Management and control method of electric power material digital intelligence professional warehouse

Abstract

The invention relates to the field of electric power material warehouse management and control, in particular to an electric power material digital intelligent professional warehouse management and control method, which comprises the following steps: acquiring electromagnetic spectrum data, identifying a central frequency point energy distribution diagram and an electromagnetic interference intensity level, constructing a three-dimensional electromagnetic field intensity distribution model of a professional warehouse, and dynamically adjusting the working mode of an RFID reader-writer; the method comprises the following steps: deploying a multi-modal check node in a storage area of a professional warehouse, and generating a composite check result through an infrared contour matching method and a UWB coordinate verification method when the confidence coefficient of electric power material data read by an RFID reader-writer is lower than a preset confidence coefficient threshold value; updating an electric power material inventory database according to a composite verification result, and performing electric power material inventory state analysis in real time through an edge computing node; the problems that inventory data distortion and material positioning deviation are caused by reduction of the accuracy rate of RFID in a strong electromagnetic environment, and inventory accuracy management of an electric power material warehouse is seriously restricted are solved.

Description

Technical Field

[0001] The present invention relates to the field of power material warehouse management and control, and specifically to a management and control method for a digital and intelligent professional warehouse for power materials. Background Art

[0002] With the continued expansion of power grid construction and the deepening of intelligent upgrades, the number of grid equipment has surged, and production operations and equipment maintenance tasks have become increasingly demanding. This has placed higher demands on emergency response capabilities and the timeliness of material supply. In particular, the rapid response of material warehouse management is directly related to the efficiency of grid repairs and power supply reliability during natural disasters and emergencies. However, traditional power material warehouse management models face multiple challenges. The diverse variety of power materials (including thousands of categories such as GIS, insulators, and relay protection devices) and the large storage volume of each type of material are significant. This leads to data lags and poor inventory visibility, making it difficult to dynamically respond to changing grid operation and maintenance needs. This results in a coexistence of redundant inventory and urgent stockouts, resulting in ineffective emergency response. Therefore, with the development of RFID technology, its use in the power industry is becoming increasingly widespread, enabling digital management of specialized power warehouses, effectively improving work efficiency and enabling more intelligent power material management.

[0003] However, some power warehouses are located near strong electromagnetic environments such as high-voltage substations and converter stations, which leads to a decrease in RFID accuracy in such scenarios, resulting in inventory data distortion and material positioning deviation, seriously restricting the accurate management of power material warehouse inventory.

[0004] Therefore, there is an urgent need for a management and control method for digital professional warehouses of electric power materials, which can effectively improve the reliability of data collection of electric power materials in strong electromagnetic environments, and then realize the dataization and visualization of electric power material management, improve the standardization and accuracy of electric power material management, and enable lean management of material flow with data elements. Summary of the Invention

[0005] (1) Technical problems to be solved

[0006] The purpose of the present invention is to provide a management and control method for a digital professional warehouse of electric power materials. In a strong electromagnetic environment, the accuracy of RFID decreases, resulting in inventory data distortion and material positioning deviation, which seriously restricts the accuracy management of the inventory of electric power material warehouses.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention provides a method for managing and controlling a digital intelligent warehouse for electric power materials, the method comprising:

[0009] S1. Obtain electromagnetic spectrum data of the storage area and high-voltage equipment area of ​​the professional warehouse, identify the center frequency energy distribution map and electromagnetic interference intensity level, and construct a three-dimensional electromagnetic field strength distribution model of the professional warehouse.

[0010] S2. Dynamically adjust the working mode of the RFID reader according to the three-dimensional electromagnetic field intensity distribution model, and adopt adaptive noise suppression in the RFID reader.

[0011] S3. Deploy multimodal verification nodes in the professional warehouse storage area. The multimodal verification nodes include RFID readers, infrared profile scanners and UWB positioning modules. When the confidence level of the power material data read by the RFID reader is lower than the preset confidence threshold, a composite verification result is generated through the infrared profile matching method and the UWB coordinate verification method.

[0012] S4. Update the power material inventory database based on the composite verification results, and perform real-time power material inventory status analysis through the edge computing node. If the inventory of power materials is lower than the dynamic safety inventory threshold, the power material replenishment instruction is triggered and a distribution plan is generated.

[0013] Furthermore, when the confidence level of the electric power material data read by the RFID reader is lower than a preset confidence threshold, the method of generating a composite verification result by using the infrared profile matching method and the UWB coordinate verification method includes:

[0014] The infrared contour matching method is as follows: when the confidence level of the electric power material data after a preset number of consecutive readings by the RFID reader is lower than a preset confidence threshold, the infrared contour scanner is activated to scan the shelf layer where the electric power material is located, and three-dimensional point cloud data containing the length, width and height of the electric power material is generated; the three-dimensional point cloud data is matched with a pre-stored standard shape feature library of electric power materials, and a contour similarity score is calculated. If the contour similarity score is higher than a preset similarity threshold, it is determined that the electric power material is effectively identified.

[0015] The UWB coordinate verification method is to obtain the coordinates of the power materials for a preset number of consecutive acquisitions according to a preset sampling interval through the UWB positioning module, and calculate the average coordinates after deleting abnormal coordinates. The average coordinates are compared with the reference coordinates recorded when the power materials are put into storage by Euclidean distance to obtain the distance deviation. When the distance deviation is less than the preset distance deviation threshold, it is determined that the coordinate verification has passed and is recorded as a valid coordinate.

[0016] When both the infrared contour matching and the UWB coordinate verification pass, the composite verification result will be marked as high credibility, and the inventory data of the power materials will be updated; if only one of the infrared contour matching and the UWB coordinate verification passes, the composite verification result will be marked as medium credibility, and a review work order containing photos of the power materials and coordinate distance deviations will be sent; if both the infrared contour matching and the UWB coordinate verification fail, the directional rescan instruction of the entire warehouse area will be automatically triggered.

