A medical material archive management method, device, equipment and medium

Abstract

This invention relates to a method, apparatus, equipment, and medium for managing medical supplies records, belonging to the field of medical supplies management technology. The method includes: acquiring target medical supplies record information; determining the maintenance needs information for each target medical supply based on its record information and a preset predictive analysis model; generating an initial maintenance plan list in conjunction with future clinical work plans; calculating the maintenance risk of the initial maintenance plan list; and optimizing and adjusting the initial maintenance plan list if the maintenance risk level exceeds a preset threshold to generate a final maintenance plan list and accompanying warning information. This application achieves the technical effects of accurately determining medical supplies maintenance needs, rationally arranging maintenance plans, reducing maintenance risks and overall costs, and ensuring the normal use of medical supplies.

Description

Technical Field

[0001] This invention relates to the field of medical supplies management technology, and in particular to a method, device, equipment and medium for managing medical supplies records. Background Technology

[0002] With the continuous development of medical technology, the types and quantities of medical supplies are increasing daily, playing a vital role in medical services. The rational management and effective maintenance of medical supplies are directly related to the quality and safety of medical services.

[0003] In traditional medical supply management, information is typically recorded manually. Medical staff or administrators manually record the identification, operation, and maintenance records of medical supplies. Maintenance plans are often based on fixed time cycles, such as regular comprehensive inspections and maintenance of equipment.

[0004] Furthermore, manual recording of information is prone to errors and omissions, leading to inaccurate and incomplete data that affects the accurate assessment of material performance status. Maintenance plans based on fixed time cycles lack flexibility and cannot be dynamically adjusted according to actual material usage and performance evolution trends. This can result in over-maintenance or under-maintenance, leading to unreasonable maintenance plans, increased maintenance costs, and medical risks. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method, apparatus, equipment and medium for managing medical supplies, and aims to solve at least one of the above-mentioned technical problems.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: Firstly, this application provides a method for managing medical supplies records, employing the following technical solution: A method for managing medical supplies records includes: Obtain the file information of each target medical supply, including the identification of the corresponding target medical supply, historical operation records, historical maintenance records, and performance evolution trends; Based on the archival information of each target medical supply and the preset predictive analysis model, the maintenance requirement information of each target medical supply is determined, including the suggested maintenance items and the suggested maintenance time window; Based on the maintenance needs information of all target medical supplies and future clinical work plans, an initial maintenance plan list is generated, which integrates all maintenance tasks to be performed and their schedules. Based on the initial maintenance plan list and the inventory and manpower information associated with each maintenance task in the initial maintenance plan list, maintenance risk is calculated to obtain the risk information of the initial maintenance plan list. The risk information includes the maintenance risk level and a list of risk factors. If the maintenance risk level is greater than a preset risk threshold, the initial maintenance plan list is optimized and adjusted based on a preset optimization algorithm and risk information to generate a final maintenance plan list and corresponding prompt information, wherein the optimization algorithm aims to reduce the maintenance risk level and overall cost.

[0007] The beneficial effects of this invention are as follows: By integrating archival information with predictive analysis models, it enables the prediction of equipment performance degradation and failure risks, reducing the risk of clinical service interruptions due to sudden equipment failures. Through intelligent collaborative scheduling of maintenance needs and future clinical work plans, potential conflicts and resource bottlenecks are identified and quantified during the planning stage, thereby generating more feasible initial plans and improving the level of medical supplies management. When the plan risk is too high, an adaptive adjustment is performed based on a risk level and factor list-driven optimization algorithm, automatically generating a final plan that achieves the optimal balance between risk, cost, and clinical assurance, and providing decision-making prompts. This reduces the workload of management personnel, improves decision-making efficiency, and ensures reliable operation of medical equipment throughout its entire lifecycle.

[0008] Based on the above technical solution, the present invention can be further improved as follows.

[0009] Furthermore, the acquisition of the file information for each target medical supply includes: For any of the target medical supplies, obtain the real-time operation data stream and spatial location sequence of the target medical supplies in a single medical operation; For any of the target medical supplies, an efficacy deviation index is calculated based on the preset theoretical performance model of the target medical supplies and the real-time operation data stream; For any of the target medical supplies, attribution analysis is performed on the efficacy deviation index based on the spatial location sequence to identify multiple potential causes; For any of the target medical supplies, based on the efficacy deviation index and multiple potential causes, update the performance evolution trend of the initial profile information corresponding to the target medical supply; For any of the target medical supplies, based on the real-time operation data stream, update the historical operation record of the initial file information corresponding to the target medical supply; For any of the target medical supplies, the file information of the target medical supplies is determined based on the target medical supplies' identification, historical maintenance records, updated performance evolution trends, and historical operation records.

[0010] The beneficial effects of adopting the above-mentioned further solutions are as follows: By acquiring the real-time operation data stream and spatial location sequence of the target medical supplies in a single medical operation, we can understand the dynamic situation of the supplies in actual use; based on the preset theoretical performance model and the real-time operation data stream, we can calculate the effectiveness deviation index, which can quantify the difference between the actual performance and theoretical performance of the supplies; by performing attribution analysis on the effectiveness deviation index based on the spatial location sequence, we can identify potential causes, which facilitates finding the reasons for performance deviation; by updating the performance evolution trend based on the effectiveness deviation index and potential causes, and by updating the historical operation records based on the real-time operation data stream, the archival information can more accurately reflect the actual condition of the supplies; finally, by determining the archival information based on the identifier, historical maintenance records, updated performance evolution trend, and historical operation records, we can provide a comprehensive data foundation for subsequent maintenance needs analysis and maintenance plan formulation.

