A workflow optimization system for the disinfection supply center based on big data analysis

Abstract

The present invention discloses an optimization system for the workflow of a disinfection supply center based on big data analysis, specifically relating to the technical field of medical data processing, and is used to solve the problems of resource crunch and timeliness lag caused by insufficient adaptation to dynamic demands in existing disinfection task scheduling; by integrating data on the time-series distribution of instrument requirements, infection risk levels, and equipment spatial distribution, combining with dynamically evaluating the priority of instrument reuse, marking department collaboration nodes, and real-time tracking the pollution risk of storage areas, dynamically correcting the disinfection resource scheduling path based on multi-dimensional decision parameters, realizing real-time balanced adaptation of task priorities and equipment loads; autonomously generating an optimization plan according to the composite weights of the pollution diffusion rate, department collaboration frequency, and equipment transportation efficiency, synchronously ensuring the scheduling timeliness of critical instruments and the environmental safety of storage areas in the event of a sudden high-load scenario, and significantly improving the emergency response ability and resource utilization efficiency of the disinfection supply center.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing, and more specifically, to an optimized system for the workflow of a disinfection supply center based on big data analysis. Background Art

[0002] When the disinfection supply center faces sudden disinfection tasks, scheduling conflicts often occur due to overlapping time windows or limited equipment resources for the instrument requirements of multiple departments. Existing methods usually allocate disinfection resources according to preset rules, such as according to department priorities or task urgency levels, and it is difficult to adapt to dynamic demand changes in real time. For example, when multiple departments submit urgent tasks simultaneously, delays in the scheduling of key instruments or idling of equipment are likely to occur, affecting the emergency response efficiency.

[0003] Existing disinfection task scheduling methods lack a dynamic coordination mechanism when dealing with concurrent demands from multiple departments, and it is difficult to balance multiple objectives of resource allocation. Especially in the case of sudden high-load scenarios, fixed rules are likely to lead to local resource squeezes or misalignment of task priorities, affecting the timeliness and safety of medical work. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an optimized system for the workflow of a disinfection supply center based on big data analysis to solve the problems proposed in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An optimized system for the workflow of a disinfection supply center based on big data analysis, comprising:

[0007] A multi-source acquisition module: obtaining multi-source real-time information of the disinfection supply center, including future instrument usage requirements, instrument infection risk levels, instrument collaborative usage information, and a spatial distribution map of disinfection equipment;

[0008] An urgency analysis module: determining the urgency of instrument reuse based on the temporal distribution of future instrument usage requirements and the instrument infection risk level;

[0009] A collaboration analysis module: determining the tightness of department collaboration according to the instrument collaborative usage information, and marking the department nodes in the current conflicting tasks;

[0010] A dynamic sorting module: setting the priority of the corresponding conflicting tasks to the highest when the urgency of instrument reuse exceeds the dynamic urgency threshold; otherwise, hierarchically sorting the conflicting tasks based on the tightness of department collaboration;

[0011] Environmental monitoring module: Monitor the air cleanliness of the storage area for disinfected instruments. If the air cleanliness exceeds the preset cleanliness threshold and indicates a risk of secondary contamination, trigger a re-evaluation of the priority of conflict tasks.

[0012] Resource allocation module: Update the spatio-temporal resource conflict path and dynamically allocate disinfection resources according to the results of the priority re-evaluation and the spatial distribution map of disinfection equipment.

[0013] In a preferred embodiment, obtain multi-source real-time information of the central sterile supply department, including:

[0014] Obtain the instrument disinfection request data submitted by each department. The instrument disinfection request data includes the instrument type, quantity, and expected disinfection completion time.

[0015] Extract future surgical schedule data from the hospital information management system and analyze the future instrument usage requirements in the surgical schedule data.

[0016] Retrieve the preset association table between instrument types and infection risk levels to determine the instrument infection risk level of the current disinfected instruments.

[0017] Statistically analyze the frequency of collaborative use of instruments in the historical combined surgery records of departments to generate inter-departmental instrument collaborative use information.

[0018] Collect the real-time position coordinates and transportation path topology data of disinfection equipment to construct a spatial distribution map of disinfection equipment.

[0019] In a preferred embodiment, determine the urgency of instrument reuse based on the temporal distribution of future instrument usage requirements and the instrument infection risk level, including:

[0020] Divide the temporal distribution of future instrument usage requirements into multiple consecutive time slices according to a preset time slice, and statistically analyze the total demand of each instrument type in each time slice.

[0021] Set the risk weighting coefficients for each instrument type according to the instrument infection risk level.

[0022] Multiply the total demand of each instrument type in the same time slice by its corresponding risk weighting coefficient to obtain the weighted demand value of each instrument type.

[0023] Accumulate the weighted demand values of the same instrument type in all time slices to generate the cumulative urgency score for the corresponding instrument type to evaluate the urgency of instrument reuse.

[0024] In a preferred embodiment, the risk weighting coefficients for each instrument type are set as follows: the high risk level corresponds to the first weighting coefficient, the medium risk level corresponds to the second weighting coefficient, and the low risk level corresponds to the third weighting coefficient.