[0017] Furthermore, the method for automatically triggering the full-area directional rescan instruction includes:

[0018] Generate a rescan instruction containing the power material code and the last valid coordinates; send a read instruction through the RFID reader within the preset initial scanning radius. If the confidence level of the read power material data exceeds the preset confidence threshold and the power material code is consistent, it is recorded as a successful judgment and the power material coordinates are updated.

[0019] If the confidence level of the read power material data exceeds the preset confidence threshold and the power material coding is inconsistent, it will be recorded as a judgment failure and the scanning radius will be reduced; if the judgment still fails, it will switch to grid scanning mode, divide the professional warehouse into grid units and send reading instructions through the RFID reader. If the judgment is successful, the power material coordinates will be locked and the power material database will be updated. Otherwise, an abnormal alarm will be triggered and automatic entry and exit will be suspended.

[0020] Furthermore, the method for dynamically adjusting the working mode of the RFID reader according to the three-dimensional electromagnetic field intensity distribution model includes:

[0021] The interference area where the RFID reader is located is obtained according to the three-dimensional electromagnetic field strength distribution model, and the working mode of the RFID reader is dynamically adjusted according to the interference area; the working mode adopts an anti-interference frequency band switching strategy in high-interference areas, adaptively and dynamically adjusts the RFID reader transmission power in medium-interference areas, and adopts a multi-tag parallel reading optimization method in low-interference areas.

[0022] When the RFID reader adopts the anti-interference frequency band switching strategy, the interference heat map in the three-dimensional electromagnetic field strength distribution model is updated synchronously, and the updated configuration file is sent to the RFID readers in the adjacent areas through the edge computing node.

[0023] Furthermore, the method of synchronously updating the interference heat map in the three-dimensional electromagnetic field intensity distribution model and sending the updated configuration file to the RFID reader in the adjacent area through the edge computing node includes:

[0024] The electromagnetic spectrum data of the interference area where the RFID reader is located and the response success rate of the RFID tag are collected in real time, and the electromagnetic interference intensity level of the corresponding grid cell in the interference heat map is updated using the Gaussian kernel density estimation algorithm.

[0025] A configuration file containing the current optimal operating frequency band, transmit power threshold, and noise suppression parameters is generated based on the updated interference heat map, and a timestamp and area code identifier are attached.

[0026] The configuration file is broadcast to the RFID readers in the adjacent areas through the edge computing node. The RFID readers in the adjacent areas receive and parse the configuration file. If the overlap between the current operating frequency band of the RFID readers in the adjacent areas and the current optimal operating frequency band in the configuration file is lower than the preset overlap threshold, the frequency band smooth switching is initiated.

[0027] Furthermore, the method for starting smooth frequency band switching includes:

[0028] The transmission power of the original working frequency band of the RFID reader in the adjacent area is gradually attenuated to the off state at a preset reduction rate. The original working frequency band is the initial working frequency range that is currently subject to excessive interference. At the same time, the transmission power is increased to the rated transmission power in the target frequency band according to a preset step size. The target frequency band is a low-interference backup frequency band analyzed through a three-dimensional electromagnetic field strength distribution model.

[0029] During the switching process, the response success rate of the RFID tag is continuously monitored. If the average response success rate within a reading cycle of a preset number of consecutive readings is lower than the preset response success rate threshold, the switching is determined to have failed, and the transmission power of the original working frequency band of the RFID reader in the adjacent area is returned to the original working frequency band and a frequency band conflict alarm message is generated.

[0030] Furthermore, the method of adopting an anti-interference frequency band switching strategy in a high-interference area, adaptively and dynamically adjusting the RFID reader transmit power in a medium-interference area, and adopting a multi-tag parallel reading optimization method in a low-interference area includes:

[0031] The anti-interference frequency band switching strategy implemented in high-interference areas is to monitor the electromagnetic field strength data in high-interference areas in real time. When it is detected that the interference intensity of the current working frequency band exceeds the preset interference intensity threshold, it switches to the preset backup anti-interference frequency band.

[0032] In the medium interference area, the RFID reader transmit power is adaptively and dynamically adjusted according to the signal strength returned by the RFID tag, and the bit error rate of the current frequency is calculated. If the bit error rate exceeds the preset bit error rate threshold, a frequency point is selected in the adjacent unoccupied frequency band.

[0033] The multi-tag parallel reading optimization method in low-interference areas dynamically adjusts the frame length of the reading round according to the number of RFID tags, so that the throughput is greater than the preset throughput threshold.

[0034] Furthermore, the method of obtaining electromagnetic spectrum data of the storage area and high-voltage equipment area of ​​the specialized warehouse, identifying the center frequency energy distribution diagram and electromagnetic interference intensity level, and constructing a three-dimensional electromagnetic field strength distribution model of the specialized warehouse includes:

[0035] Electromagnetic spectrum data is collected in real time and preprocessed through the distributed electromagnetic sensor array in the professional warehouse. The sensor array covers the storage area of ​​the professional warehouse and the adjacent high-voltage equipment area with a preset grid unit density. The electromagnetic spectrum data covers the RFID reader operating frequency band and the power frequency harmonic frequency band; the preprocessing is to delete the transient pulse interference data caused by the start and stop of high-voltage equipment, and perform sliding average filtering on the continuous spectrum data according to the preset time window to generate the center frequency energy distribution map of the grid unit.

[0036] The preprocessed electromagnetic spectrum data is used through the Gaussian kernel density estimation method to obtain the electromagnetic interference intensity level of each grid unit, and the electromagnetic interference intensity level is divided into high interference area, medium interference area and low interference area. The timestamp and spatial coordinates are marked for each grid unit to generate a three-dimensional electromagnetic field strength distribution model. The three-dimensional electromagnetic field strength distribution model represents the electromagnetic environment of the entire professional warehouse in the form of an interference heat map.

