Hospital safety production operation management ecological platform

Abstract

The present invention discloses an ecological platform for hospital production safety operation management, and relates to the technical field of hospital production safety control. The ecological platform for hospital production safety operation management of the present invention uses a risk screening module to call an industry standard risk library to comprehensively identify all potential risk points in the target hospital, and conducts a preliminary risk rating. The risk factor identification module will analyze and identify events that affect the risk, and the risk control measures module will take corresponding control measures for the risks identified. The risk factor identification module and the risk control measures module output risk change indicators through an event analysis decision module. Finally, according to the updated risk level, the corresponding risk control measures are dynamically adjusted to ensure the continuous optimization of hospital production safety and the effective control of risks. The core of the present invention is to achieve dynamic monitoring of hospital production safety and adjustment of control measures through standardized and systematic risk management.

Description

Technical Field

[0001] The present invention relates to the technical field of hospital production safety management and control, and more specifically to a hospital production safety operation management ecological platform. Background Art

[0002] As a high-risk place of "small unit, big society", the safety and stability of medical institutions are of vital importance. At present, the safety management of medical institutions mainly relies on the human and technical defense means of the security department, but in the face of highly complex and dynamically changing risks, this approach has gradually failed to adapt to the situation.

[0003] At present, some medical institutions are not fully aware of safety, responsibility, and management, and there are various problems in the entire safety production process. In the process of safety management, traditional management models are usually adopted, and there is insufficient awareness of safety risks, and a systematic safety management mechanism has not been formed. However, the existing security business has the phenomenon of information islands, and the data of subsystems such as fire protection, security, and safety production are separated. Professional business data and department data (security, general affairs, logistics, information, medical care, etc.) are separated from each other. When an emergency occurs, it is difficult to mobilize each other, and it is impossible to effectively command and dispatch. It is difficult to deal with emergency events and ensure the safety of personnel and property in the shortest time.

[0004] For highly complex and dynamically changing risks, the existing security department's manual mode of human defense + technical defense is difficult to achieve systematic security management and cannot meet the high requirements of medical institutions for safety and stability. There is insufficient awareness of the continuity of security management, failure to continuously monitor the security status and proactively discover hidden dangers. Current security does not mean future security, and the existing management method is difficult to cope with the dynamic changes of security risks.

[0005] The existing hospital production safety management and control has the following problems:

[0006] 1) The security awareness of monitoring and management personnel is not in place. They are not clear about what security work should be done and how to do it. They can only rely on third-party outsourcing companies for security management, but are not clear about the specific implementation situation;

[0007] 2) Medical institutions do not have professional risk assessment standards. Instead, each hospital uses other industry standards to create its own static risk ledger, which has not been updated for many years and cannot truly reflect the hospital's risk situation. The form is greater than the effect;

[0008] 3) The subsystems of production safety management are relatively scattered and have low integration, and cannot form an integrated safety protection system. Summary of the invention

[0009] In order to overcome the defects and deficiencies in the above-mentioned prior art, the present invention provides a hospital safety production operation management ecological platform, and the invention purpose of the present invention is to solve at least one technical problem listed in the above-mentioned prior art. The core of the present invention is to achieve dynamic monitoring of hospital safety production and adjustment of control measures through standardized and systematic risk management.

[0010] The present invention includes a risk screening module, a risk factor identification module, a risk control measures module, an event center emergency disposal module, an event analysis and decision module and a dynamic risk grading module; wherein the risk screening module calls the industry standard risk library to comprehensively identify all potential risk points in the target hospital, and performs preliminary risk rating to determine the initial level of risk; the initial level is used as the initial input parameter of the dynamic risk grading module to provide basic data for subsequent risk management; the risk factor identification module will analyze and identify key events that affect the risk, and these events will be sent to the event center emergency disposal module for processing. If the event is determined to be an important event, the emergency disposal process will be triggered to achieve rapid response and processing; the risk control measures module takes corresponding control measures for the identified risks from multiple perspectives to reduce the possibility of risk occurrence or mitigate its potential impact. The risk factor identification module and the risk control measures module output risk change indicators through the event analysis and decision module, and these indicators can be used to dynamically adjust the risk rating to obtain the updated risk level. Finally, according to the updated risk level, the corresponding risk control measures are dynamically adjusted to ensure the continuous optimization of hospital safety production and the effective control of risks.

[0011] In order to solve the above problems existing in the prior art, the present invention is implemented through the following technical solutions.

[0012] The present invention provides a hospital safety production operation management ecological platform, which includes an industry standard risk library, a risk investigation module, a risk factor identification module, a risk control measures module, an event center emergency response module, an event analysis and decision-making module, and a dynamic risk classification module;

[0013] The industry standard risk database stores risk items that have been discovered in the hospital industry;

[0014] The risk screening module calls the industry standard risk library to conduct standardized screening of existing risks in the target hospital, identifies and obtains all risk items in the target hospital; performs preliminary risk rating on all risk items, determines the initial level of each risk item; and inputs each risk item and the initial level of each risk item as preliminary risk data into the dynamic risk grading module;

[0015] The risk factor identification module collects data uploaded by terminal devices of operators, equipment detection data and / or video monitoring data, manages and analyzes risk factors in the target hospital operation process identified by means including patrol inspections, equipment detection and / or video monitoring, identifies risk factor events, and transmits risk factor events to the emergency processing module of the event center;

[0016] The risk control measures module is used to manage the operators and interact with the terminal devices of the operators; to formulate the maintenance and maintenance plans for each device and monitor the implementation of the maintenance and maintenance plans for each device; to store and manage each emergency response plan and monitor the implementation and results of each emergency response plan;

[0017] The emergency response module of the event center performs graded processing on the risk factor events reported by the risk factor identification module, performs emergency response on major events that are urgent and have a large impact, and manages the entire process of major events before, during, during, and after the event.

[0018] The event analysis and decision-making module is used to receive various event information triggered by the event center emergency response module and the risk control measures module, and use the preset risk assessment model to conduct a comprehensive analysis of the nature, frequency and correlation factors of the event to quantify the impact index of the event;

[0019] The dynamic risk grading module receives the impact index from the event analysis and decision-making module, and adjusts the level of each risk in combination with the preliminary risk data provided by the risk screening module;

[0020] According to the adjusted risk level, dynamically adjust the corresponding risk control measures in the risk control measures module.

[0021] Further preferably, in the risk screening module, a dynamic risk model is used to uniformly manage the identified risk items, and corresponding risk factor identification and control measures are provided according to the risk type of each risk item and the actual situation of the target hospital.

[0022] More preferably, the risk screening module calculates the risk score of each risk item using a dynamic risk model, and converts the risk score into a uniformly measured risk level according to the risk level interval; specifically, the risk score of the risk item calculated by the dynamic risk model is x, and its corresponding risk level interval is (x 1 ,x 2 ), the corresponding risk level interval is (D 1 ,D 2 ), then it is converted into a unified measure of risk hazard level .

[0023] More preferably, the dynamic risk model is a LEC evaluation model or a LS evaluation model.

[0024] Further preferably, the dynamic risk grading module comprehensively considers the influence of time factors and objective factors on the risk item, and obtains the risk level of the current risk at time t compared with the risk level of the preliminary risk data, which is expressed as: ;

[0025] in, It represents the impact of time factor on risk item at time t; Indicates the initial danger level of the risk item; Indicates the impact of objective factors on risk items;

[0026] The impact of time factors on risk items conforms to the bathtub curve, and the impact of objective factors on risk is calculated based on the impact degree index output by the event analysis decision module.

[0027] More preferably, the influence of time factor on risk item is calculated by using Weibull distribution, that is, the change trend of risk item with time that conforms to the bathtub curve is calculated by using Weibull distribution, and the calculation formula is: ; In the formula, t represents time, Indicates the steepness of the bathtub curve, representing the drastic change of the risk item over time in the current time interval; different types of risks have different Value, various risks The value is provided by the industry standard risk library; β represents the risk under different conditions. When β < 1, it indicates the early failure period. When β=1, it indicates the stable operation period. When β>1, it indicates the late failure period, and Introduce variables and ; Indicates the end time of the early expiration period, Indicates the end time of the stable operation period; , , , and The value of is provided by the industry standard risk library; the trend of the risk item that conforms to the bathtub curve over time is calculated using Weibull distribution and is expressed as:

[0028] .

[0029] More preferably, the impact of objective factors on risk is calculated based on the impact index output by the event analysis and decision module, specifically,

[0030] For a risk, assuming that in the initial state, the impact of objective factors on the risk is , that is, no influence. After event x occurs, the influence of objective factors increases to ,in Indicates the degree of influence of event x on objective factors, different types of time It is also different, and is determined according to the impact index output by the event analysis decision module; assuming that at time t, a total of i events have occurred, the impact of objective factors on the risk item is .

[0031] More preferably, the dynamic risk grading module comprehensively considers the influence of time factors and objective factors on the risk item F, and obtains the current risk level of risk F at time t, compared with the risk level of the preliminary risk data, expressed as ; In the formula, Indicates the current danger level of risk F; represents the initial danger level of risk F, represents the changing trend of risk items over time, Represents the impact of objective factors on the risk item F.

[0032] Further preferably, the risk factor identification module includes a patrol and inspection sub-module, which is used to set and issue patrol tasks, manage patrol points and patrol plans; the patrol and inspection sub-module issues patrol and inspection tasks to the patrol personnel's terminal devices according to its preset patrol and inspection tasks and patrol points, and the patrol personnel use their terminal devices to scan the QR code or NFC of the patrol point to identify and verify the patrol point; during the inspection process, the terminal device uploads the inspection form status to the risk factor identification module in real time; if the terminal device reports an abnormal situation, the risk factor identification module transmits the abnormal situation event to the emergency processing module of the event center.

[0033] Further preferably, the risk factor identification module includes an equipment detection submodule, which monitors real-time monitoring data related to each risk item through the Internet of Things and sensors; the sensor uploads the monitored data to the equipment detection submodule in real time, and the equipment detection submodule performs real-time analysis on the received sensor monitoring data stream according to preset thresholds and historical data. When the sensor monitoring data fluctuates significantly or exceeds the normal range, the equipment detection submodule identifies the abnormality and uploads the abnormal sensor monitoring data as a risk factor to the emergency processing module of the event center.

