A hospital equipment budget management method, system and medium based on holographic view

Abstract

The present invention relates to the field of hospital management technology, and in particular to a hospital equipment budget management method, system and medium based on a holographic view. The method comprises the following steps: obtaining hospital equipment location information data; connecting sensor data based on the hospital equipment location information data to generate a hospital equipment sensor network; wirelessly collecting sensor data from the hospital equipment sensor network to obtain a standard equipment collection data set; constructing a digital archive of the standard equipment collection data set to generate a device digital information archive; screening the hospital equipment's equipment life cycle data to obtain equipment life cycle screening data; performing multi-dimensional space data mapping based on the equipment life cycle screening data to generate a holographic view of the equipment. The present invention improves the comprehensiveness and effectiveness of hospital equipment budget management through real-time data collection, full life cycle monitoring, fault risk prediction and dynamic budget decision-making.

Description

Technical Field

[0001] The present invention relates to the field of hospital management technology, and in particular to a hospital equipment budget management method, system and medium based on holographic view. Background Art

[0002] Initially, hospital equipment procurement mainly relied on manual records and paper documents, which was not only inefficient but also prone to information asymmetry and data omissions. With the advancement of information technology, spreadsheets and database systems began to be used in equipment management, improving the efficiency of data storage and query. Entering the 21st century, hospital management has gradually transformed towards intelligence and data. The application of cloud computing and big data technologies enables hospitals to better analyze equipment usage and budget needs. However, traditional budget management still lacks intuitiveness and is difficult to achieve real-time monitoring and dynamic adjustment. In recent years, the rise of holographic view technology has provided a new solution for hospital equipment budget management. Holographic view can present complex data in a three-dimensional visualization, allowing managers to intuitively view equipment usage, maintenance history and budget allocation. However, the current traditional existing technologies often only focus on the single use stage of the equipment, ignoring the full life cycle management of the equipment, and often fail to effectively predict the risk of equipment failure, resulting in unreasonable budget allocation, which in turn leads to low comprehensiveness and effectiveness of hospital equipment budget management. Summary of the invention

[0003] Based on this, it is necessary to provide a hospital equipment budget management method, system and medium based on holographic view to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a hospital equipment budget management method based on holographic view is provided, the method comprising the following steps:

[0005] Step S1: Acquire hospital equipment location information data; connect sensor data based on the hospital equipment location information data to generate a hospital equipment sensor network; wirelessly collect sensor data from the hospital equipment sensor network to obtain a standard equipment collection data set; construct a digital archive for the standard equipment collection data set to generate a device digital information archive;

[0006] Step S2: Screening the equipment life cycle data of hospital equipment to obtain equipment life cycle screening data; performing multi-dimensional space data mapping based on the equipment life cycle screening data to generate a holographic view of the equipment; performing holographic resource allocation simulation on the equipment holographic view to generate equipment budget allocation optimization data; performing periodic holographic view updates on the equipment holographic view based on the equipment budget allocation optimization data to generate a holographic budget view of the equipment;

[0007] Step S3: collecting historical fault data of the equipment holographic budget view to obtain equipment historical fault data; predicting equipment failure risk of the equipment holographic budget view based on the equipment historical fault data to generate equipment failure risk prediction data; estimating maintenance cost of the equipment holographic budget view based on the equipment failure risk prediction data to obtain equipment risk maintenance cost data;

[0008] Step S4: Perform real-time budget warning on the equipment holographic budget view based on the equipment risk maintenance cost data to generate equipment budget warning data; perform comprehensive budget decision on the equipment holographic budget view by identifying the budget warning data, thereby generating an equipment budget decision plan to execute the hospital equipment budget management optimization task.

[0009] The present invention provides a basis for subsequent sensor layout and equipment management through accurate location information, and can improve the traceability and management efficiency of the equipment. After the sensor network is established, real-time monitoring and data collection of the equipment status can be realized, thereby improving the intelligent level of equipment management. Wireless data collection simplifies the physical layout of equipment monitoring, reduces maintenance costs, and improves the real-time and accuracy of data. By building a digital information archive of the equipment, centralized management of equipment information can be achieved, which is convenient for query and analysis at any time and improves management efficiency. By screening the data of the entire life cycle of the equipment, the use status of the equipment can be clearly understood, and the reasonable management and maintenance of the equipment can be promoted. The generation of a holographic view can intuitively display the operating status of the equipment, which is convenient for managers to quickly identify equipment problems and optimize management strategies. By simulating resource allocation, the utilization efficiency and budget allocation of the equipment can be optimized, helping hospitals to achieve the optimal configuration of resources. The regularly updated holographic budget view can reflect the changes in the equipment status, help managers adjust decisions in time, and improve the flexibility and response speed of management. The collection of historical data provides a basis for the trend analysis of equipment failures and helps identify common failures and their causes. Accurate failure risk prediction can help hospitals identify potential problems in advance and reduce the impact and maintenance costs caused by equipment failures. Accurate estimation of maintenance costs provides hospitals with a reasonable budget basis, ensures the effective use of resources, and improves the scientific nature of budget management. By real-time monitoring of maintenance costs, the risk of budget overruns can be discovered in a timely manner to ensure that equipment management is carried out within the budget. The generation of a comprehensive budget decision-making plan enables managers to formulate more reasonable budget allocation and usage strategies based on the actual use of equipment and forecast data. Through budget management optimization, hospitals can allocate resources more effectively, improve equipment utilization efficiency, and reduce overall operating costs, thereby improving the economic benefits of the hospital. Therefore, the present invention improves the comprehensiveness and effectiveness of hospital equipment budget management through real-time data collection, full life cycle monitoring, fault risk prediction, and dynamic budget decision-making.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire hospital equipment location information data;

[0012] Step S12: Deploy sensors based on the hospital equipment location information data to obtain hospital equipment sensor deployment data; perform sensor data connection on the hospital equipment sensor deployment data to generate a hospital equipment sensor network;

[0013] Step S13: wirelessly collect sensor data from the hospital equipment sensor network based on a preset data collection frequency to obtain an equipment collection data set; perform data preprocessing on the equipment collection data set to generate a standard equipment collection data set, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization;

[0014] Step S14: construct a digital archive of the standard equipment acquisition data set to generate a digital information archive of the equipment.

[0015] The present invention provides a basis for subsequent sensor deployment by accurately acquiring device location information, making the spatial layout of data collection more reasonable. It is helpful to realize the visualization and real-time monitoring of device location, and optimize the resource management and scheduling efficiency of the hospital. By reasonably deploying sensors, the comprehensiveness and accuracy of data collection are ensured, blind spots are reduced, and data quality is improved. The formed sensor network can realize data interconnection between devices, improve the ability of data sharing and collaborative work, and provide support for real-time monitoring and analysis. Wireless data collection improves the flexibility and convenience of data collection, reduces labor costs, and can reflect the working status of the equipment in real time. The implementation of data preprocessing ensures the accuracy and integrity of the data, reduces errors in subsequent analysis, and improves the reliability of data analysis. The construction of digital archives realizes the centralized management and storage of equipment information, which is convenient for subsequent data query and utilization. The generation of equipment digital information archives provides an important basis for the life cycle management, maintenance and fault prediction of equipment, and improves the scientificity and effectiveness of hospital equipment management. Through the implementation of step S1, the hospital has established a systematic and standardized process in equipment management. Real-time wireless data collection combined with precise preprocessing makes the equipment operation status visualized, providing data support for decision-making. At the same time, the construction of digital archives also facilitates the hospital's equipment management and improves the efficiency and safety of equipment use. This data-driven management model will greatly improve the hospital's operational efficiency and promote the process of digital transformation.

[0016] Preferably, step S14 includes the following steps:

[0017] Step S141: constructing a data table for the standard equipment collection data set to generate a digital archive data table, wherein the digital archive data table includes an equipment basic information table, an operation status table, a fault record table, and a maintenance record table;

[0018] Step S142: Establishing an equipment number index for the standard equipment collection data set to generate an equipment number index; archiving and storing the standard equipment collection data set according to the equipment basic information table, operation status table, fault record table, and maintenance record table according to the equipment number index to generate an initial equipment digital information file;

[0019] Step S143: Dynamically monitor the data changes of the initial device digital information file to generate device dynamic change monitoring data; automatically update the data of the initial device digital information file according to the device dynamic change monitoring data, thereby generating a device digital information file.

[0020] The present invention enhances the structuring of data by systematizing the equipment information into multiple tables, so that different types of information (such as equipment status, fault history, maintenance records, etc.) can be clearly classified and stored. The existence of various data tables makes subsequent data query, analysis and decision-making more efficient, and facilitates the comprehensive evaluation and management of equipment. The establishment of the equipment number index realizes the rapid retrieval of data and improves the efficiency of finding specific equipment information. Through data archiving and storage, the security and persistence of equipment information are ensured, data loss is avoided, and a complete historical record is provided for future query and maintenance. Dynamic monitoring ensures the real-time nature of equipment status information, so that equipment management can respond to changes in a timely manner and reduce the risk of failure. The automatic data update mechanism improves the intelligent level of data management, reduces manual intervention, improves management efficiency, and ensures the accuracy and timeliness of data. Through the implementation of step S14, the hospital can efficiently manage equipment information and build a comprehensive, dynamic and structured equipment digital archive. This archive can not only reflect the operating status, fault conditions and maintenance history of the equipment in a timely manner, but also ensure the real-time and accuracy of the information through dynamic monitoring and automatic update mechanisms. Overall, this systematic file management method improves the utilization efficiency of equipment, reduces operation and maintenance costs, and provides strong support for the hospital's equipment management.

