Hospital full-process intelligent operation and maintenance command management system based on BIM Internet of Things cooperation

Abstract

The invention discloses a hospital full-process intelligent operation and maintenance command management system based on BIM Internet of Things cooperation, and belongs to the technical field of hospital management, and the system comprises a three-dimensional visual management module which is configured to establish a three-dimensional model of a hospital building, and carries out the display and management of the hospital building; the real-time monitoring acquisition module is configured to acquire hospital multi-source data; the preprocessing analysis module is configured to perform pre-diagnosis and preventive maintenance on hospital equipment faults; the intelligent operation and maintenance command management module is configured to perform automatic management on operation and maintenance activities; and the visual display interaction module is configured to provide an interaction interface to grasp the operation and maintenance condition of the hospital anytime and anywhere. The problem that intelligent operation and maintenance command management cannot be carried out on the whole process of a hospital based on BIM Internet of Things cooperation in the prior art is solved. According to the invention, intelligent operation and maintenance command management can be carried out on the whole process of a hospital, omnibearing digital management of hospital buildings and equipment can be realized, and powerful support can be provided for efficient operation and high-quality development of the hospital.

Description

Technical Field

[0001] The present invention relates to the technical field of hospital management, and specifically to an intelligent operation and maintenance command management system for the whole process of a hospital based on BIM Internet of Things collaboration. Background Technique

[0002] Internet of Things devices refer to various physical devices, sensors, software, network structures, etc. that can be connected through a network and achieve information exchange; as a complex organizational system, a hospital needs effective operation and maintenance management to ensure the normal operation of medical facilities and the safety of patients. Among them, Internet of Things devices have important applications in hospital operation and maintenance management, which can improve the operation efficiency and service quality of the hospital, and reduce energy consumption and costs.

[0003] A Chinese patent with the publication number CN114792205B discloses a method for monitoring and alarming road surface subsidence and collapse based on a smart hospital operation and maintenance platform, including the preparation of basic data of a smart hospital and a smart hospital operation and maintenance platform; creating a three-dimensional operation and maintenance management model of a smart hospital; comprehensively analyzing the terrain deformation data monitored in real time through the smart hospital operation and maintenance platform to find abnormal data; analyzing the abnormal data, classifying and summarizing the analysis results into charts to generate high-risk areas of road surface collapse, and linking and hanging them with the three-dimensional operation and maintenance management model of the smart hospital on the smart hospital operation and maintenance platform for timely warning, and sharing the disease information with the operation and maintenance management department at the same time; after receiving the geological alarm information, the operation and maintenance management department takes corresponding emergency measures and other steps. It can accurately prompt unknown risks ahead, and thus effectively avoid accidents. However, this patent has the following defects: The existing technology cannot perform intelligent operation and maintenance command management on the whole process of a hospital based on BIM Internet of Things collaboration, cannot effectively realize the all-round digital management of hospital buildings and equipment, and cannot provide strong support for the efficient operation and high-quality development of the hospital. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent operation and maintenance command management system for the whole process of a hospital based on BIM Internet of Things collaboration, which can perform intelligent operation and maintenance command management on the whole process of a hospital based on BIM Internet of Things collaboration, can effectively realize the all-round digital management of hospital buildings and equipment, and can provide strong support for the efficient operation and high-quality development of the hospital, and solves the problems raised in the above background technique.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: An intelligent operation and maintenance command management system for the whole process of a hospital based on BIM Internet of Things collaboration, including: A three-dimensional visualization management module configured to establish a three-dimensional model of a hospital building, and perform visual display and management on the hospital building; The real-time monitoring and acquisition module is configured to perform real-time monitoring on the environmental parameters, equipment operation status, and energy consumption of the hospital, and acquire multi-source data of the hospital; Set the corresponding weight values for the environmental parameters, equipment operation status, and energy consumption of the hospital for subsequent weighted fusion of the multi-source data of the hospital; The preprocessing and analysis module is configured to preprocess and analyze the multi-source data of the hospital, and perform pre-diagnosis and preventive maintenance on hospital equipment failures; The intelligent operation and maintenance command and management module is configured to automatically assign orders and track progress, automate the management of operation and maintenance activities, and for emergencies, quickly activate the emergency plan and rapidly dispatch emergency resources; The visualization display and interaction module is configured to provide a friendly interaction interface for grasping the operation and maintenance status of the hospital at any time and anywhere.

[0006] Preferably, establish a three-dimensional model of the hospital building, and perform visualization display and management on the hospital building, including: Collect the building structure, spatial layout, and equipment installation information of the hospital based on Internet of Things devices; Based on BIM technology, establish a three-dimensional model of the hospital building according to the building structure, spatial layout, and equipment installation information of the hospital, and integrate the facility and equipment information; Perform visualization display and management on the building structure, spatial layout, and equipment installation information of the hospital according to the three-dimensional model of the hospital building, view the type, area, and status information of each room in the hospital in real time, and allocate hospital resources and optimize space.