[0025] In a preferred embodiment, the department collaboration tightness is determined according to the instrument collaboration usage information, and the department nodes in the current conflicting tasks are marked, including:

[0026] Extract the collaboration events of the same instrument type from the historical department joint surgery records, and count the continuous triggering times of the collaboration events for each pair of department combinations within a preset time window;

[0027] Set the collaboration time sensitivity parameter according to the time interval distribution law of the collaboration events, and assign a high-sensitivity mark to the collaboration events with a continuous triggering interval less than the preset threshold;

[0028] Identify the department combinations with high-sensitivity marks, and generate the department collaboration tightness hierarchy by sorting according to the continuous triggering times of the collaboration events;

[0029] Analyze the historical collaboration tightness hierarchy of the instrument application departments in the current conflicting tasks. If there are combinations ranked among the top several in the tightness hierarchy in the corresponding associated departments, mark them as key collaboration nodes;

[0030] Dynamically adjust the resource allocation path of the conflicting tasks according to the tightness hierarchy difference of the key collaboration nodes, and preferentially meet the instrument reuse requirements of the combinations with high tightness hierarchy.

[0031] In a preferred embodiment, when the urgency of instrument reuse exceeds the dynamic urgency threshold, set the priority of the corresponding conflicting task to the highest; otherwise, rank the conflicting tasks based on the department collaboration tightness, including:

[0032] Compare the cumulative urgency score with the dynamic urgency threshold, and the dynamic urgency threshold is set according to the average daily demand of the same type of instrument in the historical same period;

[0033] When the cumulative urgency score exceeds the dynamic urgency threshold, mark the sterilization priority of the associated conflicting tasks as the highest level and lock the dedicated sterilization equipment resources;

[0034] When the cumulative urgency score does not exceed the dynamic urgency threshold, extract the collaboration tightness hierarchy of the instrument application departments in the conflicting tasks, and generate a task sequence in descending order of the hierarchy ranking;

[0035] Calculate the difference in the collaboration tightness hierarchy between adjacent tasks in the task sequence. If the difference in the collaboration tightness hierarchy exceeds the preset split threshold, split them into independent processing groups;

[0036] Merge the disinfection batches according to the compatibility of the instrument sterilization parameters of the independent processing groups. The tasks within the same batch share the sterilization equipment and execute the transportation instructions synchronously.

[0037] In a preferred embodiment, the air cleanliness of the storage area for the disinfected instruments is monitored. If the air cleanliness exceeds the preset cleanliness threshold and indicates a risk of secondary contamination, a re-evaluation of the priority of the conflict tasks is triggered, including:

[0038] Obtain the air cleanliness monitoring data of the storage area for the disinfected instruments. The air cleanliness monitoring data includes the concentration of suspended particles and the number of microbial colonies;

[0039] If any of the indicators of the concentration of suspended particles and the number of microbial colonies exceeds the upper limit of the threshold range, it is marked as a secondary contamination risk event;

[0040] Calculate the pollution risk diffusion rate according to the duration of the secondary contamination risk event. The pollution risk diffusion rate is the growth rate of the pollution index per unit time;

[0041] Extract the task list involving the risk storage area in the current conflict task queue, and generate a re-evaluation task sequence from high to low according to the pollution risk diffusion rate.

[0042] In a preferred embodiment, according to the re-evaluation result of the priority and the spatial distribution map of the disinfection equipment, update the spatio-temporal resource conflict path and dynamically allocate disinfection resources, including:

[0043] Extract the re-evaluation task sequence in the re-evaluation result of the priority and the real-time position coordinates of the equipment in the spatial distribution map of the disinfection equipment;

[0044] According to the real-time position coordinates of the equipment and the path topology data of the task-related storage area, calculate the shortest transportation time of each equipment to the target area;

[0045] Generate an emergency task group according to the sorting of the re-evaluation task sequence, and mark the equipment with the shortest transportation time less than the preset response threshold as available resources;

[0046] Adjust the equipment allocation priority based on the dynamic load factor of the available resources. The dynamic load factor is the ratio of the remaining duration of the current task queue of the equipment to the maximum bearing duration;

[0047] Merge the tasks with compatible sterilization parameters in the emergency task group into the same batch, and execute the batch tasks in descending order of the equipment allocation priority.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. By constructing a multi-dimensional real-time decision-making system, the urgency of instrument reuse, the tightness of department collaboration, and the depth of dynamic environmental risk monitoring are deeply coupled to form a scheduling closed-loop with self-feedback ability; based on the characteristics of resource conflicts evolving in space and time, an optimized path that takes into account sterilization efficiency, equipment load balance, and infection prevention and control is automatically generated; when a sudden demand for high-infection-risk instruments occurs in a certain department, the current equipment transportation timeliness, the collaboration frequency of neighboring departments, and the pollution diffusion trend of the storage area are calculated simultaneously, and an emergency allocation strategy with multi-objective coordination is automatically triggered. The cross-domain data real-time interaction and dynamic weight allocation mechanism solve the problem of the separation of resource scheduling and risk prevention and control in traditional methods.