[0034] More preferably, the real-time monitoring data related to each risk item includes key parameter data under risk scenarios of automatic fire alarm system, oxygen station system, positive and negative pressure system, medical area power system and logistics support equipment.

[0035] Further preferably, the risk factor identification module includes a video surveillance sub-module, which is connected to the video surveillance system of the target hospital and uses AI intelligent analysis to identify risk factors in the video screen. When risk factors or abnormal behaviors are monitored in the video screen, the video surveillance sub-module transmits the risk factors or abnormal behaviors to the emergency processing module of the event center and generates real-time alarms and video screenshots.

[0036] Further preferably, the beforehand means that the emergency response module of the event center is connected to the various business systems of the target hospital, and the alarm information of each business system is collected centrally to perform unified scheduling management; a network connection is established between the emergency response module of the event center and the various business systems in the target hospital, and a unified data transmission format and protocol are set, and the alarm information of each business system is transmitted to the emergency response module of the event center; the emergency response module of the event center monitors and receives the alarm information sent by each business system in real time, parses the received alarm information, extracts key content, stores the parsed alarm information in a database, and displays it in a prominent manner on the monitoring interface.

[0037] Further preferably, the incident refers to that after the emergency response module of the event center receives the alarm information, it locates the specific location of the alarm through a visual map, broadcasts the alarm information through voice, and simultaneously obtains video surveillance around the alarm location to view the on-site situation and assist the processing personnel in judging the on-site situation.

[0038] More preferably, the said incident refers to that the emergency handling module of the event center retrieves the corresponding emergency response plan from the risk control measures module according to the alarm information, issues instructions to the terminal devices of relevant personnel according to the emergency response plan, directs the actions of personnel, and links the access control system to facilitate rescue or security operations.

[0039] Further preferably, the afterward refers to that the emergency processing module of the event center collects relevant data of the event from the access of alarm information, the activation of the emergency response plan to the implementation of the emergency response plan, analyzes the factors that deviate from the emergency response plan in the event, evaluates the inclusiveness of the emergency response plan, improves the emergency response plan preset in the risk control measures module, and optimizes the risk control measures.

[0040] Further preferably, the risk control measures module includes an equipment operation and maintenance sub-module, a personnel management sub-module and an emergency plan sub-module. The equipment operation and maintenance sub-module integrates equipment management, fault reporting and maintenance scheduling functions, manages hospital equipment, formulates maintenance and servicing plans for each equipment and monitors the implementation of the maintenance and servicing plans for each equipment; the personnel management sub-module records the basic information of each operator and interacts with the operator's terminal device; the emergency plan sub-module stores and manages each emergency response plan, and monitors the implementation and results of each emergency response plan.

[0041] Further preferably, the risk control measures module also includes a training and drill sub-module and a safety education sub-module. The training and drill sub-module is used to organize and manage daily training and emergency drill activities of operating personnel; the safety education sub-module provides safety education knowledge to operating personnel, can regularly issue special learning tasks, supervise learning progress and record the learning situation of each operator, conduct safety education tests on operating personnel, and after the test, generate a test report to evaluate the safety knowledge level of the operator.

[0042] Further preferably, the risk items stored in the industry standard risk library are obtained by issuing a risk questionnaire in the target hospital, classifying the risk questionnaires replied by various institutions of the target hospital, performing risk item analysis and data dependency analysis.

[0043] It is further preferred to classify the risk questionnaires replied by the institutions of the target hospital. Specifically, a deep learning algorithm is used to perform semantic analysis and vocabulary understanding on the text of the questionnaire, identify the subject objects involved in the text, and calculate the correlation between the identified subject objects and four aspects: unsafe behavior of people, unsafe behavior of objects, management defects, and unsafe factors of the environment. The four calculated correlations are compared, and the category with the highest correlation is selected as the final classification of the problem.

[0044] More preferably, the risk questionnaires answered by the institutions of the target hospital are classified, specifically, the text of the questionnaire is converted into a computable digital vector using a vector model, and the digital vectors based on the text conversion are clustered using a clustering algorithm K-means; the clustering results are analyzed using a large language model, and the topics and keywords of each category are extracted from it.

[0045] It is further preferred to classify the risk questionnaires answered by the institutions of the target hospital, specifically, directly input all the questionnaire texts into the large language model, use the large language model to cluster the questionnaire texts and summarize the themes of each category.

[0046] More preferably, the erroneous contents of the items in the questionnaire are determined through data dependency analysis; specifically, based on the dependency relationship between the survey items in the questionnaire, the interdependence between the response items is analyzed, and a dependency basis is given, through which the erroneous contents of the response items filled in the questionnaire are determined.

[0047] Compared with the prior art, the beneficial technical effects brought by the present invention are as follows:

[0048] 1. The present invention constructs a hospital-specific industry standard risk library, which is constructed based on the unique operating environment of medical institutions, integrating risk items discovered by multiple medical institutions in the medical industry, and establishing a set of industry standard risk libraries suitable for hospitals. In the later stage of the operation of the hospital safety production operation management ecological platform of the present invention, the risk items in the industry standard risk library can be updated and / or supplemented so that it can be dynamically updated. On the basis of the existence of the industry standard risk library, combined with the actual situation of each hospital, a comprehensive investigation of the risk items in the hospital is conducted to form a risk ledger for the hospital, and the specific distribution and characteristics of the risk items are identified. The industry standard risk library of the present invention is equivalent to summarizing the safety production operation experience of predecessors, combining the actual situation of the target hospital itself, and conducting a comprehensive investigation of the risk items of the target hospital. When the risk items in the industry standard risk library are updated or supplemented, the target hospital can be supplemented according to the newly updated or supplemented risk items, so as to achieve a comprehensive investigation of the risk items of the target hospital and avoid omissions and negligence of risk items.

[0049] 2. The risk screening module of the present invention uses the discovered risk items to conduct risk screening on the target hospital. The actual situation of the target hospital is combined here. When conducting the screening, it is not completely compared with the risk items in the industry standard risk library, but the risk screening module needs to obtain the actual situation of the target hospital, so as to conduct risk screening according to the actual situation of the target hospital, so as to achieve targeted risk screening. This method is more targeted rather than universal. At the same time, when conducting risk screening on the target hospital, the identified risk items are uniformly managed using the constructed dynamic risk model, and corresponding risk factor identification and control measures are provided according to the risk type of each risk item and the actual situation of the target hospital, so that the risks identified are closer to the actual situation of the target hospital, and the target hospital’s own risk ledger is established. Subsequently, targeted professional control measures and emergency plans are provided for different risk points to ensure the effective identification and precise control of risk factors unique to medical facilities, thereby improving the overall safety management level of the hospital.

[0050] 3. The risk factor identification module of the present invention manages and analyzes the risk factors in the target hospital's operation process identified by means including patrol inspections, equipment testing and / or video monitoring, and realizes dynamic analysis of the target hospital's risk factors.

[0051] 4. The hospital safety production operation management ecological platform of the present invention combines the hospital's basic space, equipment ledger data, and daily dynamic data such as inspections, equipment maintenance, and personnel training to analyze and evaluate risk point levels in real time. Based on changes in risk levels, the platform automatically and dynamically adjusts risk control measures to achieve precise management and control, especially efficient management of key safety areas. Through this mechanism, the platform can trigger corresponding emergency responses when risks increase and quickly implement optimization measures, thereby significantly reducing the possibility of accidents and improving the operational efficiency of hospital safety production.

[0052] 5. The event analysis and decision-making module in the hospital safety production operation management ecological platform of the present invention uses data analysis results to output risk change indicators in real time, providing intelligent risk decision-making support for the decision-making layer. Through dynamic monitoring and analysis of various events, the platform can adjust the risk level and control measures in a timely manner to ensure that the hospital's risk control measures always match the actual risk situation, thereby building an efficient closed-loop management system for hospital safety production and effectively improving the accuracy and timeliness of risk management.

[0053] 6. The present invention can improve the adaptability and response efficiency of risk control. Through the dynamic risk grading module, the platform uses different risk levels as the basis for adjusting control measures to ensure that management strategies change in real time with risk conditions. Risk control measures can not only be quickly initiated in high-risk situations, but also appropriately reduce intervention efforts when risks decrease to maximize resource utilization efficiency.

[0054] 7. The risk items in the industry standard risk library of the present invention are obtained by issuing questionnaires, and the obtained risk items are more suitable for the target hospital and more in line with the actual situation of the target hospital.

[0055] 8. The present invention uses a deep learning algorithm to identify the main objects of the questionnaire and then classifies them. It has clear classification standards and the analysis results are more targeted.

[0056] 9. The present invention uses vector analysis + clustering and combines a large language model to extract topics and keywords. It does not require pre-defined categories and is suitable for exploratory analysis. It can discover potential patterns in the data and the clustering results are stable.

[0057] 10. The present invention directly uses a large language model to cluster the questionnaire text and summarize the various topics. It is efficient and intelligent. The large language model can quickly and automatically complete clustering and topic summarization, saving labor costs. By utilizing the powerful capabilities of the large language model, more complex and detailed classification results can be obtained.

[0058] 11. The present invention can improve the quality of questionnaires by analyzing data dependencies and determining the contents of erroneous items in questionnaires. It can also discover and correct logical problems in questionnaire design and improve data quality. It can identify key dependencies, help understand the logical order between questions, and optimize questionnaire design. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a system architecture diagram of the hospital safety production operation management ecological platform of the present invention;

[0060] Figure 2 Schematic diagram of a bathtub curve in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings of the present invention specification to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Example 1

[0063] As a preferred embodiment of the present invention, refer to the attached specification Figure 1 As shown, this embodiment discloses a hospital safety production operation management ecological platform, which includes an industry standard risk library, a risk investigation module, a risk factor identification module, a risk control measures module, an event center emergency response module, an event analysis and decision-making module, and a dynamic risk classification module;

[0064] The industry standard risk database stores the risk items that have been discovered in the hospital industry; the industry standard risk database is like a knowledge treasure house, which stores in detail various risk items that have been discovered in the hospital industry in the past. These risk items are summarized based on a large amount of practical experience, industry research and relevant regulatory requirements, covering key areas such as medical service processes, medical equipment use, personnel management, and logistics support during hospital operations, providing an accurate and authoritative reference for subsequent risk screening and control.

