation signals from M effective antennas, calculates independent and related estimation parameters, and constructs a high-precision observation model function. This effectively overcomes the defects of unstable single signal strength and multipath interference, solving for the specific three-dimensional coordinates or precise storage location of the target measurable asset on the shelf. This allows managers to directly and quickly locate target individuals in mixed storage, overcoming the limitation of traditional RFID technology's area identification capability and solving the core pain point of difficulty in precise location.

[0010] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0011] This solution provides a method for managing metered assets in a warehouse based on RFID and IoT lighting modules. The method includes:

[0012] The first RFID antenna array is deployed in the logistics channel within the warehouse where the assets are measured; the second RFID antenna array is deployed at the warehouse entrance, transition area, and exit; and the third RFID antenna is deployed on the mobile terminal.

[0013] At a predetermined location within the warehouse, observation signals from target measurement asset tags collected by M effective antennas are simultaneously acquired; the effective antennas are from a first RFID antenna array, a second RFID antenna array, and a third RFID antenna.

[0014] The estimated parameters of the effective antennas are calculated based on the observed signals; the estimated parameters include the independent estimated parameters of each effective antenna and the correlation estimated parameters between different effective antennas.

[0015] Based on the estimated parameters, an observation model function is constructed with the RFID tag coordinates of the target measurement asset as the observation value. The observation model function is solved, and the location of the target measurement asset is obtained based on the optimal solution of the observation model function.

[0016] The location of the target measurable asset is displayed in the first state based on the IoT lighting module, and the outbound strategy of the target measurable asset is determined based on the second state display of the IoT lighting module.

[0017] A further optimization scheme is that the preset location includes a preset signal strength Be. When the third RFID antenna observes that the signal strength of the reflected signal from the target measurement asset tag at the current location exceeds the signal strength Be, the current location is determined to be a preset location.

[0018] A further optimization scheme is as follows: the independent estimation parameters include: the estimated distance and variance of the estimated distance from each effective antenna to the target metering asset tag; the related estimation parameters include the observed distance difference and variance of the observed distance difference between any two effective antennas and the target metering asset tag.

[0019] A further optimized solution is that the method for calculating the distance difference observations includes:

[0020] Calculate the observed phase difference Δθ between effective antenna i and effective antenna j. ij To eliminate label offset; where, ; Indicates the wavelength of the radio frequency signal; d i The estimated distance from the effective antenna i to the target metering asset tag can be estimated from the signal power received by the effective antenna i from the target metering asset tag; d j The estimated distance from the effective antenna j to the target metering asset tag can be estimated from the signal power received by the effective antenna i from the target metering asset tag.

[0021] Calculate the estimated distance difference ΔD between effective antenna i and effective antenna j based on the estimated distances from effective antenna i to the target metering asset tag and effective antenna j to the target metering asset tag. ij =d i -d j ;

[0022] The distance difference observation value Δd is obtained based on the phase difference observation value and the estimated distance difference. ij :

[0023] Δd ij = ;

[0024] =round( - );

[0025] Here, round() represents the rounding function; The theoretical number of cycles for the phase difference.

[0026] A further optimization scheme involves constructing an observation model function based on the estimated parameters, using the RFID tag coordinates of the target asset as the observed values; solving the observation model function; and obtaining the location of the target asset based on the optimal solution of the observation model function; including the following methods:

[0027] Construct the observation vector z using the estimated parameters of the effective antenna;

[0028] Obtain the spatial coordinates of each effective antenna Let i = 1, 2, ..., M, and configure the spatial coordinates of the target measurement asset labels as follows: ; ;

[0029] To find the optimal spatial coordinates This makes the optimal spatial coordinates The observation model function is constructed based on the distances to each effective antenna and the distance differences between them, which are closest to the estimated parameters. :

[0030] ;

[0031] The observation error covariance matrix of the observation model function is: ,matrix It is a diagonal matrix, matrix The diagonal elements are the estimated distance variances from each effective antenna to the target measurable asset tag and the observed variances of the distance differences between any two effective antennas to the target measurable asset tag;

[0032] With cost function The goal is to find the optimal solution with the minimum objective, and then determine the location of the target measurable asset based on the optimal solution.