[0007] Preferably, perform real-time monitoring on the environmental parameters, equipment operation status, and energy consumption of the hospital, and acquire multi-source data of the hospital, including: Perform real-time monitoring and acquisition on the temperature, humidity, air quality, pressure, and flow conditions in the hospital based on Internet of Things devices to determine the environmental parameters of the hospital; Perform real-time monitoring and acquisition on the on / off state, operation parameters, equipment location, operation sound, and vibration conditions of the equipment in the hospital based on Internet of Things devices to determine the equipment operation status of the hospital; Perform real-time monitoring and acquisition on the electricity consumption, water resource consumption, gas consumption, and heat energy consumption in the hospital based on Internet of Things devices to determine the energy consumption data of the hospital; Determine the multi-source data of the hospital based on Internet of Things devices according to the environmental parameters, equipment operation status, and energy consumption data of the hospital.

[0008] Preferably, set the corresponding weight values for the environmental parameters, equipment operation status, and energy consumption of the hospital, and the weight values are used for subsequent weighted fusion of the multi-source data of the hospital, including: Take the environmental parameters, the corresponding parameters of the device operating status, and the corresponding parameters of the energy consumption as the types of data items; Extract the number and types of data types included in each type of data item; Detect the correlation coefficients between the various data types included in each type of data item; Obtain the average correlation coefficient corresponding to each data type according to the correlation coefficients between each data type in each type of data item and the other data types in the same type of data item; Extract the median value of the average correlation coefficient corresponding to each data type included in the environmental parameters, and process the median value of the average correlation coefficient using the Sigmoid function to generate the weight value corresponding to the environmental parameters; Take the temperature, humidity, and air quality in the environmental parameters as the target data types; Extract the data change rate of each data type included in the other data item types corresponding to a preset unit data volume change of the target data types in the environmental parameters; wherein, the preset unit data volume corresponding to each data type is as follows: The preset unit data volume corresponding to temperature is 3°C; The preset unit data volume corresponding to humidity is 5%; The preset unit data volume corresponding to air quality is 0.1% of the standard concentration of the substances included in the air quality monitoring; Obtain the weight values corresponding to the other data item types according to the data change rate of each data type included in the other data item types corresponding to a preset unit data volume change of the target data types in the environmental parameters and the average correlation coefficient corresponding to each data type included in the other data item types.

[0009] Preferably, obtaining the weight values corresponding to the other data item types according to the data change rate of each data type included in the other data item types corresponding to a preset unit data volume change of the target data types in the environmental parameters and the average correlation coefficient corresponding to each data type included in the other data item types includes: Extract the data change rate of each data type included in the other data item types corresponding to a preset unit data volume change of the target data types in the environmental parameters; Compare the data change rate of each data type included in the other data item types with a preset change rate reference value; Select the data types with a data change rate not lower than the preset change rate reference value in the other data item types as the reference data types; Extract the average value of the correlation coefficients corresponding to the reference data types in the other data item types, and obtain the total average value of the correlation coefficients by using the average value of the correlation coefficients corresponding to the reference data types in the other data item types; Use the data change rate of each data type included in the other data item types corresponding to when the target data types in the environmental parameters all change by a preset unit data amount, and combine it with the total average value of the correlation coefficients to obtain the weight values corresponding to the other data item types.

[0010] Preferably, preprocess the hospital multi-source data, including: Clean the hospital multi-source data, and remove the noise data and duplicate data that are useless for the intelligent operation and maintenance command management of the entire hospital process; Clean the hospital multi-source data, identify the missing values and outliers in the hospital multi-source data, and check the identified missing values and outliers to determine whether they are useful for the intelligent operation and maintenance command management of the entire hospital process; If the identified missing values and outliers are useful for the intelligent operation and maintenance command management of the entire hospital process, fill the missing values and correct the outliers. If the identified missing values and outliers are useless for the intelligent operation and maintenance command management of the entire hospital process, delete the missing values and outliers.

[0011] Preferably, the preprocessing of the hospital multi-source data further includes: Normalize the hospital multi-source data to convert the hospital multi-source data into a unified data format, remove the dimension differences in the hospital multi-source data, and determine the standardized hospital multi-source data; Extract features from the hospital multi-source data, extract the feature vectors useful for the intelligent operation and maintenance command management of the entire hospital process from the hospital multi-source data, and perform weighted fusion on the feature vectors to determine the hospital feature data.

[0012] Preferably, pre-diagnose and perform preventive maintenance on hospital equipment failures, including: According to the requirements of the intelligent operation and maintenance command management of the entire hospital process based on BIM Internet of Things collaboration, collect the hospital historical data, and divide the collected hospital historical data to determine the training set and the test set; Train the deep learning model according to the training set to enable the deep learning model to autonomously learn the hospital equipment failure pre-diagnosis behavior and determine the hospital equipment failure pre-diagnosis model; Test the hospital equipment failure pre-diagnosis model according to the test set, and evaluate the performance of the hospital equipment failure pre-diagnosis model to determine whether the hospital equipment failure pre-diagnosis model can achieve the effect of pre-diagnosing hospital equipment failures; Adjust and optimize the parameters of the hospital equipment fault pre-diagnosis model according to the test evaluation results, and determine the optimal hospital equipment fault pre-diagnosis model; Deploy the optimal hospital equipment fault pre-diagnosis model to the actual hospital equipment fault pre-diagnosis environment; Input the hospital characteristic data into the hospital equipment fault pre-diagnosis model, analyze the hospital characteristic data according to the hospital equipment fault pre-diagnosis model, pre-diagnose the hospital equipment fault, determine the hospital equipment fault pre-diagnosis result, and perform preventive maintenance on the hospital equipment fault according to the hospital equipment fault pre-diagnosis result.