[0050] 2. By introducing a priority re-evaluation mechanism driven by environmental cleanliness, the potential threat of abnormal air pollution indicators to instrument safety can be captured in real time, and the pollution diffusion rate is dynamically associated with the equipment scheduling path to achieve a delay-free linkage between risk warning and resource scheduling; when the abnormal increase in the concentration of suspended particles in a certain storage area is detected, not only the backlog tasks in this area are preferentially processed, but also the task allocation logic of the equipment in the surrounding areas is actively adjusted according to the pollution propagation model. The dynamic strategy iteration based on environmental situation awareness constructs a three-dimensional safety line while ensuring the timeliness of disinfection. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic structural diagram of a system for optimizing the work process of a disinfection supply center based on big data analysis according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] Embodiment: Figure 1 A schematic structural diagram of a system for optimizing the work process of a disinfection supply center based on big data analysis according to the present invention is given. A system for optimizing the work process of a disinfection supply center based on big data analysis includes:

[0054] Multi-source acquisition module: Obtain multi-source real-time information of the disinfection supply center, including future instrument usage requirements, instrument infection risk levels, instrument collaborative usage information, and the spatial distribution map of disinfection equipment;

[0055] Urgency analysis module: Determine the urgency of instrument reuse based on the time sequence distribution of future instrument usage requirements and the instrument infection risk level;

[0056] Collaboration analysis module: Determine the department collaboration tightness based on the information of instrument collaborative use, and mark the department nodes in the current conflicting tasks;

[0057] Dynamic sorting module: When the urgency of instrument reuse exceeds the dynamic urgency threshold, set the priority of the corresponding conflicting task to the highest; otherwise, perform hierarchical sorting on the conflicting tasks based on the department collaboration tightness;

[0058] Environmental monitoring module: Monitor the air cleanliness of the instrument storage area after disinfection. If the air cleanliness exceeds the preset cleanliness threshold and indicates the risk of secondary contamination, trigger a re-evaluation of the priority of the conflicting tasks;

[0059] Resource allocation module: Update the spatio-temporal resource conflict path and dynamically allocate disinfection resources according to the results of the priority re-evaluation and the disinfection equipment spatial distribution map.

[0060] Obtain multi-source real-time information of the Central Sterile Supply Department, including future instrument usage requirements, instrument infection risk levels, instrument collaborative use information, and disinfection equipment spatial distribution map, including:

[0061] The specific process of obtaining multi-source real-time information of the Central Sterile Supply Department includes the following steps: First, obtain the instrument disinfection request data submitted by each department. The instrument disinfection request data is generated by the disinfection task submission interface of the hospital information management system and includes the instrument type, quantity, and expected disinfection completion time. Among them, the instrument type is encoded according to the hospital instrument classification standard. For example, the code A001 represents a scalpel, and B002 represents a hemostatic forceps. The expected disinfection completion time is manually input by the department according to the surgical schedule or automatically associated with the surgical plan by the system.

[0062] Extract future surgical schedule data from the hospital information management system. The future surgical schedule data includes the surgical types planned by each department within the next 24 hours, operating room allocation, and the expected instrument list. By parsing the instrument list in the surgical schedule data, extract the instrument type and quantity required for each surgery, and summarize to generate future instrument usage requirements. For example, if the orthopedic surgery schedule indicates that 5 A001-type scalpels and 2 sets of C003-type orthopedic drills are required, then the corresponding statistics are A001-type instrument future demand +5, C003-type instrument future demand +2.

[0063] Retrieve the preset association table between instrument types and infection risk levels. The preset association table is formulated by the hospital infection management department based on the clinical use scenarios of instruments and stored in the database. The instrument type codes in the association table correspond to the infection risk levels, which are divided into three levels: high risk, medium risk, and low risk. For example, the A001-type scalpel is defined as a high-risk level because it directly contacts the patient's blood, and the D004-type forceps are defined as a low-risk level because they only contact the sterile area. Query this table according to the instrument type code in the current disinfection task to determine its corresponding instrument infection risk level.

[0064] Statistically analyze the frequency of collaborative use of instruments in historical department joint surgery records. The historical department joint surgery records are stored in the surgery log database of the hospital information management system, which record the cross-departmental call records of instruments in multi-department joint surgeries in the past year. Count the number of applications for the same type of instrument by different departments within the same time period, and generate the information on the collaborative use of instruments between departments. For example, if the frequency of jointly applying for scalpel type A001 by the cardiac surgery department and the emergency department in joint surgery is 3 times per day on average, it is marked as the collaborative frequency between the cardiac surgery department and the emergency department: 3 times / day.

[0065] Collect the real-time position coordinates and transportation path topology data of disinfection equipment. The real-time position coordinates are obtained through RFID positioning tags installed on the disinfection equipment, with a positioning accuracy of 0.5 meters. The transportation path topology data includes the floor plan of the central sterile supply department, corridor passage coordinates, and elevator distribution information. By superimposing the real-time position coordinates and the topology data, construct a spatial distribution map of disinfection equipment. For example, the current coordinates of equipment X1 are (102, 205), corresponding to the 3rd shelf in the sterilization area in the floor plan, and the coordinates of equipment X2 are (305, 418), corresponding to near the orthopedic special elevator in the transportation path.

[0066] Among them, the path topology data can be updated quarterly through a laser surveying instrument and synchronized with the hospital infrastructure management system to ensure the accuracy of the floor plan and passage coordinates.

[0067] Compare the expected disinfection completion time in the instrument disinfection request data with the surgical scheduling time in the future instrument usage requirements. If the expected completion time of a certain instrument is earlier than its associated surgical scheduling time, then determine that the demand for this instrument is an urgent task.

[0068] The information on the collaborative use of instruments between departments is used to identify high-frequency collaborative department combinations. For example, if the collaborative frequency of the cardiac surgery department - emergency department combination exceeds the threshold of 5 times per day, it is marked as a high-intensity collaborative pair, and shared instrument resources are preferentially allocated in subsequent task scheduling.