[0037] The interference heat map of the three-dimensional electromagnetic field intensity distribution model is correlated with the historical working parameters of the RFID reader to establish an interference pattern feature library.

[0038] Furthermore, the method for establishing an interference pattern feature library includes:

[0039] When a certain storage area of ​​a professional warehouse continuously experiences strong fluctuations at the preset monitoring frequency and the standard deviation is greater than the preset fluctuation standard deviation threshold, it is marked as an unstable frequency band area, and the success rate of the RFID reader switching frequency band and the tag response delay data in the unstable frequency band area are recorded.

[0040] Furthermore, the method of using adaptive noise suppression in an RFID reader includes:

[0041] The adaptive noise suppression algorithm matches interference patterns through a preset interference pattern feature library and uses a wavelet packet decomposition and reconstruction method to separate and enhance the effective signal of the RFID tag.

[0042] (3) Beneficial effects

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. By constructing a three-dimensional electromagnetic field strength distribution model, dynamically adjusting the RFID reader operating mode, and applying adaptive noise suppression technology, it is possible to maintain a high tag recognition rate in a strong electromagnetic interference environment.

[0045] 2. The composite verification mechanism of multimodal verification nodes enables precise positioning and identification of power supplies, further improving data collection accuracy. This allows RFID technology to be effectively applied in strong electromagnetic environments, where it was previously inappropriate, and significantly reduces the misreading rate. This effectively prevents inventory data distortion, ensures the timely availability of supplies required for power grid equipment maintenance and emergency repairs, and significantly improves power grid repair efficiency and power supply reliability.

[0046] 3. A three-dimensional electromagnetic field strength distribution model enables differentiated operating strategies for different interference areas. Operating parameters are automatically adjusted based on real-time electromagnetic environment changes, and smooth frequency switching ensures stable operation. Furthermore, a long-term accumulation of interference pattern signatures continuously enhances the ability to predict and respond to electromagnetic interference, continuously optimizing operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flowchart of a method for managing and controlling a digital professional warehouse for electric power materials according to Example 1 of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0049] Before giving examples, it is necessary to explain the application scenarios conceived by the present invention. The present invention is a management and control method for a digital professional warehouse of electric power materials, which is applied to the management and control of professional warehouses of electric power materials in a strong electromagnetic environment, so as to improve the reliability of data collection of electric power materials, and further realize the digitization and visualization of electric power material management, and improve the standardization and accuracy of electric power material management.

[0050] Example 1: Figure 1 As shown, this embodiment provides a method for managing and controlling a digital intelligent professional warehouse for electric power materials, the method comprising:

[0051] S1. Obtain electromagnetic spectrum data of the storage area and high-voltage equipment area of ​​the professional warehouse, identify the center frequency energy distribution map and electromagnetic interference intensity level, and construct a three-dimensional electromagnetic field strength distribution model of the professional warehouse.

[0052] For example, after the electric power material warehouse next to a UHV converter station adopts digital management and control, distributed electromagnetic sensors evenly deployed in the warehouse collect electromagnetic spectrum data of the storage area and the adjacent converter station area. Using these data, a three-dimensional electromagnetic field strength distribution model covering the entire warehouse is automatically constructed, and the north side is marked as a high-interference area, the middle as a medium-interference area, and the south side as a low-interference area.

[0053] S2. Dynamically adjust the working mode of the RFID reader according to the three-dimensional electromagnetic field intensity distribution model, and adopt adaptive noise suppression in the RFID reader.

[0054] For example, the RFID reader's interference zone is determined based on a three-dimensional electromagnetic field intensity distribution model, and the reader's operating mode is dynamically adjusted based on the interference zone. RFID readers in each zone also have adaptive noise suppression enabled. This feature dynamically adjusts the receiver sensitivity threshold by analyzing background noise characteristics in real time, effectively reducing the impact of electromagnetic interference on tag recognition.

[0055] S3. Deploy multimodal verification nodes in the professional warehouse storage area. The multimodal verification nodes include RFID readers, infrared profile scanners and UWB positioning modules. When the confidence level of the power material data read by the RFID reader is lower than the preset confidence threshold, a composite verification result is generated through the infrared profile matching method and the UWB coordinate verification method.

[0056] For example, 42 sets of multimodal verification nodes were deployed within the specialized warehouse, each consisting of an RFID reader, an infrared profile scanner, and a UWB positioning module. When the north-side RFID reader attempts to read a batch of newly stored insulator tags for 12 consecutive times and the read confidence level is only 63%, below the preset confidence threshold of 85%, multimodal verification is immediately initiated to generate a composite verification result.

[0057] S4. Update the power material inventory database based on the composite verification results, and perform real-time power material inventory status analysis through the edge computing node. If the inventory of power materials is lower than the dynamic safety inventory threshold, the power material replenishment instruction is triggered and a distribution plan is generated.

[0058] For example, after comprehensive verification, the inventory database is updated with high confidence. Real-time analysis by the edge computing node shows that the total inventory of this batch of insulators after storage is 157, which is below the current dynamic safety stock threshold of 180. The dynamic safety stock threshold is automatically calculated based on historical consumption rates, current maintenance plans, and severe weather warnings. This triggers a replenishment order for power supplies and generates an optimal distribution plan based on supplier location, traffic conditions, and urgency, ensuring that grid maintenance is not impacted. Edge computing nodes serve as intelligent hubs connecting physical devices (sensors, RFID readers, etc.) with business systems (inventory management, replenishment scheduling, etc.), assuming the core functions of real-time data processing, local decision-making, and system coordination.

[0059] When the confidence level of the electric power material data read by the RFID reader is lower than a preset confidence threshold, the method for generating a composite verification result by using the infrared profile matching method and the UWB coordinate verification method includes:

[0060] The infrared contour matching method is as follows: when the confidence level of the electric power material data after a preset number of consecutive readings by the RFID reader is lower than a preset confidence threshold, the infrared contour scanner is activated to scan the shelf layer where the electric power material is located, and three-dimensional point cloud data containing the length, width and height of the electric power material is generated; the three-dimensional point cloud data is matched with a pre-stored standard shape feature library of electric power materials, and a contour similarity score is calculated. If the contour similarity score is higher than a preset similarity threshold, it is determined that the electric power material is effectively identified.