[0021] Preferably, step S2 comprises the following steps:

[0022] Step S21: define the stages of the entire life cycle of hospital equipment and generate equipment life cycle stage data, wherein the equipment life cycle stage data includes the purchase stage, the use stage, the maintenance stage, the aging stage and the replacement stage; screen the life cycle data of the equipment digital information archive based on the purchase stage, the use stage, the maintenance stage, the aging stage and the replacement stage to obtain equipment life cycle screening data;

[0023] Step S22: performing multi-dimensional spatial data mapping on the equipment life cycle stage data according to the equipment life cycle screening data to generate equipment life cycle spatial mapping data points; visualizing the equipment status of the equipment life cycle spatial mapping data points to generate a holographic view of the equipment;

[0024] Step S23: performing equipment budget consumption analysis on the equipment holographic view to generate equipment status budget consumption data; performing holographic resource allocation simulation on the equipment holographic view according to the equipment status budget consumption data to generate equipment budget allocation optimization data;

[0025] Step S24: periodically updating the device holographic view according to the device budget allocation optimization data, thereby generating a device holographic budget view.

[0026] The present invention enables equipment management to be more targeted through clear life cycle stage definitions, and facilitates the formulation of corresponding management strategies at different stages. Through life cycle data screening, the performance of the equipment at each stage can be deeply analyzed, potential problems and risks can be identified, and more effective maintenance and management measures can be implemented. Multidimensional spatial data mapping provides a comprehensive understanding of the equipment status, which helps to observe equipment performance in different dimensions, identify trends and anomalies. The generation of a holographic view of the equipment enables managers to grasp the equipment status in real time, provide visual support for decision-making, and improve management efficiency and accuracy. Budget consumption analysis can help hospitals reasonably evaluate the cost of equipment use, optimize resource allocation, and reduce unnecessary expenses. Through resource allocation simulation, hospitals can allocate budgets more effectively to ensure that the needs of equipment during use are met, thereby improving equipment utilization and service quality. Periodic updates ensure the real-time nature of equipment management information and help managers respond quickly to changes in equipment status. The holographic budget view of the equipment provides hospitals with a dynamic budget monitoring tool, which helps to formulate long-term equipment investment and management strategies and improve resource utilization efficiency. Through the implementation of step S2, the hospital can achieve comprehensive management of the entire life cycle of the equipment, systematically analyze equipment status and budget consumption, and optimize resource allocation. This move not only improves the scientificity and flexibility of equipment management, but also provides strong support for the hospital's financial planning and resource allocation. Overall, this data-driven management model helps improve the efficiency of equipment use and reduce operating costs.

[0027] Preferably, step S22 includes the following steps:

[0028] Step S221: confirming the key data indicators of the equipment life cycle stage data according to the equipment life cycle screening data to obtain the key data indicators of the equipment life cycle stage;

[0029] Step S222: Establish a multidimensional coordinate system for key data indicators of the equipment life cycle stage based on the Cartesian three-dimensional space coordinate system to generate a multidimensional space coordinate axis; map the equipment digital information archive to the multidimensional space coordinate axis to discretize the data points and generate equipment life cycle space mapping data points;

[0030] Step S223: performing mean division on the multidimensional space coordinate axis by using a preset index threshold to generate a mean division coordinate axis; performing adjacent coordinate axis space region division on the mean division coordinate axis to generate a device normal state space region and a device abnormal state space region;

[0031] Step S224: Visualize the data points of the equipment life cycle space mapping according to the equipment normal state space area and the equipment abnormal state space area to generate a holographic view of the equipment.

[0032] The present invention helps managers focus on the key links of the equipment life cycle by determining key data indicators, which is convenient for targeted management and decision-making. By confirming the key data indicators of the stage, the performance and status of the equipment can be quantified, providing a data basis for subsequent analysis. The establishment of a multidimensional coordinate system enables equipment data to be comprehensively analyzed in multiple dimensions, which is convenient for revealing the complexity of the equipment status. After the data points are discretized, the performance of the equipment in different states can be more clearly identified, providing a basis for subsequent data visualization and analysis. Mean partitioning can help managers quickly identify the normal and abnormal states of equipment operation and enhance the monitoring ability of equipment operation status. Through the segmentation of spatial regions, the operating status of the equipment can be clearly divided, providing support for subsequent abnormal detection and fault warning. Visualization not only enhances the intuitiveness of the data, but also helps managers quickly understand the operating status and potential problems of the equipment and promote timely decision-making. The holographic view of the equipment provides a full range of equipment status display, which is helpful for resource allocation, maintenance planning and fault prevention. Through the implementation of step S22, the hospital can achieve accurate analysis and effective management of the equipment life cycle stage. By confirming key data indicators, establishing multi-dimensional space, dividing and visualizing status, managers can quickly identify the normal and abnormal status of equipment and formulate corresponding management measures based on this. Overall, this process improves the scientificity and flexibility of equipment management, helps reduce the occurrence of failures, and improves the efficiency of equipment use, thereby providing hospitals with better service quality.

[0033] Preferably, step S23 includes the following steps:

[0034] Step S231: Screening the device status of the device holographic view to obtain abnormal device screening data and normal device screening data; analyzing the service life of the normal device screening data to generate normal device service life data; calculating the depreciation rate of the normal device screening data according to the normal device service life data to obtain normal device depreciation rate data;

[0035] Step S232: performing abnormal device performance analysis on the abnormal device screening data to generate abnormal device performance analysis data; performing line load calculation on the abnormal device screening data based on the abnormal device performance data to obtain abnormal device line load data;

[0036] Step S233: Perform line path connection analysis on the normal equipment screening data and the abnormal equipment screening data to generate normal equipment connection path data; perform equipment power consumption calculation on the normal equipment screening data according to the abnormal equipment line load data to obtain normal equipment power impact data;

[0037] Step S234: performing normal equipment budget consumption analysis on normal equipment depreciation rate data using normal equipment power impact data to generate normal equipment status budget consumption data; performing abnormal equipment budget consumption analysis on abnormal equipment performance analysis data using abnormal equipment line load data to generate abnormal equipment status budget consumption data;

[0038] Step S235: Integrate the normal device state budget consumption data and the abnormal device state budget consumption data to generate device state budget consumption data; perform holographic resource allocation simulation on the device holographic view according to the device state budget consumption data to generate device budget allocation optimization data.

[0039] The present invention can quickly identify abnormal and normal equipment through state screening, which is helpful to focus on monitoring and managing high-risk equipment and ensure the normal operation of the equipment. The service life analysis provides a basis for subsequent equipment maintenance and replacement decisions, avoiding failures caused by excessive use. The depreciation rate calculation can provide a reference for hospital financial management, which is helpful to evaluate the value loss of equipment and make reasonable financial arrangements. Through the performance analysis of abnormal equipment, the cause of equipment failure can be deeply understood, targeted maintenance measures can be taken, and the failure rate can be reduced. Line load calculation can help determine whether the equipment is overloaded, avoid equipment damage caused by excessive load, and ensure the safe operation of the equipment. Line path connection analysis provides the correlation between equipment, promotes the collaborative work between equipment, and improves the overall operation efficiency. Power consumption calculation can clarify the energy consumption status of normal equipment, facilitate energy management, and reduce unnecessary expenses. Budget consumption analysis can help management understand the equipment operation cost, facilitate the formulation of a reasonable budget allocation strategy, and control the hospital operation cost. Budget consumption analysis for abnormal equipment provides a basis for subsequent maintenance and replacement decisions, ensuring the rational use of resources. Data integration provides a comprehensive budget consumption perspective, which is convenient for hospitals to optimize resource allocation and improve resource utilization efficiency. Resource allocation simulation provides management with a scientific basis for decision-making, ensuring that equipment maintenance and procurement meet actual needs and improving the overall operational efficiency of the hospital. Through the implementation of step S23, the hospital can achieve comprehensive monitoring of equipment status and fine management of budgets. Through equipment status screening, performance analysis, power consumption calculation and budget optimization, managers can promptly identify problems, reasonably allocate resources, and formulate effective maintenance and procurement strategies. This not only reduces the risk of equipment failure, but also improves equipment utilization efficiency and the overall operational benefits of the hospital.

[0040] Preferably, performing holographic resource allocation simulation on the device holographic view according to the device state budget consumption data comprises:

[0041] Perform time-series consumption analysis on the equipment holographic view according to the equipment status budget consumption data to generate equipment status budget time-series consumption data; perform linear programming resource allocation on the equipment status budget time-series consumption data to generate equipment status budget resource allocation simulation data;

[0042] The budget allocation ratio of the equipment status budget resource allocation simulation data is identified to obtain budget allocation ratio identification data; the extreme value allocation ratio of the budget allocation ratio identification data is acquired, and the equipment holographic view is marked as a high-consumption device based on the acquired extreme value allocation ratio to obtain high-consumption device marking data;

[0043] The cost minimization optimization target is confirmed for the high-consumption equipment marking data to obtain the high-consumption equipment optimization target data; the budget allocation ratio identification data is allocated iteratively according to the high-consumption equipment optimization target data to generate equipment budget allocation optimization data.