[0069] The transportation path topology data in the spatial distribution map of disinfection equipment includes path distance, passage priority (such as the weight of the surgical dedicated passage is higher than that of the ordinary corridor), and real-time pedestrian flow density. Calculate the shortest transportation time for different equipment to be dispatched to the target department through the path topology data. For example, the shortest path for equipment X1 from the sterilization area to the cardiac surgery department is to go directly through Corridor 3, taking 2 minutes, while equipment X2 needs to detour through the surgical dedicated passage, taking 3.5 minutes.

[0070] The preset association table for the risk level of device infection is formulated in accordance with the "Technical Specification for Disinfection in Medical Institutions" issued by the state. The disinfection parameters corresponding to high-risk level devices need to meet the requirements of a pressure steam sterilization temperature of 134°C and a time of 5 minutes, medium-risk devices are 121°C and 15 minutes, and low-risk devices can use low-temperature plasma sterilization.

[0071] The analysis process of future device usage requirements includes time slice division. The next 24 hours are divided into periods of every 2 hours, and the total demand for each device type within each period is counted. For example, the demand for scalpel type A001 is 8 pieces in the period from 8:00 to 10:00 and 6 pieces in the period from 10:00 to 12:00, generating a demand time series distribution curve.

[0072] Based on the time series distribution of future device usage requirements and the device infection risk level, determine the urgency of device reuse, including:

[0073] The specific process of determining the urgency of device reuse includes the following steps: Divide the time series distribution of future device usage requirements into multiple consecutive periods according to the preset time slice. The duration of the preset time slice is determined according to the device call interval in the hospital operation schedule. By analyzing the fluctuation of the hourly device application volume in the historical operation records, select a time unit with stable demand fluctuations as the period length. For example, if the operation peak in a certain hospital is concentrated in the morning period and the device application volume changes significantly every 2 hours, then the time slice is set to 2 hours, and the division results are periods such as 8:00 - 10:00, 10:00 - 12:00.

[0074] When counting the total demand for each device type within each period, extract the device list of each operation from the analyzed operation schedule data, and accumulate the quantities classified by device type coding. For example, by analyzing the operation schedule data of 10 orthopedic surgeries in the period from 8:00 to 10:00, the total demand for scalpel type A001 is counted as 10 pieces, and the total demand for orthopedic drill type C003 is 6 sets.

[0075] Set the risk weighting coefficient for each device type according to the device infection risk level. The device infection risk level is set according to the classification standard of medical devices in the "Technical Specification for Disinfection in Medical Institutions". High-risk devices include those that come into contact with the damaged skin, mucous membranes of patients or invade sterile tissues. Medium-risk devices include non-invasive devices that come into contact with the intact skin of patients. Low-risk devices include those that only come into contact with clean items. The hospital infection management department formulates a mapping table between device types and risk levels and sets the weighting coefficient based on this standard. For example, the risk weighting coefficient for high-risk devices is 1.5, medium-risk is 1.2, and low-risk is 1.0. In the mapping table, the coding of scalpel type A001 is associated with a high-risk coefficient of 1.5, and the coding of hemostatic forceps type B002 is associated with a low-risk coefficient of 1.0.

[0076] Multiply the total demand of each instrument type within the same time period by its corresponding risk weighting factor to obtain the weighted demand value of each instrument type. For example, during the period from 8:00 to 10:00, 10 A001 scalpels are demanded. After multiplying by the high-risk factor of 1.5, the weighted demand value is 15. For 20 B002 hemostats demanded, after multiplying by the low-risk factor of 1.0, the weighted demand value is 20. When accumulating the weighted demand values of the same instrument type within all time periods, classify and summarize the calculation results of each time period according to the instrument type code. For example, if the weighted value of A001 scalpels is 15 from 8:00 to 10:00 and 18 from 10:00 to 12:00, the cumulative urgency score is 33.

[0077] The combined logic of time period division and risk weighting solves the defect of single-dimensional decision-making in traditional scheduling methods. For example, traditional methods only sort by application time or quantity, which may ignore the timeliness requirements of high-risk instruments. In this solution, although the total demand of B002 hemostats is higher, due to the low risk coefficient, the cumulative score of 44 may be lower than that of A001 scalpels at 33. However, after combining the threshold comparison, if the score of A001 exceeds its threshold of 30, it is still preferentially marked as a high-urgency task to ensure that infection risk control takes precedence over simple quantity requirements.

[0078] Determine the tightness of department collaboration based on the information of instrument collaborative use, and mark the department nodes in the current conflicting tasks, including:

[0079] The specific implementation process of determining the tightness of department collaboration and marking department nodes includes: extracting the historical department joint surgery records stored in the hospital information management system. The records include fields such as department number, instrument type code, application timestamp, and surgery type. For example, the record "K01 (Cardiovascular Surgery), K02 (Emergency Department), A001 (Scalpel), 2023-05-10 09:00:00, Emergency Surgery" indicates that departments K01 and K02 jointly applied for A001 scalpels at 9:00 for an emergency surgery.

[0080] Count the number of events where the interval between consecutive applications of the same instrument type by the same department combination within a preset time window (default 24 hours) is less than the preset interval threshold (default 30 minutes). For example, departments K01-K02 applied for A001 scalpels three times at 9:00, 9:15, and 9:30 on May 10, 2023. The adjacent application intervals are 15 minutes and 15 minutes respectively, both less than 30 minutes, and are counted as 3 consecutive triggers.