[0061] For example, in practical applications, when the RFID reader's reading confidence falls below a preset confidence threshold, the multimodal verification node's composite verification mechanism fully functions. For example, in the management of spare parts for primary substation maintenance, when attempting to inventory relay protection devices in inventory, a sudden increase in electromagnetic interference in the adjacent area causes the RFID reader's confidence level to drop to 58% after five consecutive reads, far below the preset threshold of 85%. An infrared profile scanner is then immediately activated to conduct a comprehensive scan of that shelf level in the warehouse, generating 3D point cloud data containing the relay protection device's precise dimensions (32.6 cm long, 25.1 cm wide, and 15.8 cm high). This data is then compared with a pre-stored library of standard relay protection device outline features, resulting in a calculated profile similarity score of 0.92, exceeding the preset similarity threshold of 0.85, confirming a valid identification of the power material.

[0062] The UWB coordinate verification method is to obtain the coordinates of the power materials for a preset number of consecutive acquisitions according to a preset sampling interval through the UWB positioning module, and calculate the average coordinates after deleting abnormal coordinates. The average coordinates are compared with the reference coordinates recorded when the power materials are put into storage by Euclidean distance to obtain the distance deviation. When the distance deviation is less than the preset distance deviation threshold, it is determined that the coordinate verification has passed and is recorded as a valid coordinate.

[0063] For example, the UWB positioning module collected the coordinates of power materials 10 times continuously at a sampling interval of once every 0.2 seconds. After statistical analysis of the data, the abnormal coordinates that exceeded the 3σ principle were deleted and the average coordinates were calculated. The average coordinates were compared with the reference coordinates recorded when the relay protection device was put into storage using the Euclidean distance calculation method. The distance deviation obtained was 0.21 meters, which was less than the preset deviation threshold of 0.3 meters, and the coordinate verification was determined to be successful.

[0064] When both the infrared contour matching and the UWB coordinate verification pass, the composite verification result will be marked as high credibility, and the inventory data of the power materials will be updated; if only one of the infrared contour matching and the UWB coordinate verification passes, the composite verification result will be marked as medium credibility, and a review work order containing photos of the power materials and coordinate distance deviations will be sent; if both the infrared contour matching and the UWB coordinate verification fail, the directional rescan instruction of the entire warehouse area will be automatically triggered.

[0065] For example, if both infrared profile matching and UWB coordinate verification are successful, the composite verification result will be marked as high confidence, and the inventory status and location information of the relay protection device will be automatically updated. For example, in another maintenance spare parts inventory, when identifying a batch of GIS combination electrical appliances, the infrared profile matching passed but the UWB coordinate verification failed, and the distance deviation reached 0.53 meters, exceeding the preset deviation threshold of 0.3 meters. The result was marked as medium confidence, and a review work order containing a photo of the GIS combination electrical appliance and the coordinate distance deviation data was automatically generated and pushed to the warehouse manager for manual confirmation. For example, when inventorying a batch of insulators of special specifications, due to excessive interference from the surrounding environment, both the infrared profile matching and the UWB coordinate verification failed, and a directional rescan instruction for the entire warehouse area was immediately and automatically triggered.

[0066] The method for automatically triggering the full-store area directional rescan instruction includes:

[0067] Generate a rescan instruction containing the power material code and the last valid coordinates; send a read instruction through the RFID reader within the preset initial scanning radius. If the confidence level of the read power material data exceeds the preset confidence threshold and the power material code is consistent, it is recorded as a successful judgment and the power material coordinates are updated.

[0068] For example, a rescan command was generated containing the insulator code (EI-220-HV782) and the last valid coordinates (shelf 14, level 3, northeast corner of the warehouse). The RFID reader first sent a read command at boosted power within an initial scanning radius of 5 meters centered on the coordinates. After three attempts, the reader detected a signal 3.2 meters from the original coordinates. With a read confidence level of 88% and consistent electrical material codes, the scan was deemed successful and the actual location coordinates of the insulators were updated.

[0069] If the confidence level of the read power material data exceeds the preset confidence threshold and the power material coding is inconsistent, it will be recorded as a judgment failure and the scanning radius will be reduced; if the judgment still fails, it will switch to grid scanning mode, divide the professional warehouse into grid units and send reading instructions through the RFID reader. If the judgment is successful, the power material coordinates will be locked and the power material database will be updated. Otherwise, an abnormal alarm will be triggered and automatic entry and exit will be suspended.

[0070] For example, during a rescan of a missing lightning arrester assembly, a signal from a power material was detected within the initial scanning radius with a 90% confidence level. However, the code displayed was "LA-110-P238," which was inconsistent with the target code "LA-110-P236." This resulted in a failure, and the scanning radius was automatically narrowed from 5 meters to 2.5 meters to continue the search. If two rounds of narrowing the search were unsuccessful, the system switched to grid scanning mode, dividing the entire warehouse into 2-meter by 2-meter grid cells and scanning each one individually. Ultimately, the target material was located approximately 8 meters from its original location, and the power material database was updated, avoiding potential delivery errors.

[0071] The method for dynamically adjusting the working mode of the RFID reader according to the three-dimensional electromagnetic field intensity distribution model includes:

[0072] The interference area where the RFID reader is located is obtained according to the three-dimensional electromagnetic field strength distribution model, and the working mode of the RFID reader is dynamically adjusted according to the interference area; the working mode adopts an anti-interference frequency band switching strategy in high-interference areas, adaptively and dynamically adjusts the RFID reader transmission power in medium-interference areas, and adopts a multi-tag parallel reading optimization method in low-interference areas.

