Disposable tableware packaging intelligent management method and system based on big data

Abstract

The invention discloses a disposable tableware packaging intelligent management method and system based on big data, and belongs to the technical field of intelligent medical treatment and logistics management, and the method comprises the following steps: S1, collecting inherent risk factors and controllable risk factors of a management unit, and collecting historical consumption data of the management unit; s2, based on the inherent risk factor and the controllable risk factor, calculating and generating a quantitative endemic area total risk score; s3, based on the total risk score of the inpatient area and the historical consumption data, calculating and generating the dynamic optimal reserve of the disposable tableware package; s4, obtaining the current actual inventory of the management unit, and comparing the current actual inventory with the dynamic optimal reserve; s5, if the current actual stock is lower than the dynamic optimal reserve, generating a replenishment task; and if the current actual inventory is not lower than the dynamic optimal reserve, returning to execute the step S4, and laying a foundation for realizing predictive and risk-driven management.

Description

Technical Field

[0001] The present invention relates to the technical fields of smart medical care and logistics management, and specifically to a big data-based intelligent management method and system for disposable tableware packaging. Background Art

[0002] When responding to public health emergencies, the operation and management of hospital isolation wards face severe challenges. Disposable tableware, a key material for ensuring basic living needs and preventing cross-infection, is a core component of infection control. Existing technologies, such as regular quantitative distribution, on-demand requisitioning, or overstocking in wards, are generally feasible under normal circumstances. However, in emergencies, when ward functions, admissions, and risk levels fluctuate rapidly, these models expose inherent technical flaws. Specifically, they fail to address the exponential coupling effect between infection prevention and control safety levels and tableware supply response speeds under the influence of public health emergencies, leading to unexpected technical contradictions that prevent both safety and timeliness, and constitute a technical challenge that urgently needs to be addressed in the current field of emergency medical supplies management.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for intelligent management of disposable tableware packaging based on big data to solve the problems raised in the above background technology.

[0005] The technical solution of the present invention is a method for intelligent management of disposable tableware packaging based on big data, comprising the following steps: S1. Collect the inherent risk factors and controllable risk factors of the management unit, and collect the historical consumption data of the management unit; S2. Calculate and generate a quantitative ward total risk score based on inherent risk factors and controllable risk factors; S3. Calculate and generate the dynamic optimal reserve of disposable tableware packaging based on the ward's total risk score and historical consumption data; S4. Obtain the current actual inventory of the management unit and compare the current actual inventory with the dynamic optimal reserve; S5. If the current actual inventory is lower than the dynamic optimal reserve, a replenishment task is generated; if the current actual inventory is not lower than the dynamic optimal reserve, the process returns to step S4. S6. In response to the replenishment task, based on the total risk score of the ward, calculate and plan the distribution path that minimizes the overall risk cost, and issue the distribution path instruction.

[0006] Preferably, S2 specifically includes: S21. Set weight coefficients corresponding to inherent risk factors and controllable risk factors respectively; S22. Perform weighted summation of inherent risk factors and controllable risk factors to generate an overall risk score for the ward.

[0007] Preferably, S3 specifically includes: S31. Calculate the deterministic demand portion based on historical consumption data; S32. Calculate the risk-adaptive safety stock portion using the ward's total risk score as an adjustment factor; S33. Sum the deterministic demand part and the risk-adaptive safety stock part to generate a dynamic optimal reserve.

[0008] Preferably, S6 specifically includes: S61. Calculate the environmental risk rating of each road section in the hospital topology diagram based on the total risk score of the ward; S62. Calculate the path risk cost of the road section based on the environmental risk rating and the estimated travel time of the road section; S63. Use the path planning algorithm and take the path risk cost as the weight of each edge in the graph to solve the delivery path.

[0009] Preferably, the inherent risk factor is a low-frequency updated parameter set according to the knowledge base of hospital infection experts; the controllable risk factor is a high-frequency updated dynamic parameter obtained based on real-time sensor data.

