Hospital building equipment intelligent operation and maintenance method and system based on multi-source identifier fusion

Abstract

The invention discloses a hospital building equipment intelligent operation and maintenance method and system based on multi-source identification fusion, and belongs to the technical field of hospital building equipment management, and the method comprises the steps: configuring and deploying multi-source identification hardware; acquiring equipment position data and equipment operation state data; performing space-time alignment and data fusion on the equipment position data and the equipment operation state data by adopting a fusion algorithm integrated with medical data time sequence compensation and space conflict pre-judgment; constructing a ternary association data model of space-equipment-operation and maintenance ternary association digital mapping based on the fused data; driving four-dimensional binding of physical space-virtual model-equipment entity-operation and maintenance process based on the ternary associated data model, carrying out view angle loading, dynamic operation and maintenance board visualization and multi-terminal synchronization, and adapting to a hospital multi-terminal collaborative operation and maintenance scene; the method can be combined with an advanced data fusion algorithm, and achieves the precise positioning of the hospital building equipment.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of hospital building equipment management, and particularly relates to a hospital building equipment intelligent operation and maintenance method and system based on multi-source identification fusion. BACKGROUND

[0002] In the field of hospital building equipment management, the current common method is to collect equipment repair information by manual recording and paper form, and to locate the equipment position by using the first two-dimensional code. This method is inefficient and prone to errors. At the same time, the location of the equipment is not accurate enough, and it is difficult to quickly and accurately find the equipment that needs to be repaired.

[0003] As the core carrier of medical activities, the equipment management of hospital buildings faces three core contradictions: 1) contradiction between single technology and complex environment. Two-dimensional codes are easily contaminated by liquids (such as blood stains in operating rooms), Bluetooth is interfered by strong magnetic fields in MRI rooms, and RFID is inaccurate in front of metal instrument cabinets. Existing solutions cannot adapt to the changing physical environment of hospitals (such as lead walls in radiology departments vs. all-glass curtain walls in outpatient areas). 2) contradiction between positioning needs and implementation costs. Operating rooms require centimeter-level positioning but have limited budgets, while general wards only need meter-level positioning but need to support batch inspections. 3) contradiction between data richness and application effectiveness. BIM models, equipment sensors, and maintenance records are isolated from each other, and maintenance personnel still need to manually switch between multiple systems for queries. SUMMARY

[0004] The purpose of the present application is to overcome the deficiencies in the prior art and provide a hospital building equipment intelligent operation and maintenance method and system based on multi-source identification fusion, which combines advanced data fusion algorithms to achieve accurate positioning of hospital equipment.

[0005] To achieve the above purpose, the present application adopts the following technical solutions:

[0006] On the one hand, the present application provides a hospital building equipment intelligent operation and maintenance method based on multi-source identification fusion, comprising the following steps:

[0007] Deploying multi-source identification hardware including two-dimensional codes, Bluetooth beacons, UWB anchor points, and RFID tags;

[0008] Collecting equipment position data and equipment operation state data using multi-source identification hardware, and adding a unified timestamp to each item of collected data using an NTP server;

[0009] Using a fusion algorithm that incorporates medical data time sequence compensation and spatial conflict prediction to perform spatio-temporal alignment and data fusion on the equipment position data and equipment operation state data;

[0010] Generate three-dimensional coordinates of medical devices in the BIM model of the hospital building based on the fused data, and construct a three-dimensional association data model of spatial-device-maintenance tripartite association digital mapping;

[0011] Based on the three-dimensional association data model, drive the four-dimensional binding of "physical space-virtual model-equipment entity-maintenance process", perform perspective loading, dynamic maintenance board visualization, and multi-terminal synchronization, and adapt to the multi-terminal collaborative maintenance scene of the hospital.

[0012] Further, the device location data includes UWB ranging value, Bluetooth RSSI signal, RFID tag ID, and two-dimensional code coordinates, and the device operating state data includes pressure and temperature sensing parameters.