[0065] The risk screening module calls the industry standard risk library to conduct standardized screening of existing risks in the target hospital, identify and obtain all risk items in the target hospital; conduct preliminary risk ratings on all risk items respectively, determine the initial level of each risk item; input each risk item and the initial level of each risk item as preliminary risk data into the dynamic risk grading module; the risk screening module plays an extremely critical pioneering role. By efficiently calling the industry standard risk library, a comprehensive and standardized screening of existing risks in the target hospital is carried out. In the screening process, advanced intelligent recognition technology combined with manual experience is used to accurately identify and obtain all risk items in the target hospital. Subsequently, a preliminary risk rating is performed for each risk item based on a scientific and reasonable risk rating system. This rating process comprehensively considers multiple factors such as the possibility of risk occurrence, the degree of harm that may be caused once it occurs, and the controllability of the risk, so as to determine the initial level of each risk item. Afterwards, each risk item and the corresponding initial level are used as preliminary risk data, and are fully and accurately input into the dynamic risk grading module, laying the foundation for subsequent dynamic risk assessment and adjustment.

[0066] The risk factor identification module collects data uploaded by the terminal devices of the operators, equipment detection data and / or video surveillance data, manages and analyzes the risk factors in the operation process of the target hospital identified by means including patrol inspections, equipment detection and / or video surveillance, identifies risk factor events, and transmits risk factor events to the emergency handling module of the event center; the risk factor identification module collects rich data uploaded by the terminal devices of the operators through a variety of advanced data collection methods. These data may include records of abnormal situations found by the operators in their daily work, feedback information from patients and their families, etc.; at the same time, it can also obtain equipment detection data in real time. These data are derived from the regular inspection and real-time monitoring systems of various medical equipment, logistics support equipment, etc., and can accurately reflect the operating status and potential failure risks of the equipment; in addition, video surveillance data is also one of its important data sources. Through high-definition surveillance cameras throughout various key areas of the hospital, the hospital's personnel flow, operating specifications, environmental safety and other aspects are fully monitored. With the help of diversified means including patrol inspections, equipment testing and video surveillance, we conduct in-depth management and detailed analysis of risk factors that arise during the operation of the target hospital. Once a risk factor event is identified, it will be transmitted to the emergency handling module of the event center as quickly as possible to ensure that the risk event can be responded to and handled in a timely manner.

[0067] The risk control measures module is used to manage operators and interact with their terminal devices; to formulate maintenance and maintenance plans for each device and monitor the implementation of the maintenance and maintenance plans for each device; to store and manage various emergency response plans, and monitor the implementation and results of various emergency response plans; the risk control measures module plays the dual role of "management executor" and "plan guardian". On the one hand, it is committed to efficient management of operators, and through establishing a close and convenient interactive channel with the terminal devices of operators, it realizes the reasonable allocation of operators' work tasks, real-time monitoring of work progress, and effective evaluation of work quality, ensuring that operators can work in strict accordance with safety regulations and operating procedures; on the other hand, it undertakes the important responsibility of formulating maintenance and maintenance plans for each device, and formulates detailed and personalized maintenance and maintenance plans according to factors such as the type, frequency of use, and service life of different equipment, and combines advanced Internet of Things technology with the equipment monitoring system to accurately monitor the implementation of the maintenance and maintenance plans of each device, and promptly discover and warn of abnormal conditions in the equipment maintenance process. At the same time, this module is also responsible for storing and managing various emergency response plans, which cover the response strategies and processes for various emergencies such as fire, earthquake, medical accidents, public health events, etc. In addition, by connecting with the information systems of various departments of the hospital, the implementation and results of various emergency response plans are monitored in real time, so as to conduct a comprehensive summary and evaluation after the emergency event, providing a strong basis for the optimization and improvement of the plan.

[0068] The emergency response module of the event center performs graded processing on the risk factor events reported by the risk factor identification module, performs emergency response on major events that are urgent and have a large impact, and manages the entire process of major events before, during, and after the event; the emergency response module of the event center is like the "emergency vanguard" of the hospital's safe production operation. When receiving the risk factor events reported by the risk factor identification module, it will quickly start the graded processing mechanism. For those major events that are urgent and have a large impact, all kinds of emergency resources within the hospital are immediately mobilized, including medical emergency teams, fire and security forces, and logistics support teams, to carry out all-round emergency response work. In the process of emergency response, the entire process of major events is managed, including risk warning and prevention measures implementation beforehand, rapid response and on-site rescue organization at the time of the incident, rescue action coordination and resource allocation optimization during the incident, and event investigation and recovery and reconstruction planning after the incident, to ensure that major events can be properly handled and the losses and impacts are minimized.

[0069] The event analysis and decision module is used to receive various event information triggered by the emergency response module and risk control measures module of the event center, and use the preset risk assessment model to comprehensively analyze the nature, frequency of occurrence and correlation factors of the event, and quantify the impact index of the event; the event analysis and decision module is mainly used to receive various event information triggered by the emergency response module and risk control measures module of the event center, which includes a detailed description of the event, the time and location of occurrence, and the personnel and equipment involved. Then, a set of highly intelligent and optimized risk assessment models that have been trained with a large amount of data are used to accurately judge the nature of the event, such as whether the event belongs to the category of technical failure, human operation error, or external environmental factors; statistical analysis is performed on the frequency of event occurrence to identify potential risk hazards with a higher frequency of occurrence; at the same time, the correlation factors are deeply analyzed to explore the internal connection between the event and the personnel, equipment, process, environment and other factors in the hospital operation management. Through comprehensive analysis of these multi-dimensional factors and the use of complex mathematical algorithms and data models, we can quantify the impact index of the incident. This index can intuitively reflect the comprehensive impact of the incident on hospital operation safety, medical service quality, patient rights protection, and social reputation, providing scientific and accurate data support for subsequent risk decision-making.

[0070] The dynamic risk grading module receives the impact index from the event analysis and decision-making module, and adjusts the level of each risk in combination with the preliminary risk data provided by the risk investigation module; the dynamic risk grading module uses a set of dynamically updated risk level algorithms, comprehensively considering multiple factors such as the changing trend of the event impact index, the inherent characteristics of the risk in the preliminary risk data, and the dynamic changes in the hospital operating environment, to make real-time and accurate level adjustments to each risk. According to the adjusted risk level, the corresponding risk control measures in the risk control measures module are dynamically adjusted through deep linkage with the risk control measures module. For example, for risk items with increased risk levels, the frequency of risk monitoring may be increased, the investment in risk prevention and control resources may be strengthened, and the emergency response process may be optimized; while for risk items with reduced risk levels, the monitoring requirements may be appropriately relaxed, and resources may be reasonably allocated to other high-risk areas, so as to achieve the optimal allocation of risk management resources and maximize the risk prevention and control effect.

[0071] Example 2

[0072] As another outstanding embodiment of the present invention, this embodiment, based on the solid foundation built by Embodiment 1, provides a more in-depth and detailed supplement and explanation of the technical solution of the present invention, and further improves the risk investigation and control mechanism of the hospital safety production operation management ecological platform.

[0073] In the risk screening module, the dynamic risk model is innovatively introduced as a powerful tool, aiming to implement comprehensive and efficient unified management of the rich and diverse risk items that have been accurately identified. This dynamic risk model is like an intelligent "risk ruler". It can be tailored for each risk item and provide highly targeted risk factor identification strategies and practical control measures based on the unique risk type of each risk item and the specific actual operating environment, medical service characteristics, staffing status and other factors of the target hospital. This means that no matter whether it is different types of risks such as medical technology risks, equipment operation risks, personnel management risks or logistics support risks, they can obtain risk response plans that are suitable for them under the in-depth analysis and precise guidance of the dynamic risk model, which greatly improves the accuracy and effectiveness of risk control.

[0074] The risk screening module uses the powerful computing power of the dynamic risk model to perform complex and precise risk score calculations for each risk item. This calculation process is not a simple numerical evaluation, but a comprehensive consideration of many key risk-related factors, such as the possibility of risk occurrence, the severity of potential hazards, the frequency of risk exposure and other multi-dimensional indicators. After obtaining the risk score x calculated by the dynamic risk model for each risk item, in order to achieve an intuitive comparison of the risk levels between different risk items and unified quantitative management, the risk score is converted into a uniformly measured risk risk level based on a pre-set and scientifically reasonable risk level interval.

[0075] Specifically, the risk score of the risk item calculated by the dynamic risk model is x, and its corresponding risk level range is (x 1 ,x 2 ), the corresponding risk level interval is (D 1 ,D 2 ), then it is converted into a unified measure of risk hazard level This conversion process is like translating risk information expressed in different "languages" into a universal and easy-to-understand "language", allowing hospital operation managers to clearly understand the relative degree of danger of various risks at a glance, thereby providing an extremely convenient and scientific basis for subsequent decisions such as risk resource allocation and control priority determination.

[0076] As a typical example of this embodiment, the dynamic risk model adopted can be the LEC evaluation model or LS evaluation model which is widely used in the field of risk assessment and has significant results. The LEC evaluation model comprehensively evaluates the size of the risk by multiplying the three key factors of the likelihood of an accident (Likelihood), the frequency of exposure to a dangerous environment (Exposure), and the possible consequences (Consequence) of the accident. Its evaluation results can more comprehensively reflect the actual situation of the risk, and are particularly suitable for risk assessment in various complex operating environments. The LS evaluation model focuses on the product calculation of the two core elements of the likelihood of a risk event (Likelihood) and the severity of the consequences (Severity) to determine the risk level. The model is relatively simple and intuitive, and performs well in some scenarios where the accuracy of risk assessment is high and data acquisition is relatively easy. Whether it is the LEC evaluation model or the LS evaluation model, it can fully play its role in the risk investigation module of the present invention by virtue of its unique advantages and characteristics, helping the hospital's safe production operation management ecological platform to more accurately identify, evaluate and control risks, and build a solid line of defense for the safe and stable operation of the hospital.