[0010] Preferably, the calculation of the risk-adaptive safety stock portion is also based on a preset risk sensitivity coefficient; the risk sensitivity coefficient is an adjustable strategy parameter set by the hospital management according to the emergency response level and the abundance of supplies.

[0011] Preferably, the calculation of the path risk cost is also based on a preset time risk preference coefficient; the time risk preference coefficient is used to balance the weights of timeliness and safety in path planning.

[0012] An intelligent management system for disposable tableware packaging based on big data, including: Data collection module, used to collect inherent risk factors, controllable risk factors and historical consumption data of the management unit; The risk scoring module is used to calculate and generate a quantitative overall risk score for the ward based on inherent risk factors and controllable risk factors; A reserve calculation module is used to calculate the dynamic optimal reserve of disposable tableware packaging based on the ward's total risk score and historical consumption data; The inventory monitoring module is used to obtain the current actual inventory, compare it with the dynamic optimal reserve, and generate a replenishment task when the current actual inventory is lower than the dynamic optimal reserve; The path planning module is used to respond to replenishment tasks, calculate and plan the distribution path that minimizes the overall risk cost based on the total risk score of the ward, and issue distribution path instructions.

[0013] The present invention provides an intelligent management method and system for disposable tableware packaging based on big data through improvements. Compared with the existing technology, it has the following improvements and advantages: 1. This invention achieves dynamic quantification and precise perception of hospital-acquired infection risk. By collecting inherent and controllable risk factors from management units and applying a ward-wide overall risk scoring model, this system transforms abstract risk into a precise, dynamically updated quantitative indicator. It also combines stable baseline risk with immediate environmental changes to generate an overall ward risk score that serves as a basis for unified decision-making across the entire system. This scoring mechanism enables managers to gain data-driven insights into the risk distribution and dynamic evolution of the entire hospital, laying the foundation for predictive, risk-driven management. 2. This invention establishes a risk-adaptive dynamic optimal reserve strategy, making safety stock levels a direct function of risk levels, not just demand fluctuations. When a critically ill patient in an isolation ward causes its controllable risk factor to rise, the system's calculated overall risk score increases accordingly, automatically increasing the dynamic optimal reserve level for disposable tableware packaging in that ward. This allows for a preemptive buffer before the risk actually materializes into a supply disruption, reducing the risk of tableware supply disruptions in high-risk wards and shortening supply response times. 3. The present invention plans an intelligent delivery path that minimizes the overall risk cost. When responding to a replenishment task, the present invention calculates and plans a delivery path based on the total risk score of the ward through a path risk cost model. It defines a new cost metric, namely risk equivalent time, which quantifies and unifies the intangible infection risk and the tangible time cost. The path planning algorithm uses this as the weight to solve the problem, and the result is no longer the shortest path in the traditional sense, but a path with the lowest comprehensive exposure risk. When a delivery task needs to pass through a corridor at the door of a high-risk ward, even if the path is the shortest, the system will assign it an extremely high path risk cost due to the high environmental risk rating of this section, thereby actively planning an alternative path that bypasses the low-risk area. This design deeply integrates infection control strategies into logistics execution, making every material delivery an active risk avoidance behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further explained below in conjunction with the accompanying drawings and Examples: Figure 1 This is a flow chart of a big data-based intelligent management method for disposable tableware packaging of the present invention. DETAILED DESCRIPTION

[0015] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0016] Example 1: See also Figure 1 , the present invention provides a method for intelligent management of disposable tableware packaging based on big data, comprising the following steps: S1, collecting inherent risk factors and controllable risk factors of a management unit, and collecting historical consumption data of the management unit; S2. Calculate and generate a quantitative ward total risk score based on inherent risk factors and controllable risk factors; S3. Calculate and generate the dynamic optimal reserve of disposable tableware packaging based on the ward's total risk score and historical consumption data; S4. Obtain the current actual inventory of the management unit and compare the current actual inventory with the dynamic optimal reserve; S5. If the current actual inventory is lower than the dynamic optimal reserve, a replenishment task is generated; if the current actual inventory is not lower than the dynamic optimal reserve, the process returns to step S4. S6. In response to the replenishment task, based on the total risk score of the ward, calculate and plan the distribution path that minimizes the overall risk cost, and issue the distribution path instruction.