[0073] When the RFID reader adopts the anti-interference frequency band switching strategy, the interference heat map in the three-dimensional electromagnetic field strength distribution model is updated synchronously, and the updated configuration file is sent to the RFID readers in the adjacent areas through the edge computing node.

[0074] The method of synchronously updating the interference heat map in the three-dimensional electromagnetic field intensity distribution model and sending the updated configuration file to the RFID reader in the adjacent area through the edge computing node includes:

[0075] The electromagnetic spectrum data of the interference area where the RFID reader is located and the response success rate of the RFID tag are collected in real time, and the electromagnetic interference intensity level of the corresponding grid cell in the interference heat map is updated using the Gaussian kernel density estimation algorithm.

[0076] A configuration file containing the current optimal operating frequency band, transmit power threshold, and noise suppression parameters is generated based on the updated interference heat map, and a timestamp and area code identifier are attached.

[0077] The configuration file is broadcast to the RFID readers in the adjacent areas through the edge computing node. The RFID readers in the adjacent areas receive and parse the configuration file. If the overlap between the current operating frequency band of the RFID readers in the adjacent areas and the current optimal operating frequency band in the configuration file is lower than the preset overlap threshold, the frequency band smooth switching is initiated.

[0078] For example, when the RFID reader in the high-interference zone on the north side switches to the 915MHz frequency band, the interference heat map in the three-dimensional electromagnetic field intensity distribution model is synchronously updated, marking the area as "Frequency Band Switched Area" and adding the timestamp "2024-10-12 15:28:36." The edge computing node then generates a configuration file containing the latest operating parameters, including the current optimal operating frequency band of 915MHz, a transmit power of 30dBm, and noise suppression parameters. It also adds the timestamp and the region code identifier "North Zone-A001" to the configuration file. This configuration file is broadcast in real time by the edge computing node to the adjacent RFID readers in the high-interference zone on the north side, namely, the RFID readers numbered "North Zone-A002," "North Zone-A003," and "Central Zone-B001." After receiving and parsing the configuration file, these RFID readers found that the "North District-A002" and "North District-A003" readers were currently operating in the 865MHz frequency band, with an overlap of only 10% with the 915MHz frequency in the configuration file, which is lower than the preset 30% overlap threshold. Therefore, smooth frequency band switching was immediately initiated for these two RFID readers.

[0079] The method for starting frequency band smooth switching includes:

[0080] The transmission power of the original working frequency band of the RFID reader in the adjacent area is gradually attenuated to the off state at a preset reduction rate. The original working frequency band is the initial working frequency range that is currently subject to excessive interference. At the same time, the transmission power is increased to the rated transmission power in the target frequency band according to a preset step size. The target frequency band is a low-interference backup frequency band analyzed through a three-dimensional electromagnetic field strength distribution model.

[0081] During the switching process, the response success rate of the RFID tag is continuously monitored. If the average response success rate within a reading cycle of a preset number of consecutive readings is lower than the preset response success rate threshold, the switching is determined to have failed, and the transmission power of the original working frequency band of the RFID reader in the adjacent area is returned to the original working frequency band and a frequency band conflict alarm message is generated.

[0082] For example, during the smooth switching of frequency bands, the transmission power of the "North District-A002" RFID reader / writer in the original working frequency band of 865MHz gradually decayed from 26dBm at a rate of 2dBm per second. At the same time, the transmission power in the target frequency band of 915MHz was increased in steps of 2dBm per second, from the initial 10dBm to the rated 30dBm. The entire switching process lasted about 12 seconds, during which the RFID reader / writer device remained in a continuous working state, avoiding the offline time of the device in the traditional switching method. During the switching process, the response success rate of the RFID tag was monitored in real time. The average response success rate of the "North District-A002" reader / writer during the switching process was 86%, which was higher than the preset response success rate threshold of 80%, and the switching was determined to be successful. However, the "North District-A003" reader was affected by a temporary external interference source during the switching process, and the average response success rate of five consecutive read cycles dropped to 76%. It was determined that the switching had failed, and the transmission power of the RFID reader was immediately reduced to 26dBm of the original 865MHz frequency band. An alarm message "Frequency band 915MHz is interfered with, frequency band switching failed" was generated and pushed to the management platform to remind the administrator to investigate the temporary interference source.

[0083] Furthermore, the method of adopting an anti-interference frequency band switching strategy in a high-interference area, adaptively and dynamically adjusting the RFID reader transmit power in a medium-interference area, and adopting a multi-tag parallel reading optimization method in a low-interference area includes:

[0084] The anti-interference frequency band switching strategy implemented in high-interference areas is to monitor the electromagnetic field strength data in high-interference areas in real time. When it is detected that the interference intensity of the current working frequency band exceeds the preset interference intensity threshold, it switches to the preset backup anti-interference frequency band.

[0085] In the medium interference area, the RFID reader transmit power is adaptively and dynamically adjusted according to the signal strength returned by the RFID tag, and the bit error rate of the current frequency is calculated. If the bit error rate exceeds the preset bit error rate threshold, a frequency point is selected in the adjacent unoccupied frequency band.

[0086] The multi-tag parallel reading optimization method in low-interference areas dynamically adjusts the frame length of the reading round according to the number of RFID tags, so that the throughput is greater than the preset throughput threshold.