[0033] A further optimization scheme is as follows: the cost function for: .

[0034] A further optimization scheme is as follows: the cost function The goal is to find the optimal solution with the minimum objective, and then determine the location of the target measurable asset based on the optimal solution. This includes the following methods:

[0035] T1, initial configuration estimate ;

[0036] In T2, during the k-th iteration, the Jacobian matrix of the observation model is calculated. ;

[0037] T3, based on Jacobian matrix Calculate update step size : ;

[0038] T4, based on the k-th iteration estimate and update step size Update the estimate for the (k+1)th iteration. : ;

[0039] T5, repeat steps T2-T4 until... If the value is less than a set threshold, the optimal solution is finally obtained. ;

[0040] T6. Based on the known locations of each material bin in the warehouse, estimate the optimal solution. The probability of falling entirely into a certain bin is used to select the bin with the highest probability as the target measurement asset.

[0041] A further optimization scheme is that the method for determining the outbound strategy includes:

[0042] During the process of warehousing each measurable asset, the warehousing time is recorded and entered into the IoT lighting module;

[0043] The IoT lighting module prioritizes displaying the second status of the assets that were received earlier, based on their arrival time.

[0044] The decision on whether to release the target measured asset is based on the second status display of the IoT lighting module.

[0045] This solution also provides a metering asset warehouse management system based on RFID and IoT lighting modules, used to implement the aforementioned metering asset warehouse management method based on RFID and IoT lighting modules. The system includes:

[0046] The data acquisition module includes a first RFID antenna array deployed in the logistics channel within the warehouse for measuring assets, a second RFID antenna array deployed at the warehouse entrance, transition point and exit, and a third RFID antenna deployed on a mobile terminal.

[0047] The acquisition module is used to simultaneously acquire observation signals from target measurement asset tags at preset locations within the warehouse using M effective antennas; the effective antennas are from a first RFID antenna array, a second RFID antenna array, and a third RFID antenna.

[0048] The parameter estimation module is used to calculate the estimated parameters of the effective antennas based on the observed signals; the estimated parameters include the independent estimated parameters of each effective antenna and the correlation estimated parameters between different effective antennas;

[0049] The positioning module is used to construct an observation model function based on the estimated parameters, with the RFID tag coordinates of the target measurement asset mapped as the observation values, solve the observation model function, and obtain the positioning of the target measurement asset based on the optimal solution of the observation model function.

[0050] The outbound module is used to display the first status of the target metered asset based on the location of the IoT lighting module, and to determine the outbound strategy of the target metered asset based on the second status display of the IoT lighting module.

[0051] A further optimized solution is to position the transition port between the qualified measurement asset area and the withdrawn measurement asset area.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. This invention provides a method and system for warehouse management of metered assets based on RFID and IoT lighting modules. By deploying a fixed first RFID antenna array, a second RFID antenna array, and a movable third RFID antenna array deployed on a mobile terminal, a multi-dimensional dynamic collaborative sensing network is formed. The observation signals from M effective antennas are fused, and the estimated parameters of independent and related estimated parameters are calculated to construct a high-precision observation model function. This effectively overcomes the defects of unstable single signal strength and multipath interference, and solves the specific three-dimensional coordinates or precise storage location of the target metered asset on the shelf. This allows managers to directly and quickly locate the target individual stored in a mixed environment, changing the limitation of traditional RFID technology that can only identify areas and solving the core pain point of difficulty in precise location.

[0054] 2. This invention provides a method and system for managing measurable assets in a warehouse based on RFID and IoT lighting modules. By deploying antenna arrays at key nodes such as logistics channels, entrances, transition points, and exits in the warehouse, and linking them with mobile terminals, the system can automatically track the entire process of measurable assets from entry to exit. No manual scanning is required at the entry and exit points; the system automatically records and verifies data, avoiding human error. Within the warehouse, combined with precise positioning, inventory counting can be automated, resulting in an order-of-magnitude improvement in both efficiency and accuracy.