[0017] This embodiment provides a big data-based intelligent management method for disposable tableware packaging, which establishes a complete operational process that integrates data collection, risk quantification, dynamic reserve calculation, inventory monitoring, task generation, and intelligent path planning. This method directly links risk assessment results with inventory and logistics decisions, achieving automated closed-loop control from risk perception to material response. The fundamental purpose is to unify the originally conflicting safety and timeliness goals into a computable and optimizable framework through a data-driven actuarial model, thereby maximizing the efficiency and reliability of material supply and reducing overall operating costs while ensuring infection control requirements.

[0018] Example 2 S2 specifically includes: S21. Set weight coefficients corresponding to inherent risk factors and controllable risk factors respectively; S22. Perform a weighted summation of inherent risk factors and controllable risk factors to generate an overall risk score for the ward; Intrinsic risk factors are low-frequency updated parameters set based on the knowledge base of hospital infection experts; controllable risk factors are high-frequency updated dynamic parameters obtained based on real-time sensor data.

[0019] In this embodiment, the calculation of the ward total risk score aims to transform the complex nosocomial infection risk into a standardized quantitative indicator. To achieve this goal, this step introduces a ward total risk score model. The inherent logic is to decompose risk into two dimensions: relatively stable inherent risk and real-time controllable risk. Through dynamic weighted summation, it can accurately reflect the comprehensive risk level at a specific time point. The mathematical expression of this model is: Where, Representative Management Unit At the moment The dimensionless total risk score is the core basis for subsequent decision-making; Is the unique identifier of the management unit, is the timestamp; For the management unit The inherent risk factor is a low-frequency updated parameter whose value is set based on static information such as the physical layout of the ward and basic isolation conditions in the hospital infection expert knowledge base; This value can be quantified using a scoring table; for example, for each management unit Score and sum according to the following rules: Ward type: single isolation ward is worth 0 points; double ward is worth 5 points; multi-person room is worth 10 points; Isolation facilities: 0 points if equipped with a negative pressure ventilation system; 3 points if equipped with an independent anteroom but without negative pressure ventilation; 5 points if equipped with no independent anteroom or effective isolation facilities; Area location: 0 points for being located in an independent infection building of the hospital; 2 points for being located in the isolation terminal area of ​​a common building; 5 points for being mixed with other common wards or located on a main pedestrian route; It is the sum of the above scores and can be normalized as needed; For the management unit At the moment A controllable risk factor, which is a dynamic parameter updated at high frequency. Its value is derived from data collected by the real-time sensor network, such as patient density, medical staff flow frequency, and ventilation system air exchange efficiency. This value can be generated by normalizing and weighting the data of each sensor; the raw readings of each sensor Through a maximum-minimum normalization function Map to interval, where and is the preset safety threshold or historical extreme value; it can be calculated using the following formula:

[0020] Where, is the real-time patient density, is the frequency of medical care flow, is the air exchange efficiency of the ventilation system; are the corresponding weight coefficients, satisfying , its value can be set by hospital infection experts according to the contribution of different factors to the risk of infection; please note that ventilation efficiency The coefficient before is negative, indicating that the higher the ventilation efficiency, the lower the risk; and are the top-level weight coefficients corresponding to the inherent and controllable risk factors, respectively. Both are dimensionless parameters and satisfy , its settings can be adjusted by hospital management according to the emergency response level, or optimized through regression analysis of historical infection event data; : Raw readings of the sensor; : Preset safety threshold or historical minimum value of sensor readings; : Preset safety threshold or historical maximum value of sensor readings; : Maximum-minimum normalization function, used to map the raw sensor readings to interval; For example, a logistic regression model can be constructed with whether an infection event occurred within a specific historical time window as the dependent variable, and the inherent risk factor of the corresponding management unit within the time window as and controllable risk factors The mean of is taken as the independent variable, and the coefficients obtained by model fitting are used to objectively determine the weights of the two. This model runs at a preset frequency, such as every five minutes, and continuously updates the risk score of each management unit based on the latest data input, forming a dynamic risk heat map covering the entire hospital, thereby providing accurate, dynamic and quantifiable input for subsequent material management decisions.