[0077] Example 3

[0078] As another valuable embodiment of the present invention, this embodiment, based on the solid and innovative foundation built by Embodiment 1 and Embodiment 2, has carried out a more in-depth and comprehensive expansion and sublimation of the technical solution of the present invention, further enhancing the excellent performance of the hospital safety production operation management ecological platform in dynamic risk assessment and precise classification.

[0079] In this embodiment, the dynamic risk grading module demonstrates a high degree of intelligent and refined management capabilities. It has made a breakthrough in comprehensively considering the complex and far-reaching impact of time factors and objective factors on risk items. In the long river of time, risk is not an unchanging static entity, but like a dynamically evolving living organism, its degree of danger will change significantly over time. At the same time, various objective factors in the operation of the hospital, such as medical technology innovation, personnel structure adjustments, policy and regulatory changes, etc., will also act as "stimulus signals" from the external environment, and have an impact that cannot be ignored on the degree of danger of risk items. By constructing a specific mathematical model, at time t, compared with the degree of danger determined by the preliminary risk data, the degree of danger of the current risk can be accurately expressed as ;in, It represents the impact of time factor on risk item at time t; Indicates the initial danger level of the risk item; Represents the impact of objective factors on risk items.

[0080] The impact of time factors on risk items presents a very regular bathtub curve feature. This bathtub curve vividly depicts the changing trajectory of risk during its life cycle: in the early failure period, risk is like a new unstable factor, and the probability of failure or adverse consequences is relatively high. As time goes by, it gradually enters the stable operation period, when the risk is in a relatively stable and controllable state. However, when it enters the late failure period, due to equipment aging, outdated technology and other reasons, the risk will rise again.

[0081] In order to accurately calculate the impact of this complex time factor, this embodiment uses the powerful mathematical tool Weibull distribution. The Weibull distribution is used to calculate the change trend of the risk item that conforms to the bathtub curve over time. The calculation formula is: ; In the formula, t represents time, Indicates the steepness of the bathtub curve, representing the drastic change of the risk item over time in the current time interval; different types of risks have different Value, various risks The value is provided by the industry standard risk library; β represents the risk under different conditions. When β < 1, it indicates the early failure period. When β=1, it indicates the stable operation period. When β>1, it indicates the late failure period, and Introduce variables and ; Indicates the end time of the early expiration period, Indicates the end time of the stable operation period; , , , and The value of is provided by the industry standard risk library; the trend of the risk item that conforms to the bathtub curve over time is calculated using Weibull distribution and is expressed as:

[0082] .

[0083] The impact of objective factors on risk is closely linked to the event analysis and decision-making module. Its specific calculation method is cleverly derived from the impact index output by the event analysis and decision-making module. For a risk, assuming that in the initial state, the impact of objective factors on the risk is , that is, at this time, objective factors have not yet had a substantial impact on the risk. After event x occurs, the impact of objective factors increases to ,in Indicates the degree of influence of event x on objective factors, different types of time It is also different, and is determined according to the impact index output by the event analysis decision module; assuming that at time t, a total of i events have occurred, the impact of objective factors on the risk item is This calculation method is like gradually superimposing the "impact" of each event on the objective factors, thereby comprehensively and accurately reflecting the comprehensive effect of objective factors on risk items under the influence of multiple events.

[0084] Finally, the dynamic risk grading module can accurately calculate the current risk level of risk F at time t compared with the risk level of the preliminary risk data by organically integrating the influence of time factors and objective factors. ; In the formula, Indicates the current danger level of risk F; represents the initial danger level of risk F, represents the changing trend of risk items over time, It represents the impact of objective factors on the risk item F. Through this comprehensive calculation model, the hospital safety production operation management ecological platform can accurately grasp the changes in the degree of danger of various risks at different time points and under different objective environments in real time, thereby providing the most scientific and reasonable decision-making basis for the risk control measures module, ensuring that the hospital can always maintain a high degree of safety and stability in a complex and changing operating environment, and provide patients with high-quality and safe medical services.

[0085] Example 4

[0086] As another excellent and critical embodiment of the present invention, this embodiment, based on fully absorbing the profound technical background and rich innovative achievements constructed by Embodiment 1, Embodiment 2 and Embodiment 3, provides a more in-depth, comprehensive and meticulous supplement and explanation of the technical solution of the present invention, and further optimizes and improves the functional architecture and operation mechanism of the risk factor identification module in the hospital safety production operation management ecological platform.

[0087] The risk factor identification module includes a patrol inspection submodule, which has powerful and flexible functions. It can not only scientifically and reasonably set and issue patrol inspection tasks according to the hospital's operation layout, equipment distribution, and past risk records, but also accurately locate and manage patrol inspection points, and carefully plan patrol inspection plans. In the actual operation process, the patrol inspection submodule, based on its pre-set patrol inspection tasks and patrol inspection points, is like an accurate information "transmitter tower", and timely issues patrol inspection task instructions to the terminal equipment of the patrol personnel. The patrol personnel use their convenient terminal equipment to scan the QR code of the patrol inspection point or use near-field communication (NFC) technology to identify and verify the patrol inspection point. This advanced identification and verification method not only ensures the precise matching of the patrol personnel's identity and the patrol inspection point, but also greatly improves the efficiency and accuracy of the patrol inspection work. During the patrol inspection process, the terminal equipment is like a keen "information collector", uploading the details of the patrol inspection list to the risk factor identification module in real time, so that the risk factor identification module can grasp the patrol inspection progress and various types of information found in real time. Once the terminal device reports an abnormal situation, the risk factor identification module will quickly and decisively transmit the abnormal situation event to the emergency processing module of the event center to ensure that the abnormal situation can be handled promptly and effectively, just like sounding an emergency alarm for the safe operation of the hospital and nipping potential risks in the bud.

[0088] The risk factor identification module includes an equipment detection submodule, which builds a real-time monitoring data network covering various risk items through the Internet of Things and sensor monitoring technology. It is closely connected with the sensors related to each risk item, and always monitors the key parameter data of the fire automatic alarm system, oxygen station system, positive and negative pressure system, medical area power system and logistics support equipment risk scenarios, and uploads the monitored data to the equipment detection submodule in real time. The equipment detection submodule uses advanced data analysis algorithms to perform real-time analysis on the received sensor monitoring data stream based on preset thresholds and rich historical data. When the sensor monitoring data fluctuates significantly or exceeds the normal range, the equipment detection submodule can quickly and accurately identify abnormal situations with its powerful intelligent recognition capabilities. At this time, it will upload the abnormal sensor monitoring data as a risk factor to the emergency processing module of the event center, providing extremely critical first-hand data for subsequent risk assessment and emergency disposal.

[0089] The risk factor identification module includes a video monitoring submodule, which is connected to the video monitoring system of the target hospital and uses AI intelligent analysis to identify risk factors in the video screen. When risk factors or abnormal behaviors are detected in the video screen, the video monitoring submodule transmits the risk factors or abnormal behaviors to the emergency processing module of the event center and generates real-time alarms and video screenshots. These real-time alarm information is like a sharp alarm sound, which can attract the attention of relevant personnel at the first time, and the video screenshots provide intuitive and powerful evidence support for subsequent event investigation and analysis, so that hospital operation managers can quickly understand the scene and details of the incident, so as to formulate response strategies and solutions more efficiently, adding a pair of intelligent and reliable "eyes" to the overall safety operation of the hospital, and comprehensively guaranteeing the safe and stable operation of the hospital.

[0090] In this embodiment, the risk factor identification module builds a multi-level, comprehensive, and intelligent risk factor identification system through the collaborative work and information sharing of the patrol inspection sub-module, the equipment detection sub-module, and the video monitoring sub-module. It can accurately and timely discover various risk factors in the hospital operation process, and quickly transmit them to the event center emergency processing module for subsequent processing, providing a solid and powerful guarantee for the efficient and stable operation of the hospital's safe production operation management ecological platform, ensuring that the hospital can provide patients with high-quality medical services in a safe and orderly environment.

[0091] Example 5

[0092] As another key embodiment of the present invention, this embodiment further expands and explains the technical solution in detail based on Embodiment 1, Embodiment 2, Embodiment 3 and Embodiment 4. In this embodiment, the emergency response module of the event center implements full-process management for events, covering four stages: before, during, during and after the event.

[0093] In the pre-event stage, the emergency response module of the event center establishes a docking link with each business system of the target hospital, centrally gathers the alarm data of each business system through the data acquisition interface, and uses a unified scheduling algorithm for comprehensive management. Relying on the network communication protocol, the emergency response module of the event center is connected to each business system in the hospital, and a standardized data transmission format and communication protocol specification are set to ensure that the alarm information of each business system is accurately transmitted to the emergency response module of the event center. This module uses a real-time monitoring mechanism to receive the alarm information pushed by each business system, uses a data parsing engine to analyze the received alarm information, extracts key data fields, and stores the parsed alarm information in a structured database. At the same time, it is prominently displayed in a specific format on the monitoring interface to facilitate monitoring personnel to detect and view it in time.

[0094] When an incident occurs, that is, at the incident stage, the emergency response module of the event center, after receiving the alarm information, uses the visual map tool to accurately locate the specific geographical coordinates of the alarm, and uses the speech synthesis and broadcasting system to broadcast the alarm information. At the same time, the video surveillance data stream around the alarm location is obtained through the video surveillance integrated interface, so that the processing personnel can view the actual situation on the scene in real time, provide them with intuitive on-site situation awareness, and assist the processing personnel in making accurate judgments and analytical decisions on the on-site situation.

[0095] Entering the mid-event stage, the emergency handling module of the incident center retrieves the emergency response plan that matches it from the plan library of the risk control measures module based on the key features and parameters of the alarm information. Based on the content of the plan, the execution instructions are sent to the terminal devices of relevant personnel according to the preset instruction push mechanism, and the personnel are directed to carry out corresponding actions through the command and dispatch system. In addition, a linkage interface is established with the access control system to provide convenient conditions such as access control and path guidance for the actions of rescue personnel or security personnel based on the needs of the emergency response process, ensuring the efficient and smooth implementation of emergency actions.