[0011] Furthermore, the calculation of the efficacy deviation index based on the preset theoretical performance model of the target medical supplies and the real-time operation data stream includes: Based on the real-time operation data stream, the measured performance field distribution of the target medical supplies in the actual operation environment is calculated by inversion. Based on the preset theoretical performance model and the current operating parameters, the theoretical performance field distribution of the target medical supplies under the spatial location sequence is calculated; Based on the spatial difference between the measured performance field distribution and the theoretical performance field distribution, the effectiveness deviation index is determined.

[0012] The beneficial effects of adopting the above-mentioned further scheme are: the measured performance field distribution is calculated based on the inversion of real-time operation data stream, the theoretical performance field distribution is calculated based on the preset theoretical performance model and the current operation parameters, and the effectiveness deviation index is determined according to the spatial difference between the two. This accurately quantifies the deviation between the actual performance and theoretical performance of the target medical supplies, provides accurate data support for subsequent record information updates and maintenance needs analysis, and helps to manage medical supply records more scientifically.

[0013] Furthermore, the determination of maintenance requirements for each target medical supply based on its archival information and a pre-defined predictive analysis model includes: For any of the target medical supplies, obtain the real-time status data of the target medical supplies and the clinical department information associated with the target medical supplies; Based on department type and pre-defined clinical business criticality mapping rules, the clinical criticality weight of each target medical supply is determined. Based on a preset predictive analysis model, the real-time status data of each target medical supply, historical maintenance records, and performance evolution trends, the health risk value and risk factors of each target medical supply are calculated. The preset predictive analysis model is a machine learning model trained based on historical fault data and operational data. Based on the health risk value and clinical criticality weight of each target medical supply, the risk level of each target medical supply is determined; If the risk level is greater than the set threshold, then based on the risk level, the type of risk factor and the clinical criticality weight, the maintenance requirement information of each target medical supply is matched from the preset multi-dimensional strategy library.

[0014] The beneficial effects of adopting the above-mentioned further solutions are as follows: By acquiring real-time status data of the target medical supplies and related clinical department information, and combining this with actual situation analysis, clinical criticality weights are determined to reflect the differences in the importance of supplies to different departments; a machine learning predictive analysis model trained based on historical fault data and operational data is used to calculate health risk values ​​and risk factors, improving the accuracy of calculations; risk levels are determined by combining health risk values ​​and clinical criticality weights, making risk assessment more comprehensive; and maintenance requirement information is matched from a pre-set multi-dimensional strategy library based on risk level, risk factor type, and clinical criticality weights, enabling precise formulation of maintenance plans to ensure the normal operation of medical supplies and meet medical business needs.

[0015] Furthermore, based on the maintenance needs information of all target medical supplies and future clinical work plans, an initial maintenance plan list is generated, including: From the aforementioned future clinical work plan, extract the departmental-level material occupancy plan, scheduling plan, and predicted patient flow fluctuation curve for the future set time period; For each target medical supply, based on the identifier of the target medical supply, the identifier is associated with the occupation period in the department-level supply occupation plan to identify the unavailability conflict time window of the target medical supply; The suggested maintenance time window in each maintenance requirement is checked for conflict with the unavailability conflict time window of the corresponding target medical supplies, and maintenance requirement information with time conflicts is filtered out. Based on the patient flow fluctuation information, clinical criticality weight, health risk value level, and conflict time interval corresponding to the maintenance request information with time conflicts, the priority of each maintenance request information with time conflicts is determined. Based on each priority, maintenance request information with time conflicts is sorted to obtain a maintenance request priority queue. Based on the order of the maintenance requirement priority queue, the execution time for each maintenance requirement is determined in a non-conflict time window, and a schedule for maintenance requirement information with time conflicts is generated. The initial maintenance plan list is generated based on the scheduling of maintenance needs with time conflicts and the suggested maintenance time windows for maintenance needs without time conflicts.

[0016] The beneficial effects of adopting the above-mentioned further approach are as follows: by extracting relevant information from future clinical work plans, identifying unavailable conflict time windows for target medical supplies, screening maintenance needs with time conflicts, determining priorities and ranking them by comprehensively considering patient flow fluctuations, clinical criticality weights, health risk levels, and conflict time intervals, rationally scheduling maintenance needs with time conflicts, and finally combining non-conflicting maintenance needs to generate an initial maintenance plan list, making the maintenance plan more in line with the actual clinical work situation, improving the rationality and effectiveness of medical supply maintenance arrangements, and reducing conflicts between maintenance and clinical work.

[0017] Furthermore, the maintenance risk calculation based on the initial maintenance plan list and the inventory and manpower information associated with each maintenance task in the initial maintenance plan list includes: For each maintenance task in the initial maintenance plan list, a material risk index is determined based on the inventory information associated with the maintenance task and the schedule and spare parts list of the maintenance task. The inventory information includes the current inventory quantity, safety stock threshold and in-transit order information. The material risk index is calculated by weighting the degree to which the current inventory quantity is lower than the safety stock threshold and the probability of the in-transit order meeting the demand in a timely manner. For each maintenance task in the initial maintenance plan list, a first human resource risk index is determined based on the human resource information associated with the maintenance task and the task requirements of the maintenance task. The human resource information includes the number of personnel, workload and skill level. The human resource risk index is calculated based on the ratio of the number of personnel who meet the skill requirements to the task requirements and the degree to which the workload of personnel exceeds a preset threshold. Based on the material risk index and manpower risk index of each maintenance task, the risk information of the initial maintenance plan list is determined.