[0055] 3. This invention provides a method and system for warehouse management of metered assets based on RFID and IoT lighting modules. It combines high-precision positioning results with the status control of IoT lighting modules (such as LED indicator lights). When the system locates the target metered asset, it can automatically light up or change the status (such as color or flashing) of the IoT lighting module in its storage location, converting digital positioning information into intuitive physical guidance signals. During picking or inventory operations, operators do not need to repeatedly check the screen coordinates and physical positions; they only need to follow the light guidance to quickly find the target.

[0056] 4. This invention provides a method and system for managing metering asset warehouses based on RFID and IoT lighting modules. It upgrades existing metering asset warehouses to intelligent systems, standardizes storage locations, and implements first-in-first-out (FIFO) IoT lighting guidance function based on IoT lighting modules. This solves the problem of FIFO storage age of electricity meters in the warehouse, and greatly reduces the workload of querying calibration dates and checking each meter in the warehouse in the original system. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0058] Figure 1 This is a schematic diagram of a method for managing a metered asset warehouse based on RFID and IoT lighting modules;

[0059] Figure 2 This is a schematic diagram of the structure of a metering asset warehouse management system based on RFID and IoT lighting modules;

[0060] Figure 3 A diagram showing the comparison of the time for a rapid inventory count of the entire warehouse after the renovation and before the renovation.

[0061] Figure 4 Screenshot of the inventory management interface of the asset warehouse management software;

[0062] Figure 5 This is a screenshot of part of the inventory data interface. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0064] Existing RFID-based warehouse management systems cannot accurately locate individual assets when dealing with typical warehousing conditions involving batch and mixed storage. Therefore, this solution provides the following embodiments to address the aforementioned technical problems:

[0065] Example 1

[0066] This embodiment provides a method for managing a metered asset warehouse based on RFID and IoT lighting modules, such as... Figure 1 As shown, the method includes:

[0067] Step 1: Deploy the first RFID antenna array in the logistics channel within the warehouse where the assets are measured; deploy the second RFID antenna array at the warehouse entrance, transition point, and exit; and deploy the third RFID antenna on the mobile terminal.

[0068] Step 2: At a predetermined location within the warehouse, simultaneously acquire observation signals from target measurement asset tags collected by M effective antennas; the effective antennas are from the first RFID antenna array, the second RFID antenna array, and the third RFID antenna.

[0069] The preset location includes a preset signal strength Be. When the third RFID antenna observes that the signal strength of the reflected signal from the target measurement asset tag at the current location exceeds the signal strength Be, the current location is determined to be a preset location. The third RFID antenna needs to operate when precise positioning is required. For the system's automatic full-database rapid inventory count, identification and inventory can be performed based solely on the data collected by the first and second RFID antenna arrays.

[0070] Specifically, the third RFID antenna is deployed on a handheld terminal or a turnover vehicle; the first RFID antenna array is fixedly arranged on the main and branch passages in the warehouse; based on the x and y distances after weighting by the joint distribution γ.

[0071] Step 3: Calculate the estimated parameters of the effective antennas based on the observed signals; the estimated parameters include the independent estimated parameters of each effective antenna and the correlation estimated parameters between different effective antennas;

[0072] The independent estimation parameters include: the estimated distance and variance of the estimated distance from each effective antenna to the target metering asset tag; the related estimation parameters include the observed distance difference and variance of the observed distance difference between any two effective antennas and the target metering asset tag.

[0073] The method for calculating the distance difference observations includes:

[0074] For the same target tag, the inherent offset of the phase values ​​measured by different antennas is the same. Therefore, for effective antenna i and effective antenna j, the phase difference observation value can be calculated. ;in, The inherent phase offset of effective antenna i can be measured through calibration by the antenna manufacturer. The inherent phase offset of the effective antenna j can be measured through calibration at the antenna manufacturer.

[0075] S31, Calculate the observed phase difference Δθ between effective antenna i and effective antenna j. ij To eliminate label offset; where Δθ ij =4π(d i -d j ) / λ; where λ represents the wavelength of the radio frequency signal; d i This represents the estimated distance from the effective antenna i to the target metering asset tag, and the signal power P received from the target metering asset tag by the effective antenna i. m,i Estimated; d j The estimated distance from the effective antenna j to the target metering asset tag can be estimated from the signal power received by the effective antenna i from the target metering asset tag; specifically, P m,i = P0 −10nlog10(d i )+X σ Where n represents the path loss index, which is typically between 2 and 4 between warehouse shelves, and is calibrated based on the actual environment; X σ This represents a random variable, which in this embodiment is a zero-mean Gaussian distribution N(0,σ). 2 ).