[0044] The present invention can reveal the energy consumption trend of the equipment through time-series consumption analysis, and help the management understand the operating efficiency of the equipment in different time periods. By analyzing the time-series consumption data of the equipment, the peak period of consumption can be found, providing a basis for subsequent resource allocation and optimizing the equipment use plan. The linear programming method can achieve optimal resource allocation, so that resource allocation meets budget constraints and actual needs, and avoids resource waste. Through scientific resource allocation, the effective operation of the equipment is ensured, the operating cost is reduced, and the economic benefit of the equipment is improved. The identification of the budget allocation ratio helps managers to clearly understand the budget allocation of different equipment and provide data support for subsequent optimization. By analyzing the allocation ratio, unreasonable resource allocation can be found and adjusted in time. The identification of the extreme value allocation ratio helps to determine which equipment has excessive consumption and carry out targeted management. The marking of high-consumption equipment can attract the attention of managers and facilitate the priority treatment of existing faults or efficiency problems. Confirming the optimization target provides a clear direction for the improvement of high-consumption equipment, enabling the management to formulate practical optimization measures and reduce the overall equipment operating cost. By setting the goal of minimizing costs, the efficiency of resource use can be effectively improved and the economic benefit of the equipment can be improved. Through iterative allocation, the budget allocation data is optimized, so that the resource allocation is more reasonable and the equipment utilization efficiency is improved. Continuous adjustments and optimizations during the iteration process ensure the flexible use of resources and adapt to changes in equipment status and demand. Holographic resource allocation simulation ensures efficient use and reasonable allocation of equipment resources through time-series consumption analysis, linear programming, budget ratio identification and cost optimization. This process can not only reduce the operating cost of equipment and improve economic benefits, but also help management to monitor equipment status in real time, make scientific management decisions, and improve the overall efficiency and quality of hospital equipment management.

[0045] Preferably, step S3 comprises the following steps:

[0046] Step S31: collecting historical fault data of the equipment holographic budget view to obtain historical fault data of the equipment;

[0047] Step S32: Divide the equipment historical fault data into data sets to generate a model training set and a model test set; use a long short-term memory neural network algorithm to train the model training set to generate an equipment risk prediction pre-model; perform model optimization iteration on the equipment risk prediction pre-model through the model test set to generate an equipment risk prediction model;

[0048] Step S33: Import the equipment holographic budget view into the equipment risk prediction model to predict equipment failure risk and generate equipment failure risk prediction data; estimate the maintenance cost of the equipment holographic budget view based on the equipment failure risk prediction data to obtain equipment risk maintenance cost data.

[0049] The present invention provides an important basis for subsequent analysis and model building by collecting historical fault data, and can more comprehensively understand the failure mode and law of the equipment. By analyzing historical data, potential fault hazards and laws can be discovered, providing data support for equipment maintenance decisions. Data set division ensures the effectiveness of model training and testing, avoids overfitting, and improves the generalization ability of the model. The long short-term memory neural network algorithm can effectively capture the long-term dependence in time series data and improve the accuracy of fault prediction. Model optimization iteration ensures the accuracy and reliability of the prediction model. By continuously adjusting the model parameters, the prediction ability of the model in different situations can be improved. The optimized model can better adapt to the actual operating status of the equipment and improve the accuracy and timeliness of equipment failure risk prediction. Fault risk prediction can identify potential equipment failures in a timely manner, help managers take measures in advance, and reduce the losses caused by sudden failures. By real-time monitoring of equipment failure risks, the operation and maintenance strategy of the equipment can be optimized, and the overall reliability and service life of the equipment can be improved. Maintenance cost estimation can help management to reasonably plan maintenance budgets, optimize resource allocation, and avoid excessive expenditures. Accurate maintenance cost data provides a basis for formulating more efficient equipment management policies, which helps to improve the operational efficiency and economic benefits of hospitals.

[0050] In this specification, a hospital equipment budget management system based on a holographic view is provided, which is used to execute the above-mentioned hospital equipment budget management method based on a holographic view. The hospital equipment budget management system based on a holographic view includes:

[0051] A digital archive construction module is used to obtain hospital equipment location information data; connect sensor data based on the hospital equipment location information data to generate a hospital equipment sensor network; wirelessly collect sensor data from the hospital equipment sensor network to obtain a standard equipment collection data set; construct a digital archive for the standard equipment collection data set to generate a device digital information archive;

[0052] The holographic budget simulation module is used to screen the data of the entire life cycle of hospital equipment to obtain equipment life cycle screening data; perform multi-dimensional space data mapping based on the equipment life cycle screening data to generate a holographic view of the equipment; perform holographic resource allocation simulation on the equipment holographic view to generate equipment budget allocation optimization data; perform periodic updates on the equipment holographic view based on the equipment budget allocation optimization data to generate a holographic budget view of the equipment;

[0053] The fault risk prediction module is used to collect historical fault data of the equipment holographic budget view to obtain the equipment historical fault data; to predict the equipment fault risk of the equipment holographic budget view through the equipment historical fault data to generate the equipment fault risk prediction data; to estimate the maintenance cost of the equipment holographic budget view based on the equipment fault risk prediction data to obtain the equipment risk maintenance cost data;

[0054] The budget management decision module is used to make real-time budget warnings on the equipment holographic budget view based on the equipment risk maintenance cost data and generate equipment budget warning data; by identifying the budget warning data, a comprehensive budget decision is made on the equipment holographic budget view, thereby generating an equipment budget decision plan to execute the hospital equipment budget management optimization operation.

[0055] The beneficial effect of the present invention is that through sensor network connection and wireless data collection, real-time monitoring of equipment location information is achieved, and the accuracy of equipment management is improved. The construction of digital archives provides reliable basic data for subsequent data analysis and decision-making, and promotes the systematization and standardization of data. The screening of equipment life cycle data helps to fully understand the use status and historical background of the equipment, ensuring that all important factors are taken into account when allocating the budget. The generated holographic view provides an intuitive equipment resource allocation simulation, so that managers can optimize the budget more scientifically, and improve the efficiency and effectiveness of budget management. By collecting historical failure data of the equipment, common failure modes of the equipment can be identified, so as to make effective failure risk prediction. This forward-looking management method can help hospitals prevent potential problems, reduce equipment downtime, reduce maintenance costs, and ensure that the equipment operates in the best condition. Real-time budget warning based on risk maintenance cost data can timely identify and deal with budget overspending or potential risks, thereby ensuring the financial health of the hospital. The generation of a comprehensive budget decision-making plan ensures the scientificity and rationality of budget allocation, improves the transparency of equipment management and the basis for decision-making, and ultimately achieves the optimization of hospital equipment budget management. Therefore, the present invention improves the comprehensiveness and effectiveness of hospital equipment budget management through real-time data collection, full life cycle monitoring, failure risk prediction and dynamic budget decision-making.

[0056] A computer-readable storage medium stores a computer program, which, when executed, implements the above-mentioned hospital equipment budget management method based on holographic view. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of the steps of a hospital equipment budget management method based on a holographic view;

[0058] Figure 2 for Figure 1Detailed implementation steps of step S2 in the flowchart;

[0059] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0060] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0061] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0062] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0063] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0064] To achieve this, please refer to Figures 1 to 3 , a hospital equipment budget management method based on holographic view, the method comprising the following steps:

[0065] Step S1: Acquire hospital equipment location information data; connect sensor data based on the hospital equipment location information data to generate a hospital equipment sensor network; wirelessly collect sensor data from the hospital equipment sensor network to obtain a standard equipment collection data set; construct a digital archive for the standard equipment collection data set to generate a device digital information archive;

[0066] Step S2: Screening the equipment life cycle data of hospital equipment to obtain equipment life cycle screening data; performing multi-dimensional space data mapping based on the equipment life cycle screening data to generate a holographic view of the equipment; performing holographic resource allocation simulation on the equipment holographic view to generate equipment budget allocation optimization data; performing periodic holographic view updates on the equipment holographic view based on the equipment budget allocation optimization data to generate a holographic budget view of the equipment;

[0067] Step S3: collecting historical fault data of the equipment holographic budget view to obtain equipment historical fault data; predicting equipment failure risk of the equipment holographic budget view based on the equipment historical fault data to generate equipment failure risk prediction data; estimating maintenance cost of the equipment holographic budget view based on the equipment failure risk prediction data to obtain equipment risk maintenance cost data;

[0068] Step S4: Perform real-time budget warning on the equipment holographic budget view based on the equipment risk maintenance cost data to generate equipment budget warning data; perform comprehensive budget decision on the equipment holographic budget view by identifying the budget warning data, thereby generating an equipment budget decision plan to execute the hospital equipment budget management optimization task.