[0018] The beneficial effects of adopting the above-mentioned further solutions are: by quantitatively calculating the degree to which material inventory guarantees plan execution and the degree to which human resources match plan requirements, the previous vague judgments relying on experience are transformed into precise diagnosis based on data. This enables management to clearly identify potential resource bottlenecks before plan execution, providing a reliable basis for subsequent risk warnings and plan optimization, and reducing the risk of maintenance task delays or clinical service interruptions due to insufficient resource preparation.

[0019] Furthermore, based on a preset optimization algorithm and risk information, the initial maintenance plan list is optimized and adjusted to generate a final maintenance plan list, including: Based on the maintenance risk level, the global search strength of the optimization algorithm is determined, wherein the higher the maintenance risk level, the greater the global search strength is set; Based on the proportion of each type of risk factor in the risk factor list, the local search weights of the optimization algorithm for different risk types are determined. Based on the global search strength and the local search weight, the parameters of the optimization algorithm are configured, and the initial maintenance plan list is iteratively optimized based on the configured optimization algorithm to generate multiple candidate optimization plans; Based on a preset evaluation function, a comprehensive score is calculated for each candidate optimization plan. The evaluation function is set based on the estimated maintenance risk level, estimated comprehensive cost, and availability of equipment in key clinical areas corresponding to the candidate optimization plan. The candidate optimization plan with the highest comprehensive score is selected as the final maintenance plan list.

[0020] The beneficial effects of adopting the above-mentioned further approach are as follows: by quantifying the maintenance risk level into a global search strength to guide the search scope, and parsing the risk factor list into local search weights to guide the search direction, the optimization algorithm can dynamically adjust its optimization focus. For high-risk plans, a more comprehensive and in-depth exploration will be initiated to avoid getting trapped in local optima; at the same time, resources will be prioritized towards the most prominent risk categories for targeted optimization. Through quantitative evaluation of multiple candidate plans using an evaluation function, a final maintenance plan list that achieves the best balance between controllable risk, cost optimization, and clinical assurance is selected. This improves the quality and efficiency of optimization decisions and reduces the risk of resource waste or clinical interruption due to unreasonable planning.

[0021] Secondly, this application provides a medical supplies record management device, which adopts the following technical solution: A medical supplies record management device, comprising: The acquisition module is used to acquire the file information of each target medical supply. The file information includes the identifier of the corresponding target medical supply, historical operation records, historical maintenance records, and performance evolution trends. The maintenance analysis module is used to determine the maintenance requirements of each target medical supply based on the archive information of each target medical supply and the preset predictive analysis model. The maintenance requirements include suggested maintenance items and suggested maintenance time windows. The initial maintenance plan generation module is used to generate an initial maintenance plan list based on the maintenance requirements information of all target medical supplies and future clinical work plans. The initial maintenance plan list integrates all maintenance tasks to be performed and their schedules. The risk calculation module is used to perform maintenance risk calculation based on the initial maintenance plan list and the inventory and manpower information associated with each maintenance task in the initial maintenance plan list, and to obtain the risk information of the initial maintenance plan list. The risk information includes the maintenance risk level and the risk factor list. An optimization module is used to optimize and adjust the initial maintenance plan list based on a preset optimization algorithm and risk information if the maintenance risk level is greater than a preset risk threshold, thereby generating a final maintenance plan list and corresponding prompt information, wherein the optimization algorithm aims to reduce the maintenance risk level and overall cost.

[0022] Thirdly, this application provides an electronic device that adopts the following technical solution: An electronic device includes a memory and a processor, wherein the memory stores a computer program capable of being loaded by the processor and executing the medical supplies record management method according to any one of the first aspects.

[0023] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium storing a computer program capable of being loaded by a processor and executing the medical supplies record management method according to any one of the first aspects.

[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0025] Figure 1 A flowchart illustrating a medical supplies record management method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a medical supplies file management device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0028] This application provides a method for managing medical supplies records. This method can be executed by an electronic device, which can be a server or a mobile terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The mobile terminal device can be a laptop computer, a desktop computer, etc., but is not limited to these.

[0029] like Figure 1 As shown, a method for managing medical supplies records mainly includes: S1, Obtain the file information of each target medical supply, wherein the file information includes the identifier of the corresponding target medical supply, historical operation records, historical maintenance records, and performance evolution trends; In this embodiment of the application, S1 specifically includes the following sub-steps: For any of the target medical supplies, obtain the real-time operation data stream and spatial location sequence of the target medical supplies in a single medical operation; For any of the target medical supplies, an efficacy deviation index is calculated based on the preset theoretical performance model of the target medical supplies and the real-time operation data stream; For any of the target medical supplies, attribution analysis is performed on the efficacy deviation index based on the spatial location sequence to identify multiple potential causes; For any of the target medical supplies, based on the efficacy deviation index and multiple potential causes, update the performance evolution trend of the initial profile information corresponding to the target medical supply; For any of the target medical supplies, based on the real-time operation data stream, update the historical operation record of the initial file information corresponding to the target medical supply; For any of the target medical supplies, the file information of the target medical supplies is determined based on the target medical supplies' identification, historical maintenance records, updated performance evolution trends, and historical operation records.