[0076] The phase difference observation value Δθ is calculated based on the above formula. ij Label offset was eliminated, and the accuracy was extremely high. Based on hyperbolic positioning technology, the phase difference observation value Δθ ij In fact, it represents the distance difference between the target tag and the two effective antennas.

[0077] S32, calculate the estimated distance difference ΔD between effective antenna i and effective antenna j based on the estimated distance from effective antenna i to the target metering asset tag and the estimated distance from effective antenna j to the target metering asset tag. ij =d i -d j ;

[0078] S33, Based on the phase difference observation and the estimated distance difference, the distance difference observation value Δd is obtained. ij :

[0079] Δd ij = ;

[0080] =round( - );

[0081] Here, round() represents the rounding function; The theoretical number of cycles representing the phase difference.

[0082] Phase difference observation Δθ ij The system itself still suffers from 2π ambiguity. This scheme uses the estimated distance difference obtained from the signal strength RSSI to resolve the ambiguity; based on the phase θ... i With distance d i The relationship model yielded the aforementioned distance difference observations. Calculation formula; specific phase θ i With distance d i The relational model is: θ i =(4πd i / λ+θ r,i mod 2π; mod 2π represents the phase ambiguity term. Since the phase has a period of 2π, the phase measurement value θ is affected. i Since the distance is always between [0, 2π), this scheme introduces a phase ambiguity term to provide ambiguous distance variation information, and then uses the distance difference observation Δd. ij To resolve the blurring, the phase difference value of the blurring is restored to an approximate absolute distance scale.

[0083] Since the signal strength and phase measurements of a single antenna cannot be used for direct positioning, this scheme uses the precise coordinates of multiple antennas (spatial diversity) as reference points. By measuring the distance or distance change information from the target tag to these reference points, the coordinates of the target tag can be solved.

[0084] Step four: Construct an observation model function based on the estimated parameters, using the RFID tag coordinates of the target asset as the observed values; solve the observation model function; and obtain the location of the target asset based on the optimal solution of the observation model function. This step specifically includes the following methods:

[0085] S41, construct the observation vector using the estimated parameters of the effective antenna. ;

[0086] S42, Obtain the spatial coordinates of each effective antenna. Let i = 1, 2, ..., M, and configure the spatial coordinates of the target measurement asset labels as follows: ; ;

[0087] S43, to find the optimal spatial coordinates This makes the optimal spatial coordinates The observation model function is constructed based on the distances to each effective antenna and the distance differences between them, which are closest to the estimated parameters. :

[0088] ;

[0089] The observation error covariance matrix of the observation model function is: ,matrix It is a diagonal matrix, matrix The diagonal elements are the estimated distance variances from each effective antenna to the target measurable asset tag and the observed variances of the distance differences between any two effective antennas to the target measurable asset tag;

[0090] S44, with cost function The goal is to find the optimal solution with the minimum objective, and then determine the location of the target measurable asset based on the optimal solution.

[0091] In step S44, the cost function for: Specifically, the observation model function is a nonlinear least squares problem.

[0092] The specific methods for solving the observation model function include:

[0093] T1, initial configuration estimate Initial estimate The positioning can be configured based on a rough estimate of the distance using RSSI or the result of the previous iteration.

[0094] In T2, during the k-th iteration, the Jacobian matrix of the observation model is calculated. ;

[0095] T3, based on Jacobian matrix Calculate update step size : ;

[0096] T4, based on the k-th iteration estimate and update step size Update the estimate for the (k+1)th iteration. : ;

[0097] T5, repeat steps T2-T4 until... If the value is less than a set threshold, the optimal solution is finally obtained. ;

[0098] T6. Based on the known locations of each material bin in the warehouse, estimate the optimal solution. The probability of falling entirely into a certain bin is used to select the bin with the highest probability as the target measurement asset.