[0021] Example 3 S3 specifically includes: S31. Calculate the deterministic demand portion based on historical consumption data; S32. Calculate the risk-adaptive safety stock portion using the ward's total risk score as an adjustment factor; S33, summing the deterministic demand portion and the risk-adaptive safety stock portion to generate a dynamic optimal reserve; The calculation of the risk-adaptive safety stock is also based on a preset risk sensitivity coefficient; the risk sensitivity coefficient is an adjustable strategic parameter set by the hospital management based on the emergency response level and the abundance of supplies; In this implementation, the dynamic optimal reserve level is calculated using an inventory model that incorporates a risk adjustment factor. This model is motivated by the fact that traditional inventory theory focuses solely on demand uncertainty, failing to consider the dramatic increase in the likelihood of supply disruptions and their consequences in high-risk environments. Therefore, this solution, building on the classical inventory model, uses the total risk score calculated in the previous step as a key adjustment variable to dynamically adjust the safety stock level. The mathematical expression is: ; The logical structure of this formula is divided into two parts: the first part is the average consumption during the lead time to meet deterministic demand; the second part is the risk-adaptive safety stock defined in the present invention; wherein, Represents a management unit against The optimal reserve quantity recommended for type tableware, its physical dimension is quantity; is the historical average daily consumption, in units of quantity / day; is the average replenishment lead time, in days; is the standard deviation of daily consumption, in units of number / day; The results are directly quoted from the aforementioned risk scoring model; is a dimensionless risk sensitivity coefficient, which is an adjustable strategic parameter and is set by the hospital management according to the emergency response level and the abundance of supplies. For example, in normal conditions, when supplies are abundant, a lower risk sensitivity can be set, such as When entering the early warning period of a specific infectious disease or when supplies are tight, in order to increase safety redundancy, the coefficient can be increased to or higher. These values ​​are only examples. The specific values ​​can be calibrated by the hospital according to the actual situation. The dimensions of the two items on the right side of the formula are (number / day) × (day). The result is quantity, which is consistent with the dimension on the left side. The application effect of this model is that when the risk score of a management unit is When it rises, even if its historical demand fluctuates If the system remains unchanged, it will also proactively increase its safety stock level to proactively hedge against potential supply disruptions caused by high-risk environments, ensuring the continuity of supply of critical materials; : Model of disposable tableware.

[0022] Example 4 S6 specifically includes: S61. Calculate the environmental risk rating of each road section in the hospital topology diagram based on the total risk score of the ward; S62. Calculate the path risk cost of the road section based on the environmental risk rating and the estimated travel time of the road section; S63. Use the path planning algorithm and use the path risk cost as the weight of each edge in the graph to solve the delivery path; The calculation of path risk cost is also based on the preset time risk preference coefficient; the time risk preference coefficient is used to balance the weights of timeliness and safety in path planning.

[0023] In order to make the setting of this parameter more standardized, a recommended value linked to the hospital emergency response level can be preset, for example: Daily status, first-level response: security requirements are high, timeliness is secondary, can be set , that is, 70% of the weight in the path cost is given to risk.