[0030] In the above embodiments, real-time operational data streams can be obtained through data acquisition sensors installed on medical supplies, such as pressure sensors and flow sensors. These sensors can sense various operating parameters of the medical supplies in real time and transmit data. Spatial location sequences can be determined using positioning devices, such as indoor positioning systems. Then, based on the preset theoretical performance model of the target medical supplies and the real-time operational data stream, an effectiveness deviation index is calculated. Next, based on the spatial location sequence, attribution analysis is performed on the effectiveness deviation index to identify multiple potential causes. For example, if a surgical instrument is found to have a large effectiveness deviation, analyzing its spatial location sequence may reveal that it is due to long-term placement in a humid environment causing rust, thus affecting performance. Subsequently, based on the effectiveness deviation index and multiple potential causes, the performance evolution trend of the initial file information corresponding to the target medical supplies is updated. If effectiveness deviations are detected multiple times with similar causes, it can be determined that the performance of the medical supplies is evolving in a negative direction. Then, based on the real-time operational data stream, the historical operation records of the initial file information corresponding to the target medical supplies are updated. Finally, based on the target medical supplies' identification, historical maintenance records, updated performance evolution trends, and historical operation records, the file information of the target medical supplies is determined.

[0031] The calculation of the efficacy deviation index based on the preset theoretical performance model of the target medical supplies and the real-time operation data stream includes: Based on the real-time operation data stream, the measured performance field distribution of the target medical supplies in the actual operation environment is calculated by inversion. Based on the preset theoretical performance model and the current operating parameters, the theoretical performance field distribution of the target medical supplies under the spatial location sequence is calculated; Based on the spatial difference between the measured performance field distribution and the theoretical performance field distribution, the effectiveness deviation index is determined.

[0032] For example, for a medical imaging device, the spatial distribution of its performance indicators such as image resolution and contrast during actual use can be deduced from the real-time operational data it receives. Then, based on a preset theoretical performance model and current operating parameters, the theoretical performance field distribution of the target medical supply in a spatial location sequence can be calculated—that is, the performance distribution the medical supply should possess under ideal conditions. Finally, based on the spatial difference between the measured performance field distribution and the theoretical performance field distribution, an efficacy deviation index is determined.

[0033] In the above embodiments, attribution analysis is performed on the effectiveness deviation index to identify multiple potential causes, including: Analyze the changes in the spatial location sequence during the single medical procedure; If the changes exceed a preset spatial stability threshold, the potential cause is determined to include an unexpected spatial displacement of the target medical supplies during operation.

[0034] If the changes do not exceed the spatial stability threshold, then analyze whether the fluctuation characteristics of the efficacy deviation index or the real-time operation data stream are synchronized with the periodic characteristics of the patient's physiological signal time series data; if they are synchronized, then determine that the potential cause includes operational environment interference modulated by periodic physiological activities.

[0035] The above embodiments further include: adaptively calibrating at least one parameter in the preset theoretical performance model based on multiple performance records generated by the target medical supplies in multiple medical operations, generating a calibrated theoretical performance model, and using the calibrated theoretical performance model to calculate a new efficacy deviation index.

[0036] S2, based on the file information of each target medical supply and the preset predictive analysis model, determine the maintenance requirement information of each target medical supply, wherein the maintenance requirement information includes suggested maintenance items and suggested maintenance time windows; In this embodiment of the application, determining the maintenance requirements information for each target medical supply based on its archival information and a pre-set predictive analysis model includes: For any of the target medical supplies, obtain the real-time status data of the target medical supplies and the clinical department information associated with the target medical supplies; Based on department type and pre-defined clinical business criticality mapping rules, the clinical criticality weight of each target medical supply is determined. Based on a preset predictive analysis model, the real-time status data of each target medical supply, historical maintenance records, and performance evolution trends, the health risk value and risk factors of each target medical supply are calculated. The preset predictive analysis model is a machine learning model trained based on historical fault data and operational data. Based on the health risk value and clinical criticality weight of each target medical supply, the risk level of each target medical supply is determined; If the risk level is greater than the set threshold, then based on the risk level, the type of risk factor and the clinical criticality weight, the maintenance requirement information of each target medical supply is matched from the preset multi-dimensional strategy library.

[0037] In the above embodiments, real-time status data can be obtained through monitoring equipment connected to medical supplies, and clinical department information can be queried from the hospital's information management system.

[0038] Based on department type and pre-defined clinical criticality mapping rules, the clinical criticality weight of each target medical supply is determined. For example, the clinical criticality weight of medical supplies used in the operating room is higher than that of supplies used in general wards.

[0039] Then, based on the preset predictive analysis model, the real-time status data of each target medical supply, historical maintenance records and performance evolution trends, the health risk value and risk factors of each target medical supply are calculated. The preset predictive analysis model is a machine learning model trained based on historical fault data and operational data. It can accurately predict the health risk of the supplies based on the input data.