[0099] Step 5: Based on the location of the target metered asset using the IoT lighting module, display the first state and determine the outbound strategy of the target metered asset based on the second state display of the IoT lighting module.

[0100] The methods for determining the outbound strategy in step five include:

[0101] S51, during the process of warehousing each measured asset, record the warehousing time and input it into the IoT lighting module;

[0102] S52, the IoT lighting module prioritizes displaying the second status of the measured assets with earlier entry times according to their entry time;

[0103] S53 determines whether to issue the target measured asset based on the second status display of the IoT lighting module.

[0104] When meters are dispatched, the IoT light module on the bin of the first meter to be dispatched will flash to remind users to prioritize using meters from that batch. If the wrong meter is selected, the system will issue a warning when the meter is dispatched. This effectively avoids the problem of high inventory age of metered assets.

[0105] This solution transforms the asset warehouse into a unified intelligent warehousing unit. By deploying radio frequency channels, fixed sensing systems, mobile antenna arrays, and a lighting guidance system composed of IoT lighting modules, it not only fundamentally solves the technical defects of traditional RFID technology in dense warehousing environments, but also improves the precision and intelligence of the management of the metered asset warehouse through the integration of precise positioning and lighting guidance.

[0106] Example 2

[0107] This embodiment provides a metered asset warehouse management system based on RFID and IoT lighting modules, such as... Figure 2 As shown, the system for implementing the metered asset warehouse management method based on RFID and IoT lighting modules of Embodiment 1 includes:

[0108] The data acquisition module includes a first RFID antenna array deployed in the logistics channel within the warehouse for measuring assets, a second RFID antenna array deployed at the warehouse entrance, transition point and exit, and a third RFID antenna deployed on a mobile terminal.

[0109] The acquisition module is used to simultaneously acquire observation signals from target measurement asset tags at preset locations within the warehouse using M effective antennas; the effective antennas are from a first RFID antenna array, a second RFID antenna array, and a third RFID antenna.

[0110] The parameter estimation module is used to calculate the estimated parameters of the effective antennas based on the observed signals; the estimated parameters include the independent estimated parameters of each effective antenna and the correlation estimated parameters between different effective antennas;

[0111] The positioning module is used to construct an observation model function based on the estimated parameters, with the RFID tag coordinates of the target measurement asset mapped as the observation values, solve the observation model function, and obtain the positioning of the target measurement asset based on the optimal solution of the observation model function.

[0112] The outbound module is used to display the first status of the target metered asset based on the location of the IoT lighting module, and to determine the outbound strategy of the target metered asset based on the second status display of the IoT lighting module.

[0113] The transition port is located between the qualified measurement asset area and the withdrawn measurement asset area.

[0114] This embodiment modifies a certain measurement asset warehouse management system according to the method of this solution. Specifically, the process of realizing rapid inventory of the entire warehouse based on RFID and IoT lighting modules in the modified measurement asset warehouse management system is as follows:

[0115] The administrator clicks "Start Global Inventory" in the system. Through the reader cluster, all first and second RFID antenna arrays are simultaneously activated, completing a full inventory scan within 10-15 minutes. After the data is aggregated and deduplicated by edge computing nodes, it is compared with the system's book inventory to automatically generate an inventory report and pinpoint the last known location of any discrepancies in the meters for verification. Screenshots of specific inventory data are shown below. Figure 5 As shown.

[0116] The upgraded metering asset warehouse management system based on RFID and IoT lighting modules enables the rapid retrieval of designated electricity meters as follows:

[0117] The system receives picking requests from work orders or manual input. Pickers enter the warehouse area at preset locations within the warehouse. M effective antennas simultaneously collect observation signals from the target measurable asset tags. The data is used for edge computing to determine the location of the target measurable asset. Indicator lights in the main and branch channels guide the picker to the target bin. The outbound strategy for the target measurable asset is determined based on the second status display of the IoT lighting module.