[0096] In the post-event stage, the emergency response module of the event center comprehensively collects relevant data records from the alarm information access starting point, the emergency response plan activation time to the entire emergency response plan implementation process through the data collection interface. The data analysis model is used to deeply analyze the deviation factors that are inconsistent with the emergency response plan during the event handling process, and the plan evaluation algorithm is used to evaluate the adaptability and inclusiveness of the emergency response plan. Based on the analysis and evaluation results, the emergency response plan pre-set in the risk control measures module is optimized and adjusted in a targeted manner, thereby improving the effectiveness and accuracy of the overall risk control measures.

[0097] Example 6

[0098] As another preferred embodiment of the present invention, this embodiment is based on the above-mentioned embodiment 1, embodiment 2, embodiment 3, embodiment 4 or embodiment 5, and further supplements and elaborates on the technical solution of the present invention in detail. In this embodiment, the risk control measures module is mainly composed of an equipment operation and maintenance submodule, a personnel management submodule and an emergency plan submodule. The equipment operation and maintenance submodule integrates core functional modules such as equipment asset management, fault report generation and upload, and maintenance task scheduling, and manages the entire life cycle of various types of equipment in the hospital. It formulates maintenance and maintenance plans for each equipment based on multi-dimensional factors such as equipment type, frequency of use, and operating status, and monitors the execution progress and actual effect of each equipment maintenance and maintenance plan in real time through the equipment operation monitoring system. The personnel management submodule constructs an operator information database to record the basic information such as identity information, position information, skill qualifications, etc. of each operator, and uses a mobile terminal application (APP) or desktop management system to interact with the operator's terminal device for information and push tasks. As a repository for emergency response plans, the emergency plan submodule is responsible for the storage, updating and version management of the plans. It also monitors the implementation status and final result feedback of each emergency response plan in real time during the actual execution process through the data monitoring interface, providing data support for the optimization and improvement of the plans.

[0099] As a preferred implementation scheme of this embodiment, the risk control measures module also covers a training and drill sub-module and a safety education sub-module. The training and drill sub-module formulates training plans and emergency drill plans for operators based on the hospital's operational management needs and risk prevention and control requirements, and organizes and manages the implementation of various training activities and emergency drill activities, including functions such as training resource allocation, drill scenario design, and drill process evaluation. As a dissemination platform for safety education knowledge for operators, the safety education sub-module regularly publishes special learning tasks and learning materials, uses a learning progress tracking algorithm to monitor the learning progress of operators, and records the learning history data of each operator. Regularly conduct safety education tests on operators. After the test, use the intelligent evaluation system to generate a test report to quantitatively evaluate the safety knowledge level of operators, providing a basis for subsequent personalized training and safety education strategy adjustments.

[0100] Example 7

[0101] As another preferred embodiment of the present invention, the technical solution of the present invention is further supplemented and elaborated in detail in combination with the above-mentioned embodiment 1, embodiment 2, embodiment 3, embodiment 4, embodiment 5 and embodiment 6. In this embodiment, the risk items stored in the industry standard risk library are obtained by means of a questionnaire.

[0102] As an example of this embodiment, a risk questionnaire is issued in the target hospital. After the questionnaire is collected, the text of the questionnaire is semantically analyzed and vocabulary understood using a deep learning algorithm to identify the subject objects involved in the text, which may be people, objects, management or environmental factors, etc. For the identified subject objects, the correlation is calculated with four aspects: unsafe behavior of people, unsafe behavior of objects, management defects, and unsafe factors of the environment. For example, the correlation with unsafe behavior of people is determined by analyzing the words related to human behavior (such as "negligence" and "illegal operation" etc.) that appear in the text; the correlation with the unsafe state of objects is determined by analyzing the words related to the state of objects (such as "damage" and "failure" etc.). The four calculated correlations are compared, and the category with the highest correlation is selected as the final classification of the problem.

[0103] As another example of this embodiment, a risk questionnaire is issued in the target hospital. After the questionnaires are collected, the questionnaire text is first clustered. Through the pre-trained semantic model, the text of each survey item in the survey file is converted into a vector, and clustering is performed based on the text vector using a clustering algorithm (K-means).

[0104] Specifically, a vector model is used to convert text into a computable digital vector, usually using word embedding technology such as Word2Vec and GloVe. These technologies represent each word as a low-dimensional vector, so that words with similar semantics are closer in the vector space. Texts corresponding to vectors with high similarity are classified into one category. A similarity threshold can be set. When the similarity of two text vectors exceeds the threshold, they are classified into the same category. Using a large language model to analyze the clustering results can provide a deeper understanding of the theme and characteristics of each type of text. The large language model has powerful language understanding and generation capabilities, and can extract key information and patterns from large amounts of text data.

[0105] The large language model generally summarizes the topic of each category in the following ways: (1) Analyze the keywords and key phrases of representative texts in the category. The large model can identify important words in the text and summarize the topic keywords based on these words. (2) Generate descriptive sentences for the category. The large model can generate concise and accurate descriptive sentences based on the text content in the category to summarize the topic of the category. (3) Compare the differences between different categories. The large model can compare the text characteristics of different categories and highlight the uniqueness of each category, thereby better summarizing the topic.

[0106] After obtaining the clustering results, the topics and keywords of each category are extracted from it. Here, the large language model is used again to input the text of each category into the large language model, so that the large language model can summarize the topics and keywords of each category based on these texts.

[0107] As another example of this embodiment, a risk questionnaire is issued in the target hospital. After the questionnaires are collected, all the questionnaire texts are directly input into the large language model, and the large language model is directly allowed to cluster and summarize the themes of each category.

[0108] Based on the questionnaire completion and hospital situation description information of the hospital, the LEC evaluation method is used to input the relevant data into the large language model. The large language model selects the appropriate scores for L accident, the possibility of event occurrence, E the frequency of personnel exposure to dangerous environments and C the possible consequences of accidents according to the standards in the LEC table to obtain the risk value score and the corresponding risk judgment level.

[0109] Through data dependency analysis, the erroneous content of the items in the questionnaire can be determined. Specifically, based on the dependency relationship between the survey items in the questionnaire, the interdependence between the response items is analyzed, and the dependency basis is given. The erroneous content of the response items filled in the questionnaire can be determined through the dependency basis.

[0110] In this embodiment, the above different text analysis methods have different focuses, and multiple methods can be used in combination when performing multi-angle analysis. For example, logical dependency analysis is first used to ensure the logical accuracy of the questionnaire, and then cluster analysis is performed, and finally risk score prediction is performed, so as to comprehensively analyze all aspects of the safety physical examination questionnaire.

[0111] Example 8

[0112] As another preferred embodiment of the present invention, in combination with the above-mentioned Embodiment 1, Embodiment 2, Embodiment 3, Embodiment 4, Embodiment 5, Embodiment 6 or Embodiment 7, this embodiment provides an overall description of the hospital safety production operation management ecological platform of the present invention.

[0113] This embodiment provides a hospital safety production operation management ecological platform, the core of which is to achieve dynamic monitoring of hospital safety production and adjustment of control measures through systematic risk management.

[0114] Hospital safety production operation management ecological platform, refer to the instruction manual Figure 1 As shown, it includes industry standard risk library, risk investigation module, risk factor identification module, risk control measures module, event center emergency response module, event analysis and decision module and dynamic risk classification module. Its process is as follows:

[0115] First, the risk screening module calls the industry standard risk library to comprehensively identify all potential risk points in the target hospital and conducts a preliminary risk rating to determine the initial level of risk. This initial level will serve as an input parameter for the dynamic risk grading module to provide basic data for subsequent risk management;

[0116] Subsequently, the risk factor identification module will analyze and identify the key events that affect the risk. These events will be sent to the emergency disposal module of the event center for further processing. If the event is determined to be an important event, the emergency disposal process will be triggered to achieve rapid response and processing. At the same time, the risk control measures module will take corresponding control measures for the identified risks from multiple angles to reduce the possibility of risk occurrence or mitigate its potential impact. The risk factor identification module and the risk control measures module output risk change indicators through the event analysis decision module. These indicators will be used to dynamically adjust the risk rating to obtain an updated risk level. Finally, according to the updated risk level, the corresponding risk control measures are dynamically adjusted to ensure the continuous optimization of hospital safety production and the effective control of risks.

[0117] The above-mentioned risk screening module is the source of initial risk data for the hospital's production safety operation management ecological platform. It conducts standardized screening of existing risks in the hospital based on the industry standard risk library, and comprehensively identifies and acquires all risks within the hospital. Subsequently, the identified risk points are uniformly managed through a dynamic risk model, and corresponding risk factor identification and control measures are provided based on the risk type and the actual situation of the hospital. The dynamic risk model uses the LEC or LS evaluation method to make a preliminary judgment on the risk and calculate the risk hazard score. The score is converted into a uniformly measured risk hazard degree based on the risk level range to ensure the uniformity of the risk level, thereby providing basic data for subsequent dynamic risk management. Specifically, the risk score calculated by the dynamic risk model for the risk item is x, and the corresponding risk level range is (x 1 ,x 2 ), the corresponding risk level interval is (D 1 ,D 2 ), then it is converted into a unified measure of risk hazard level .