[0040] Based on the health risk value and clinical criticality weight of each target medical supply, the risk level of each target medical supply is determined. If the risk level exceeds a set threshold, maintenance requirement information for each target medical supply is matched from a pre-set multi-dimensional strategy library based on the risk level, the type of risk factor, and the clinical criticality weight. By accurately determining the maintenance requirements according to the actual situation of different supplies, the one-size-fits-all approach to maintenance planning used in traditional methods is avoided.

[0041] S3, based on the maintenance needs information of all target medical supplies and future clinical work plans, generates an initial maintenance plan list, which integrates all maintenance tasks to be performed and their schedules; In this embodiment of the application, S3 includes the following sub-steps: From the aforementioned future clinical work plan, extract the departmental-level material occupancy plan, scheduling plan, and predicted patient flow fluctuation curve for the future set time period; For each target medical supply, based on the identifier of the target medical supply, the identifier is associated with the occupation period in the department-level supply occupation plan to identify the unavailability conflict time window of the target medical supply; The suggested maintenance time window in each maintenance requirement is checked for conflict with the unavailability conflict time window of the corresponding target medical supplies, and maintenance requirement information with time conflicts is filtered out. Based on the patient flow fluctuation information, clinical criticality weight, health risk value level, and conflict time interval corresponding to the maintenance request information with time conflicts, the priority of each maintenance request information with time conflicts is determined. Based on each priority, maintenance request information with time conflicts is sorted to obtain a maintenance request priority queue. Based on the order of the maintenance requirement priority queue, the execution time for each maintenance requirement is determined in a non-conflict time window, and a schedule for maintenance requirement information with time conflicts is generated. The initial maintenance plan list is generated based on the scheduling of maintenance needs with time conflicts and the suggested maintenance time windows for maintenance needs without time conflicts.

[0042] In the above implementation, the departmental-level material occupancy plan, scheduling plan, and predicted patient flow fluctuation curve for a specified future time period are extracted from future clinical work plans. This information can be obtained from the hospital's scheduling system and data analysis system.

[0043] A future clinical work plan represents a comprehensive schedule of all planned medical activities for a future period, pre-established by the hospital or department, and serves as the constraint framework for scheduling. A departmental material occupancy plan refers to the detailed usage schedule derived from this work plan, broken down by department and down to each piece of medical equipment or high-value consumables. A scheduling plan is used to represent the sequence of time arrangements for specific clinical activities such as surgery, examination, and treatment in a specific room or on specific equipment. A predicted patient flow fluctuation curve refers to graphical data generated by a predictive model based on historical data and seasonal factors, reflecting the changing trends of the number of patients visiting the hospital or the number of surgeries in different future periods.

[0044] For conflict tasks, a multi-factor dynamic priority calculation model is introduced. This model comprehensively considers the following factors: Due to business disruptions, scheduling maintenance during peak patient hours can exacerbate clinical pressure. Therefore, it is necessary to refer to traffic fluctuation information to appropriately avoid or increase task priority in order to complete the task quickly. Safety risks: The higher the clinical criticality and current health risk level of the material itself, the higher the priority should be given to its maintenance. Scheduling feasibility is assessed; shorter conflict intervals indicate a more urgent adjustment window, potentially requiring special handling. Through calculation, all conflicting tasks are assigned a quantified priority and ordered.

[0045] Subsequently, in order of priority, for each conflicting task, within the allowable fluctuation range of its maintenance time window, a time slot is found that avoids the "busy period" of the material while still accommodating the original plan as much as possible, and a slot is reserved for this task. Finally, all successfully scheduled tasks are integrated with the originally non-conflicting tasks to form a complete initial maintenance plan list containing specific execution times.

[0046] This generated initial maintenance plan list fully considers the actual needs of clinical work, avoids conflicts between maintenance and clinical work, and improves the efficiency of medical supplies use and the rationality of maintenance.

[0047] In some embodiments, the process of extracting information from future work plans and generating a priority queue can be implemented in a variety of ways: A parallel processing approach based on graph model and constraint solving is adopted: The future timeline, all involved medical supplies, departments, maintenance teams, and key tools are abstracted as nodes in a graph. Clinical scheduling occupancy, maintenance task requirements, and resource dependencies are abstracted as edges with attributes, constructing a resource-time dependency graph representing all constraints.

[0048] In the graph model, the conflict between maintenance task requirements and clinical occupancy manifests as a competition between resource nodes over time. By traversing all maintenance tasks, task subgraphs with resource competition are automatically identified. For each conflict subgraph, initial priority weights are calculated using graph algorithms based on the attributes of the task nodes (e.g., clinical criticality, health risk).

[0049] The set of conflicting tasks, their initial priorities, and related constraints are input into a lightweight constraint solver. The solver aims to maximize the degree of freedom in scheduling high-priority tasks, fine-tuning the priority order of tasks over several iterations (e.g., appropriately increasing the priority weight of tasks with extremely high resource contention), ultimately outputting a more feasible, optimized maintenance requirement priority queue under the given constraints.