[0013] Preferably, automatically assign orders and track the progress, automate the management of operation and maintenance activities. For emergencies, quickly activate the emergency plan and rapidly dispatch emergency resources, including: Determine the hospital equipment fault operation and maintenance personnel according to the hospital equipment fault cause, automatically assign orders to the hospital equipment fault operation and maintenance personnel, and intelligently guide the hospital equipment fault operation and maintenance personnel to the hospital equipment fault location for maintenance based on intelligent navigation; And track the progress of hospital equipment fault preventive maintenance in real time, standardize the daily operation and maintenance work process, and automate the management of operation and maintenance activities such as reporting, inspection, and maintenance of hospital equipment faults. For hospital emergencies, give early warnings in a timely manner, quickly activate the emergency plan, guide on-site personnel to respond quickly, and rapidly dispatch emergency resources.

[0014] Preferably, provide a friendly interaction interface for grasping the hospital operation and maintenance status at any time and place, including: According to the hospital equipment fault pre-diagnosis result and combined with the hospital equipment fault preventive maintenance plan, form a hospital full-process intelligent operation and maintenance command and management report, and display it in a visual form on the mobile terminal to hospital managers, operation and maintenance personnel, and medical staff for grasping the hospital operation and maintenance status at any time and place.

[0015] Compared with the prior art, the beneficial effects of the present invention are: The present invention utilizes BIM technology to establish a three-dimensional model of a hospital building, integrates facility and equipment information, and realizes visual management of information such as building structure, spatial layout, and equipment installation. By installing Internet of Things devices such as sensors and monitoring cameras, it monitors environmental parameters, equipment operation status, and energy consumption of the hospital in real time, collects multi-source data of the hospital, and uses big data analysis technology to preprocess and analyze the collected multi-source data of the hospital, providing decision-making support for operation and maintenance management, realizing pre-diagnosis and preventive maintenance of hospital equipment failures, automatically dispatching orders and tracking progress according to the hospital equipment failure situation, streamlining and standardizing the daily operation and maintenance work processes, realizing automated management of operation and maintenance activities such as repair requests, inspections, and maintenance. In case of emergencies, it quickly activates the emergency plan, guides on-site personnel to respond quickly, realizes rapid dispatching of emergency resources, improves emergency handling capabilities, and provides a friendly interaction interface for hospital managers, operation and maintenance personnel, medical staff, etc., supports mobile access, facilitates grasping the hospital operation and maintenance status anytime and anywhere, can conduct intelligent operation and maintenance command management for the entire hospital process based on BIM Internet of Things collaboration, effectively realizes all-round digital management of hospital buildings and equipment, and provides strong support for the efficient operation and high-quality development of the hospital. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 FIG. is a module diagram of the hospital full-process intelligent operation and maintenance command management system based on BIM Internet of Things collaboration of the present invention; Figure 2 FIG. is a flowchart of the hospital full-process intelligent operation and maintenance command management system based on BIM Internet of Things collaboration of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] To solve the problems of the existing technology that it cannot conduct intelligent operation and maintenance command management for the entire hospital process based on BIM Internet of Things collaboration, cannot effectively realize all-round digital management of hospital buildings and equipment, and cannot provide strong support for the efficient operation and high-quality development of the hospital, please refer to Figure 1 - Figure 2 This embodiment provides the following technical solutions: The hospital full-process intelligent operation and maintenance command management system based on BIM Internet of Things collaboration includes: a three-dimensional visualization management module, a real-time monitoring and acquisition module, a preprocessing and analysis module, an intelligent operation and maintenance command management module, and a visualization display and interaction module.

[0019] Specifically, through the interaction among the three-dimensional visualization management module, real-time monitoring and acquisition module, preprocessing and analysis module, intelligent operation and maintenance command management module, and visualization display and interaction module, intelligent operation and maintenance command management of the entire hospital process can be carried out based on BIM-IoT collaboration, effectively realizing the all-round digital management of hospital buildings and equipment, and providing strong support for the efficient operation and high-quality development of the hospital.

[0020] Among them, the three-dimensional visualization management module is used to establish a three-dimensional model of the hospital building, and conduct visual display and management of the hospital building; In this embodiment, establishing a three-dimensional model of the hospital building and conducting visual display and management of the hospital building includes: Collecting the building structure, spatial layout, and equipment installation information of the hospital based on Internet of Things devices; Based on BIM technology, establishing a three-dimensional model of the hospital building according to the building structure, spatial layout, and equipment installation information of the hospital, and integrating the information of facilities and equipment; Conducting visual display and management of the building structure, spatial layout, and equipment installation information of the hospital according to the three-dimensional model of the hospital building, and real-time viewing the type, area, and status information of each room in the hospital, and allocating hospital resources and optimizing the space.