[0024] Emergency, three-level response: Timeliness is the priority, delivery must be made as soon as possible, can be set , that is, 80% of the weight of the path cost is given to time; General status, secondary response: both are taken into account and can be set ; In this implementation, delivery route planning with overall risk cost minimization aims to calculate a route that strikes the optimal balance between timeliness and safety. The technical motivation is that traditional route planning algorithms use time or distance as the sole optimization objective, which presents significant drawbacks in hospital environments where strict infection risk control is required. This solution designs a new cost function that takes into account the environmental exposure risk of the route and weights it with the time cost. The overall risk cost model for this route is mathematically expressed as: ;in, Represents the complete delivery path The total risk cost, whose physical dimension is seconds, can be understood as the risk equivalent time; A road with multiple sections The path of the composition; An edge in the hospital topology graph, such as a corridor or elevator; To pass the road The estimated travel time in seconds; For road sections The dimensionless environmental risk rating is determined by the total risk score of all management units adjacent to the road segment. The spatial weighted average is obtained to represent the exposure risk level of the road section; The spatial weighted average can be calculated using the inverse distance weighted method; for any road segment in the map , environmental risk rating The calculation formula is as follows:

[0025] in, is the adjacent road segment in the topology graph The snap-in, is the total number of all neighboring management units; It's a road section The geometric center point to the management unit Euclidean distance of the center point; Is a snap-in At the moment Total risk score; It is a power exponent, usually set to 2. p=2 means that the influence of distance decays inversely proportional to the square. This is a classic model widely used in spatial interpolation. It can effectively reflect the dominant role of neighboring unit risk, indicating that the farther the distance, the lower the weight of the risk score decreases inversely proportional to the square. is a dimensionless time-risk preference coefficient in the interval [0, 1], which is used to balance the weight of timeliness and safety in path planning; the dimensions of each item on the right side of the formula are all seconds to ensure dimensional consistency; when responding to delivery tasks, the system is based on the latest Update each edge in the graph of , and then use this The formula is used as the weight of the edges in path planning algorithms such as A* or Dijkstra to solve the optimal path that minimizes the overall risk cost; this method ensures that the distribution process actively avoids high-risk areas and materializes infection control strategies in every link of logistics.

[0026] Example 5 A big data-based intelligent management system for disposable tableware packaging includes: a data acquisition module for collecting inherent risk factors, controllable risk factors and historical consumption data of a management unit; The risk scoring module is used to calculate and generate a quantitative overall risk score for the ward based on inherent risk factors and controllable risk factors; A reserve calculation module is used to calculate the dynamic optimal reserve of disposable tableware packaging based on the ward's total risk score and historical consumption data; The inventory monitoring module is used to obtain the current actual inventory, compare it with the dynamic optimal reserve, and generate a replenishment task when the current actual inventory is lower than the dynamic optimal reserve; The path planning module is used to respond to replenishment tasks, calculate and plan the distribution path that minimizes the overall risk cost based on the total risk score of the ward, and issue distribution path instructions.

[0027] The present invention also provides an intelligent management system for implementing the above method; the system is composed of a series of functionally independent hardware or software modules, which work together to achieve a complete closed-loop management function; the system specifically includes: a data acquisition module, which serves as the input interface of the system and is responsible for continuously collecting multi-source data from the hospital infection expert knowledge base, real-time sensor network and historical order database; a risk scoring module, which serves as the core analysis unit of the system and has a built-in and execution of the aforementioned ward total risk scoring model to convert the raw data into a quantitative risk score; a reserve calculation module, which receives the output of the risk scoring module and runs the aforementioned dynamic optimal reserve model to provide each management unit with a certain amount of risk. The system generates dynamic inventory recommendations for each material of the element; an inventory monitoring module is responsible for real-time monitoring of the actual inventory of each management unit and comparing it with the dynamic optimal reserve generated by the reserve calculation module. When the actual inventory is lower than the reserve, a replenishment task is automatically generated; and a path planning module, after receiving a replenishment task, the module immediately calls the aforementioned overall risk cost model, combines the latest risk data, calculates and plans the optimal distribution path, and then issues the path instructions to the distribution personnel or automated logistics equipment; through the organic integration and seamless collaboration of these modules, the entire system can adaptively respond to changes in environmental risks and material demands, thereby realizing efficient, accurate and safe management of emergency materials.