[0118] like Figure 3 As shown in the diagram, this is a comparison of the time required for a rapid inventory count of the entire warehouse after the renovation versus before the renovation. Figure 3 It is evident that the number of inventory counts posted during the comprehensive inventory check has significantly decreased after the upgrade, resulting in a substantial improvement in efficiency. A screenshot of the inventory management interface of the asset management software based on RFID and IoT lighting modules is shown below. Figure 4As shown, inventory management includes qualified inventory counts, withdrawn asset counts, full inventory counts, inventory records, and inventory shortage handling.

[0119] Example 3

[0120] This embodiment provides a computer-readable medium storing a computer program. The computer program, when executed by a processor, can implement the metering asset warehouse management method based on RFID and IoT lighting modules as described in Embodiment 1, specifically performing the following steps:

[0121] Step 1: Deploy the first RFID antenna array in the logistics channel within the warehouse where the assets are measured; deploy the second RFID antenna array at the warehouse entrance, transition point, and exit; and deploy the third RFID antenna on the mobile terminal.

[0122] Step 2: At a predetermined location within the warehouse, simultaneously acquire observation signals from target measurement asset tags collected by M effective antennas; the effective antennas are from the first RFID antenna array, the second RFID antenna array, and the third RFID antenna.

[0123] Step 3: Calculate the estimated parameters of the effective antennas based on the observed signals; the estimated parameters include the independent estimated parameters of each effective antenna and the correlation estimated parameters between different effective antennas;

[0124] Step 4: Construct an observation model function based on the estimated parameters, using the RFID tag coordinates of the target measurement asset as the observed values; solve the observation model function; and obtain the location of the target measurement asset based on the optimal solution of the observation model function.

[0125] Step 5: Based on the location of the target metered asset using the IoT lighting module, display the first state and determine the outbound strategy of the target metered asset based on the second state display of the IoT lighting module.

[0126] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for managing metering assets warehouse based on RFID and IOT light module, characterized in that, The method comprises: deploying a first RFID antenna array in a logistics channel in a warehouse of metering assets, deploying a second RFID antenna array at an entrance, a transition and an exit of the warehouse, and deploying a third RFID antenna on a mobile terminal; synchronously acquiring observation signals of a target metering asset tag collected by M effective antennas at preset positions in the warehouse; the effective antennas come from the first RFID antenna array, the second RFID antenna array and the third RFID antenna; calculating estimated parameters of the effective antennas based on the observation signals; the estimated parameters comprise independent estimated parameters of each effective antenna and related estimated parameters between different effective antennas; constructing an observation model function with the RFID tag coordinate mapping of the target metering asset as an observation value according to the estimated parameters, solving the observation model function, and obtaining the positioning of the target metering asset according to an optimal solution of the observation model function; performing first state display on the positioning of the target metering asset based on an Internet of Things light module, and determining an out-of-warehouse strategy of the target metering asset according to second state display of the Internet of Things light module.

2. The method of claim 1, wherein the method is a method of managing metered assets in a warehouse using RFID and IoT based lighting modules. The preset positions comprise a preset signal strength Be, and when the third RFID antenna observes that the signal strength of the target metering asset tag reflection signal at a current position exceeds the signal strength Be, it is determined that the current position belongs to the preset positions.

3. The method of claim 1, wherein the method is a method of managing metered assets in a warehouse using RFID and IoT based lighting modules, characterized in that, The independent estimated parameters comprise estimated distances and estimated distance variances of each effective antenna to the target metering asset tag, and the related estimated parameters comprise distance difference observation values and distance difference observation value variances of any two effective antennas to the target metering asset tag.

4. The method of claim 3, wherein the method is based on RFID and IoT lighting module based metered asset store management. The distance difference observation value calculation method comprises: computing a phase difference observation Δθ between effective antenna i and effective antenna j ij to eliminate tag bias; wherein Δθ ij = 4π(d i -d j ) / λ; λ represents the wavelength of the radio frequency signal; d i represents the estimated distance from effective antenna i to the target metrology asset tag, which can be estimated from the signal power received by effective antenna i; d j represents the estimated distance from effective antenna j to the target metrology asset tag, which can be estimated from the signal power received by effective antenna i The estimated distance difference ΔD between active antenna i and active antenna j is calculated from the estimated distance of active antenna i to the target metrology asset tag and the estimated distance of active antenna j to the target metrology asset tag ij = d i - d j ; Based on the phase difference observation value and the estimated distance difference, the distance difference observation value Δd is obtained ij : Δd ij = ; = round( - ); wherein round() denotes a rounding function; Theoretical number of periods of phase difference.