[0021] Among them, the real-time monitoring and acquisition module is used to conduct real-time monitoring of the environmental parameters, equipment operation status, and energy consumption of the hospital, and collect multi-source data of the hospital; In this embodiment, conducting real-time monitoring of the environmental parameters, equipment operation status, and energy consumption of the hospital and collecting multi-source data of the hospital includes: Based on Internet of Things devices, conducting real-time monitoring and acquisition of the temperature, humidity, air quality, pressure, and flow conditions in the hospital, and determining the environmental parameters of the hospital; Specifically, the temperature is to monitor the indoor and outdoor temperatures through temperature sensors to ensure that the medical environment meets the comfort requirements of patients and the operation requirements of medical equipment; the humidity is to monitor the air humidity through humidity sensors to avoid mold growth caused by excessive humidity or affect the comfort of patients due to too low humidity; the air quality is to monitor indicators such as CO2 concentration and PM2.5 through air quality sensors to evaluate whether the air quality meets the medical environment requirements; the pressure is to monitor the pressure changes in the ventilation system through pressure sensors to ensure reasonable air flow distribution; the flow is to monitor the fresh air volume, drainage volume, etc. through flow sensors for optimizing the operation of the ventilation and drainage systems.

[0022] Based on Internet of Things devices, conducting real-time monitoring and acquisition of the on / off state, operation parameters, equipment location, operation sound, and vibration conditions of the equipment in the hospital, and determining the equipment operation status of the hospital; Specifically, the power-on and power-off status refers to the opening and closing times of the recording device, facilitating the statistics of the device usage frequency; the operating parameters refer to current, voltage, power, etc., used to evaluate the operating efficiency and energy consumption of the device; the device location refers to using the positioning function of the Internet of Things device to real-time master the device location, facilitating rapid dispatching and operation and maintenance; the operating sound and vibration refer to monitoring whether the device is operating abnormally through a sound sensor or a vibration sensor, and giving early warnings of potential faults.

[0023] Based on the Internet of Things device, the electricity consumption, water resource consumption, gas consumption and heat energy consumption in the hospital are monitored and collected in real time to determine the energy consumption data of the hospital; Specifically, the electricity consumption refers to monitoring the electricity usage of each department and device, analyzing the energy consumption trend, and formulating energy-saving measures; the water resource consumption refers to recording the water consumption, optimizing the water resource allocation, and reducing waste; the gas consumption refers to monitoring the gas volume used in the hospital and evaluating the energy consumption efficiency; the heat energy consumption refers to the heat energy usage of the heating system, used for analyzing the energy consumption distribution and optimization.

[0024] According to the environmental parameters, device operating status and energy consumption data of the hospital, the multi-source data of the hospital based on the Internet of Things device is determined.

[0025] Specifically, corresponding weight values are set for the environmental parameters, device operating status and energy consumption of the hospital, and the weight values are used for subsequent weighted fusion of the multi-source data of the hospital, including: Taking the environmental parameters, corresponding parameters of the device operating status and corresponding parameters of the energy consumption as data item types; Extracting the number and types of data types included in each data item type; Detecting the correlation coefficients between each data type included in each data item type; Obtaining the average correlation coefficient corresponding to each data type according to the correlation coefficients between each data type in each data item type and other data types in the same data item type; Extracting the median of the average correlation coefficients corresponding to each data type included in the environmental parameters, and processing the median of the average correlation coefficients using the Sigmoid function to generate the weight value corresponding to the environmental parameters; Taking the temperature, humidity and air quality in the environmental parameters as target data types; Extracting the data change rates of each data type included in other data item types corresponding to a preset unit data volume change of the target data type in the environmental parameters; among them, the preset unit data volumes corresponding to each data type are as follows: The preset unit data volume corresponding to the temperature is 3°C; The preset unit data volume corresponding to the humidity is 5%; The preset unit data volume corresponding to air quality is 0.1% of the standard concentration of the substance content included in air quality monitoring; Based on the data change rate of each data type included in other data item types corresponding to a preset unit data volume change for each target data type in the environmental parameters, and combining the average value of the correlation coefficients corresponding to each data type included in other data item types, the weight value corresponding to other data item types is obtained.