[0069] The present invention provides a basis for subsequent sensor layout and equipment management through accurate location information, and can improve the traceability and management efficiency of the equipment. After the sensor network is established, real-time monitoring and data collection of the equipment status can be realized, thereby improving the intelligent level of equipment management. Wireless data collection simplifies the physical layout of equipment monitoring, reduces maintenance costs, and improves the real-time and accuracy of data. By building a digital information archive of the equipment, centralized management of equipment information can be achieved, which is convenient for query and analysis at any time and improves management efficiency. By screening the data of the entire life cycle of the equipment, the use status of the equipment can be clearly understood, and the reasonable management and maintenance of the equipment can be promoted. The generation of a holographic view can intuitively display the operating status of the equipment, which is convenient for managers to quickly identify equipment problems and optimize management strategies. By simulating resource allocation, the utilization efficiency and budget allocation of the equipment can be optimized, helping hospitals to achieve the optimal configuration of resources. The regularly updated holographic budget view can reflect the changes in the equipment status, help managers adjust decisions in time, and improve the flexibility and response speed of management. The collection of historical data provides a basis for the trend analysis of equipment failures and helps identify common failures and their causes. Accurate failure risk prediction can help hospitals identify potential problems in advance and reduce the impact and maintenance costs caused by equipment failures. Accurate estimation of maintenance costs provides hospitals with a reasonable budget basis, ensures the effective use of resources, and improves the scientific nature of budget management. By real-time monitoring of maintenance costs, the risk of budget overruns can be discovered in a timely manner to ensure that equipment management is carried out within the budget. The generation of a comprehensive budget decision-making plan enables managers to formulate more reasonable budget allocation and usage strategies based on the actual use of equipment and forecast data. Through budget management optimization, hospitals can allocate resources more effectively, improve equipment utilization efficiency, and reduce overall operating costs, thereby improving the economic benefits of the hospital. Therefore, the present invention improves the comprehensiveness and effectiveness of hospital equipment budget management through real-time data collection, full life cycle monitoring, fault risk prediction, and dynamic budget decision-making.

[0070] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a hospital equipment budget management method based on a holographic view of the present invention. In this example, the hospital equipment budget management method based on a holographic view includes the following steps:

[0071] Step S1: Acquire hospital equipment location information data; connect sensor data based on the hospital equipment location information data to generate a hospital equipment sensor network; wirelessly collect sensor data from the hospital equipment sensor network to obtain a standard equipment collection data set; construct a digital archive for the standard equipment collection data set to generate a device digital information archive;

[0072] In an embodiment of the present invention, the location information of various types of equipment is collected by utilizing the hospital's internal location identification system (such as RFID, Bluetooth beacon, Wi-Fi positioning, etc.). The location information of the equipment is recorded, including specific location parameters such as floor, department, and room number. The collected location information data is formatted and unified into a structured format for subsequent processing. The standardized location information is stored in the device location information database to ensure that it is available for query and real-time update. A unique identification code is configured for each hospital device and registered in the sensor network to establish the digital identity of the device. Based on the location information data, a connection is established between the hospital device and the sensor through a wireless connection method (such as Wi-Fi, Bluetooth or Internet of Things protocol) to form a sensor network. Ensure that the sensor network covers all devices and that the data transmission is stable and low-latency. Perform a comprehensive test of the network connection to eliminate signal interference and unstable connection factors to ensure the real-time and continuity of sensor data transmission. When necessary, adjust the sensor layout or the position of the signal amplifier to enhance signal coverage. Start the sensor data acquisition module to regularly collect information such as the status, usage, and location changes of each device. Monitor the collected data stream in real time to ensure data integrity and accuracy. Format the collected data and convert all data into a standard data format for unified management. Perform data cleaning to remove duplicate, abnormal or erroneous data to ensure the standardization and validity of the data set. Store the standardized data on a centralized data platform to generate a standard equipment collection data set. This data set should include equipment status records, location changes, sensor performance data, etc. Gather the location information, status data, usage frequency, maintenance records and other information of each device to generate the initial archive data for each device. Establish a digital archive structure for each device, including fields such as basic equipment information, location information, sensor data history, maintenance records, etc. Enter the sorted archive data into the digital archive system to generate a digital information archive for the device. The archive of each device should have a real-time update function so that it can be dynamically updated according to the latest data collected in the sensor network.

[0073] Step S2: Screening the equipment life cycle data of hospital equipment to obtain equipment life cycle screening data; performing multi-dimensional space data mapping based on the equipment life cycle screening data to generate a holographic view of the equipment; performing holographic resource allocation simulation on the equipment holographic view to generate equipment budget allocation optimization data; performing periodic holographic view updates on the equipment holographic view based on the equipment budget allocation optimization data to generate a holographic budget view of the equipment;

[0074] In an embodiment of the present invention, the complete life cycle data related to the equipment is obtained, including the purchase date, usage frequency, maintenance record, failure history, expected life, depreciation, etc. of the equipment. All relevant data are classified and stored by using a data acquisition module or a database query system for subsequent processing. The collected data is screened to remove irrelevant or redundant information to ensure the accuracy and consistency of the screened data. The data is labeled according to the different states of the equipment (such as procurement, operation, maintenance, decommissioning, etc.) to obtain standardized equipment life cycle screening data. Key data features such as usage frequency, failure rate, maintenance cost, depreciation value, remaining life, etc. are extracted from the equipment life cycle screening data. Weights are assigned to the feature data of each device, and different features are prioritized according to the actual needs of the hospital and the importance of the equipment. The extracted equipment data is mapped to a multidimensional data space (such as time, cost, status, location, etc.) to construct a multidimensional space data model for each device. Similar devices are grouped through feature projection and data clustering analysis to centrally present the device status and needs in the holographic view. Generate a holographic view of the equipment from the data mapped in multi-dimensional space, and visualize the various status information and usage characteristics of the equipment through three-dimensional charts, heat maps or state maps. The view should be interactive in real time and support users to query, retrieve and dynamically view equipment information. Set resource allocation rules based on the frequency of use, failure risk and maintenance requirements of the equipment, such as allocating more resources to high-frequency and key equipment. Consider the hospital's annual budget and the priority of equipment maintenance and replacement, and set the resource budget ratio for each device. Simulate resource allocation on the holographic view to calculate the budget requirements and consumption of various types of equipment under different circumstances. Use simulation algorithms (such as Monte Carlo simulation or linear programming) to optimize resource allocation to reduce equipment resource waste and balance budget consumption. Convert the resource allocation simulation results into equipment budget allocation optimization data, and mark the budget priority, resource requirements and optimization allocation suggestions for each device. The optimization data should include adjustment suggestions and potential risks to provide managers with a scientific basis for resource allocation. Based on the equipment budget allocation optimization data, establish a periodic data update mechanism to regularly collect equipment status changes and new data. The update content includes equipment usage, maintenance records, budget consumption and life cycle stage changes. The holographic view is automatically refreshed based on the updated data, so that the equipment status and budget allocation are optimized and synchronized in real time. The resource requirements and budget allocation of the equipment are recalculated at each update to ensure that the holographic view reflects the current equipment status. The periodically updated equipment holographic view is further integrated to generate an equipment holographic budget view, which contains real-time resource allocation suggestions and budget consumption. This holographic budget view can help hospital managers to understand the dynamic adjustment of equipment resource allocation in a timely manner, so as to reasonably allocate annual budget and equipment maintenance resources.

[0075] Step S3: collecting historical fault data of the equipment holographic budget view to obtain equipment historical fault data; predicting equipment failure risk of the equipment holographic budget view based on the equipment historical fault data to generate equipment failure risk prediction data; estimating maintenance cost of the equipment holographic budget view based on the equipment failure risk prediction data to obtain equipment risk maintenance cost data;

[0076] In an embodiment of the present invention, the historical fault records of the equipment are extracted from the hospital equipment management system, including data such as fault type, fault cause, repair time, maintenance cost and impact degree. The fault data is standardized to ensure the consistency and accuracy of the data field, and the standardized historical fault data of the equipment is obtained. According to the time sequence of the fault occurrence, the historical fault data is converted into time series data to facilitate subsequent analysis and prediction. According to different fault types of the equipment (such as hardware fault, software fault, power fault, etc.), the data is classified and labeled so as to perform detailed analysis under different fault types. The fault frequency and fault interval time of the equipment in a specific period are calculated to generate the fault frequency and interval data of the equipment to provide a reference for subsequent risk prediction. A statistical or machine learning model suitable for equipment fault prediction is selected, such as a time series analysis model (such as ARIMA), a fault tree analysis (FTA), or a deep learning model based on a long short-term memory network (LSTM). According to the fault data characteristics of the equipment, the parameters of the prediction model are set to ensure that the model can accurately reflect the fault trend of the equipment. The selected prediction model is input with the fault data time series to perform fault trend analysis and predict the future equipment failure probability. Output equipment failure risk prediction data, including the possibility of failure, expected time of occurrence, and relative risk of various types of failures. Based on the predicted failure probability and failure impact, the equipment failure risk is divided into different levels (such as low, medium, and high risk). Mark the failure risk level and import the prediction data into the equipment holographic budget view to display the equipment failure risk status in real time. According to the equipment failure risk level, determine the type of maintenance required (such as preventive maintenance, emergency repair, regular inspection, etc.). Classify maintenance needs of different risk levels so that resources can be allocated in a targeted manner in maintenance cost estimation. Set cost estimation parameters for each maintenance type based on the hospital's equipment maintenance resources (such as manpower, parts, working hours, etc.) and historical maintenance cost data. Calculate the expected maintenance frequency and required resources for different equipment types and failure risk levels to generate an accurate maintenance cost estimation model. Input the equipment failure risk prediction data and maintenance cost parameters into the cost estimation model, calculate the maintenance cost of each equipment at different risk levels, and generate equipment risk maintenance cost data. The data includes the total expected maintenance cost, the sub-item cost by maintenance type, and the annual maintenance budget recommendation for the equipment. Import equipment risk maintenance cost data into the equipment holographic budget view, and dynamically adjust the maintenance plan according to changes in risk prediction. Optimize maintenance costs to achieve reasonable allocation of hospital resources, and provide real-time cost prediction and risk analysis in the holographic view. After each update, the equipment holographic budget view should display the current equipment's failure risk status, maintenance cost estimation data, and annual maintenance recommendations for the equipment, so that managers can make maintenance decisions in a timely manner.This dynamic maintenance plan helps hospitals effectively control maintenance costs without affecting normal equipment use, and prevent failures of high-risk equipment in advance.