[0013] Further, one two-dimensional code is deployed per device, and laser etching stainless steel signs are used; one Bluetooth beacon is deployed per 30m 2 , a button battery is configured for power supply and the transmission power is adjusted to avoid the electrocardiogram frequency band; UWB anchors are deployed according to the correction strategy, POE power supply and multipath suppression antennas are used; RFID tags are used for mobile assets and are encapsulated with epoxy resin.

[0014] Further, the correction strategy deployment of the UWB anchor point includes:

[0015] Calculate the diagonal length L diag of the target area of the UWB anchor point deployment;

[0016] According to the positioning accuracy requirement δ, determine the initial anchor point spacing according to the formula ;

[0017] According to the metal interference coefficient β, the metal density weight ω and the metal interference index ρ metal of the hospital scene, the actual deployment spacing d med is obtained by using the following formula for spacing correction;

[0018]

[0019] In the formula, β is the electromagnetic compatibility coefficient of the medical device, 0.92 for the operating room and 1.05 for the general ward; ω is the metal density weight, which is positively correlated with the number of CT devices and lead walls, and is dynamically valued between 0 and 1; ρ metal is the metal interference index, which is collected in real time by a spectrum analyzer and is in the interval of 0-0.8.

[0020] Further, a fusion algorithm that integrates medical data time series compensation and space conflict prediction is used, and the formula is represented as:

[0021]

[0022] In the formula, Kk For Kalman gain; z k UWB measurement; H k λ·τ is the observation matrix. seq For medical data time-series compensation, λ represents the time-series weight of equipment such as anesthesia machines / monitors, and τ represents the time-series weight of the equipment. seq It is the data lag coefficient; γ conf γ is the spatial conflict prediction coefficient. When the BIM model detects equipment congestion, γ conf The value was reduced from 1 to 0.85, prioritizing the maintenance of positioning continuity.

[0023] Furthermore, the medical data time-series compensation term λ·τ seq Based on the data upload characteristics of different medical devices, corresponding time-series weights λ and data lag coefficients τ are set. seq Spatial conflict prediction coefficient γ conf The algorithm dynamically adjusts based on the equipment congestion status detected by the BIM model, adapting the fusion algorithm to the hospital equipment operation and spatial layout scenarios.

[0024] Furthermore, the UWB ranging values ​​and Bluetooth RSSI signals are weighted and fused, and the RFID / Bluetooth coordinates are transformed to the BIM coordinate system through the ICP point cloud registration algorithm to achieve spatiotemporal alignment.

[0025] When the deviation between the QR code and UWB positioning is greater than 0.5m, the conflict resolution protocol is activated, and a dual-threshold decision model is used to quantify the judgment as follows:

[0026]

[0027] In the formula, σ metal It is the standard deviation of metallic interference, calculated using the multipath components of the UWB signal; σ0 = 0.3, used as the measured threshold in the hospital setting;

[0028] If the area is determined to be a metal-dense area, the UWB data is corrected using a UWB compensation algorithm that combines a medical scene correction coefficient α. In the metal-dense area, α is set to 0.8, and in the open area, it is set to 1.2.

[0029] If the area is not in a densely populated metal area, QR code data will be used first, and multi-source verification will be triggered simultaneously.

[0030] Furthermore, the construction rules for the ternary association data model must satisfy:

[0031] The spatial dimension and the equipment dimension are linked through BIM coordinates and PM codes, with a linking error of <0.5m;

[0032] A trigger relationship is established between the device dimension and the operation and maintenance dimension through real-time operating parameters and maintenance work order IDs. When the device operating parameters exceed the threshold, a maintenance work order is automatically generated in the operation and maintenance dimension.

[0033] The three-dimensional data are aligned with timestamps, with a synchronization accuracy error of <1ms, and together drive the four-dimensional binding of "physical space - virtual model - device entity - operation and maintenance process".