[0026] The technical effects of the above technical solution are as follows: By extracting the number and types of data types in the data item types, detecting the correlation coefficients between the data types, and calculating the average value of the correlation coefficients, etc., the internal relationship between different data types can be considered more comprehensively and accurately. The weight value generated on this basis can make the multi-source data in the hospital more reasonably allocate the proportion of each data during weighted fusion, thereby improving the accuracy of data fusion and providing a more reliable basis for subsequent analysis and decision-making based on the fused data. Using the Sigmoid function to process the intermediate value of the average correlation coefficient of the data types in the environmental parameters to generate the weight value corresponding to the environmental parameters, the Sigmoid function has the characteristics of mapping the value to a specific interval and smoothing the data, etc., which can make the determination of the environmental parameter weight more scientific and reasonable, avoid extreme cases of weight values, and at the same time consider the comprehensive correlation degree between the data types inside the environmental parameters. Taking temperature, humidity, and air quality in the environmental parameters as target data types, considering the data change rate of each data type in other data item types when they change a preset unit data volume, and combining the average value of the correlation coefficients of the data types in other data item types to obtain the weight value corresponding to other data item types, this can more accurately reflect the mutual influence relationship between the environmental parameters and other data item types, thereby more accurately reflecting the importance of different data item types in multi-source data fusion, helping to deeply explore the potential associations between multi-source data, and improving the comprehensiveness and accuracy of the analysis of the overall operation status of the hospital. By analyzing the parameters corresponding to the energy consumption situation and determining its weight value, the status of the energy consumption data can be highlighted in multi-source data fusion, helping the hospital to more clearly understand the relationship between energy consumption and other environmental parameters, equipment operation status and other factors, thereby providing more powerful data support for energy management and optimization, and helping the hospital to take targeted measures to reduce energy consumption and improve energy utilization efficiency.

[0027] Specifically, based on the data change rate of each data type included in other data item types corresponding to a preset unit data volume change for each target data type in the environmental parameters, and combining the average value of the correlation coefficients corresponding to each data type included in other data item types, the weight value corresponding to other data item types is obtained, including: Extract the data change rate of each data type included in other data item categories when the target data type in the environmental parameters all changes by a preset unit data volume; Compare the data change rate of each data type included in other data item categories with a preset change rate reference value; Filter out the data types in other data item categories whose data change rate is not lower than the preset change rate reference value as reference data types; Extract the average value of the correlation coefficients corresponding to the reference data types in the other data item categories, and obtain the total average value of the correlation coefficients by using the average value of the correlation coefficients corresponding to the reference data types in the other data item categories; Use the data change rate of each data type included in other data item categories when the target data type in the environmental parameters all changes by a preset unit data volume, combined with the total average value of the correlation coefficients, to obtain the weight value corresponding to the other data item categories; Among them, the weight value corresponding to the other data item categories is obtained through the following formula: ; Among them, W represents the weight value corresponding to the other data item categories; m represents the number of data types included in the other data item categories; G i represents the average value of the correlation coefficients corresponding to the i-th data type included in the other data item categories; B i represents the data change rate of the i-th data type included in the other data item categories when the target data type all changes by a preset unit data volume; G p represents the total average value of the correlation coefficients; B p represents the average value of the data change rates corresponding to all reference data types.

[0028] The construction process of the above mathematical model is as follows: Step 1: Determine the data change rate of the influencing factors of the weight and the correlation coefficient. The weight W needs to reflect the following two core factors at the same time; Step 2: Construct the contribution value of a single data type. The contribution value of each data type needs to combine its data change rate and the correlation coefficient. The structure is as follows: ; Among them, represents the geometric mean of the data change rate, which not only considers the response of a single data type (B i ), but also incorporates the average response of the overall reference data types (B p ), avoiding the interference of extreme values. And, is used to constrain the influence of the correlation coefficient. Specifically, Used to map the correlation coefficient corresponding data volume to [0, 1] and ensure that the denominator is non - negative; Select a smaller mapped value of the correlation coefficient to prevent the contribution value from increasing abnormally due to an overly small denominator.

[0029] Step 3: Take the average of the contribution values of the m data types to reflect the overall impact, and compress the total contribution value to the [0, 1] interval through the Sigmoid function to ensure the stability of the weight values. The final weight value acquisition model is as follows: ; Among them, W represents the weight values corresponding to other data item types; m represents the number of data types included in other data item types; G i represents the average correlation coefficient corresponding to the i - th data type included in other data item types; B i represents the data change rate of the i - th data type included in other data item types when the target data type changes by a preset unit data volume; G p represents the total average correlation coefficient; B p represents the average value of the data change rates corresponding to all reference data types.

[0030] The technical effects of the above - mentioned technical solutions are as follows: By comparing the data change rates of each data type in other data item types with a preset reference value, data types with data change rates not lower than the reference value are selected as reference data types, which can eliminate data types that are less affected by the change of the target data type of environmental parameters, focus on key data types, improve the data accuracy of subsequent analysis and processing, and further improve the reliability of performance index calculation based on these data. Extract the average correlation coefficient corresponding to the reference data type and calculate the total average correlation coefficient, and combine the data change rate to obtain the weight value, comprehensively considering the degree of association between data types and the degree of influence by environmental parameter changes, making the weight determination more scientific and reasonable, which helps to more accurately fuse multi - source data and improve the data fusion performance. Analyzing and determining the weight according to the impact of environmental parameter changes on other data item types can enable the system to better adapt to the fluctuations of environmental parameters in the hospital environment, reasonably allocate the weights of each data item type under different environmental conditions, and improve the system's adaptability and overall performance to complex and changeable environments.