[0081] The collaborative time sensitivity parameter is set according to the median of the continuous application intervals for the same type of equipment by the same department combination in historical data. The specific method is: after excluding abnormal data with an interval of more than 24 hours, calculate the median of the remaining intervals. For example, the median interval of 100 applications for A001 scalpels by departments K01-K02 is 15 minutes, so its sensitivity parameter is set to 15 minutes. If the actual application interval is ≤15 minutes, it is marked as a high-sensitivity event. After identifying all high-sensitivity department combinations, a closeness hierarchy list is generated in descending order of the number of continuous triggers. For example, departments K01-K02 trigger 5 high-sensitivity events within 24 hours, and departments K03-K04 trigger 3 times, then the hierarchy list is K01-K02 (5 times), K03-K04 (3 times).

[0082] When analyzing the historical collaboration density level of the equipment application department in the current conflict task, if its associated department ranks in the top N in the hierarchy list (N is set proportionally according to the total number of departments, N=10 when there are 50 departments, and N=5 when there are 20 departments), it is marked as a key collaboration node. For example, in the current conflict task, department K01 applies for A001 scalpel, and its associated department K02 ranks first in the hierarchy list (N=5), then K02 is marked as a key node.

[0083] The resource allocation path is dynamically adjusted according to the key node's closeness level difference (the difference between the current node ranking and other node rankings). The key node's closeness level difference threshold is set according to the 75% quantile of the level difference in the historical tasks. For example, if the 75% quantile is 2 for the past 100 tasks, the difference threshold is set to 2. If K01-K02 ranks first and K01-K05 ranks fourth in the current task, and the key node's closeness level difference 3≥2, the path optimization is triggered, and the A001 scalpel is preferentially allocated to the K01-K02 path, and the path is marked as a high-priority channel in the spatial distribution map of disinfection equipment.

[0084] The preset interval threshold is set based on the turnover requirements of the hospital's routine surgeries. For example, if an operation takes an average of 1 hour, a 30-minute interval can cover the instrument preparation time for adjacent operations. The median calculation of the collaborative time sensitivity parameter excludes abnormal intervals (such as > 24 hours) caused by equipment failure or system errors to ensure the objectivity of the parameters. The 75% quantile calculation of the hierarchical difference adjustment threshold is based on the historical task data of the past three months and is updated monthly to ensure timeliness. If there is no historical data (such as a newly established department), the default parameters for the entire hospital (interval threshold of 30 minutes, sensitivity parameter of 30 minutes) are used until data accumulation is completed.

[0085] When the urgency of device reuse exceeds the dynamic urgency threshold, the priority of the corresponding conflicting task is set to the highest; otherwise, the conflicting tasks are ranked based on the closeness of departmental collaboration, including:

[0086] The specific implementation process of setting priorities and hierarchical sorting includes: comparing the cumulative urgency score with the dynamic urgency threshold, which is set according to the emergency task response plan formulated by the hospital infection control department. The specific rule is to take 120% of the daily average demand peak of the same type of medical devices in the same historical period (such as the past three months). For example, the daily average demand peak of scalpel type A001 in the second quarter of 2023 is 20, then the dynamic urgency threshold is set to 24.

[0087] When the cumulative urgency score exceeds the dynamic urgency threshold, for example, the current cumulative score of scalpel type A001 is 26 (generated by weighted calculation of period demand), exceeding the threshold of 24, mark the sterilization priority of the associated conflict task as the highest level, and lock the dedicated sterilization equipment resources reserved for emergency tasks. The dedicated equipment resource is a high-temperature sterilization cabinet (equipment number X1), with its parameters set to 134°C and 5 minutes. Other tasks are not allowed to occupy it until the emergency task is completed.

[0088] When the cumulative urgency score does not exceed the dynamic urgency threshold, for example, the cumulative score of hemostatic forceps type B002 is 18, not exceeding the threshold of 20, extract the data of the cooperation tightness level of the departments applying for medical devices in the conflict task. The data of the cooperation tightness level comes from the weekly updated ranking table of department combination tightness. For example, departments K01 - K02 (cardiac surgery - emergency department) rank first in the cooperation tightness level of scalpel type A001, and departments K03 - K04 (orthopedics - general surgery) rank third. Generate the task sequence in descending order of the level ranking as tasks K01 - K02 and tasks K03 - K04.

[0089] When calculating the difference in the cooperation tightness level between adjacent tasks in the task sequence, for example, the difference between K01 - K02 (level 1) and K03 - K04 (level 3) in the task sequence is 2. The difference threshold is set according to the distribution of level differences in historical tasks. The specific rule is to take the 75th percentile of the level differences in the past 100 tasks. For example, the 75th percentile in the statistical result is 1, then the splitting threshold is set to 1. If the current difference of 2 exceeds the threshold of 1, split the task with a high difference into independent processing groups. For example, split tasks K01 - K02 and tasks K03 - K04 into two independent groups for processing.

[0090] When merging disinfection batches according to the compatibility of the sterilization parameters of the medical devices in the independent processing groups, the compatibility judgment is based on the operating specifications of the hospital disinfection supply center, requiring that the deviation of the sterilization temperature of the medical devices within the same batch does not exceed ±2°C and the deviation of the sterilization time does not exceed ±10%. For example, scalpel type A001 (134°C, 5 minutes) and orthopedic drill type C003 (134°C, 6 minutes) can be merged into the same batch, sharing sterilization equipment X1 and being transported synchronously, while hemostatic forceps type B002 (121°C, 15 minutes) is in a separate batch due to parameter differences.