[0087] For example, refined operating mode strategies were implemented for RFID readers in different interference zones. In the high-interference zone on the north side, the interference intensity in the 860-870MHz operating frequency band suddenly rose to 85dB, exceeding the preset 75dB interference threshold. The anti-interference frequency band switching strategy was immediately activated, switching the RFID reader to the pre-tested 915MHz backup frequency band. This band is sufficiently isolated from the main interference band to effectively avoid the main interference source generated by the converter station. RFID readers in the medium-interference zone use adaptive power adjustment technology. By analyzing the signal strength data returned by the RFID tag in real time, the transmit power is automatically increased from 18dBm to 22dBm when the RFID tag feedback signal strength is below -60dBm. When the RFID tag feedback signal strength is above -50dBm, the transmit power is reduced to 20dBm, ensuring recognition rate while optimizing energy consumption. The BER of the current operating frequency, 865MHz, was simultaneously calculated. When the BER reached 1.8% during a count, exceeding the preset BER threshold of 1.5%, the system automatically and briefly switched to the adjacent, unoccupied 868MHz frequency to avoid misidentification. The reader / writer in the southern low-interference zone employed a multi-tag parallel reading optimization method. Based on the average number of 60 RFID tags counted per count in this area, the frame length of each read cycle was dynamically adjusted to 12. This resulted in a throughput of 140 tags per second, far exceeding the preset threshold of 100 tags, significantly improving the efficiency of rapidly warehousing large quantities of materials.

[0088] The method of obtaining electromagnetic spectrum data of the storage area and high-voltage equipment area of ​​the specialized warehouse, identifying the center frequency energy distribution diagram and the electromagnetic interference intensity level, and constructing a three-dimensional electromagnetic field strength distribution model of the specialized warehouse includes:

[0089] Electromagnetic spectrum data is collected in real time and preprocessed through the distributed electromagnetic sensor array in the professional warehouse. The sensor array covers the storage area of ​​the professional warehouse and the adjacent high-voltage equipment area with a preset grid unit density. The electromagnetic spectrum data covers the RFID reader operating frequency band and the power frequency harmonic frequency band; the preprocessing is to delete the transient pulse interference data caused by the start and stop of high-voltage equipment, and perform sliding average filtering on the continuous spectrum data according to the preset time window to generate the center frequency energy distribution map of the grid unit.

[0090] The preprocessed electromagnetic spectrum data is used through the Gaussian kernel density estimation method to obtain the electromagnetic interference intensity level of each grid unit, and the electromagnetic interference intensity level is divided into high interference area, medium interference area and low interference area. The timestamp and spatial coordinates are marked for each grid unit to generate a three-dimensional electromagnetic field strength distribution model. The three-dimensional electromagnetic field strength distribution model represents the electromagnetic environment of the entire professional warehouse in the form of an interference heat map.

[0091] The interference heat map of the three-dimensional electromagnetic field intensity distribution model is correlated with the historical working parameters of the RFID reader to establish an interference pattern feature library.

[0092] For example, 230 electromagnetic sensors were deployed in a 5m x 5m grid cell density within a 2,000m2 specialized warehouse space, covering the entire storage area and the adjacent high-voltage converter equipment area. The sensors collected electromagnetic spectrum data, including the 860-960MHz frequency band commonly used by RFID readers, as well as the 50Hz power frequency and its harmonics (100Hz, 150Hz, 200Hz, etc.). An intelligent preprocessing algorithm automatically identifies and removes electromagnetic pulse interference caused by the daily startup and shutdown of high-voltage equipment. These interferences typically last no more than 200ms but can reach peaks of 90dB. A sliding average filter was applied to the continuous spectrum data using a preset 30-second time window to generate a center frequency energy distribution map for each grid cell. This clearly shows that the 860-870MHz frequency band is significantly interfered with in the northern area, and the interference energy decays with increasing distance from the converter station. Using Gaussian kernel density estimation, the electromagnetic interference intensity of each grid cell was quantified and graded. Areas with interference intensities greater than 70 dB were designated as high interference (primarily concentrated in the northern area near the converter station), those between 40 and 70 dB were designated as medium interference (primarily distributed in the central part of the warehouse), and those below 40 dB were designated as low interference (primarily located in the southern area, away from the converter station). A timestamp and three-dimensional spatial coordinates were attached to each grid cell to construct a complete three-dimensional electromagnetic field intensity distribution model. This model visualizes the electromagnetic environment of the entire warehouse as an interference heat map, with different colors representing different levels of interference intensity—for example, red indicates high interference, yellow indicates medium interference, and green indicates low interference. The interference heat map derived from this three-dimensional electromagnetic field intensity distribution model was correlated with the historical operating parameters of RFID readers in each area (including operating frequency, transmit power, and tag response rate), thereby constructing a unique interference pattern signature library for the specialized warehouse.

[0093] The method for establishing an interference pattern feature library comprises:

[0094] When a certain storage area of ​​a professional warehouse continuously experiences strong fluctuations at the preset monitoring frequency and the standard deviation is greater than the preset fluctuation standard deviation threshold, it is marked as an unstable frequency band area, and the success rate of the RFID reader switching frequency band and the tag response delay data in the unstable frequency band area are recorded.

[0095] For example, during the establishment of the interference pattern feature library, it is identified that the standard deviation of the field strength fluctuation in the north area at the 866MHz monitoring frequency reaches 12dB, which is much higher than the preset 3dB fluctuation standard deviation threshold, and the area is automatically marked as a frequency band unstable area. Records revealed that when RFID readers in this area attempted to switch frequency bands, the success rate from 865MHz to 915MHz was 92%, while the success rate for switching to 902MHz was only 43%. The average response delay of tags in different frequency bands was also recorded: 320 milliseconds for 865MHz, 140 milliseconds for 915MHz, and 480 milliseconds for 902MHz. This indicates that the 915MHz band has the best anti-interference performance in this area. Furthermore, the interference intensity in the unstable region of this band is positively correlated with the operating load of the converter station. Whenever the converter station load exceeds 80% of its rated capacity, the interference intensity increases by 15-20dB. Based on this, the frequency band switching prediction strategy was optimized to prepare for frequency band switching in advance when the converter station load begins to increase, effectively reducing RFID recognition interruption time. Furthermore, through long-term accumulation and machine learning analysis, the interference pattern feature library has been continuously enriched and improved, and the ability to predict and respond to electromagnetic interference has been continuously improved, enabling the specialized warehouse to maintain stable and efficient material management capabilities in complex electromagnetic environments.