[0031] Using the sine function transformation and taking the minimum value as the denominator avoids the excessive influence of certain data types on weight calculation due to abnormal average correlation coefficients, balances the roles of correlation coefficients of different data types in weight calculation, and makes the weight calculation more robust. This term combines the change rate B i of a single data type with the average change rate B of the reference data typep Combined, it takes into account the characteristics of individual data type changes and the overall reference data type change level in a smooth manner, enabling the weight calculation to more comprehensively reflect the data change situation and enhancing the representational ability of the weight value for the actual data changes. The calculation result is mapped to the interval (0, 1) through the Sigmoid function, making the final weight value within a reasonable range, meeting the general requirement of the weight representing the importance degree, providing a standardized and appropriate weight for the weighted fusion of multi-source data, improving the data fusion performance, and further enhancing the accuracy of various analyses and decisions based on the fused data.

[0032] Among them, the preprocessing and analysis module is used to preprocess and analyze the multi-source data of the hospital, and perform pre-diagnosis and preventive maintenance on the hospital equipment failures; In this embodiment, the preprocessing of the multi-source data of the hospital includes: Clean the multi-source data of the hospital, removing the noise data and duplicate data that are useless for the intelligent operation and maintenance command and management of the entire hospital process; Clean the multi-source data of the hospital, identify the missing values and abnormal values in the multi-source data of the hospital, and check the identified missing values and abnormal values to determine whether they are useful for the intelligent operation and maintenance command and management of the entire hospital process; If the identified missing values and abnormal values are useful for the intelligent operation and maintenance command and management of the entire hospital process, fill the missing values and correct the abnormal values. If the identified missing values and abnormal values are useless for the intelligent operation and maintenance command and management of the entire hospital process, delete the missing values and abnormal values.

[0033] In this embodiment, the preprocessing of the multi-source data of the hospital further includes: Normalize the multi-source data of the hospital, convert the multi-source data of the hospital into a unified data format, remove the dimension differences in the multi-source data of the hospital, and determine the standardized multi-source data of the hospital; Extract features from the multi-source data of the hospital, extract the feature vectors useful for the intelligent operation and maintenance command and management of the entire hospital process from the multi-source data of the hospital, and perform weighted fusion on the feature vectors to determine the hospital feature data.

[0034] In this embodiment, the pre-diagnosis and preventive maintenance of the hospital equipment failures include: According to the requirements of the intelligent operation and maintenance command and management of the entire hospital process based on the BIM-IoT collaboration, collect the historical data of the hospital, and divide the collected historical data of the hospital to determine the training set and the test set; Train the deep learning model according to the training set, enabling the deep learning model to autonomously learn the pre-diagnosis behavior of the hospital equipment failures and determine the pre-diagnosis model of the hospital equipment failures; Test the hospital equipment fault pre-diagnosis model according to the test set, evaluate the performance of the hospital equipment fault pre-diagnosis model, and determine whether the hospital equipment fault pre-diagnosis model can achieve the effect of pre-diagnosing hospital equipment faults; Adjust and optimize the parameters of the hospital equipment fault pre-diagnosis model according to the test evaluation results to determine the best hospital equipment fault pre-diagnosis model; Deploy the best hospital equipment fault pre-diagnosis model to the actual hospital equipment fault pre-diagnosis environment; Input the hospital feature data into the hospital equipment fault pre-diagnosis model, analyze the hospital feature data according to the hospital equipment fault pre-diagnosis model, pre-diagnose the hospital equipment faults, determine the hospital equipment fault pre-diagnosis results, and perform preventive maintenance on the hospital equipment faults according to the hospital equipment fault pre-diagnosis results.

[0035] Among them, the intelligent operation and maintenance command and management module is used for automatic order dispatching and progress tracking, automatically managing the operation and maintenance activities. For emergencies, quickly start the emergency plan and quickly dispatch the emergency resources; In this embodiment, automatic order dispatching and progress tracking, automatically managing the operation and maintenance activities. For emergencies, quickly start the emergency plan and quickly dispatch the emergency resources, including: Determine the hospital equipment fault operation and maintenance personnel according to the hospital equipment fault cause, automatically dispatch orders to the hospital equipment fault operation and maintenance personnel, and intelligently guide the hospital equipment fault operation and maintenance personnel to the hospital equipment fault location for maintenance based on intelligent navigation; And track the progress of the hospital equipment fault preventive maintenance in real time, standardize the daily operation and maintenance work processes, and automatically manage the operation and maintenance activities of reporting, inspection, and maintenance of hospital equipment faults; For hospital emergencies, give early warnings in a timely manner, quickly start the emergency plan, guide the on-site personnel to respond quickly, and quickly dispatch the emergency resources.

[0036] Among them, the visual display and interaction module is used to provide a friendly interaction interface for mastering the hospital operation and maintenance status at any time and anywhere.