5. The method of managing metered assets in a vault based on RFID and IoT light module as claimed in claim 3, wherein, constructing an observation model function with the RFID tag coordinate mapping of the target metering asset as an observation value according to the estimated parameters, solving the observation model function, and obtaining the positioning of the target metering asset according to an optimal solution of the observation model function; The method comprises: Constructing an observation vector with estimated parameters of an effective antenna ; obtaining spatial coordinates of each active antenna , i = 1, 2,..., M, the spatial coordinates of the target metrology asset tag are configured as ; ; to find the optimal spatial coordinates such that the optimal spatial coordinates The observation model function is constructed with the goal that the distance and distance difference to each effective antenna are closest to the estimated parameters : ； where T denotes transpose; denotes the distance between two spatial coordinates; The observation error covariance matrix of the observation model function is , the matrix is a diagonal matrix, and the diagonal elements of the matrix are the estimated distance variance of each effective antenna to the target metrology asset tag and the distance difference observation value variance of any two effective antennas to the target metrology asset tag; The cost function The optimal solution is solved with the objective function being minimized, and the positioning of the target metrology asset is obtained based on the optimal solution.

6. The method of claim 5, wherein the method is based on RFID and IoT lighting module based metered asset store management, characterized in that, The cost function is: .

7. The method of managing metered assets storehouse based on RFID and Internet of Things light module according to claim 1, characterized in that, The cost function The optimal solution is solved by targeting the minimum, and the positioning of the target measurement asset is obtained according to the optimal solution, specifically comprising the method: T1, configure initial estimate ; T2, in the kth iteration, compute the Jacobian matrix H of the observation model (k) ; T3, based on Jacobian matrix Computing update step : ; T4, based on the estimate of the kth iteration and update step update the estimate of the k+1th iteration : ​ T5, repeat steps T2-T4 until less than a set threshold, resulting in an optimal solution ; T6, according to the known position of each bin in the warehouse, estimate the optimal solution The probability of completely falling into each bin, select the bin with the highest probability as the target asset positioning.

8. The method for metered asset store management based on RFID and Internet of Things light module according to claim 1, characterized in that, The out-of-warehouse strategy determination method comprises: recording the in-warehouse time and inputting the Internet of Things light module during the in-warehouse process of each metering asset; preferentially performing second state display on the metering assets with earlier in-warehouse time than the metering assets with later in-warehouse time by the Internet of Things light module according to the in-warehouse time; determining whether to out-of-warehouse the target metering asset according to the second state display of the Internet of Things light module.

9. A metering asset store management system based on RFID and Internet of Things light module, characterized in that, The system comprises: a collection module comprising a first RFID antenna array deployed in a logistics channel in a warehouse of metering assets, a second RFID antenna array deployed at an entrance, a transition and an exit of the warehouse, and a third RFID antenna deployed on a mobile terminal; the collection module is used for synchronously collecting observation signals of a target metering asset tag by M effective antennas at preset positions in the warehouse; the effective antennas come from the first RFID antenna array, the second RFID antenna array and the third RFID antenna; a parameter estimation module configured to calculate estimated parameters of the effective antennas based on the observation signals; the estimated parameters comprise independent estimated parameters of each effective antenna and related estimated parameters between different effective antennas; and The positioning module is configured to construct an observation model function taking the RFID tag coordinate mapping of the target metrology asset as an observation value according to the estimation parameter, solve the observation model function, and obtain the positioning of the target metrology asset according to an optimal solution of the observation model function. The delivery module is configured to display a first state of the positioning of the target metrology asset based on the Internet of Things light module, and determine a delivery strategy of the target metrology asset according to a second state of the Internet of Things light module.

10. The RFID and IoT based lighting module enabled metered asset store management system as claimed in claim 9 wherein, The transition port is arranged between the qualified metrology asset area and the disassembly and return metrology asset area.

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