[0034] Furthermore, the view loading is triggered by scanning a code or near-field signal. When the code is scanned, the view configuration file is requested through the grid_id in the QR code. When the near-field signal is triggered, the view is automatically woken up and a simplified view is loaded based on the RSSI signal strength.

[0035] Based on the device and operation and maintenance dimensions of the ternary association data model, the device status is displayed in a linked manner, including normal, warning, fault, and under maintenance;

[0036] Through the four-dimensional binding relationship of the ternary association data model, the maintenance completion confirmation of OperationTablet, the status update of Server, and the termination of red alerts of ARGlasses are ensured to be mapped in real time in the ternary association data model, so as to achieve cross-terminal status consistency.

[0037] The ternary correlation data model includes spatial data, equipment data, and operation and maintenance data. Spatial data includes BIM coordinates and point cloud matching data, while equipment data is identified as PM using PM coding rules. code =Type (1：4) -Year install -Seq (3d) Operation and maintenance data includes maintenance records and fault records.

[0038] On the other hand, the present invention provides an intelligent operation and maintenance system for hospital building equipment based on multi-source identifier fusion, comprising:

[0039] Multi-source identification hardware module, used to deploy multi-source identification hardware including QR codes, Bluetooth beacons, UWB anchors and RFID tags;

[0040] The data processing module uses multi-source identification hardware to collect device location data and device operating status data, and uses an NTP server to add a unified timestamp to the collected data.

[0041] The multimodal data fusion module employs a fusion algorithm that incorporates medical data temporal compensation and spatial conflict prediction to perform spatiotemporal alignment and data fusion on the device location data and device operating status data;

[0042] Furthermore, based on the fused data, the three-dimensional coordinates of medical equipment in the hospital building BIM model are generated, and a three-element association data model with a three-element association digital mapping of space-equipment-operation and maintenance is constructed.

[0043] The virtual-real interaction management module is used to drive the four-dimensional binding of "physical space-virtual model-equipment entity-operation and maintenance process" based on the three-element association data model, and to perform perspective loading, dynamic operation and maintenance dashboard visualization and multi-terminal synchronization, adapting to the multi-terminal collaborative operation and maintenance scenario of the hospital.

[0044] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: The intelligent operation and maintenance method and system for hospital building equipment based on multi-source identifier fusion provided by this invention utilizes multiple identifier technologies such as QR codes, Bluetooth, UWB, and RFID to acquire multi-source identifier fusion data, based on fusion algorithms, data fusion processing, and precise positioning. Combined with advanced data fusion algorithms, it achieves precise positioning of hospital building equipment; through the fusion of BIM models and sensor data, and by employing spatiotemporal alignment and conflict resolution technologies, the equipment management system can more accurately reflect the real-time status of the equipment; it provides real-time equipment status visualization, which can dynamically display the status of the equipment through real-time feedback in a virtual environment, facilitating rapid response by operation and maintenance personnel. Attached Figure Description

[0045] Figure 1 A flowchart illustrating an intelligent operation and maintenance method for hospital building equipment based on multi-source identifier fusion, provided as an embodiment of the present invention.

[0046] Figure 2 This is a block diagram of an intelligent operation and maintenance system for hospital building equipment based on multi-source identifier fusion, provided as an embodiment of the present invention.

[0047] Figure 3 This is a flowchart of multi-source identifier data fusion provided in an embodiment of the present invention.

[0048] Figure 4 This is a flowchart illustrating a virtual-real collaborative management process provided in an embodiment of the present invention.

[0049] Figure 5 This is a schematic diagram of a dynamic operation and maintenance dashboard for medical equipment provided in an embodiment of the present invention.

[0050] Figure 6 This is a schematic diagram of grid_id in a QR code provided in an embodiment of the present invention.

[0051] Figure 7 This is a schematic diagram illustrating conflict resolution as provided in an embodiment of the present invention.