[0028] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A big data-based intelligent management method for disposable tableware packaging, characterized by: The steps include: S1. Collect the inherent risk factors and controllable risk factors of the management unit, and collect the historical consumption data of the management unit; S2. Calculate and generate a quantitative ward total risk score based on inherent risk factors and controllable risk factors; S3. Calculate and generate the dynamic optimal reserve of disposable tableware packaging based on the ward's total risk score and historical consumption data; S4. Obtain the current actual inventory of the management unit and compare the current actual inventory with the dynamic optimal reserve; S5. If the current actual inventory is lower than the dynamic optimal reserve, a replenishment task is generated; If the current actual inventory is not less than the dynamic optimal reserve, return to step S4; S6. In response to the replenishment task, based on the total risk score of the ward, calculate and plan the distribution path that minimizes the overall risk cost, and issue the distribution path instruction.

2. The method for intelligent management of disposable tableware packaging based on big data according to claim 1, characterized in that: S2 specifically includes: S21. Set weight coefficients corresponding to inherent risk factors and controllable risk factors respectively; S22. Perform weighted summation of inherent risk factors and controllable risk factors to generate an overall risk score for the ward.

3. The method for intelligent management of disposable tableware packaging based on big data according to claim 1, characterized in that: S3 specifically includes: S31. Calculate the deterministic demand portion based on historical consumption data; S32. Calculate the risk-adaptive safety stock portion using the ward's total risk score as an adjustment factor; S33. Sum the deterministic demand part and the risk-adaptive safety stock part to generate a dynamic optimal reserve.

4. The method for intelligent management of disposable tableware packaging based on big data according to claim 1, characterized in that: S6 specifically includes: S61. Calculate the environmental risk rating of each road section in the hospital topology diagram based on the total risk score of the ward; S62. Calculate the path risk cost of the road section based on the environmental risk rating and the estimated travel time of the road section; S63. Use the path planning algorithm and take the path risk cost as the weight of each edge in the graph to solve the delivery path.

5. The method for intelligent management of disposable tableware packaging based on big data according to claim 2, characterized in that: Intrinsic risk factors are low-frequency updated parameters set based on the knowledge base of hospital infection experts; controllable risk factors are high-frequency updated dynamic parameters obtained based on real-time sensor data.

6. The method for intelligent management of disposable tableware packaging based on big data according to claim 3, characterized in that: The calculation of the risk-adaptive safety stock portion is also based on a preset risk sensitivity coefficient; the risk sensitivity coefficient is an adjustable strategy parameter set by the hospital management based on the emergency response level and material abundance.

7. The method for intelligent management of disposable tableware packaging based on big data according to claim 4, characterized in that: The calculation of path risk cost is also based on the preset time risk preference coefficient; the time risk preference coefficient is used to balance the weights of timeliness and safety in path planning.

8. A big data-based intelligent management system for disposable tableware packaging, based on the big data-based intelligent management method for disposable tableware packaging according to any one of claims 1 to 7, characterized in that: include: Data collection module, used to collect inherent risk factors, controllable risk factors and historical consumption data of the management unit; The risk scoring module is used to calculate and generate a quantitative overall risk score for the ward based on inherent risk factors and controllable risk factors; A reserve calculation module is used to calculate the dynamic optimal reserve of disposable tableware packaging based on the ward's total risk score and historical consumption data; The inventory monitoring module is used to obtain the current actual inventory, compare it with the dynamic optimal reserve, and generate a replenishment task when the current actual inventory is lower than the dynamic optimal reserve; The path planning module is used to respond to replenishment tasks, calculate and plan the distribution path that minimizes the overall risk cost based on the total risk score of the ward, and issue distribution path instructions.

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