[0037] In this embodiment, providing a friendly interaction interface for mastering the hospital operation and maintenance status at any time and anywhere, including: According to the hospital equipment fault pre-diagnosis results and combined with the hospital equipment fault preventive maintenance plan, form a hospital full-process intelligent operation and maintenance command and management report, and display it in a visual form on the mobile terminal to hospital managers, operation and maintenance personnel, and medical staff for mastering the hospital operation and maintenance status at any time and anywhere.

[0038] It should be noted that, in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0039] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A hospital full-process intelligent operation and maintenance command management system based on BIM and Internet of Things collaboration, characterized in that Including: A three-dimensional visualization management module, configured to establish a three-dimensional model of the hospital building, and perform visual display and management on the hospital building; A real-time monitoring and data collection module, configured to perform real-time monitoring on the environmental parameters, equipment operation status, and energy consumption of the hospital, and collect multi-source data of the hospital; Set corresponding weight values for the environmental parameters, equipment operation status, and energy consumption of the hospital, which are used for subsequent weighted fusion of the multi-source data of the hospital; A preprocessing and analysis module, configured to preprocess and analyze the multi-source data of the hospital, and perform pre-diagnosis and preventive maintenance on hospital equipment failures; An intelligent operation and maintenance command management module, configured to automatically assign orders and track progress, automate the management of operation and maintenance activities, and for emergencies, quickly start the emergency plan and rapidly dispatch emergency resources; A visual display and interaction module, configured to provide a friendly interaction interface for grasping the hospital operation and maintenance status at any time and anywhere.

2. The hospital full-process intelligent operation and maintenance command management system based on BIM and IoT collaboration according to claim 1, characterized in that Establish a three-dimensional model of the hospital building, and perform visual display and management on the hospital building, including: Collect the building structure, spatial layout, and equipment installation information of the hospital based on Internet of Things devices; Based on BIM technology, establish a three-dimensional model of the hospital building according to the building structure, spatial layout, and equipment installation information of the hospital, and integrate the facility and equipment information; Perform visual display and management on the building structure, spatial layout, and equipment installation information of the hospital according to the three-dimensional model of the hospital building, view the type, area, and status information of each room in the hospital in real time, and allocate hospital resources and optimize space.

3. The hospital full-process intelligent operation and maintenance command management system based on BIM-IoT collaboration according to claim 1, wherein, Perform real-time monitoring on the environmental parameters, equipment operation status, and energy consumption of the hospital, and collect multi-source data of the hospital, including: Perform real-time monitoring and collection on the temperature, humidity, air quality, pressure, and flow conditions in the hospital based on Internet of Things devices to determine the environmental parameters of the hospital; Perform real-time monitoring and collection on the on / off state, operation parameters, equipment location, operation sound, and vibration conditions of the equipment in the hospital based on Internet of Things devices to determine the equipment operation status of the hospital; Perform real-time monitoring and collection on the power consumption, water resource consumption, gas consumption, and heat energy consumption in the hospital based on Internet of Things devices to determine the energy consumption data of the hospital; Determine the multi-source data of the hospital based on Internet of Things devices according to the environmental parameters, equipment operation status, and energy consumption data of the hospital.

4. The hospital full-process intelligent operation and maintenance command management system based on BIM and Internet of Things collaboration according to claim 3, characterized in that, Set corresponding weight values for the environmental parameters, equipment operation status, and energy consumption of the hospital, including: Take the corresponding parameters of the environmental parameters, equipment operation status, and energy consumption as the types of data items; Extract the number and types of data types included in each data item type; Detect the correlation coefficients between the various data types included in each data item type; Obtain the average correlation coefficient corresponding to each data type according to the correlation coefficients between each data type in each data item type and other data types in the same data item type; Extract the average intermediate value of the correlation coefficients corresponding to each data type contained in the environmental parameters, and process the average intermediate value of the correlation coefficients using the Sigmoid function to generate the weight values corresponding to the environmental parameters; Take the temperature, humidity, and air quality in the environmental parameters as the target data types; Extract the data change rates of each data type contained in other data item categories when the target data type in the environmental parameters changes by a preset unit data volume; wherein, the preset unit data volumes corresponding to each data type are as follows: The preset unit data volume corresponding to temperature is 3°C; The preset unit data volume corresponding to humidity is 5%; The preset unit data volume corresponding to air quality is 0.1% of the standard concentration of substances contained in air quality monitoring; Obtain the weight values corresponding to other data item categories based on the data change rates of each data type contained in other data item categories when the target data types in the environmental parameters all change by a preset unit data volume and the average correlation coefficients corresponding to each data type contained in other data item categories.