[0050] S4. Based on the initial maintenance plan list and the inventory and manpower information associated with each maintenance task in the initial maintenance plan list, perform maintenance risk calculation to obtain the risk information of the initial maintenance plan list. The risk information includes the maintenance risk level and a list of risk factors. In this embodiment of the application, the step of calculating maintenance risk based on the initial maintenance plan list and the inventory and manpower information associated with each maintenance task in the initial maintenance plan list includes: For each maintenance task in the initial maintenance plan list, a material risk index is determined based on the inventory information associated with the maintenance task and the schedule and spare parts list of the maintenance task. The inventory information includes the current inventory quantity, safety stock threshold and in-transit order information. The material risk index is calculated by weighting the degree to which the current inventory quantity is lower than the safety stock threshold and the probability of the in-transit order meeting the demand in a timely manner. For each maintenance task in the initial maintenance plan list, a first human resource risk index is determined based on the human resource information associated with the maintenance task and the task requirements of the maintenance task. The human resource information includes the number of personnel, workload and skill level. The human resource risk index is calculated based on the ratio of the number of personnel who meet the skill requirements to the task requirements and the degree to which the workload of personnel exceeds a preset threshold. Based on the material risk index and manpower risk index of each maintenance task, the risk information of the initial maintenance plan list is determined.

[0051] In the above embodiments, inventory information represents the storage status data of spare parts and consumables required to perform the specific maintenance task.

[0052] S5. If the maintenance risk level is greater than the preset risk threshold, the initial maintenance plan list is optimized and adjusted based on the preset optimization algorithm and risk information to generate the final maintenance plan list and corresponding prompt information, wherein the optimization algorithm aims to reduce the maintenance risk level and overall cost.

[0053] In this embodiment of the application, the step of optimizing and adjusting the initial maintenance plan list based on a preset optimization algorithm and risk information to generate a final maintenance plan list includes: Based on the maintenance risk level, the global search strength of the optimization algorithm is determined, wherein the higher the maintenance risk level, the greater the global search strength is set; Based on the proportion of each type of risk factor in the risk factor list, the local search weights of the optimization algorithm for different risk types are determined. Based on the global search strength and the local search weight, the parameters of the optimization algorithm are configured, and the initial maintenance plan list is iteratively optimized based on the configured optimization algorithm to generate multiple candidate optimization plans; Based on a preset evaluation function, a comprehensive score is calculated for each candidate optimization plan. The evaluation function is set based on the estimated maintenance risk level, estimated comprehensive cost, and availability of equipment in key clinical areas corresponding to the candidate optimization plan. The candidate optimization plan with the highest comprehensive score is selected as the final maintenance plan list.

[0054] In the above implementation, this risk-based adaptive optimization process can be achieved in a variety of ways.

[0055] For example, an adaptive optimization framework based on simulated annealing is employed, encoding the initial maintenance plan list into a data structure that the optimization algorithm can process. This encoding is then used as the initial current solution for the simulated annealing algorithm. Simultaneously, a pre-defined mapping table is consulted based on the maintenance risk level to determine the initial temperature and cooling rate.

[0056] In each iteration, a new solution needs to be generated from the current solution. Various neighborhood movement operations are defined, such as time shift, task swapping, and resource reallocation. Different selection probabilities are assigned to these operations based on local search weights. For example, if the weight of material risk is high, the probability of moving the task to a time after its critical spare parts inventory is sufficient will increase.

[0057] As a further optional implementation in the embodiments of this application, the method further includes: Based on the identifier of the target medical supplies, obtain the business data associated with the target medical supplies, wherein the business data includes at least equipment depreciation cost, energy consumption cost, consumable cost, operator man-hours, research project information, and clinical billing information. Using the target medical equipment, the patient associated with this medical operation, the operator, consumables, research projects, and billing items as entities, and using equipment usage relationships, cost attribution relationships, and value contribution relationships as edges, a dynamic knowledge graph is constructed or updated; wherein, the equipment effectiveness deviation index and attribution analysis results are stored in the knowledge graph as performance attributes of the target medical equipment entity. Based on the knowledge graph, a virtual cost accounting unit is created for this medical operation. Through a dynamic allocation model, equipment depreciation, energy consumption, consumables, and labor costs are aggregated into this unit to obtain the accurate total cost of this medical operation. Based on the scientific research, teaching, and technical difficulty attributes of the entities associated in the knowledge graph, the non-financial value contribution of this medical operation is calculated through predefined contribution factor rules. Based on the precise total cost, the non-financial value contribution, and the equipment effectiveness deviation index, a comprehensive benefit assessment result for the target medical equipment is generated and stored in its individualized performance file. By monitoring and analyzing the usage process of medical supplies, the originally scattered and static files can be transformed into dynamic and intelligent management tools.

[0058] This method integrates archival information with predictive analytics models to predict equipment performance degradation and failure risks, reducing the risk of clinical service disruptions due to sudden equipment failures. By intelligently co-scheduling maintenance needs with future clinical work plans, it identifies and quantifies potential conflicts and resource bottlenecks during the planning phase, generating more feasible initial plans and improving medical supplies management. When the plan risk is too high, an optimization algorithm driven by risk level and factor list adaptively adjusts the plan, automatically generating a final plan that achieves the optimal balance between risk, cost, and clinical assurance, and providing decision-making suggestions. This reduces the workload of management personnel, improves decision-making efficiency, and ensures reliable operation of medical equipment throughout its entire lifecycle.

[0059] Figure 2 A schematic diagram of a medical supplies record management device 200 is shown.