[0091] The 120% ratio of the dynamic urgency threshold is set according to the emergency response level of the hospital infection management department for public health emergencies. When the cumulative score exceeds the threshold, a red warning mechanism is triggered to forcibly enable dedicated equipment resources. The splitting threshold of the collaboration tightness level difference is recalculated monthly based on the task data of the latest 30 days. For example, if the 75th percentile of the level difference in a certain month is updated to 2, the splitting threshold is synchronously adjusted to 2. Without historical data support (such as the newly introduced type D004 endoscope), the default threshold of similar instruments is adopted. For example, the default threshold for endoscope instruments is 130% of the historical daily average demand.

[0092] The determination of sterilization parameter compatibility requires manual review and confirmation. For example, after the system automatically combines A001 and C003 into the same batch, the supervisor of the disinfection supply center needs to review in the operation interface whether the parameter deviation (temperature 0°C, time +1 minute) is within the allowable range, and then execute the batch operation after confirmation.

[0093] Monitor the air cleanliness of the storage area for instruments after disinfection. If the air cleanliness exceeds the preset cleanliness threshold and indicates the risk of secondary contamination, trigger a re-evaluation of the priority of conflicting tasks, including:

[0094] The specific implementation process of triggering the priority re-evaluation includes: obtaining the air cleanliness monitoring data of the storage area for instruments after disinfection. The air cleanliness monitoring data is collected in real time by the particle counters and microbial samplers deployed in the storage area, and includes two indicators: the particle concentration and the number of microbial colonies. The particle concentration is in units of the number of particles per cubic meter, and the number of microbial colonies is measured in colony forming units per cubic meter.

[0095] Compare the particle concentration and the number of microbial colonies with the preset cleanliness threshold range respectively. The preset cleanliness threshold range is set according to the national standard "Management Specification for Hospital Disinfection Supply Center". The particle concentration threshold range is 0 - 35,000 particles per cubic meter, and the number of microbial colonies threshold range is 0 - 200 CFU per cubic meter. If any indicator exceeds the upper limit of the threshold range, for example, the measured particle concentration is 42,000 particles per cubic meter and the number of microbial colonies is 180 CFU per cubic meter, it is marked as a secondary contamination risk event, and the start time and current time of the event are recorded to calculate the duration.

[0096] When calculating the pollution risk diffusion rate based on the duration of the secondary contamination risk event, the pollution risk diffusion rate is the growth rate of the pollution index per unit time.

[0097] The specific calculation method of the pollution risk diffusion rate is: (current pollution index value - initial pollution index value) / duration.

[0098] For example, if the initial concentration of suspended particles in a certain storage area is 35,000 particles per cubic meter and it rises to 42,000 particles per cubic meter after 3 hours, the diffusion rate is (42,000 - 35,000) / 3 = 2,300 particles / (cubic meter·hour). If the number of microbial colonies in the same event rises from 180 CFU per cubic meter to 220 CFU per cubic meter, the diffusion rate is (220 - 180) / 3 ≈ 13.3 CFU / (cubic meter·hour). The higher value of the diffusion rates of the two indicators is taken as the final pollution risk diffusion rate.

[0099] When extracting the task list of the risk storage areas involved in the current conflict task queue, all tasks marked as "uncompleted" in the task management system of the disinfection supply center and whose associated instrument storage locations match the risk storage areas are included in the list. For example, if a pollution risk event occurs in storage area A and there are 5 tasks in the task queue related to the instrument storage in area A, then these 5 tasks are extracted to generate a list. When generating the re-evaluation task sequence from high to low according to the pollution risk diffusion rate, for example, if the diffusion rate of task 1 is 2,300 particles / (cubic meter·hour), task 2 is 1,500 particles / (cubic meter·hour), and task 3 is 1,000 particles / (cubic meter·hour), then the re-evaluation task sequence is sorted as task 1, task 2, task 3.

[0100] The calculation of the pollution risk diffusion rate needs to exclude abnormal data caused by equipment failures or instantaneous fluctuations. For example, if the concentration of suspended particles suddenly increases from 35,000 to 50,000 within 5 minutes and then immediately drops back, such data is regarded as invalid and re-collected.

[0101] The extraction rules of the task list include strict matching between the instrument storage location and the risk area. For example, the storage location code of the instrument in the task must be exactly the same as the code of the risk storage area to avoid mis-associating tasks in other areas.

[0102] The calculation of the duration is accurate to minutes. For example, if the risk event lasts from 9:00 to 10:30, the duration is 1.5 hours. If the pollution index fluctuates beyond the threshold during the duration, the time of the last time it exceeds the threshold is used as the current time to calculate the duration. For example, if the concentration of suspended particles exceeds the threshold from 9:00 to 9:30, drops back within the threshold from 9:30 to 10:00, and then exceeds again at 10:00, the duration is counted as 1 hour from 9:00 to 10:00. The rule for selecting the higher value of the pollution risk diffusion rate is to give priority to the indicator that has a greater impact on the safety of the instrument. For example, the diffusion rate priority of the microbial colony number exceeding the standard is higher than that of the suspended particle concentration.

[0103] According to the priority re-evaluation results and the disinfection equipment space distribution map, update the space-time resource conflict path and dynamically allocate disinfection resources, including:

[0104] The specific implementation process of updating the spatio-temporal resource conflict path and dynamically allocating disinfection resources includes: extracting the re-evaluation task sequence in the priority re-evaluation result and the real-time position coordinates of the devices in the disinfection equipment spatial distribution map. The re-evaluation task sequence comes from the task list sorted in descending order of the pollution risk diffusion rate generated in step S5. For example, task 1 (diffusion rate: 2,300 particles per hour), task 2 (1,500 particles per hour), task 3 (1,000 particles per hour). The real-time position coordinates of the devices are collected in real time through RFID positioning tags deployed on the disinfection equipment. For example, the coordinates of device X1 are (102, 205), and the coordinates of device X2 are (305, 418).