[0118] The above-mentioned risk factor identification module includes patrol inspection submodule, equipment monitoring submodule and video analysis submodule. Among them, patrol inspection is an important means to discover and identify risk factors. By setting basic data such as patrol tasks, patrol points and plan information, the patrol inspection submodule will automatically issue tasks to the corresponding patrol personnel. The patrol personnel use the wireless intelligent patrol terminal to scan the QR code or NFC recognition to verify the patrol point, and at the same time can effectively avoid cheating in paper filling. During the patrol process, the terminal stores and uploads the situation of the patrol point to the platform in real time. The reported abnormal situation will also be automatically uploaded to the emergency disposal module of the event center, which is convenient for the subsequent processing of the emergency disposal module of the event center. Equipment monitoring is an important means to monitor fixed areas in real time and identify potential risks. The equipment monitoring submodule monitors real-time data related to risks through the Internet of Things smart sensors. It is suitable for key parameter data monitoring in risk scenarios such as automatic fire alarm, oxygen station system, positive and negative pressure system, medical area power system, logistics support equipment (such as power distribution, air conditioning, elevator, boiler, etc.). The sensor uploads the monitored data to the ecological platform in real time, and the ecological platform analyzes the received data stream in real time according to the preset threshold and historical data. When the sensor detects that the data has a more obvious fluctuation or exceeds the normal range, the ecological platform automatically identifies the anomaly and uploads the risk factor to the emergency response module of the event center. Video surveillance uses AI intelligent analysis to identify risk factors in the video screen. It is an important means of real-time monitoring of areas that cannot be quantitatively monitored, such as battery storage areas, UPS rooms, electrical rooms, warehouses, electric sheds, hazardous chemical warehouses, and key personnel activity areas. The ecological platform automatically identifies high-risk factors such as smoke, flames, crowds, and crowds through AI analysis technology. When the ecological platform detects risk factors or abnormal behavior, it will automatically push the monitoring results to the emergency response module of the event center and generate real-time alarms and video screenshots for relevant personnel to follow up.

[0119] The above-mentioned emergency response module of the event center is to grade the risk factor events reported in the risk factor identification module. For major events that are urgent and have a large impact, emergency response needs to be carried out through the emergency command center. The whole process of major events is handled before, during, during and after the event. The steps include:

[0120] Before the above, the ecological platform collects information from various alarm systems by connecting to the hospital fire protection business subsystem, and centrally manages the dispatch. The connected systems include: fire alarm host, smart fire protection system, medical alarm system, intrusion alarm, access control alarm, video surveillance system, etc. Each system transmits the alarm information to the emergency command center through the network. The alarm connection process is as follows:

[0121] (1) System preparation and connection. Ensure that all relevant alarm systems, such as the fire alarm host, smart fire protection system, one-button alarm system, intrusion alarm system, access control alarm system, etc., operate normally and establish stable network connections with each other and with the ecological platform (or the emergency response module of the event center). Set a unified data transmission format and protocol so that the alarm information issued by each system can be accurately identified and received by the ecological platform (or the emergency response module of the event center);

[0122] (2) Generation and sending of alarm information:

[0123] ——The sensors of the fire alarm host continuously monitor environmental parameters such as smoke and temperature. Once the detection value exceeds the preset safety threshold, an alarm signal containing detailed information such as the alarm location, type (such as smoke alarm, high temperature alarm), time, etc. is immediately generated and sent to the ecological platform (or the emergency response module of the event center) through the network;

[0124] ——The smart fire protection system collects real-time data on the operating status of fire protection facilities (such as whether the fire pump is working properly, whether the sprinkler head is blocked, etc.) and the water level and pressure information of fire protection water. When an abnormality is detected, this data is integrated with the alarm information and sent;

[0125] ——When a person triggers the alarm button, the one-button alarm system obtains the button location information, quickly generates and sends an alarm signal, and can also attach a brief description of the situation entered by the on-site personnel;

[0126] ——The intrusion alarm system uses infrared, microwave and other detection technologies to sense illegal intrusions, and sends alarm messages with information such as the intrusion location, intrusion direction, and suspicious target characteristics;

[0127] ——When the access control alarm system detects an abnormal access control opening (door opening timeout), it sends the access control location, abnormal opening time, etc. to the emergency command center;

[0128] (3) The ecological platform (or the emergency response module of the event center) receives and processes:

[0129] ——The receiving end of the ecological platform (or the emergency response module of the event center) monitors and receives the alarm information sent by each system in real time. After receiving the alarm information, it parses the information and extracts key content, such as the alarm source system, alarm location, alarm type and severity, etc.;

[0130] ——Store the parsed alarm information in the database and display it in a striking manner on the monitoring interface of the ecological platform, such as popping up an alarm window, sounding an alarm, etc., to attract the attention of the on-duty personnel.

[0131] The above incident can quickly locate the alarm location and obtain on-site images for auxiliary judgment. After receiving the alarm, the ecological platform locates the specific location of the alarm through a visual map, broadcasts the alarm information through voice, and automatically connects to the surrounding video surveillance to view the on-site situation, assisting the on-duty personnel to quickly and accurately judge the on-site situation. The specific process of police linkage is as follows:

[0132] (1) Receiving alarm information: The ecological platform receives alarm information from various alarm systems (such as fire alarm host, intrusion alarm system, etc.);

[0133] (2) Map positioning: Through the equipment deployment space, the alarm location can be quickly and accurately located on the visual map, and the alarm location can be displayed through animation;

[0134] (3) Video surveillance linkage: Automatically link with surrounding video surveillance to quickly select images related to the alarm location from a large number of surveillance images;

[0135] (4) Intelligent screen screening: Based on the alarm type and location information, the nearest surveillance screen is intelligently selected. For example, a fire alarm will prioritize the display of the screen near the fire point, and an intrusion alarm will display the screen of a suspicious person breaking into the area, thereby confirming the on-site situation from multiple angles.

[0136] In the above-mentioned incident, personnel and materials are accurately dispatched according to the plan to ensure the orderly implementation of emergency operations. According to the plan, the on-duty personnel command the actions of personnel and deploy materials according to the prompts. The plan automatically notifies relevant personnel and links access control to facilitate rescue and security operations. The specific process is as follows:

[0137] (1) Plan activation: The ecological platform quickly activates the corresponding traction emergency command plan based on the type and location of the alarm event;

[0138] (2) Personnel dispatch planning: Regarding personnel dispatch, the specific tasks of each rescue team and security personnel are clarified, and a clear action route is planned, such as which channel the fire brigade will enter the fire scene from, where the rescue team will start the rescue, etc.

[0139] (3) Material dispatch and arrangement: For material dispatch, accurately inform the material storage location, transportation route and usage distribution, such as the allocation of fire-fighting equipment and first aid materials;

[0140] (4) Information notification: Automatically notify relevant personnel through various communication methods (such as SMS, voice, system messages, etc.) to ensure that they can obtain instruction information in a timely manner;

[0141] (6) Facility linkage: Linked with facilities such as access control, for example, to quickly open passages for rescue personnel and restrict unauthorized personnel from entering dangerous areas.

[0142] After the incident, analyze the factors that deviate from the plan, evaluate the inclusiveness of the plan, improve the risk control links in the emergency plan such as fire protection, alarm, and command process, optimize risk control measures, and enhance emergency response capabilities. The specific process is as follows:

[0143] (1) Data collection: After the incident is handled, all relevant data from the entire process of alarm information access, alarm information linkage to command and dispatch are collected, including alarm records of various systems, command information from the command center, personnel and material dispatch status, on-site feedback information, etc.

[0144] (2) Link analysis: Carefully study each link in the event process, including the execution time, execution status, and results of each link;

[0145] (3) Determination of deviation factors: Compare the actual implementation situation with the planned emergency plan to find out the factors that deviate from the planned plan, such as rescue personnel not arriving at the scene according to the planned route, and materials not being deployed in time.

[0146] (4) Cause analysis: For the deviation factors found, analyze the causes in depth, which may be communication failure, insufficient personnel training, defects in the plan itself, etc.

[0147] (5) Inclusiveness assessment: assess the emergency plan’s tolerance to these deviation factors and determine the plan’s flexibility and adaptability in the face of such situations;

[0148] (6) Plan revision: Based on the analysis results, make reasonable revisions to relevant risk controls, such as adjusting command processes, optimizing material reserve plans, etc.;

[0149] (7) Optimization of measures: Starting from the improvement of fire-fighting facility maintenance, optimization of alarm systems, and improvement of command and dispatch processes, the overall emergency management level will be comprehensively improved.

[0150] The risk control measures module includes an equipment operation and maintenance submodule, a personnel management submodule, an emergency plan submodule, a training and drill submodule, and a safety education submodule. The risk control measures module can be dynamically adjusted according to the risk level. When the risk level increases or decreases, the management measures related to the corresponding risk are adjusted synchronously to achieve accurate risk control.

[0151] The equipment operation and maintenance submodule effectively identifies and prevents potential failures through regular maintenance and upkeep of equipment, significantly reducing the probability of sudden accidents. This submodule includes a one-stop maintenance center and a maintenance task module. The one-stop maintenance center integrates multiple functions such as equipment management, fault reporting, and maintenance scheduling to achieve rapid response and centralized management of hospital equipment problems, thereby improving maintenance efficiency, shortening equipment downtime, and ensuring the continuity of medical services. The equipment operation and maintenance submodule formulates personalized maintenance plans based on the different types and usage conditions of equipment, and automatically generates and issues corresponding maintenance tasks, aiming to extend the life of the equipment and optimize operating performance.

[0152] The personnel management submodule centrally manages security personnel. This submodule records the basic information, job responsibilities, shift schedule, etc. of each security personnel. By collecting attendance data and work task records, it automatically calculates working hours, overtime hours and related performance indicators, thereby achieving reasonable human resource allocation to meet different security management needs.

[0153] The emergency plan submodule is used to store and manage various emergency response plans. The emergency plan submodule contains a series of preset emergency plans, covering measures for dealing with emergencies such as fire, equipment failure and violent incidents. When this submodule detects an emergency, it can quickly call the corresponding plan, issue instructions to relevant security personnel, and provide step-by-step operation instructions. Security personnel receive dispatch instructions through terminal devices and execute corresponding emergency tasks in a timely manner.

[0154] The training and drill submodule is used to organize and manage the daily training and emergency drill activities of security personnel. The training and drill submodule has training courses, emergency drills and personnel assessment management functions. According to job responsibilities, different types of professional training and drill activities are regularly arranged. The training and drill submodule records the training results and skill improvement status of each security personnel in detail to form a training file.

[0155] The safety education submodule provides systematic safety knowledge education for security personnel and safety management personnel. The safety education submodule covers a variety of educational resources such as video courses, graphic tutorials and online tests. It can regularly release special learning tasks, monitor learning progress and record the learning status of each person. After the test, the system generates a test report to help evaluate and improve the safety knowledge level of personnel.