[0060] like Figure 2 As shown, a medical supplies record management device 200 mainly includes: The acquisition module 201 is used to acquire the file information of each target medical supply. The file information includes the identifier of the corresponding target medical supply, historical operation records, historical maintenance records, and performance evolution trends. The maintenance analysis module 202 is used to determine the maintenance requirements information for each target medical supply based on the file information of each target medical supply and the preset predictive analysis model. The maintenance requirements information includes suggested maintenance items and suggested maintenance time windows. The initial maintenance plan generation module 203 is used to generate an initial maintenance plan list based on the maintenance requirement information of all target medical supplies and future clinical work plans. The initial maintenance plan list integrates all maintenance tasks to be performed and their schedules. The risk calculation module 204 is used to perform maintenance risk calculation based on the initial maintenance plan list and the inventory and manpower information associated with each maintenance task in the initial maintenance plan list, and to obtain the risk information of the initial maintenance plan list, the risk information including the maintenance risk level and the risk factor list; The optimization module 205 is used to optimize and adjust the initial maintenance plan list based on a preset optimization algorithm and risk information if the maintenance risk level is greater than a preset risk threshold, and generate a final maintenance plan list and corresponding prompt information, wherein the optimization algorithm aims to reduce the maintenance risk level and overall cost.

[0061] In one example, the module in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0062] For example, when modules in a device can be implemented via a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling programs. Alternatively, these modules can be integrated together as a system-on-a-chip (SOC).

[0063] In this application, various objects such as messages / information / devices / network elements / systems / apparatus / actions / operations / processes / concepts may be named. It is understood that these specific names do not constitute a limitation on the relevant objects. The names may be changed depending on the scenario, context, or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from their functions and technical effects embodied / performed in the technical solution.

[0064] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0065] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0066] Figure 3 This is a structural block diagram of an electronic device 300 according to an embodiment of this application.

[0067] like Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302, and may further include one or more of an information input / output (I / O) interface 303, a communication component 304, and a communication bus 305.

[0068] The processor 301 controls the overall operation of the electronic device 300 to complete all or part of the steps in the aforementioned medical supplies record management method. The memory 302 stores various types of data to support the operation of the electronic device 300. This data may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as one or more of Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0069] I / O interface 303 provides an interface between processor 301 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 304 is used to test wired or wireless communication between electronic device 300 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 304 may include a Wi-Fi component, a Bluetooth component, and an NFC component.

[0070] The communication bus 305 may include a path for transmitting information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0071] The electronic device 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute a medical supplies record management method given in the above embodiments.

[0072] The following describes the computer-readable storage medium provided in the embodiments of this application. The computer-readable storage medium described below can be referred to in correspondence with the medical supplies file management method described above.

[0073] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for managing medical supplies records.

[0074] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0075] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0076] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for managing medical supplies records, characterized in that, include: Obtain the file information of each target medical supply, including the identification of the corresponding target medical supply, historical operation records, historical maintenance records, and performance evolution trends; Based on the archival information of each target medical supply and the preset predictive analysis model, the maintenance requirement information of each target medical supply is determined, including the suggested maintenance items and the suggested maintenance time window; Based on the maintenance needs information of all target medical supplies and future clinical work plans, an initial maintenance plan list is generated, which integrates all maintenance tasks to be performed and their schedules. Based on the initial maintenance plan list and the inventory and manpower information associated with each maintenance task in the initial maintenance plan list, maintenance risk is calculated to obtain the risk information of the initial maintenance plan list. The risk information includes the maintenance risk level and a list of risk factors. If the maintenance risk level is greater than a preset risk threshold, the initial maintenance plan list is optimized and adjusted based on a preset optimization algorithm and risk information to generate a final maintenance plan list and corresponding prompt information, wherein the optimization algorithm aims to reduce the maintenance risk level and overall cost.

2. The method for managing medical supplies records according to claim 1, characterized in that, The acquisition of the file information for each target medical supply includes: For any of the target medical supplies, obtain the real-time operation data stream and spatial location sequence of the target medical supplies in a single medical operation; For any of the target medical supplies, an efficacy deviation index is calculated based on the preset theoretical performance model of the target medical supplies and the real-time operation data stream; For any of the target medical supplies, attribution analysis is performed on the efficacy deviation index based on the spatial location sequence to identify multiple potential causes; For any of the target medical supplies, based on the efficacy deviation index and multiple potential causes, update the performance evolution trend of the initial profile information corresponding to the target medical supply; For any of the target medical supplies, based on the real-time operation data stream, update the historical operation record of the initial file information corresponding to the target medical supply; For any of the target medical supplies, the file information of the target medical supplies is determined based on the target medical supplies' identification, historical maintenance records, updated performance evolution trends, and historical operation records.

3. The method for managing medical supplies records according to claim 2, characterized in that, The calculation of the efficacy deviation index based on the preset theoretical performance model of the target medical supplies and the real-time operation data stream includes: Based on the real-time operation data stream, the measured performance field distribution of the target medical supplies in the actual operation environment is calculated by inversion. Based on the preset theoretical performance model and the current operating parameters, the theoretical performance field distribution of the target medical supplies under the spatial location sequence is calculated; Based on the spatial difference between the measured performance field distribution and the theoretical performance field distribution, the effectiveness deviation index is determined.