[0105] Calculate the shortest transportation time from each device to the target area according to the real-time position coordinates of the devices and the path topology data of the task-associated storage area. The path topology data includes the floor plan, corridor coordinates, and elevator distribution information. For example, the shortest path for device X1 from the coordinates (102, 205) to storage area A is to go directly through Corridor 3, which takes 2 minutes, and device X2 needs to detour through the dedicated surgical channel, which takes 3.5 minutes.

[0106] When generating the emergency task group according to the sorting of the re-evaluation task sequence, tasks 1, 2, and 3 are included in the emergency task group in the original sequence. Mark the devices with the shortest transportation time less than the preset response threshold as available resources. The preset response threshold is set according to the 75th percentile of the historical transportation time. For example, by counting the transportation time of the devices to each area in the past three months, the 75th percentile is 3 minutes, so the response threshold is set to 3 minutes. The transportation time of device X1 is 2 minutes < 3 minutes, so it is marked as an available resource, and the 3.5 minutes of device X2 > 3 minutes, so it is marked as an unavailable resource.

[0107] Adjust the device allocation priority based on the dynamic load factor of the available resources. The dynamic load factor is the ratio of the remaining duration of the device's current task queue to the maximum bearing duration. For example, the remaining duration of device X1's current task queue is 1 hour, and the maximum bearing duration is 8 hours, so the dynamic load factor is 1 / 8 = 0.125. For device X3, the remaining duration is 2 hours, and the maximum bearing duration is 6 hours, so the load factor is 2 / 6 ≈ 0.333. The lower the load factor, the higher the priority.

[0108] The remaining duration of the device's current task queue is obtained in real time from the disinfection equipment status monitoring system, and the maximum bearing duration is set according to the technical manual provided by the equipment manufacturer. For example, the maximum bearing duration of autoclave X1 is 8 hours.

[0109] When merging tasks with compatible sterilization parameters in the emergency task group into the same batch, the compatibility of sterilization parameters is determined according to the national standard "Operating Specifications for Hospital Central Supply and Distribution". It is required that the deviation of the sterilization temperature of the instruments within the same batch does not exceed ±2°C and the deviation of the sterilization time does not exceed ±10%. For example, for Task 1 (Type A001 scalpel, 134°C, 5 minutes) and Task 3 (Type C003 orthopedic drill bit, 134°C, 6 minutes), the temperature deviation is 0°C and the time deviation is +1 minute (+20%). Since the time deviation exceeds the limit, they cannot be merged. However, Task 2 (Type D004 forceps, 134°C, 5 minutes) can be merged with Task 1 into the same batch. When executing batch tasks in descending order of equipment allocation priority, the priority of Equipment X1 is 0.125, which is higher than that of Equipment X3 at 0.333. The merged Task 1-2 batch is preferentially allocated to Equipment X1 for execution, and Task 3 is separately allocated to Equipment X3.

[0110] The monthly update rule for the preset response threshold is to take the 75th percentile of the latest 30-day transportation time. For example, if the statistical percentile drops to 2.5 minutes in a certain month, the threshold is synchronously updated to 2.5 minutes. The calculation of the dynamic load factor excludes abnormal data during the equipment maintenance period. For example, the task queue data during the daily fixed maintenance period (14:00 - 14:30) of Equipment X1 is not involved in the calculation. The determination of sterilization parameter compatibility requires manual review. For example, after the system automatically determines that Tasks 1-2 cannot be merged, with a temperature deviation of ±2°C and a time deviation of ±10%, batches cannot be merged if they exceed the range.

[0111] The above formulas are all calculated by taking the numerical values after dimensionless. The formulas are obtained by collecting a large amount of data and performing software simulation to get a formula that is closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0112] It should be noted that the present invention can be deployed on the device itself to achieve embedded applications, or can also run on a PC with a user interface or other terminals, so as to meet various hardware environments and usage requirements.

[0113] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0114] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0115] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings, direct couplings, or communication connections shown or discussed with each other can be through some interfaces, and the indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0116] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0117] In addition, in each embodiment of the present application, each functional module can be integrated into a processing module, can exist physically alone for each module, or two or more modules can be integrated into one module.