5. The hospital full-process intelligent operation and maintenance command management system based on BIM and IoT collaboration according to claim 4, wherein, Obtain the weight values corresponding to other data item categories based on the data change rates of each data type contained in other data item categories when the target data types in the environmental parameters all change by a preset unit data volume and the average correlation coefficients corresponding to each data type contained in other data item categories, including: Extract the data change rates of each data type contained in other data item categories when the target data types in the environmental parameters all change by a preset unit data volume; Compare the data change rates of each data type contained in other data item categories with a preset change rate reference value; Screen out the data types corresponding to the data change rates in other data item categories that are not lower than the preset change rate reference value as the reference data types; Extract the average correlation coefficients corresponding to the reference data types in the other data item categories, and obtain the total average correlation coefficient using the average correlation coefficients corresponding to the reference data types in the other data item categories; Obtain the weight values corresponding to other data item categories by combining the data change rates of each data type contained in other data item categories when the target data types in the environmental parameters all change by a preset unit data volume with the total average correlation coefficient.

6. The hospital full-process intelligent operation and maintenance command and management system based on BIM and Internet of Things collaboration according to claim 1, characterized in that, Perform preprocessing on the hospital multi-source data, including: Clean the hospital multi-source data, removing the noise data and duplicate data that are useless for the intelligent operation and maintenance command and management of the entire hospital process; Clean the hospital multi-source data, identify the missing values and outliers in the hospital multi-source data, and check the identified missing values and outliers in the hospital multi-source data to determine whether the identified missing values and outliers are useful for the intelligent operation and maintenance command and management of the entire hospital process; If the identified missing values and outliers are useful for the intelligent operation and maintenance command and management of the hospital's entire process, fill in the missing values and correct the outliers. If the identified missing values and outliers are useless for the intelligent operation and maintenance command and management of the hospital's entire process, delete the missing values and outliers.

7. The hospital full-process intelligent operation and maintenance command management system based on BIM and IoT collaboration according to claim 4, characterized in that, The preprocessing of the hospital's multi-source data also includes: Normalize the hospital's multi-source data to convert the hospital's multi-source data into a unified data format, remove the dimensional differences in the hospital's multi-source data, and determine the standardized hospital multi-source data; Extract features from the hospital's multi-source data, extract the feature vectors useful for the intelligent operation and maintenance command and management of the hospital's entire process from the hospital's multi-source data, and perform weighted fusion on the feature vectors to determine the hospital's feature data.

8. The hospital full-process intelligent operation and maintenance command management system based on BIM and Internet of Things collaboration according to claim 7, characterized in that, Perform pre-diagnosis and preventive maintenance on hospital equipment failures, including: According to the requirements of the intelligent operation and maintenance command and management of the hospital's entire process based on BIM-IoT collaboration, collect the hospital's historical data, and divide the collected hospital's historical data to determine the training set and the test set; Train the deep learning model according to the training set to enable the deep learning model to autonomously learn the pre-diagnosis behavior of hospital equipment failures and determine the pre-diagnosis model of hospital equipment failures; Test the pre-diagnosis model of hospital equipment failures according to the test set, evaluate the performance of the pre-diagnosis model of hospital equipment failures, and determine whether the pre-diagnosis model of hospital equipment failures can achieve the effect of pre-diagnosing hospital equipment failures; Adjust and optimize the parameters of the pre-diagnosis model of hospital equipment failures according to the test evaluation results to determine the best pre-diagnosis model of hospital equipment failures; Deploy the best pre-diagnosis model of hospital equipment failures and deploy the best pre-diagnosis model of hospital equipment failures in the actual pre-diagnosis environment of hospital equipment failures; Input the hospital's feature data into the pre-diagnosis model of hospital equipment failures, analyze the hospital's feature data according to the pre-diagnosis model of hospital equipment failures, pre-diagnose the hospital equipment failures, determine the pre-diagnosis results of hospital equipment failures, and perform preventive maintenance on the hospital equipment failures according to the pre-diagnosis results of hospital equipment failures.

9. The hospital full-process intelligent operation and maintenance command management system based on BIM-IoT collaboration according to claim 8, characterized in that Automatically dispatch orders and track progress, automate the management of operation and maintenance activities. For emergencies, quickly activate the emergency plan and quickly dispatch emergency resources, including: Determine the operation and maintenance personnel for hospital equipment failures according to the causes of hospital equipment failures, automatically dispatch orders to the operation and maintenance personnel for hospital equipment failures, and intelligently guide the operation and maintenance personnel for hospital equipment failures to the location of hospital equipment failures for maintenance based on intelligent navigation; And track the progress of preventive maintenance of hospital equipment failures in real time, standardize the daily operation and maintenance work process, and automate the management of operation and maintenance activities such as repair, inspection, and maintenance of hospital equipment failures; For hospital emergencies, give early warnings in a timely manner, quickly activate the emergency plan, guide on-site personnel to respond quickly, and quickly dispatch emergency resources.

10. The hospital full-process intelligent operation and maintenance command management system based on BIM and Internet of Things collaboration according to claim 9, characterized in that, Provide a friendly interaction interface for grasping the hospital's operation and maintenance status at any time, including: Based on the pre-diagnosis results of hospital equipment failures and combined with the preventive maintenance plan for hospital equipment failures, an intelligent operation and maintenance command management report for the entire hospital process is formed and presented to hospital administrators, operation and maintenance personnel, and medical staff in a visual form on the mobile device, so as to grasp the operation and maintenance status of the hospital at any time and anywhere.

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