[0077] Step S4: Perform real-time budget warning on the equipment holographic budget view based on the equipment risk maintenance cost data to generate equipment budget warning data; perform comprehensive budget decision on the equipment holographic budget view by identifying the budget warning data, thereby generating an equipment budget decision plan to execute the hospital equipment budget management optimization task.

[0078] In an embodiment of the present invention, by reading the real-time maintenance cost data in the equipment holographic budget view, including the current budget consumption, the predicted maintenance expenditure, and the risk level of each device. The equipment maintenance frequency and the change trend of the maintenance cost are continuously monitored to capture the situation of exceeding the expected budget consumption or risk increase in real time. According to the annual budget allocation of the hospital and the risk level of the equipment, the budget warning thresholds of different risk levels are set. For example, the maintenance cost of high-risk equipment can be set with a lower budget warning threshold, and the low-risk equipment can be set with a higher threshold. Configure an automated monitoring mechanism, and once the real-time maintenance cost approaches or exceeds the warning threshold, the system will trigger a budget warning. When the maintenance cost of the equipment exceeds the warning threshold, the corresponding equipment budget warning data is generated. The budget warning data includes the equipment number, the warning reason (such as exceeding the threshold, frequent failures, etc.), the specific data exceeding the threshold, and the recommended countermeasures. The budget warning data is integrated into the equipment holographic budget view so that the budget warning status of each device can be displayed in real time in the budget management interface. When the maintenance cost of the equipment exceeds the warning threshold, the corresponding equipment budget warning data is generated. The budget warning data includes the equipment number, the warning reason (such as exceeding the threshold, frequent failures, etc.), the specific data exceeding the threshold, and the recommended countermeasures. Integrate budget warning data into the equipment holographic budget view so that the budget warning status of each device can be displayed in real time in the budget management interface. Formulate budget allocation rules, such as giving priority to the maintenance budget for high-frequency failure equipment and appropriately delaying maintenance for low-risk equipment. According to the warning data and priority of the equipment, the budget funds are reasonably divided into different equipment risk levels and warning levels to achieve efficient fund allocation. According to the priority sorting and budget decision rules, a budget decision plan containing specific budget allocation amounts, maintenance schedules, and priority equipment is generated. The budget decision plan can be further refined into monthly, quarterly or annual maintenance budget plans to ensure transparent and efficient use of funds for equipment maintenance. Submit the budget decision plan to the hospital equipment management system, and allocate maintenance funds, adjust the maintenance cycle of equipment, or increase the inspection frequency of high-risk equipment according to the plan. During the execution process, monitor the operating status and budget consumption of the equipment in real time, and dynamically adjust the plan to ensure that the capital investment meets the actual needs of the equipment. According to the real-time changes in the equipment budget warning data, adjust the maintenance frequency, maintenance content, budget allocation, etc. of the equipment. For example, when the failure rate of the equipment decreases, its maintenance frequency and budget can be reduced accordingly. Regularly analyze the deviation between early warning data and actual execution, optimize the budget allocation decision for the next cycle through data feedback, and improve the scientificity and rationality of budget allocation.

[0079] Preferably, step S1 comprises the following steps:

[0080] Step S11: Acquire hospital equipment location information data;

[0081] Step S12: Deploy sensors based on the hospital equipment location information data to obtain hospital equipment sensor deployment data; perform sensor data connection on the hospital equipment sensor deployment data to generate a hospital equipment sensor network;

[0082] Step S13: wirelessly collect sensor data from the hospital equipment sensor network based on a preset data collection frequency to obtain an equipment collection data set; perform data preprocessing on the equipment collection data set to generate a standard equipment collection data set, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization;

[0083] Step S14: construct a digital archive of the standard equipment acquisition data set to generate a digital information archive of the equipment.

[0084] In an embodiment of the present invention, the location information of all equipment in the hospital is obtained, including the room number, floor, department where the equipment is located, and location coordinates (such as operating room, ICU, etc.), so as to plan the sensor layout. The hospital information management system (HIS) or equipment management system is used to automatically obtain the equipment location information to ensure the accuracy and real-time nature of the location data. The collected location information data is stored in the equipment database, the accuracy of the location data is verified, and the duplication and error of the location information are eliminated to ensure the accuracy of the subsequent sensor deployment. Based on the hospital equipment location information data, a sensor deployment plan is formulated, including sensor type selection, quantity allocation, and location layout. According to the type, importance, and frequency of use of each device, the sensor layout density is determined, and more sensors are preferentially equipped for key medical equipment (such as life monitoring equipment, imaging equipment, etc.) to improve the comprehensiveness of data monitoring. According to the planning plan, the sensor is deployed at the designated location of the equipment, and the corresponding parameters (such as data collection frequency, sensor connection protocol, etc.) are configured to generate hospital equipment sensor deployment data. Ensure the stable connection and normal operation of the sensor to avoid signal interference or data collection interruption. Data connection is performed on the hospital equipment sensor deployment data, a sensor network is established, and seamless connection between the equipment and the sensor is achieved to generate a hospital equipment sensor network. Use the Internet of Things (IoT) technology to manage each node of the device sensor network to ensure the stability of data transmission. According to the preset data collection frequency, the hospital device sensor network is automatically collected to ensure real-time and periodic data collection. The data collection frequency can be personalized according to the use of the equipment to ensure the timeliness and accuracy of the data. Use the sensor network to wirelessly collect data from each device to ensure that the signal is not interfered during the data collection process and obtain the device collection data set. Use wireless transmission protocols (such as Wi-Fi, Bluetooth or LoRa, etc.) to complete data collection and transmission to ensure the real-time and integrity of device data. Preprocess the device collection data set to generate a standard device collection data set. Data preprocessing includes the following steps: remove invalid, abnormal or duplicate data to ensure data quality; apply filtering algorithms to eliminate noise in the collected data and improve data accuracy; use mean interpolation, time series prediction or multiple interpolation methods to fill missing data to avoid errors caused by incomplete data; normalize the data to ensure data format consistency for subsequent analysis and application. Based on the standard equipment collection data set, build a digital archive for each device, which includes basic information of the device (such as device number, type, location), real-time operation data, maintenance records, fault history, etc. Store the digital archives of all devices in the hospital's equipment management database to ensure the security and accessibility of the archive data. Assign a unique identification code to the equipment archive to facilitate subsequent data tracking and retrieval. Set up an automatic update mechanism to ensure that the real-time data and status changes of each device can be synchronized to the digital archive.Regularly back up archival data to prevent data loss and provide long-term data support for equipment archives to assist in equipment management and decision-making analysis.

[0085] Preferably, step S14 includes the following steps:

[0086] Step S141: constructing a data table for the standard equipment collection data set to generate a digital archive data table, wherein the digital archive data table includes an equipment basic information table, an operation status table, a fault record table, and a maintenance record table;

[0087] Step S142: Establishing an equipment number index for the standard equipment collection data set to generate an equipment number index; archiving and storing the standard equipment collection data set according to the equipment basic information table, operation status table, fault record table, and maintenance record table according to the equipment number index to generate an initial equipment digital information file;

[0088] Step S143: Dynamically monitor the data changes of the initial device digital information file to generate device dynamic change monitoring data; automatically update the data of the initial device digital information file according to the device dynamic change monitoring data, thereby generating a device digital information file.

[0089] In the embodiment of the present invention, the data table structure of the standard equipment collection data set is designed according to the management requirements of the hospital equipment, and includes four main tables: Equipment basic information table: stores basic information of the equipment, such as equipment number, name, type, model, installation location, manufacturer, etc. Operation status table: records the real-time operation status of the equipment, including operation time, power consumption data, performance status, etc., to ensure the visualization of the equipment status. Fault record table: records the fault information of the equipment, such as fault time, fault type, impact range, repair status, etc. Maintenance record table: records the maintenance information of the equipment, including maintenance time, maintenance personnel, maintenance content, maintenance results, etc. Based on the data table design, the information in the standard equipment collection data set is sorted and classified to generate the above four digital archive data tables to ensure the integrity and standardization of the archive data. These four data tables are created and initialized in the hospital's database system to provide structural support for subsequent data archiving and updating. A unique equipment number index is created for each device in the standard equipment collection data set as the unique identifier of the device to ensure accurate association of the data. The equipment number index can be a unique code composed of the equipment type, serial number and department code, which is convenient for rapid retrieval and matching. According to the equipment number index, the standard equipment collection data is archived and stored according to the structure of four digital archive data tables (equipment basic information table, operation status table, fault record table, and maintenance record table). Through data archiving, the historical data and real-time data of each device are classified and stored in the corresponding data table to generate the initial equipment digital information archive and realize the structured management of equipment information. In the standard equipment collection data set, the real-time status, fault events and maintenance operations of the equipment are continuously monitored to generate equipment dynamic change monitoring data. Compare the real-time data and archive data, automatically detect and record equipment status changes, fault occurrences and maintenance conditions to ensure the real-time nature of the data. According to the equipment dynamic change monitoring data, the relevant data tables in the initial equipment digital information archive are automatically updated to ensure that the archive information is consistent with the actual equipment status. Set up an automatic update mechanism. When the equipment status, fault record or maintenance record changes, the system will automatically write the new data into the corresponding data table to generate the latest version of the equipment digital information archive. Regularly perform data consistency checks on the equipment's digital information archives to ensure that the data in the archives is free of duplication and errors, and that the data table structure and content are complete. Generate regular backups of the archive data to ensure that it can be restored in the event of data loss or error, thereby ensuring the long-term availability of the archives.