[0118] If the above-mentioned function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0119] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0120] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A workflow optimization system for the disinfection supply center based on big data analysis, characterized in that, Including: Multi-source acquisition module: Obtain multi-source real-time information of the sterilization supply center, including future instrument usage requirements, instrument infection risk levels, instrument collaborative usage information, and the spatial distribution map of disinfection equipment; Urgency analysis module: Determine the urgency of instrument reuse based on the temporal distribution of future instrument usage requirements and the instrument infection risk level, including: Divide the temporal distribution of future instrument usage requirements into multiple consecutive time periods according to a preset time slice, and count the total demand of each instrument type in each time period; Set the risk weighting coefficient for each instrument type according to the instrument infection risk level; Multiply the total demand of each instrument type in the same time period by its corresponding risk weighting coefficient to obtain the weighted demand value of each instrument type; Accumulate the weighted demand values of the same instrument type in all time periods to generate the cumulative urgency score of the corresponding instrument type to evaluate the urgency of instrument reuse; Collaboration analysis module: Determine the tightness of department collaboration based on instrument collaborative usage information and mark the department nodes in the current conflict task, including: Extract the collaborative events of the same instrument type in the historical department joint operation records, and count the continuous trigger times of the collaborative events for each pair of department combinations within a preset time window; Set the collaborative time sensitivity parameter according to the time interval distribution law of the collaborative events, and assign a high sensitivity mark to the collaborative events with a continuous trigger interval less than the preset threshold; Identify the department combinations with high sensitivity marks, and generate the department collaboration tightness hierarchy according to the number of continuous trigger times of the collaborative events; Analyze the historical collaboration tightness hierarchy of the instrument application department in the current conflict task. If there are combinations ranked among the top several in the tightness hierarchy in the corresponding associated departments, mark them as key collaboration nodes; Dynamically adjust the resource allocation path of the conflict task according to the tightness hierarchy difference of the key collaboration nodes, and preferentially meet the instrument reuse requirements of the combinations with a high tightness hierarchy; Dynamic sorting module: Set the priority of the corresponding conflict task to the highest when the urgency of instrument reuse exceeds the dynamic urgency threshold; otherwise, rank the conflict tasks hierarchically based on the department collaboration tightness; Environmental monitoring module: Monitor the air cleanliness of the area where the sterilized instruments are stored. If the air cleanliness exceeds the preset cleanliness threshold and indicates the risk of secondary pollution, trigger a re-evaluation of the priority of the conflict task; Resource allocation module: Update the spatio-temporal resource conflict path and dynamically allocate disinfection resources according to the re-evaluation result of the priority and the spatial distribution map of the disinfection equipment, including: Extract the re-evaluation task sequence in the re-evaluation result of the priority and the real-time position coordinates of the equipment in the spatial distribution map of the disinfection equipment; Calculate the shortest transportation time from each equipment to the target area according to the real-time position coordinates of the equipment and the path topology data of the task-associated storage area; Generate an emergency task group according to the sorting of the re-evaluation task sequence, and mark the equipment with the shortest transportation time less than the preset response threshold as available resources; Adjust the equipment allocation priority based on the dynamic load factor of the available resources. The dynamic load factor is the ratio of the remaining duration of the equipment's current task queue to the maximum bearing duration; Merge the tasks with compatible sterilization parameters in the emergency task group into the same batch, and execute the batch tasks in descending order of the equipment allocation priority.

2. The optimized system for the work process of a disinfection supply center based on big data analysis according to claim 1, characterized in that, Obtain multi-source real-time information of the sterilization supply center, including: Obtain the instrument disinfection request data submitted by each department, where the instrument disinfection request data includes the instrument type, quantity, and expected disinfection completion time; Extract the future surgical schedule data from the hospital information management system and analyze the future instrument usage requirements in the surgical schedule data; Retrieve the preset association table of instrument types and infection risk levels to determine the instrument infection risk level of the current disinfected instruments; Statistically analyze the frequency of collaborative use of instruments in the historical department joint surgery records to generate the instrument collaborative use information between departments; Collect the real-time position coordinates and transportation path topology data of the disinfection equipment to construct a spatial distribution map of the disinfection equipment.

3. The optimized system for the workflow of a disinfection supply center based on big data analysis according to claim 1, wherein, Set the risk weighting coefficients for each instrument type as follows: the high risk level corresponds to the first weighting coefficient, the medium risk level corresponds to the second weighting coefficient, and the low risk level corresponds to the third weighting coefficient.

4. The optimized system for the work process of a disinfection supply center based on big data analysis according to claim 1, characterized in that When the urgency of instrument reuse exceeds the dynamic urgency threshold, set the priority of the corresponding conflict task to the highest; Otherwise, perform a hierarchical ranking of the conflict tasks based on the department collaboration tightness, including: Compare the cumulative urgency score with the dynamic urgency threshold, and the dynamic urgency threshold is set according to the average daily demand of the same type of instruments in the historical same period; When the cumulative urgency score exceeds the dynamic urgency threshold, mark the sterilization priority of the associated conflict task as the highest level and lock the dedicated sterilization equipment resources; When the cumulative urgency score does not exceed the dynamic urgency threshold, extract the collaboration tightness level of the instrument application department in the conflict task, and generate a task sequence in descending order of the level ranking; Calculate the difference in the collaboration tightness levels between adjacent tasks in the task sequence. If the difference in the collaboration tightness levels exceeds the preset split threshold, split them into independent processing groups; Merge the disinfection batches according to the compatibility of the instrument sterilization parameters of the independent processing groups. Tasks within the same batch share the sterilization equipment and execute the transportation instructions synchronously.

5. The optimized system for the work process of a disinfection supply center based on big data analysis according to claim 1, wherein Monitor the air cleanliness of the instrument storage area after disinfection. If the air cleanliness exceeds the preset cleanliness threshold and indicates the risk of secondary contamination, trigger a re-evaluation of the priority of the conflict task, including: Obtain the air cleanliness monitoring data of the instrument storage area after disinfection, where the air cleanliness monitoring data includes the suspended particle concentration and the number of microbial colonies; If any of the indicators of the suspended particle concentration and the number of microbial colonies exceeds the upper limit of the threshold range, mark it as a secondary contamination risk event; Calculate the pollution risk diffusion rate based on the duration of the secondary contamination risk event. The pollution risk diffusion rate is the growth rate of the pollution index per unit time; Extract the task list involving the risk storage area in the current conflict task queue and generate a re-evaluation task sequence in descending order of the pollution risk diffusion rate.

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