4. The method for managing medical supplies records according to claim 2, characterized in that, The maintenance requirements for each target medical supply are determined based on its archival information and a pre-defined predictive analysis model, including: For any of the target medical supplies, obtain the real-time status data of the target medical supplies and the clinical department information associated with the target medical supplies; Based on department type and pre-defined clinical business criticality mapping rules, the clinical criticality weight of each target medical supply is determined. Based on a preset predictive analysis model, the real-time status data of each target medical supply, historical maintenance records, and performance evolution trends, the health risk value and risk factors of each target medical supply are calculated. The preset predictive analysis model is a machine learning model trained based on historical fault data and operational data. Based on the health risk value and clinical criticality weight of each target medical supply, the risk level of each target medical supply is determined; If the risk level is greater than the set threshold, then based on the risk level, the type of risk factor and the clinical criticality weight, the maintenance requirement information of each target medical supply is matched from the preset multi-dimensional strategy library.

5. A method for managing medical supplies records according to claim 4, characterized in that, Based on the maintenance needs information of all target medical supplies and future clinical work plans, an initial maintenance plan list is generated, including: From the aforementioned future clinical work plan, extract the departmental-level material occupancy plan, scheduling plan, and predicted patient flow fluctuation curve for the future set time period; For each target medical supply, based on the identifier of the target medical supply, the identifier is associated with the occupation period in the department-level supply occupation plan to identify the unavailability conflict time window of the target medical supply; The suggested maintenance time window in each maintenance requirement is checked for conflict with the unavailability conflict time window of the corresponding target medical supplies, and maintenance requirement information with time conflicts is filtered out. Based on the patient flow fluctuation information, clinical criticality weight, health risk value level, and conflict time interval corresponding to the maintenance request information with time conflicts, the priority of each maintenance request information with time conflicts is determined. Based on each priority, maintenance request information with time conflicts is sorted to obtain a maintenance request priority queue. Based on the order of the maintenance requirement priority queue, the execution time for each maintenance requirement is determined in a non-conflict time window, and a schedule for maintenance requirement information with time conflicts is generated. The initial maintenance plan list is generated based on the scheduling of maintenance needs with time conflicts and the suggested maintenance time windows for maintenance needs without time conflicts.

6. The method for managing medical supplies records according to claim 1, characterized in that, The maintenance risk calculation, based on the initial maintenance plan list and the associated inventory and manpower information for each maintenance task in the initial maintenance plan list, includes: For each maintenance task in the initial maintenance plan list, a material risk index is determined based on the inventory information associated with the maintenance task and the schedule and spare parts list of the maintenance task. The inventory information includes the current inventory quantity, safety stock threshold and in-transit order information. The material risk index is calculated by weighting the degree to which the current inventory quantity is lower than the safety stock threshold and the probability of the in-transit order meeting the demand in a timely manner. For each maintenance task in the initial maintenance plan list, a first human resource risk index is determined based on the human resource information associated with the maintenance task and the task requirements of the maintenance task. The human resource information includes the number of personnel, workload and skill level. The human resource risk index is calculated based on the ratio of the number of personnel who meet the skill requirements to the task requirements and the degree to which the workload of personnel exceeds a preset threshold. Based on the material risk index and manpower risk index of each maintenance task, the risk information of the initial maintenance plan list is determined.

7. A method for managing medical supplies records according to claim 6, characterized in that, The initial maintenance plan list is optimized and adjusted based on a preset optimization algorithm and risk information to generate a final maintenance plan list, including: Based on the maintenance risk level, the global search strength of the optimization algorithm is determined, wherein the higher the maintenance risk level, the greater the global search strength is set; Based on the proportion of each type of risk factor in the risk factor list, the local search weights of the optimization algorithm for different risk types are determined. Based on the global search strength and the local search weight, the parameters of the optimization algorithm are configured, and the initial maintenance plan list is iteratively optimized based on the configured optimization algorithm to generate multiple candidate optimization plans; Based on a preset evaluation function, a comprehensive score is calculated for each candidate optimization plan. The evaluation function is set based on the estimated maintenance risk level, estimated comprehensive cost, and availability of equipment in key clinical areas corresponding to the candidate optimization plan. The candidate optimization plan with the highest comprehensive score is selected as the final maintenance plan list.

8. A medical supplies record management device, characterized in that, include: The acquisition module is used to acquire the file information of each target medical supply. The file information includes the identifier of the corresponding target medical supply, historical operation records, historical maintenance records, and performance evolution trends. The maintenance analysis module is used to determine the maintenance requirements of each target medical supply based on the archive information of each target medical supply and the preset predictive analysis model. The maintenance requirements include suggested maintenance items and suggested maintenance time windows. The initial maintenance plan generation module is used to generate an initial maintenance plan list based on the maintenance requirements information of all target medical supplies and future clinical work plans. The initial maintenance plan list integrates all maintenance tasks to be performed and their schedules. The risk calculation module is used to perform maintenance risk calculation based on the initial maintenance plan list and the inventory and manpower information associated with each maintenance task in the initial maintenance plan list, and to obtain the risk information of the initial maintenance plan list. The risk information includes the maintenance risk level and the risk factor list. An optimization module is used to optimize and adjust the initial maintenance plan list based on a preset optimization algorithm and risk information if the maintenance risk level is greater than a preset risk threshold, thereby generating a final maintenance plan list and corresponding prompt information, wherein the optimization algorithm aims to reduce the maintenance risk level and overall cost.

9. An electronic device, characterized in that, Includes a processor, which is coupled to a memory; The processor is configured to execute a computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Includes a computer program or instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1-7.

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