[0096] The method for adopting adaptive noise suppression in an RFID reader includes:

[0097] The adaptive noise suppression algorithm matches interference patterns through a preset interference pattern feature library and uses a wavelet packet decomposition and reconstruction method to separate and enhance the effective signal of the RFID tag.

[0098] For example, the RFID reader / writer in the power supply digital warehouse utilizes advanced adaptive noise suppression technology, significantly improving data reading reliability in strong electromagnetic environments. When the RFID reader / writer in the high-interference zone on the north side activates, it first accesses a pre-set interference pattern signature library and matches the current electromagnetic environment characteristics with the stored patterns. For example, during a strong interference event caused by the startup of nearby equipment at a substation, the reader / writer quickly identified an 87% match between the interference pattern and the "high-voltage switch operation interference pattern" in the signature library, and immediately applied the corresponding noise suppression parameters. In the raw signal received by the RFID reader / writer, the valid RFID tag signal was drowned out by strong background noise, resulting in a signal-to-noise ratio of only 0.6. A five-layer wavelet packet decomposition and reconstruction method was used to process the signal. The original signal was first decomposed into sub-signals in different frequency bands. Threshold suppression was performed in the higher frequency domain (corresponding to the interference band), while enhancement processing was performed in the frequency bands where the RFID tag signal was concentrated. This method successfully improved the signal-to-noise ratio to 2.8, rendering the previously difficult-to-identify RFID tag signal clearly discernible. An adaptive noise suppression algorithm effectively improves tag recognition rates in high-interference environments, significantly reducing the startup frequency of multimodal verification nodes and improving overall system efficiency. When interference patterns change, such as when harmonic distribution changes due to equipment maintenance at a nearby substation, the system can adjust wavelet packet decomposition parameters in real time to dynamically adapt to the new interference characteristics, ensuring that RFID readers maintain high data collection reliability in a variety of complex electromagnetic environments.

[0099] Finally, it should be noted that although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for managing and controlling a digitalized professional warehouse for electric power materials, characterized in that: The method comprises: Obtain electromagnetic spectrum data for the specialized warehouse storage area and high-voltage equipment area, identify the center frequency energy distribution diagram and electromagnetic interference intensity level, and construct a three-dimensional electromagnetic field strength distribution model for the specialized warehouse; Dynamically adjust the working mode of the RFID reader according to the three-dimensional electromagnetic field intensity distribution model, and adopt adaptive noise suppression in the RFID reader; Deploy multimodal verification nodes in the professional warehouse storage area. The multimodal verification nodes include RFID readers, infrared profile scanners, and UWB positioning modules. When the confidence level of the power material data read by the RFID reader is lower than the preset confidence threshold, a composite verification result is generated through the infrared profile matching method and the UWB coordinate verification method. The power material inventory database is updated based on the composite verification results, and the power material inventory status is analyzed in real time through the edge computing node. If the inventory level of power materials is lower than the dynamic safety inventory threshold, the power material replenishment instruction is triggered and a distribution plan is generated.

2. A method for managing and controlling a digital intelligent warehouse for electric power materials according to claim 1, characterized in that: When the confidence level of the electric power material data read by the RFID reader is lower than a preset confidence threshold, the method for generating a composite verification result by using the infrared profile matching method and the UWB coordinate verification method includes: The infrared contour matching method is as follows: when the confidence level of the electric power material data read by the RFID reader is lower than a preset confidence threshold after a preset number of consecutive readings, the infrared contour scanner is activated to scan the shelf layer where the electric power material is located, generating three-dimensional point cloud data containing the length, width and height of the electric power material; the three-dimensional point cloud data is matched with a pre-stored standard shape feature library of electric power materials, and a contour similarity score is calculated. If the contour similarity score is higher than a preset similarity threshold, it is determined that the electric power material is effectively identified; The UWB coordinate verification method is to obtain the coordinates of the power materials for a preset number of consecutive acquisitions according to a preset sampling interval through the UWB positioning module, and calculate the average coordinate after deleting the abnormal coordinates. The average coordinate is compared with the reference coordinate recorded when the power materials are put into storage by Euclidean distance to obtain the distance deviation. When the distance deviation is less than the preset distance deviation threshold, it is determined that the coordinate verification has passed and is recorded as a valid coordinate. When both the infrared contour matching and the UWB coordinate verification pass, the composite verification result will be marked as high credibility, and the inventory data of the power materials will be updated; if only one of the infrared contour matching and the UWB coordinate verification passes, the composite verification result will be marked as medium credibility, and a review work order containing photos of the power materials and coordinate distance deviations will be sent; if both the infrared contour matching and the UWB coordinate verification fail, the directional rescan instruction of the entire warehouse area will be automatically triggered.

3. The method for controlling a digital intelligent warehouse for electric power materials according to claim 2 is characterized in that: The method for automatically triggering the full-area directional rescan instruction includes: Generate a rescan command containing the power material code and the last valid coordinates; send a read command through the RFID reader within the preset initial scanning radius. If the confidence level of the read power material data exceeds the preset confidence threshold and the power material code is consistent, the judgment is recorded as successful and the power material coordinates are updated; If the confidence level of the read power material data exceeds the preset confidence threshold and the power material coding is inconsistent, it will be recorded as a judgment failure and the scanning radius will be reduced; if the judgment still fails, it will switch to grid scanning mode, divide the professional warehouse into grid units and send reading instructions through the RFID reader. If the judgment is successful, the power material coordinates will be locked and the power material database will be updated. Otherwise, an abnormal alarm will be triggered and automatic entry and exit will be suspended.