[0156] The event analysis and decision-making module is used to receive various event information triggered by the emergency response module and risk control measures module of the event center, and use the preset risk assessment model to conduct a comprehensive analysis of factors such as the nature, frequency and correlation of the event, so as to quantify the impact index D of the event. This index D not only reflects the direct impact of a single event, but also reveals the potential correlation and systemic risks between events, providing data support for subsequent dynamic risk grading.

[0157] The dynamic risk grading module receives the risk change indicator data from the event analysis and decision-making center module, and adjusts the level of each risk in real time based on the preliminary risk data provided by the risk screening module. The specific operation process includes:

[0158] (1) Give priority to the impact of time factors on the risk system environment. The degree of risk will change with the use time. Considering the early failure period, stable operation period, and late failure period, the overall change of risk over time conforms to the bathtub curve;

[0159] (2) Consider the impact of objective factors on the risk system environment. Changes in the operating environment that lead to abnormal equipment status and equipment failures can increase the risk level. In other words, regular equipment maintenance can reduce the risk level.

[0160] (3) Taking into account the impact of time factors and objective factors on risk points, the current risk level at time t is obtained compared with the initial risk level: ;in, It represents the impact of time factor on risk item at time t; Indicates the initial danger level of the risk item; Indicates the impact of objective factors on risk items;

[0161] (4) Obtain the calculated risk level and determine the changed risk level based on the risk level zoning.

[0162] Furthermore, in the above item (1), the Weibull distribution is used to calculate the impact of the time factor on the risk item, that is, the Weibull distribution is used to calculate the change trend of the risk item that conforms to the bathtub curve over time. The calculation formula is: ; In the formula, t represents time, Indicates the steepness of the bathtub curve, representing the drastic change of the risk item over time in the current time interval; different types of risks have different Value, various risks The value is provided by the industry standard risk library; β represents the risk under different conditions. When β < 1, it indicates the early failure period. When β=1, it indicates the stable operation period. When β>1, it indicates the late failure period, and Introduce variables and ; Indicates the end time of the early expiration period, Indicates the end time of the stable operation period; , , , and The value of is provided by the industry standard risk library; the trend of the risk item that conforms to the bathtub curve over time is calculated using Weibull distribution and is expressed as:

[0163] .

[0164] Furthermore, in the above item (2), the objective factors that affect the risk level are considered. During the operation process, the objective factors that affect the risk level are calculated through real-time monitoring of safety equipment, AI intelligent analysis and early warning, and operation and maintenance of equipment and facilities. For a risk, assuming that in the initial state, the impact of objective factors on the risk is , that is, no influence. After event x occurs, the influence of objective factors increases to ,in Indicates the degree of influence of event x on objective factors, different types of time It is also different, and is determined according to the impact index output by the event analysis decision module; assuming that at time t, a total of i events have occurred, the impact of objective factors on the risk item is .

[0165] Furthermore, in the above item (4), the dynamic risk grading module comprehensively considers the influence of time factors and objective factors on the risk item F, and obtains the current risk level of risk F at time t, compared with the risk level of the preliminary risk data, expressed as ; In the formula, Indicates the current danger level of risk F; represents the initial danger level of risk F, represents the changing trend of risk items over time, It indicates the impact of objective factors on the risk item F. The final value is defined according to the risk range and its risk level is adjusted dynamically.

[0166] Example 9

[0167] As another preferred embodiment of the present invention, this embodiment further elaborates and supplements the technical solution of the present invention through an example. This embodiment provides a hospital safety production operation management ecological platform, which includes an industry standard risk library, a risk investigation module, a risk factor identification module, a risk control measure module, an event center emergency response module, an event analysis and decision module, and a dynamic risk classification module.

[0168] The risk screening module conducts standardized screening of existing risks in the hospital based on the industry standard risk library, comprehensively identifies and obtains all risks in the hospital. Subsequently, the identified risk points are uniformly managed through a dynamic risk model, and corresponding risk factor identification and control measures are provided according to the risk type and the actual situation of the hospital. Using the LEC or LS evaluation method, the risk is initially judged and the risk score is converted into a uniformly measured risk hazard degree according to the risk level range, thereby providing basic data for subsequent dynamic risk management.

[0169] After the risk is counted, it is necessary to convert the calculated risk score into a unified risk level. In this article, risk X is evaluated by LEC and scored as L: possible, but not often, E: once a week, or accidental exposure, and C: 1 death, as shown in Table 1 below. ,Under the LEC method, the score range corresponding to the risk level is [70,160) as shown in Table 2 and Table 3, which belongs to general risk, and the corresponding level of danger level range is [25%,50%), which is converted into a unified risk danger level D, as , In its initial state, risk X is a general risk with a risk level of 43%.

[0170] Table 1 shows the evaluation of LEC evaluation method

[0171]

[0172] Table 2 shows the score range corresponding to the level under the LEC method

[0173]

[0174] Table 3 shows the risk level range corresponding to the risk level

[0175]

[0176] The risk factor identification module includes a patrol inspection submodule, an equipment monitoring submodule and a video analysis submodule. Among them, patrol inspection is an important means to discover and identify risk factors. By setting basic data such as patrol tasks, patrol points and plan information, the patrol inspection submodule will automatically issue tasks to the corresponding patrol personnel. The patrol personnel use the wireless intelligent patrol terminal to scan the QR code or NFC identification to verify the patrol point, and at the same time can effectively avoid cheating in paper filling. During the patrol process, the terminal stores and uploads the situation of the patrol point to the platform in real time. The reported abnormal situation will also be automatically uploaded to the emergency disposal module of the event center, which is convenient for the subsequent processing of the emergency disposal module of the event center. Equipment monitoring is an important means to monitor fixed areas in real time and identify potential risks. The equipment monitoring submodule monitors real-time data related to risks through the Internet of Things smart sensors, which is suitable for key parameter data monitoring in risk scenarios such as automatic fire alarms, oxygen station systems, positive and negative pressure systems, medical area power systems, and logistics support equipment (such as power distribution, air conditioning, elevators, boilers, etc.). The sensor uploads the monitored data to the ecological platform in real time, and the ecological platform analyzes the received data stream in real time according to the preset threshold and historical data. When the sensor detects that the data has a more obvious fluctuation or exceeds the normal range, the ecological platform automatically identifies the anomaly and uploads the risk factor to the emergency response module of the event center. Video surveillance uses AI intelligent analysis to identify risk factors in the video screen. It is an important means of real-time monitoring of areas that cannot be quantitatively monitored, such as battery storage areas, UPS rooms, electrical rooms, warehouses, electric sheds, hazardous chemical warehouses, and key personnel activity areas. The ecological platform automatically identifies high-risk factors such as smoke, flames, crowds, and crowds through AI analysis technology. When the ecological platform detects risk factors or abnormal behavior, it will automatically push the monitoring results to the emergency response module of the event center and generate real-time alarms and video screenshots for relevant personnel to follow up.

[0177] The emergency response module of the event center is used to grade the risk factor events reported in the risk factor identification module. For major events that are urgent and have a large impact, emergency response must be carried out through the emergency command center.

[0178] The risk control measures module includes an equipment operation and maintenance submodule, a personnel management submodule, an emergency plan submodule, a training and drill submodule, and a safety education submodule.

[0179] The event analysis and decision-making module is used to receive various event information triggered by the emergency response module and risk control measures module of the event center, and use the preset risk assessment model to conduct a comprehensive analysis of factors such as the nature, frequency and correlation of the event, so as to quantify the impact index D of the event. This index D not only reflects the direct impact of a single event, but also reveals the potential correlation and systemic risks between events, providing data support for subsequent dynamic risk grading.

[0180] Furthermore, in this embodiment, through risk factor screening, a total of 100 events were found within one year. After event analysis and decision-making, 6 of them were related to risk X. Combined with the nature of the event and the length of the impact of the risk factor on risk X, the impact of each event on risk X was obtained, as shown in Table 4 below:

[0181] Table 4 shows the impact of each event on risk X

[0182]

[0183] Among them, an impact degree greater than 1 means that the risk will have a negative impact on the event, and the event makes the risk level of risk X greater. A value less than 1 means the opposite, and a value equal to 1 means that the event has no impact on the current risk.

[0184] The dynamic risk grading module receives the risk change indicator data from the event analysis and decision center module, and adjusts the level of each risk in real time in combination with the preliminary risk data provided by the risk screening module. In this embodiment, the initial risk level of risk D is D=43%, which is a general risk. After one year, the definition of the risk level is: ;

[0185] It is necessary to consider the events of this year and the impact of objective risk factors on risk. First, consider the impact of time factors on the risk system environment, and combine the bathtub curve to calculate the impact of time factors on risk at a specified time. Combine the formula and let , , , , as well as , which is 15, 2, 18, 0.5, 1, and 3, indicating that the current type of risk enters the stable operation period from the early failure period in the second year and enters the late failure period in the 18th year. Under the conditions of this paper, the impact of time factors on this type of risk is as follows: Figure 2 shown.

[0186] Considering only the time factor, in the first year, the risk level is: .

[0187] When only the time factor is considered, in the first year, according to Table 3, the current level is still general risk.

[0188] Considering that there were multiple risk factor events during this year, each risk factor event had a different degree of impact after event analysis and decision-making. Considering the impact of objective factors and the impact of event factors, in the first year, the risk level is: .

[0189] After comprehensive consideration, according to Table 3, the risk level is dynamically adjusted to a higher risk.