[0052] Figure 8 This is a schematic diagram of multi-terminal synchronization provided in an embodiment of the present invention. Detailed Implementation

[0053] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0054] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0055] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0056] Example

[0057] like Figure 1 As shown, this embodiment of the invention provides an intelligent operation and maintenance method for hospital building equipment based on multi-source identifier fusion, including the following steps:

[0058] Configure and deploy multi-source identification hardware including QR codes, Bluetooth beacons, UWB anchors, and RFID tags;

[0059] Multi-source identification hardware is used to collect device location data and device operating status data, and an NTP server is used to add a unified timestamp to the collected data.

[0060] A fusion algorithm incorporating medical data temporal compensation and spatial conflict prediction is used to perform spatiotemporal alignment and data fusion of the device location data and device operating status data;

[0061] Based on the fused data, the three-dimensional coordinates of medical equipment in the hospital building BIM model are generated, and a three-element association data model with three-element association digital mapping of space-equipment-operation and maintenance is constructed.

[0062] Based on the aforementioned ternary relational data model, a four-dimensional binding of "physical space - virtual model - equipment entity - operation and maintenance process" is driven, enabling perspective loading, dynamic operation and maintenance dashboard visualization, and multi-terminal synchronization, adapting to multi-terminal collaborative operation and maintenance scenarios in hospitals.

[0063] In some embodiments, the device location data includes UWB ranging values, Bluetooth RSSI signals, RFID tag IDs, and QR code coordinates, and the device operating status data includes pressure and temperature sensing parameters.

[0064] Applying Gaussian filtering to UWB ranging values ​​can effectively reduce the impact of environmental noise on ranging accuracy, improve the accuracy of subsequent data fusion, and ensure the quality of equipment data acquisition in complex hospital environments.

[0065] One QR code is deployed per device, using laser-etched stainless steel signage; one Bluetooth beacon is deployed every 30 square meters, powered by a button battery and with its transmission power adjusted to avoid the ECG frequency band; UWB anchors are deployed according to a modified strategy, using PoE power supply and multipath suppression antennas; RFID tags are used for mobile assets and are encapsulated in epoxy resin. See Table 1 below.

[0066] Table 1

[0067] Identification type Deployment density Power supply mode Medical scene adaptive design QR code 1 per device Passive Operating room with laser etching stainless steel sign Bluetooth beacon 1 per 30 square meters Button cell (3 years) Adjustable transmission power to avoid ECG frequency band UWB anchor point 4 per room in operating room POE power supply Metal environment multipath suppression antenna RFID Mobile asset tag Passive Epoxy encapsulation resistant to formaldehyde disinfection

[0068] The deployment of the UWB anchor point correction strategy includes:

[0069] Calculate the diagonal length L of the target area for UWB anchor deployment. diag ;

[0070] Based on the positioning accuracy requirement δ, according to the formula Determine the initial spacing of the anchor points;

[0071] Combining the metal interference coefficient β, metal density weight ω, and metal interference index ρ in a hospital setting metal The actual deployment spacing d is obtained by using the following formula for spacing correction. med ;

[0072]

[0073] In the formula, β is the electromagnetic compatibility coefficient of medical equipment, taken as 0.92 for operating rooms and 1.05 for general wards; ω is the metal density weight, which is positively correlated with the number of CT equipment and lead walls, and is dynamically assigned a value of 0-1; ρ metalThe metal interference index is collected in real time by a spectrum analyzer, ranging from 0 to 0.8.

[0074] In this embodiment, taking an operating room as an example, the room's length and width are first measured to be 6m × 5m, then the diagonal length L diag =7.81. If a positioning accuracy of δ = 0.05 is required, the calculated reference spacing d ≈ 2.76. Since the operating room is a metal-dense environment, the final anchor point spacing is taken as 2.76 × 0.8 = 2.21. In actual deployment, one anchor point is installed at each of the four corners. See Table 2 below.