4. The method for controlling a digital intelligent warehouse for electric power materials according to claim 1 is characterized in that: The method for dynamically adjusting the working mode of the RFID reader according to the three-dimensional electromagnetic field intensity distribution model includes: The interference area where the RFID reader is located is obtained based on the three-dimensional electromagnetic field intensity distribution model, and the working mode of the RFID reader is dynamically adjusted according to the interference area; the working mode adopts an anti-interference frequency band switching strategy in high-interference areas, adaptively and dynamically adjusts the RFID reader transmission power in medium-interference areas, and adopts a multi-tag parallel reading optimization method in low-interference areas; When the RFID reader adopts the anti-interference frequency band switching strategy, the interference heat map in the three-dimensional electromagnetic field strength distribution model is updated synchronously, and the updated configuration file is sent to the RFID readers in the adjacent areas through the edge computing node.

5. The method for controlling a digital intelligent warehouse for electric power materials according to claim 4 is characterized in that: The method of synchronously updating the interference heat map in the three-dimensional electromagnetic field intensity distribution model and sending the updated configuration file to the RFID reader in the adjacent area through the edge computing node includes: Real-time collection of electromagnetic spectrum data and RFID tag response success rate in the interference area where the RFID reader is located, and the use of Gaussian kernel density estimation algorithm to update the electromagnetic interference intensity level of the corresponding grid cell in the interference heat map; Generate a configuration file containing the current optimal operating frequency band, transmit power threshold, and noise suppression parameters based on the updated interference heat map, and append a timestamp and region code identifier. The configuration file is broadcast to the RFID readers in the adjacent areas through the edge computing node. The RFID readers in the adjacent areas receive and parse the configuration file. If the overlap between the current operating frequency band of the RFID readers in the adjacent areas and the current optimal operating frequency band in the configuration file is lower than the preset overlap threshold, the frequency band smooth switching is initiated.

6. A method for managing and controlling a digital intelligent warehouse for electric power materials according to claim 5, characterized in that: The method for starting frequency band smooth switching includes: The transmission power of the RFID reader / writer in the adjacent area is gradually attenuated at a preset reduction rate to the off state in the original operating frequency band, where the original operating frequency band is the initial operating frequency range currently experiencing excessive interference. At the same time, the transmission power is increased in a preset step size to the rated transmission power in the target frequency band, where the target frequency band is a low-interference backup frequency band analyzed using a three-dimensional electromagnetic field intensity distribution model. During the switching process, the response success rate of the RFID tag is continuously monitored. If the average response success rate within a reading cycle of a preset number of consecutive readings is lower than the preset response success rate threshold, the switching is determined to have failed, and the transmission power of the original working frequency band of the RFID reader in the adjacent area is returned to the original working frequency band and a frequency band conflict alarm message is generated.

7. The method for controlling a digital intelligent warehouse for electric power materials according to claim 4 is characterized in that: The method of adopting an anti-interference frequency band switching strategy in a high-interference area, adaptively and dynamically adjusting the RFID reader transmit power in a medium-interference area, and adopting a multi-tag parallel reading optimization method in a low-interference area includes: The anti-interference frequency band switching strategy implemented in high-interference areas is to monitor the electromagnetic field strength data in high-interference areas in real time. When it is detected that the interference intensity of the current working frequency band exceeds the preset interference intensity threshold, it switches to the preset backup anti-interference frequency band; In the medium interference area, the RFID reader transmit power is adaptively and dynamically adjusted according to the signal strength returned by the RFID tag. The BER of the current frequency is calculated. If the BER exceeds the preset BER threshold, a frequency is selected in the adjacent unoccupied frequency band. The multi-tag parallel reading optimization method in low-interference areas dynamically adjusts the frame length of the reading round according to the number of RFID tags, so that the throughput is greater than the preset throughput threshold.

8. The method for controlling a digital intelligent warehouse for electric power materials according to claim 1 is characterized in that: The method of obtaining electromagnetic spectrum data of the storage area and high-voltage equipment area of ​​the specialized warehouse, identifying the center frequency energy distribution diagram and the electromagnetic interference intensity level, and constructing a three-dimensional electromagnetic field strength distribution model of the specialized warehouse includes: Electromagnetic spectrum data is collected and preprocessed in real time through a distributed electromagnetic sensor array within the specialized warehouse. The sensor array covers the specialized warehouse's storage area and adjacent high-voltage equipment area with a preset grid cell density. The electromagnetic spectrum data covers the RFID reader operating frequency band and the power frequency harmonic band. The preprocessing removes transient pulse interference data generated by the start and stop of high-voltage equipment, performs a sliding average filter on the continuous spectrum data according to a preset time window, and generates a center frequency energy distribution map for the grid cells. The preprocessed electromagnetic spectrum data is used to obtain the electromagnetic interference intensity level of each grid cell through the Gaussian kernel density estimation method. The electromagnetic interference intensity level is divided into high interference area, medium interference area and low interference area. Each grid cell is annotated with a timestamp and spatial coordinates to generate a three-dimensional electromagnetic field intensity distribution model. The three-dimensional electromagnetic field intensity distribution model represents the electromagnetic environment of the entire professional warehouse in the form of an interference heat map. The interference heat map of the three-dimensional electromagnetic field intensity distribution model is correlated with the historical working parameters of the RFID reader to establish an interference pattern feature library.

9. A method for managing and controlling a digital intelligent warehouse for electric power materials according to claim 8, characterized in that: The method for establishing an interference pattern feature library comprises: When a certain storage area of ​​a professional warehouse continuously experiences strong fluctuations at the preset monitoring frequency and the standard deviation is greater than the preset fluctuation standard deviation threshold, it is marked as an unstable frequency band area, and the success rate of the RFID reader switching frequency band and the tag response delay data in the unstable frequency band area are recorded.

10. The method for controlling a digital intelligent warehouse for electric power materials according to claim 1 is characterized in that: The method for adopting adaptive noise suppression in an RFID reader includes: The adaptive noise suppression algorithm matches interference patterns through a preset interference pattern feature library and uses a wavelet packet decomposition and reconstruction method to separate and enhance the effective signal of the RFID tag.

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