[0190] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with the technology to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. Hospital safety production operation management ecological platform, characterized by: The ecological platform includes an industry standard risk library, a risk investigation module, a risk factor identification module, a risk control measures module, an event center emergency response module, an event analysis and decision-making module, and a dynamic risk grading module; The industry standard risk database stores risk items that have been discovered in the hospital industry; The risk screening module calls the industry standard risk library to conduct a standardized screening of existing risks in the target hospital, identify and obtain all risk items in the target hospital; perform preliminary risk rating on all risk items respectively, and determine the initial level of each risk item; Input each risk item and its initial level into the dynamic risk grading module as preliminary risk data; In the risk screening module, a dynamic risk model is used to uniformly manage the identified risk items, and corresponding risk factor identification and control measures are provided according to the risk type of each risk item and the actual situation of the target hospital; The risk screening module uses the dynamic risk model to calculate the risk score of each risk item, and converts the risk score into a uniformly measured risk degree according to the risk level interval; specifically, the risk score of the risk item calculated by the dynamic risk model is x, and its corresponding risk level interval is (x1, x2), and the corresponding risk degree interval is (D1, D2), which is converted into a uniformly measured risk degree ; The risk factor identification module collects data uploaded by terminal devices of operators, equipment detection data and / or video monitoring data, manages and analyzes risk factors in the target hospital operation process identified by means including patrol inspections, equipment detection and / or video monitoring, identifies risk factor events, and transmits risk factor events to the emergency processing module of the event center; The risk control measures module is used to manage the operators and interact with the terminal devices of the operators; to formulate the maintenance and maintenance plans for each device and monitor the implementation of the maintenance and maintenance plans for each device; to store and manage each emergency response plan and monitor the implementation and results of each emergency response plan; The emergency response module of the event center performs graded processing on the risk factor events reported by the risk factor identification module, performs emergency response on major events that are urgent and have a large impact, and manages the entire process of major events before, during, during, and after the event. The event analysis and decision-making module is used to receive various event information triggered by the event center emergency response module and the risk control measures module, and use the preset risk assessment model to conduct a comprehensive analysis of the nature, frequency and correlation factors of the event to quantify the impact index of the event; The dynamic risk grading module receives the impact index from the event analysis and decision-making module, and adjusts the level of each risk in combination with the preliminary risk data provided by the risk screening module; the dynamic risk grading module comprehensively considers the impact of time factors and objective factors on risk items, and obtains the degree of danger compared with the preliminary risk data at time t; the impact of time factors on risk items conforms to the bathtub curve, and the impact of time factors on risk items is calculated using Weibull distribution; the impact of objective factors on risk is calculated based on the impact index output by the event analysis and decision-making module; the current danger level of risk F is expressed as ; In the formula, Indicates the current danger level of risk F; represents the initial danger level of risk F, represents the changing trend of risk items over time, Represents the impact of objective factors on risk item F; According to the adjusted risk level, dynamically adjust the corresponding risk control measures in the risk control measures module.

2. The hospital safety production operation management ecological platform according to claim 1, characterized in that: The dynamic risk model is a LEC evaluation model or a LS evaluation model.

3. The hospital safety production operation management ecological platform according to claim 1, characterized in that: The Weibull distribution is used to calculate the changing trend of the risk item that conforms to the bathtub curve over time. The calculation formula is: ; In the formula, t represents time, Indicates the steepness of the bathtub curve, representing the drastic change of the risk item over time in the current time interval; different types of risks have different Value, various risks The value is provided by the industry standard risk library; β represents the risk under different conditions. When β < 1, it indicates the early failure period. When β=1, it indicates the stable operation period. When β>1, it indicates the late failure period, and Introduce variables ; Indicates the end time of the early expiration period, Indicates the end time of the stable operation period; , , , and The value of is provided by the industry standard risk library; the trend of the risk item that conforms to the bathtub curve over time is calculated using Weibull distribution and is expressed as: 。 4. The hospital safety production operation management ecological platform according to claim 1, characterized in that: The impact of objective factors on risk is calculated based on the impact index output by the event analysis and decision-making module, specifically, For a risk, assuming that in the initial state, the impact of objective factors on the risk is , that is, no influence. After event x occurs, the influence of objective factors increases to ,in Indicates the degree of influence of event x on objective factors, different types of time It is also different, and is determined according to the impact index output by the event analysis decision module; assuming that at time t, a total of i events have occurred, the impact of objective factors on the risk item is .

5. The hospital safety production operation management ecological platform according to any one of claims 1 to 4, characterized in that: The risk factor identification module includes a patrol inspection submodule, which is used to set and issue patrol inspection tasks, manage patrol inspection points and patrol inspection plans; The inspection submodule issues inspection tasks to the terminal devices of the inspectors according to their preset inspection tasks and inspection points. The inspectors use their terminal devices to scan the QR code or NFC of the inspection point to identify and verify the inspection point. During the inspection process, the terminal device uploads the inspection form status to the risk factor identification module in real time. If the terminal device reports an abnormal situation, the risk factor identification module transmits the abnormal situation event to the emergency processing module of the event center.

6. The hospital safety production operation management ecological platform according to claim 5, characterized in that: The risk factor identification module also includes an equipment detection submodule, which monitors real-time monitoring data related to each risk item through the Internet of Things and sensors; the sensor uploads the monitored data to the equipment detection submodule in real time, and the equipment detection submodule performs real-time analysis on the received sensor monitoring data stream according to preset thresholds and historical data. When the sensor monitoring data fluctuates significantly or exceeds the normal range, the equipment detection submodule identifies the abnormality and uploads the abnormal sensor monitoring data as a risk factor to the emergency processing module of the event center.

7. The hospital safety production operation management ecological platform according to claim 6, characterized in that: The real-time monitoring data related to each risk item include key parameter data under risk scenarios of automatic fire alarm system, oxygen station system, positive and negative pressure system, medical area power system and logistics support equipment.

8. The hospital safety production operation management ecological platform as claimed in claim 5, characterized in that: The risk factor identification module includes a video monitoring sub-module, which is connected to the video monitoring system of the target hospital and uses AI intelligent analysis to identify risk factors in the video screen. When risk factors or abnormal behaviors are monitored in the video screen, the video monitoring sub-module transmits the risk factors or abnormal behaviors to the emergency processing module of the event center and generates real-time alarms and video screenshots.

9. The hospital safety production operation management ecological platform according to any one of claims 1 to 4, characterized in that: The so-called beforehand means that the emergency response module of the event center is connected to the various business systems of the target hospital, and the alarm information of each business system is collected centrally to carry out unified scheduling management; a network connection is established between the emergency response module of the event center and the various business systems in the target hospital, and a unified data transmission format and protocol are set, and the alarm information of each business system is transmitted to the emergency response module of the event center; the emergency response module of the event center monitors and receives the alarm information sent by each business system in real time, parses the received alarm information, extracts key content, stores the parsed alarm information in the database, and displays it in a striking manner on the monitoring interface.

10. The hospital safety production operation management ecological platform according to any one of claims 1 to 4, characterized in that: The incident refers to that after the emergency response module of the event center receives the alarm information, it locates the specific location of the alarm through a visual map, broadcasts the alarm information through voice, and simultaneously obtains video surveillance around the alarm location to check the on-site situation and assist the processing personnel in judging the on-site situation.

11. The hospital safety production operation management ecological platform according to any one of claims 1 to 4, characterized in that: The so-called "in the process" means that the emergency handling module of the event center retrieves the corresponding emergency response plan from the risk control measures module according to the alarm information, issues instructions to the terminal devices of relevant personnel according to the emergency response plan, directs personnel to take action, and links the access control system to facilitate rescue or security operations.

12. The hospital safety production operation management ecological platform according to any one of claims 1 to 4, characterized in that: The so-called afterward means that the emergency processing module of the event center collects relevant data from the access of alarm information, the activation of emergency response plans to the implementation of emergency response plans, analyzes the factors that deviate from the emergency response plans in the events, evaluates the inclusiveness of the emergency response plans, improves the emergency response plans preset in the risk control measures module, and optimizes the risk control measures.

13. The hospital safety production operation management ecological platform according to any one of claims 1 to 4, characterized in that: The risk control measures module includes an equipment operation and maintenance submodule, a personnel management submodule and an emergency plan submodule. The equipment operation and maintenance submodule integrates equipment management, fault reporting and maintenance scheduling functions, manages hospital equipment, formulates maintenance and servicing plans for each equipment and monitors the implementation of the maintenance and servicing plans for each equipment; the personnel management submodule records the basic information of each operator and interacts with the operator's terminal equipment; the emergency plan submodule stores and manages each emergency response plan, and monitors the implementation and results of each emergency response plan.

14. The hospital safety production operation management ecological platform according to claim 13, characterized in that: The risk control measures module also includes a training and drill sub-module and a safety education sub-module. The training and drill sub-module is used to organize and manage daily training and emergency drill activities of operators; the safety education sub-module provides safety education knowledge to operators, can regularly issue special learning tasks, supervise learning progress and record the learning situation of each operator, conduct safety education tests on operators, and after the test, generate a test report to evaluate the safety knowledge level of the operators.

15. The hospital safety production operation management ecological platform according to any one of claims 1 to 4, characterized in that: The risk items stored in the industry standard risk library are obtained by issuing a risk questionnaire in the target hospital, classifying the risk questionnaires replied by the institutions of the target hospital, performing risk item analysis and data dependency analysis.

16. The hospital safety production operation management ecological platform according to claim 15, characterized in that: The risk questionnaires replied by the institutions of the target hospitals were classified. Specifically, the deep learning algorithm was used to perform semantic analysis and vocabulary understanding on the text of the questionnaire, identify the subject objects involved in the text, and calculate the correlation between the identified subject objects and the four aspects of unsafe behaviors of people, unsafe behaviors of objects, management defects, and unsafe factors of the environment. The four calculated correlations were compared, and the category with the highest correlation was selected as the final classification of the problem.

17. The hospital safety production operation management ecological platform according to claim 15, characterized in that: The risk questionnaires replied by the institutions of the target hospitals were classified. Specifically, the text of the questionnaire was converted into a computable digital vector using a vector model, and the digital vectors based on the text conversion were clustered using the clustering algorithm K-means; the clustering results were analyzed using a large language model, and the topics and keywords of each category were extracted.

18. The hospital safety production operation management ecological platform according to claim 15, characterized in that: The risk questionnaires replied by the institutions of the target hospital were classified. Specifically, all the questionnaire texts were directly input into the large language model, and the large language model was used to cluster the questionnaire texts and summarize the themes of each category.

19. The hospital safety production operation management ecological platform according to claim 15, characterized in that: Through data dependency analysis, the erroneous content of the items in the questionnaire is determined; specifically, based on the dependency relationship between the survey items in the questionnaire, the interdependence between the response items is analyzed, and the dependency basis is given, and the erroneous content of the response items filled in the questionnaire is determined through the dependency basis.

Images