[0075] Table 2

[0076] Traditional scheme Technical scheme of the present application Fixed interval deployment (e.g. 3m) Adapt to room size and accuracy requirements dynamically Ignore environmental electromagnetic interference Compensate for metal influence through correction coefficient Need to adjust repeatedly by actual measurement Complete theoretical deployment at one time

[0077] In some embodiments, a fusion algorithm incorporating medical data temporal compensation and spatial conflict prediction is adopted, expressed by the formula:

[0078]

[0079] In the formula, K k For Kalman gain; z k UWB measurement; H k λ·τ is the observation matrix. seq For medical data time-series compensation, λ represents the time-series weight of equipment such as anesthesia machines / monitors, and τ represents the time-series weight of the equipment. seq It is the data lag coefficient; γ conf γ is the spatial conflict prediction coefficient. When the BIM model detects equipment congestion, γ conf The value was reduced from 1 to 0.85, prioritizing the maintenance of positioning continuity.

[0080] Among them, the medical data time-series compensation term λ·τ seq Based on the data upload characteristics of different medical devices (such as anesthesia machines, monitors, etc.), corresponding time-series weights λ and data lag coefficients τ are set. seq Spatial conflict prediction coefficient γ conf The algorithm dynamically adjusts based on the equipment congestion status detected by the BIM model, adapting the fusion algorithm to the hospital equipment operation and spatial layout scenarios.

[0081] In some embodiments, UWB ranging values ​​and Bluetooth RSSI signals are weighted and fused, and the RFID / Bluetooth coordinates are converted to the BIM coordinate system through the ICP point cloud registration algorithm to achieve spatiotemporal alignment.

[0082] When the deviation between the QR code and UWB positioning is greater than 0.5m, the conflict resolution protocol is activated, and a dual-threshold decision model is used to quantify the judgment as follows:

[0083]

[0084] In the formula, σ metal It is the standard deviation of metallic interference, calculated using the multipath components of the UWB signal; σ0 = 0.3, used as the measured threshold in the hospital setting;

[0085] If the area is determined to be a metal-dense area, the UWB data is corrected using a UWB compensation algorithm that combines a medical scene correction coefficient α. In the metal-dense area, α is set to 0.8, and in the open area, it is set to 1.2.

[0086] If the area is not in a densely populated metal area, QR code data will be used first, and multi-source verification will be triggered simultaneously.

[0087] Specifically, during UWB anchor point deployment, the electromagnetic compatibility coefficient β of medical devices is flexibly set according to different medical scenarios, such as 0.92 for operating rooms and 1.05 for general wards, to adapt to the electromagnetic environment of different scenarios and adjust the anchor point spacing accordingly; the metal density weight ω and the metal interference index ρ are also used. metal Dynamically assign values ​​based on the distribution of metal equipment and interference in the actual scenario to ensure that the anchor point spacing is adapted to the complex environment of the hospital.

[0088] In some embodiments, the construction rules of the ternary association data model must satisfy:

[0089] The spatial dimension and the equipment dimension are linked through BIM coordinates and PM codes, with a linking error of <0.5m;

[0090] A trigger relationship is established between the device dimension and the operation and maintenance dimension through real-time operating parameters and maintenance work order IDs. When the device operating parameters exceed the threshold, a maintenance work order is automatically generated in the operation and maintenance dimension.

[0091] The three-dimensional data are aligned with timestamps, with a synchronization accuracy error of <1ms, and together drive the four-dimensional binding of "physical space - virtual model - device entity - operation and maintenance process".

[0092] Among them, the spatial dimension includes BIM three-dimensional coordinates, physical space partition coding (such as operating room number OR-001), and metal interference level (calculated in real time through UWB signal);

[0093] The associated PM code at the device level is:

[0094] (\(\text{PM}_{\text{code}}=\text{Type}_{(1:4)}-\text{Year}_{\text{install}}-\text{Seq}_{(3d)}\)), real-time running parameters (pressure, temperature, etc.), historical fault codes.