[0090] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0091] Step S21: define the stages of the entire life cycle of hospital equipment and generate equipment life cycle stage data, wherein the equipment life cycle stage data includes the purchase stage, the use stage, the maintenance stage, the aging stage and the replacement stage; screen the life cycle data of the equipment digital information archive based on the purchase stage, the use stage, the maintenance stage, the aging stage and the replacement stage to obtain equipment life cycle screening data;

[0092] Step S22: performing multi-dimensional spatial data mapping on the equipment life cycle stage data according to the equipment life cycle screening data to generate equipment life cycle spatial mapping data points; visualizing the equipment status of the equipment life cycle spatial mapping data points to generate a holographic view of the equipment;

[0093] Step S23: performing equipment budget consumption analysis on the equipment holographic view to generate equipment status budget consumption data; performing holographic resource allocation simulation on the equipment holographic view according to the equipment status budget consumption data to generate equipment budget allocation optimization data;

[0094] Step S24: periodically updating the device holographic view according to the device budget allocation optimization data, thereby generating a device holographic budget view.

[0095] In the embodiment of the present invention, the whole life cycle of the equipment is defined according to the different use stages of the hospital equipment, including the following stages: Purchase stage: equipment purchase, installation and commissioning stage, including equipment purchase cost and installation information. Use stage: daily use and operation stage of the equipment, including operation frequency and status information. Maintenance stage: regular inspection and maintenance stage of the equipment, recording the maintenance requirements and maintenance records of the equipment. Aging stage: the stage when the performance of the equipment gradually declines, including the aging speed and the degree of performance decline. Replacement stage: the stage when the equipment reaches the end of its service life or needs to be replaced due to a failure, including replacement cost and replacement decision support data. After definition, the equipment life cycle stage data is generated to ensure that the life cycle status data of each device is clearly classified. Based on the equipment digital information archive, the equipment data is screened according to each stage (purchase, use, maintenance, aging, replacement), and relevant information is extracted to form equipment life cycle screening data. Through data screening, it is ensured that the information of each stage in the archive data is independent and clear, and structured stage data is provided for subsequent analysis. According to the equipment life cycle data screening, the multi-dimensional spatial data mapping technology is used to convert the equipment life cycle information into spatial data points to generate equipment life cycle spatial mapping data points: each data point contains multi-dimensional information such as equipment usage status, maintenance frequency, aging speed, budget consumption, etc., so as to intuitively display the equipment status in space. The equipment life cycle spatial mapping data points are visualized to generate a holographic view of the equipment. The holographic view visualizes the position of the equipment in the life cycle and its current status, making the overall status of the equipment clear at a glance. Through the graphical interface, it is possible to intuitively observe information such as equipment usage status, budget allocation, aging trend, etc., which is convenient for quick decision-making. Through the equipment holographic view, the budget consumption of the equipment at each stage is analyzed to generate equipment status budget consumption data. This data includes equipment usage consumption, maintenance costs, aging repair costs, etc., to help identify high-consumption equipment and stages. Analyze the budget consumption trend of the equipment to provide a reference for subsequent resource allocation. Based on the equipment status budget consumption data, the holographic resource allocation simulation is used to reasonably allocate resources for the equipment at different life cycle stages to generate equipment budget allocation optimization data. In the simulation process, by optimizing resource allocation, reasonable maintenance and resource allocation of the equipment are achieved, and the budget pressure on equipment maintenance and replacement is reduced. According to the equipment budget allocation optimization data, set the update cycle of the equipment holographic view, and regularly refresh the equipment status, budget consumption and other information to ensure the real-time and accuracy of the equipment information. During the update process, the system will dynamically update the equipment status, budget consumption, maintenance frequency and other information to generate the latest equipment holographic budget view. After the periodic update, the updated holographic view is stored as the equipment holographic budget view. This view can provide real-time budget, status and life cycle stage visualization support for the hospital's equipment management, which is helpful for resource optimization and equipment replacement decision-making.

[0096] Preferably, step S22 includes the following steps:

[0097] Step S221: confirming the key data indicators of the equipment life cycle stage data according to the equipment life cycle screening data to obtain the key data indicators of the equipment life cycle stage;

[0098] Step S222: Establish a multidimensional coordinate system for key data indicators of the equipment life cycle stage based on the Cartesian three-dimensional space coordinate system to generate a multidimensional space coordinate axis; map the equipment digital information archive to the multidimensional space coordinate axis to discretize the data points and generate equipment life cycle space mapping data points;

[0099] Step S223: performing mean division on the multidimensional space coordinate axis by using a preset index threshold to generate a mean division coordinate axis; performing adjacent coordinate axis space region division on the mean division coordinate axis to generate a device normal state space region and a device abnormal state space region;

[0100] Step S224: Visualize the data points of the equipment life cycle space mapping according to the equipment normal state space area and the equipment abnormal state space area to generate a holographic view of the equipment.

[0101] In an embodiment of the present invention, by filtering data according to the equipment life cycle, key data indicators of each stage are extracted so as to effectively mark important information of the equipment at different stages. The key data indicators of each stage cover the following contents: Purchase stage: equipment purchase cost, installation time. Use stage: equipment use frequency, running time, load condition. Maintenance stage: maintenance frequency, maintenance cost, failure rate. Aging stage: aging speed, performance degradation rate. Replacement stage: replacement estimated cost, recommended replacement time. After confirming the key data indicators of the stage, the key data indicators of the equipment life cycle stage are generated to provide a basis for the subsequent establishment of a multidimensional space coordinate system and data mapping. Based on the Cartesian three-dimensional space coordinate system, the multidimensional space coordinate axis is constructed for the confirmed key data indicators of the equipment life cycle stage to generate a multidimensional space coordinate axis. Each dimension corresponds to a key data indicator, ensuring that the equipment life cycle data can fully reflect the information of each stage in the space coordinate system. The key data in the equipment digital information archive is mapped to the multidimensional space coordinate axis, presented in the form of discrete data points, and the equipment life cycle space mapping data points are generated. The discretized data points are positioned in space in the form of coordinates, which can represent the important status and numerical information of the equipment at each life cycle stage. According to the preset indicator threshold, the multi-dimensional space coordinate axis is mean-divided to generate the mean-divided coordinate axis, so that the key indicators of each stage are divided according to the middle point of the value, which is convenient for the subsequent regional segmentation. The mean division ensures that each data point can be relatively accurately divided into normal or abnormal state. According to the mean-divided coordinate axis, the adjacent coordinate axis space area is divided to generate the equipment normal state space area and the equipment abnormal state space area. The normal state space area contains data points whose operating status and indicators are within the safe range, while the abnormal state space area identifies the equipment data points with potential risks or performance degradation. According to the equipment normal state space area and the equipment abnormal state space area, the equipment life cycle space mapping data points are visualized. The visualization process uses colors and marks to distinguish the data points in normal and abnormal states, so that the status of the equipment is clear at a glance in the spatial graphics. After the visualization is completed, the data points are integrated to generate a holographic view of the equipment, which fully displays the life cycle stage, status and key indicators of the equipment. The holographic view provides an intuitive view of the equipment status through a graphical display method, providing an important reference for the hospital's equipment management and decision-making.

[0102] Preferably, step S23 includes the following steps:

[0103] Step S231: Screening the device status of the device holographic view to obtain abnormal device screening data and normal device screening data; analyzing the service life of the normal device screening data to generate normal device service life data; calculating the depreciation rate of the normal device screening data according to the normal device service life data to obtain normal device depreciation rate data;

[0104] Step S232: performing abnormal device performance analysis on the abnormal device screening data to generate abnormal device performance analysis data; performing line load calculation on the abnormal device screening data based on the abnormal device performance data to obtain abnormal device line load data;

[0105] Step S233: Perform line path connection analysis on the normal equipment screening data and the abnormal equipment screening data to generate normal equipment connection path data; perform equipment power consumption calculation on the normal equipment screening data according to the abnormal equipment line load data to obtain normal equipment power impact data;

[0106] Step S234: performing normal equipment budget consumption analysis on normal equipment depreciation rate data using normal equipment power impact data to generate normal equipment status budget consumption data; performing abnormal equipment budget consumption analysis on abnormal equipment performance analysis data using abnormal equipment line load data to generate abnormal equipment status budget consumption data;

[0107] Step S235: Integrate the normal device state budget consumption data and the abnormal device state budget consumption data to generate device state budget consumption data; perform holographic resource allocation simulation on the device holographic view according to the device state budget consumption data to generate device budget allocation optimization data.