[0095] As a specific embodiment, it is coded as: PM code =Type (1:4)-Year install -Seq (3d) .

[0096] Among them, Type (1：4) Device type abbreviation (first 4 characters); Year install : Year of equipment installation; Seq (3d) Three-digit serial number. For example, the 5th anesthesia machine installed in 2023: PM code =ANES-2023-005.

[0097] The device sensors are connected via the OPC UA protocol, and the data update frequency is configurable (typically 1 second / time for operating room equipment).

[0098] From an operations and maintenance perspective, this includes binding maintenance plans (including countdown to the next maintenance), repair work order IDs, and digital twin identifiers for operations and maintenance personnel.

[0099] Real-time acquisition of device sensor data via OPC UA protocol (updated every 1 second) drives dynamic updates of the ternary association model, achieving four-dimensional binding of "physical space - virtual model - device entity - operation and maintenance process".

[0100] In some embodiments, view loading is triggered based on QR code scanning or near-field signals. When QR code scanning is triggered, the view configuration file is requested through the grid ID in the QR code. When near-field signaling is triggered, a simplified view is automatically woken up and loaded based on the RSSI signal strength.

[0101] like Figure 5 As shown in Table 3 below, the device status is displayed in conjunction with the device and operation and maintenance dimensions of the ternary association data model, including normal, warning, fault, and under maintenance.

[0102] Table 3

[0103] Device status Model performance Data update frequency Normal Green translucent 5 minutes Warning Yellow flicker (1 Hz) 30 seconds Fault Red + explosion particle effect Real-time Under repair Blue rotating marker Manually triggered

[0104] Through the four-dimensional binding relationship of the ternary association data model, the maintenance completion confirmation of OperationTablet, the status update of Server, and the termination of red alerts of ARGlasses are ensured to be mapped in real time in the ternary association data model, so as to achieve cross-terminal status consistency.

[0105] The ternary correlation data model includes spatial data, equipment data, and operation and maintenance data. Spatial data includes BIM coordinates and point cloud matching data, while equipment data is identified as PM using PM coding rules. code =Type (1：4) -Year install -Seq (3d)Operation and maintenance data includes maintenance records and fault records.

[0106] like Figure 8 As shown, the multi-terminal synchronization solution strictly follows the interaction process of OperationTablet, Server, Database, WebEngine, and ARGlasses, ensuring that status change messages are synchronized in real time and accurately across all terminals, adapting to hospital multi-terminal collaborative operation and maintenance scenarios.

[0107] On the other hand, the present invention provides an intelligent operation and maintenance system for hospital building equipment based on multi-source identifier fusion, comprising:

[0108] Multi-source identification hardware module, used to deploy multi-source identification hardware including QR codes, Bluetooth beacons, UWB anchors and RFID tags;

[0109] The data processing module uses multi-source identification hardware to collect device location data and device operating status data, and uses an NTP server to add a unified timestamp to the collected data.

[0110] The multimodal data fusion module employs a fusion algorithm that incorporates medical data temporal compensation and spatial conflict prediction to perform spatiotemporal alignment and data fusion on the device location data and device operating status data;

[0111] Furthermore, based on the fused data, the three-dimensional coordinates of medical equipment in the hospital building BIM model are generated, and a three-element association data model with a three-element association digital mapping of space-equipment-operation and maintenance is constructed.

[0112] The virtual-real interaction management module is used to drive the four-dimensional binding of "physical space-virtual model-equipment entity-operation and maintenance process" based on the three-element association data model, and to perform perspective loading, dynamic operation and maintenance dashboard visualization and multi-terminal synchronization, adapting to the multi-terminal collaborative operation and maintenance scenario of the hospital.