[0108] In the embodiment of the present invention, a holographic view is formed by collecting real-time data from device sensors and monitoring systems. The collected data is cleaned, and missing values ​​and outliers are processed to improve data quality. Classification algorithms such as decision trees or support vector machines are used to classify the status of devices and identify normal devices and abnormal devices. The depreciation rate of normal devices is calculated based on the financial model, using a formula such as: depreciation rate = original value of equipment - residual value ÷ service life; IoT sensors are used to monitor the operating status of abnormal devices and collect performance data. The performance data is processed using data analysis libraries in programming languages ​​such as Python or R. The device load is calculated based on the power formula: line load = current × voltage; the device and its connection relationship are modeled as a graph, and the connection path is analyzed using a depth-first search or breadth-first search algorithm. According to the device power parameters and load data, the power consumption is calculated: power consumption = line load × time; combined with the equipment service life and power consumption, a budget consumption prediction model is established. Through linear regression or multivariate regression analysis, the key factors affecting budget consumption are determined to generate consumption prediction data. The budget consumption data of normal and abnormal devices are integrated using ETL (extract, transform, load) tools. Build an optimization model, apply optimization algorithms to optimally allocate equipment resources, and generate optimization suggestions. Use simulation software to simulate resource allocation plans to verify their effectiveness and feasibility.

[0109] Preferably, performing holographic resource allocation simulation on the device holographic view according to the device state budget consumption data comprises:

[0110] Perform time-series consumption analysis on the equipment holographic view according to the equipment status budget consumption data to generate equipment status budget time-series consumption data; perform linear programming resource allocation on the equipment status budget time-series consumption data to generate equipment status budget resource allocation simulation data;

[0111] The budget allocation ratio of the equipment status budget resource allocation simulation data is identified to obtain budget allocation ratio identification data; the extreme value allocation ratio of the budget allocation ratio identification data is acquired, and the equipment holographic view is marked as a high-consumption device based on the acquired extreme value allocation ratio to obtain high-consumption device marking data;

[0112] The cost minimization optimization target is confirmed for the high-consumption equipment marking data to obtain the high-consumption equipment optimization target data; the budget allocation ratio identification data is allocated iteratively according to the high-consumption equipment optimization target data to generate equipment budget allocation optimization data.

[0113] In an embodiment of the present invention, by collecting device state budget consumption data, ensure that the data contains information such as timestamp, device ID, budget consumption value, etc. Clean the data, process missing values ​​and outliers to ensure the accuracy of the analysis. Use a time series analysis method (such as an ARIMA model) to perform time series consumption analysis on the device state budget consumption data, generate device state budget time series consumption data, and reflect the budget consumption trend of each device in different time periods. According to the device state budget time series consumption data, a linear programming model is constructed. Define the objective function (such as minimizing the total cost) and constraints (such as device resource restrictions). Use a linear programming solver (such as SciPy's linprog or Gurobi) to solve the model, generate device state budget resource allocation simulation data, and indicate the optimal resource allocation scheme among each device. By analyzing the device state budget resource allocation simulation data, calculate the budget allocation ratio of each device, that is, the ratio of the budget consumption of each device to the total budget. Visualize the budget allocation ratio (such as using a bar chart) to facilitate identification and analysis of the resource allocation of each device. Use statistical methods (such as quantile analysis) to identify extreme values ​​(highest and lowest ratios) in the budget allocation ratio and generate budget allocation ratio identification data. Based on the obtained extreme value allocation ratio, high-consumption devices are marked. The budget allocation ratio of these devices is significantly higher than the preset threshold. For the marked high-consumption devices, determine the optimization goal of minimizing costs. For example, a goal is set to minimize its budget consumption without affecting the normal operation of the equipment. Use optimization algorithms (such as genetic algorithms or simulated annealing algorithms) to confirm the goals and seek the best budget allocation strategy. According to the optimization target data of high-consumption devices, iteratively adjust the budget allocation ratio identification data. In each iteration, refer to the actual consumption of the equipment and the optimization target to gradually optimize the resource allocation. After several iterations, generate the final equipment budget allocation optimization data to ensure that resources are reasonably and effectively allocated.

[0114] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0115] Step S31: collecting historical fault data of the equipment holographic budget view to obtain historical fault data of the equipment;

[0116] Step S32: Divide the equipment historical fault data into data sets to generate a model training set and a model test set; use a long short-term memory neural network algorithm to train the model training set to generate an equipment risk prediction pre-model; perform model optimization iteration on the equipment risk prediction pre-model through the model test set to generate an equipment risk prediction model;

[0117] Step S33: Import the equipment holographic budget view into the equipment risk prediction model to predict equipment failure risk and generate equipment failure risk prediction data; estimate the maintenance cost of the equipment holographic budget view based on the equipment failure risk prediction data to obtain equipment risk maintenance cost data.

[0118] In an embodiment of the present invention, the source of historical fault data is determined, including equipment logs, maintenance records, sensor data, and equipment monitoring systems. Use an automated script or data crawling tool (such as BeautifulSoup or Scrapy in Python) to extract historical fault data from a specified source. Ensure that the data includes information such as equipment ID, fault type, occurrence time, duration, maintenance measures, and related environmental conditions. Store the collected historical fault data in a database (such as MySQL, PostgreSQL) or a data file (such as CSV, JSON format) to prepare for subsequent analysis. Use a data partitioning method (such as 70% training set, 30% test set) to randomly divide the historical fault data into a model training set and a model test set. This can be implemented using Python's train_test_split function. Perform feature extraction on the model training set, extract key features that affect equipment failures (such as equipment age, ambient temperature, load, etc.), and perform normalization. Use a deep learning framework (such as TensorFlow or Keras) to build an LSTM model. Use the model test set to verify and optimize the trained LSTM model, and improve the model performance by adjusting hyperparameters (such as learning rate, batch size, etc.) and model structure (such as increasing the number of LSTM layers). Calculate the prediction accuracy of the model (such as mean square error MSE) and tune the model until satisfactory performance is achieved. Input the data of the equipment holographic budget view into the trained equipment risk prediction model. Generate equipment failure risk prediction data based on the output of the model to predict the probability of future equipment failure. Estimate maintenance costs based on equipment failure risk prediction data. The following formula can be used: Maintenance cost = failure probability × average maintenance cost per failure; for high-risk equipment, maintenance and resource allocation are prioritized. Visualize the failure risk prediction data and maintenance cost estimation results (such as using Matplotlib or Seaborn library) to facilitate analysis and understanding by decision makers, generate reports, and provide them to relevant managers to support equipment maintenance and resource allocation decisions.

[0119] In this specification, a hospital equipment budget management system based on a holographic view is provided, which is used to execute the above-mentioned hospital equipment budget management method based on a holographic view. The hospital equipment budget management system based on a holographic view includes:

[0120] A digital archive construction module is used to obtain hospital equipment location information data; connect sensor data based on the hospital equipment location information data to generate a hospital equipment sensor network; wirelessly collect sensor data from the hospital equipment sensor network to obtain a standard equipment collection data set; construct a digital archive for the standard equipment collection data set to generate a device digital information archive;

[0121] The holographic budget simulation module is used to screen the data of the entire life cycle of hospital equipment to obtain equipment life cycle screening data; perform multi-dimensional space data mapping based on the equipment life cycle screening data to generate a holographic view of the equipment; perform holographic resource allocation simulation on the equipment holographic view to generate equipment budget allocation optimization data; perform periodic updates on the equipment holographic view based on the equipment budget allocation optimization data to generate a holographic budget view of the equipment;

[0122] The fault risk prediction module is used to collect historical fault data of the equipment holographic budget view to obtain the equipment historical fault data; to predict the equipment fault risk of the equipment holographic budget view through the equipment historical fault data to generate the equipment fault risk prediction data; to estimate the maintenance cost of the equipment holographic budget view based on the equipment fault risk prediction data to obtain the equipment risk maintenance cost data;

[0123] The budget management decision module is used to make real-time budget warnings on the equipment holographic budget view based on the equipment risk maintenance cost data and generate equipment budget warning data; by identifying the budget warning data, a comprehensive budget decision is made on the equipment holographic budget view, thereby generating an equipment budget decision plan to execute the hospital equipment budget management optimization operation.

[0124] The beneficial effect of the present invention is that through sensor network connection and wireless data collection, real-time monitoring of equipment location information is achieved, and the accuracy of equipment management is improved. The construction of digital archives provides reliable basic data for subsequent data analysis and decision-making, and promotes the systematization and standardization of data. The screening of equipment life cycle data helps to fully understand the use status and historical background of the equipment, ensuring that all important factors are taken into account when allocating the budget. The generated holographic view provides an intuitive equipment resource allocation simulation, so that managers can optimize the budget more scientifically, and improve the efficiency and effectiveness of budget management. By collecting historical failure data of the equipment, common failure modes of the equipment can be identified, so as to make effective failure risk prediction. This forward-looking management method can help hospitals prevent potential problems, reduce equipment downtime, reduce maintenance costs, and ensure that the equipment operates in the best condition. Real-time budget warning based on risk maintenance cost data can timely identify and deal with budget overspending or potential risks, thereby ensuring the financial health of the hospital. The generation of a comprehensive budget decision-making plan ensures the scientificity and rationality of budget allocation, improves the transparency of equipment management and the basis for decision-making, and ultimately achieves the optimization of hospital equipment budget management. Therefore, the present invention improves the comprehensiveness and effectiveness of hospital equipment budget management through real-time data collection, full life cycle monitoring, failure risk prediction and dynamic budget decision-making.