[0113] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent operation and maintenance of hospital building equipment based on multi-source identifier fusion, characterized in that, Including the following steps: Configure and deploy multi-source identification hardware including QR codes, Bluetooth beacons, UWB anchors, and RFID tags; Multi-source identification hardware is used to collect device location data and device operating status data, and an NTP server is used to add a unified timestamp to the collected data. A fusion algorithm incorporating medical data temporal compensation and spatial conflict prediction is used to perform spatiotemporal alignment and data fusion of the device location data and device operating status data; Based on the fused data, the three-dimensional coordinates of medical equipment in the hospital building BIM model are generated, and a three-element association data model with three-element association digital mapping of space-equipment-operation and maintenance is constructed. Based on the aforementioned ternary relational data model, a four-dimensional binding of "physical space - virtual model - equipment entity - operation and maintenance process" is driven, enabling perspective loading, dynamic operation and maintenance dashboard visualization, and multi-terminal synchronization, adapting to multi-terminal collaborative operation and maintenance scenarios in hospitals.

2. The intelligent operation and maintenance method for hospital building equipment based on multi-source identifier fusion according to claim 1, characterized in that, The device location data includes UWB ranging values, Bluetooth RSSI signals, RFID tag IDs, and QR code coordinates. The device operating status data includes pressure and temperature sensing parameters.

3. The intelligent operation and maintenance method for hospital building equipment based on multi-source identifier fusion according to claim 2, characterized in that, One QR code is deployed per device, using laser-etched stainless steel signage; one Bluetooth beacon is deployed per 30 square meters, powered by a button battery and with its transmission power adjusted to avoid the ECG frequency band; UWB anchors are deployed according to a correction strategy, using POE power supply and multipath suppression antennas; RFID tags are used for mobile assets and are encapsulated in epoxy resin.

4. The intelligent operation and maintenance method for hospital building equipment based on multi-source identifier fusion according to claim 3, characterized in that, The deployment of the UWB anchor point correction strategy includes: Calculate the diagonal length L of the target area for UWB anchor deployment. diag ; Based on the positioning accuracy requirement δ, according to the formula Determine the initial spacing of the anchor points; Combining the metal interference coefficient β, metal density weight ω, and metal interference index ρ in a hospital setting metal The actual deployment spacing d is obtained by using the following formula for spacing correction. med ; In the formula, β is the electromagnetic compatibility coefficient of medical equipment, taken as 0.92 for operating rooms and 1.05 for general wards; ω is the metal density weight, which is positively correlated with the number of CT equipment and lead walls, and is dynamically assigned a value of 0-1; ρ metal The metal interference index is collected in real time by a spectrum analyzer, ranging from 0 to 0.

8.

5. The intelligent operation and maintenance method for hospital building equipment based on multi-source identifier fusion according to claim 2, characterized in that, A fusion algorithm incorporating temporal compensation and spatial conflict prediction of medical data is adopted, and the formula is expressed as follows: In the formula, K k For Kalman gain; z k UWB measurement; H k λ·τ is the observation matrix. seq For medical data time-series compensation, λ represents the time-series weight of equipment such as anesthesia machines / monitors, and τ represents the time-series weight of the equipment. seq It is the data lag coefficient; γ conf γ is the spatial conflict prediction coefficient. When the BIM model detects equipment congestion, γ conf The value was reduced from 1 to 0.85, prioritizing the maintenance of positioning continuity.

6. The intelligent operation and maintenance method for hospital building equipment based on multi-source identifier fusion according to claim 5, characterized in that, The medical data time-series compensation term λ·τ seq Based on the data upload characteristics of different medical devices, corresponding time-series weights λ and data lag coefficients τ are set. seq Spatial conflict prediction coefficient γ conf The algorithm dynamically adjusts based on the equipment congestion status detected by the BIM model, adapting the fusion algorithm to the hospital equipment operation and spatial layout scenarios.

7. The intelligent operation and maintenance method for hospital building equipment based on multi-source identifier fusion according to claim 5, characterized in that, We weighted and fused UWB ranging values ​​and Bluetooth RSSI signals, and used the ICP point cloud registration algorithm to transform RFID / Bluetooth coordinates to the BIM coordinate system to achieve spatiotemporal alignment. When the deviation between the QR code and UWB positioning is greater than 0.5m, the conflict resolution protocol is activated, and a dual-threshold decision model is used to quantify the judgment as follows: In the formula, σ metal It is the standard deviation of metallic interference, calculated using the multipath components of the UWB signal; σ0 = 0.3, used as the measured threshold in the hospital setting; If the area is determined to be a metal-dense area, the UWB data is corrected using a UWB compensation algorithm that combines a medical scene correction coefficient α. In the metal-dense area, α is set to 0.8, and in the open area, it is set to 1.

2. If the area is not in a densely populated metal area, QR code data will be used first, and multi-source verification will be triggered simultaneously.

8. The intelligent operation and maintenance method for hospital building equipment based on multi-source identifier fusion according to claim 1, characterized in that, The construction rules for the ternary association data model must satisfy: The spatial dimension and the equipment dimension are linked through BIM coordinates and PM codes, with a linking error of <0.5m; A trigger relationship is established between the device dimension and the operation and maintenance dimension through real-time operating parameters and maintenance work order IDs. When the device operating parameters exceed the threshold, a maintenance work order is automatically generated in the operation and maintenance dimension. The three-dimensional data are aligned with timestamps, with a synchronization accuracy error of <1ms, and together drive the four-dimensional binding of "physical space - virtual model - device entity - operation and maintenance process".

9. The intelligent operation and maintenance method for hospital building equipment based on multi-source identifier fusion according to claim 1, characterized in that, View loading is triggered by scanning a code or near-field signal. When triggered by scanning a code, the view configuration file is requested through the grid_id in the QR code. When triggered by near-field signal, the view is automatically woken up and loaded with a simplified view based on the RSSI signal strength. Based on the device and operation and maintenance dimensions of the ternary association data model, the device status is displayed in a linked manner, including normal, warning, fault, and under maintenance; Through the four-dimensional binding relationship of the ternary association data model, the maintenance completion confirmation of OperationTablet, the status update of Server, and the termination of red alerts of ARGlasses are ensured to be mapped in real time in the ternary association data model, so as to achieve cross-terminal status consistency. The ternary correlation data model includes spatial data, equipment data, and operation and maintenance data. Spatial data includes BIM coordinates and point cloud matching data, while equipment data is identified as OM using PM coding rules. code =Type (1：4) -Year install -Seq (3d) Operation and maintenance data includes maintenance records and fault records.

10. A hospital building equipment intelligent operation and maintenance system based on multi-source identifier fusion, characterized in that, include: Multi-source identification hardware module, used to deploy multi-source identification hardware including QR codes, Bluetooth beacons, UWB anchors and RFID tags; The data processing module uses multi-source identification hardware to collect device location data and device operating status data, and uses an NTP server to add a unified timestamp to the collected data. The multimodal data fusion module employs a fusion algorithm that incorporates medical data temporal compensation and spatial conflict prediction to perform spatiotemporal alignment and data fusion on the device location data and device operating status data; Furthermore, based on the fused data, the three-dimensional coordinates of medical equipment in the hospital building BIM model are generated, and a three-element association data model with a three-element association digital mapping of space-equipment-operation and maintenance is constructed. The virtual-real interaction management module is used to drive the four-dimensional binding of "physical space-virtual model-equipment entity-operation and maintenance process" based on the three-element association data model, and to perform perspective loading, dynamic operation and maintenance dashboard visualization and multi-terminal synchronization, adapting to the multi-terminal collaborative operation and maintenance scenario of the hospital.

Images