[0125] The present invention also provides a computer-readable storage medium storing a computer program, which implements the above-mentioned hospital equipment budget management method based on holographic view when executed.

[0126] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0127] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A hospital equipment budget management method based on holographic view, characterized in that: The following steps are involved: Step S1: Obtain hospital equipment location information data; Connect sensor data based on hospital equipment location information data to generate a hospital equipment sensor network; Wirelessly collect sensor data from the hospital equipment sensor network to obtain a standard equipment collection data set; construct a digital archive of the standard equipment collection data set to generate a digital information archive of the equipment; Step S2: Screening the equipment life cycle data of hospital equipment to obtain equipment life cycle screening data; performing multi-dimensional spatial data mapping based on the equipment life cycle screening data to generate a holographic view of the equipment; Perform holographic resource allocation simulation on the equipment holographic view to generate equipment budget allocation optimization data; Periodically updating the device holographic view according to the device budget allocation optimization data, thereby generating a device holographic budget view; Step S2 includes the following steps: Step S21: define the stages of the entire life cycle of hospital equipment and generate equipment life cycle stage data, wherein the equipment life cycle stage data includes the purchase stage, the use stage, the maintenance stage, the aging stage and the replacement stage; screen the life cycle data of the equipment digital information archive based on the purchase stage, the use stage, the maintenance stage, the aging stage and the replacement stage to obtain equipment life cycle screening data; Step S22: Perform multi-dimensional spatial data mapping on the equipment life cycle stage data according to the equipment life cycle screening data to generate equipment life cycle spatial mapping data points; visualize the equipment status of the equipment life cycle spatial mapping data points to generate a holographic view of the equipment; Step S22 includes the following steps: Step S221: confirming the key data indicators of the equipment life cycle stage data according to the equipment life cycle screening data to obtain the key data indicators of the equipment life cycle stage; Step S222: Establish a multidimensional coordinate system for key data indicators of the equipment life cycle stage based on the Cartesian three-dimensional space coordinate system to generate a multidimensional space coordinate axis; map the equipment digital information archive to the multidimensional space coordinate axis to discretize the data points and generate equipment life cycle space mapping data points; Step S223: performing mean division on the multidimensional space coordinate axis by using a preset index threshold to generate a mean division coordinate axis; performing adjacent coordinate axis space region division on the mean division coordinate axis to generate a device normal state space region and a device abnormal state space region; Step S224: Visualizing the data points of the equipment life cycle space mapping according to the equipment normal state space area and the equipment abnormal state space area to generate a holographic view of the equipment; Step S23: performing equipment budget consumption analysis on the equipment holographic view to generate equipment status budget consumption data; performing holographic resource allocation simulation on the equipment holographic view according to the equipment status budget consumption data to generate equipment budget allocation optimization data; Step S24: periodically updating the device holographic view according to the device budget allocation optimization data, thereby generating a device holographic budget view; Step S3: collecting historical fault data of the equipment holographic budget view to obtain equipment historical fault data; predicting equipment failure risk of the equipment holographic budget view based on the equipment historical fault data to generate equipment failure risk prediction data; estimating maintenance cost of the equipment holographic budget view based on the equipment failure risk prediction data to obtain equipment risk maintenance cost data; Step S4: Perform real-time budget warning on the equipment holographic budget view based on the equipment risk maintenance cost data to generate equipment budget warning data; perform comprehensive budget decision on the equipment holographic budget view by identifying the budget warning data, thereby generating an equipment budget decision plan to execute the hospital equipment budget management optimization task.

2. The hospital equipment budget management method based on holographic view according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire hospital equipment location information data; Step S12: Deploy sensors based on the hospital equipment location information data to obtain hospital equipment sensor deployment data; perform sensor data connection on the hospital equipment sensor deployment data to generate a hospital equipment sensor network; Step S13: wirelessly collect sensor data from the hospital equipment sensor network based on a preset data collection frequency to obtain an equipment collection data set; perform data preprocessing on the equipment collection data set to generate a standard equipment collection data set, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization; Step S14: construct a digital archive of the standard equipment acquisition data set to generate a digital information archive of the equipment.

3. The hospital equipment budget management method based on holographic view according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: constructing a data table for the standard equipment collection data set to generate a digital archive data table, wherein the digital archive data table includes an equipment basic information table, an operation status table, a fault record table, and a maintenance record table; Step S142: Establishing an equipment number index for the standard equipment collection data set to generate an equipment number index; archiving and storing the standard equipment collection data set according to the equipment basic information table, operation status table, fault record table, and maintenance record table according to the equipment number index to generate an initial equipment digital information file; Step S143: Dynamically monitor the data changes of the initial device digital information file to generate device dynamic change monitoring data; automatically update the data of the initial device digital information file according to the device dynamic change monitoring data, thereby generating a device digital information file.

4. The hospital equipment budget management method based on holographic view according to claim 1 is characterized in that: Step S23 includes the following steps: Step S231: Screening the device status of the device holographic view to obtain abnormal device screening data and normal device screening data; analyzing the service life of the normal device screening data to generate normal device service life data; calculating the depreciation rate of the normal device screening data according to the normal device service life data to obtain normal device depreciation rate data; Step S232: performing abnormal device performance analysis on the abnormal device screening data to generate abnormal device performance analysis data; performing line load calculation on the abnormal device screening data based on the abnormal device performance data to obtain abnormal device line load data; Step S233: Perform line path connection analysis on the normal equipment screening data and the abnormal equipment screening data to generate normal equipment connection path data; perform equipment power consumption calculation on the normal equipment screening data according to the abnormal equipment line load data to obtain normal equipment power impact data; Step S234: performing normal equipment budget consumption analysis on normal equipment depreciation rate data using normal equipment power impact data to generate normal equipment status budget consumption data; performing abnormal equipment budget consumption analysis on abnormal equipment performance analysis data using abnormal equipment line load data to generate abnormal equipment status budget consumption data; Step S235: Integrate the normal device state budget consumption data and the abnormal device state budget consumption data to generate device state budget consumption data; perform holographic resource allocation simulation on the device holographic view according to the device state budget consumption data to generate device budget allocation optimization data.

5. The hospital equipment budget management method based on holographic view according to claim 4 is characterized in that: The holographic resource allocation simulation of the device holographic view based on the device status budget consumption data includes: Perform time-series consumption analysis on the equipment holographic view according to the equipment status budget consumption data to generate equipment status budget time-series consumption data; perform linear programming resource allocation on the equipment status budget time-series consumption data to generate equipment status budget resource allocation simulation data; The budget allocation ratio of the equipment status budget resource allocation simulation data is identified to obtain budget allocation ratio identification data; the extreme value allocation ratio of the budget allocation ratio identification data is acquired, and the equipment holographic view is marked as a high-consumption device based on the acquired extreme value allocation ratio to obtain high-consumption device marking data; The cost minimization optimization target is confirmed for the high-consumption equipment marking data to obtain the high-consumption equipment optimization target data; the budget allocation ratio identification data is allocated iteratively according to the high-consumption equipment optimization target data to generate equipment budget allocation optimization data.

6. The hospital equipment budget management method based on holographic view according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: collecting historical fault data of the equipment holographic budget view to obtain historical fault data of the equipment; Step S32: Divide the equipment historical fault data into data sets to generate a model training set and a model test set; use a long short-term memory neural network algorithm to train the model training set to generate an equipment risk prediction pre-model; perform model optimization iteration on the equipment risk prediction pre-model through the model test set to generate an equipment risk prediction model; Step S33: Import the equipment holographic budget view into the equipment risk prediction model to predict equipment failure risk and generate equipment failure risk prediction data; estimate the maintenance cost of the equipment holographic budget view based on the equipment failure risk prediction data to obtain equipment risk maintenance cost data.

7. A hospital equipment budget management system based on holographic view, characterized in that: For executing the hospital equipment budget management method based on holographic view as claimed in claim 1, the hospital equipment budget management system based on holographic view comprises: A digital archive construction module is used to obtain hospital equipment location information data; connect sensor data based on the hospital equipment location information data to generate a hospital equipment sensor network; wirelessly collect sensor data from the hospital equipment sensor network to obtain a standard equipment collection data set; construct a digital archive for the standard equipment collection data set to generate a device digital information archive; The holographic budget simulation module is used to screen the data of the entire life cycle of hospital equipment to obtain equipment life cycle screening data; perform multi-dimensional space data mapping based on the equipment life cycle screening data to generate a holographic view of the equipment; perform holographic resource allocation simulation on the equipment holographic view to generate equipment budget allocation optimization data; perform periodic updates on the equipment holographic view based on the equipment budget allocation optimization data to generate a holographic budget view of the equipment; The fault risk prediction module is used to collect historical fault data of the equipment holographic budget view to obtain the equipment historical fault data; to predict the equipment fault risk of the equipment holographic budget view through the equipment historical fault data to generate the equipment fault risk prediction data; to estimate the maintenance cost of the equipment holographic budget view based on the equipment fault risk prediction data to obtain the equipment risk maintenance cost data; The budget management decision module is used to make real-time budget warnings on the equipment holographic budget view based on the equipment risk maintenance cost data and generate equipment budget warning data; by identifying the budget warning data, a comprehensive budget decision is made on the equipment holographic budget view, thereby generating an equipment budget decision plan to execute the hospital equipment budget management optimization operation.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the hospital equipment budget management method based on holographic view as described in any one of claims 1 to 6 is implemented.

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