Needle recycling management platform based on Internet of Things

Abstract

The invention discloses a needle recovery management platform based on the Internet of Things, relates to the technical field of recovery management, solves the technical problems of multi-path planning in different scenes and dynamic recovery adjustment in emergency situations, and provides a needle recovery management platform based on the Internet of Things through multi-dimensional classification according to purposes, pollution levels and specifications and coverage of special needle treatment requirements. The method can reduce infection risk, support a mobile terminal to upload a novel needle sample, automatically update an identification model and adapt to hospital consumable iteration, solve the problem that a traditional system is invalid in category update, distinguish small scenes from large scenes, adapt to a hospital complex physical structure, weight priority through distance and emergency degree for the small scenes, insert in on-road nodes and preferentially select high floors, and improve the accuracy of the system. And for a large scene, temporary storage center pressure, transportation junction constraints, global rough planning and local fine optimization are fused, and the building crossing efficiency is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of recycling management, in particular to a needle recycling management platform based on Internet of Things. BACKGROUND

[0002] In hospital operation, needle recycling management is a key link of medical waste disposal.

[0003] However, the traditional management mode has many disadvantages. The needle is only classified in a basic way, and there is a lack of fine classification of infection risk and specification adaptability. New needle is easy to be identified after being introduced, which increases the medical risk. The recycling threshold relies on manual experience and is not dynamically adjusted in combination with historical data, department attributes and container space restrictions, which leads to overflow risk or resource waste. The recycling path is generated only according to fixed rules, and there is no dynamic adjustment capability for sudden situations. The recycling efficiency and emergency response are insufficient. The task allocation of the recycling vehicle and the transportation hub constraints are not included in the planning, which is easy to cause uneven load and illegal traffic, and increases the labor and time cost. SUMMARY

[0004] In view of the defects of the prior art, the present application provides a needle recycling management platform based on Internet of Things, which solves the problems of multi-path planning in different scenarios and dynamic adjustment of recycling in sudden situations.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a needle recycling management platform based on Internet of Things, comprising: A recycling management supervision module is used for establishing a needle identification library according to the basic information transmitted by the needle information acquisition module, classifying the needle, obtaining the corresponding department and label based on the source of the needle, obtaining the recycling container corresponding to the department, synchronizing the mapping relationship of container ID-department code-location coordinates in real time, comparing the number of needles in the recycling container with the recycling threshold, marking the department to be recycled, and transmitting the department to be recycled information to the adaptive analysis processing module. An adaptive analysis processing module is used for analyzing the obtained department to be recycled information, obtaining the department to be recycled, classifying the scene based on the number of departments to obtain large scene conditions and small scene conditions, taking the department to be recycled with the most needles as the starting point of the path for the small scene conditions, determining the priority by quantifying and scoring the distance and recycling emergency degree, and generating small scene path information. For the large scene conditions, a layer model is constructed, a heuristic function and a dynamic weight distribution model are designed, and large scene path information is generated. The dynamic monitoring adjustment module is configured to acquire real-time monitoring information, determine whether a sudden situation exists, generate a dynamic adjustment signal if the sudden situation exists, analyze the dynamic adjustment signal, insert the sudden situation according to the minimum disturbance principle and re-plan a path in a small-scene situation, re-allocate tasks and paths with the optimal resource configuration as a target in a large-scene situation, generate dynamic adjustment information, and transmit the dynamic adjustment information to the recycling management information output module.

[0006] As a further scheme of the present application, the needle information acquisition module and the recycling management information output module are further included. The needle information acquisition module is configured to acquire basic information of the needle, the basic information including a needle type, a needle source, and a recycling time, and transmit the acquired basic information to the recycling management monitoring module. The recycling management information output module is configured to display the acquired small-scene path information, large-scene path information, or dynamic adjustment information to corresponding management personnel.

[0007] As a further scheme of the present application, the specific way in which the recycling management monitoring module marks the to-be-recycled department is as follows: Based on the needle source, a corresponding department is acquired, and is marked as i, and i=1, 2, …, j, where j represents the type of the department, then a recycling container corresponding to the department i is acquired, and the number of needles in the recycling container is acquired, and is compared with a recycling threshold value; If the number of needles is greater than the recycling threshold value, it indicates that the corresponding department needs to be recycled, and the corresponding department is marked as a to-be-recycled department, otherwise, if the number of needles is less than the recycling threshold value, it indicates that the corresponding department does not reach the recycling standard, and is marked as a normal monitoring department.

[0008] As a further scheme of the present application, the specific way in which the self-adaptive analysis processing module analyzes the acquired to-be-recycled department information is as follows: All to-be-recycled departments are acquired and are marked as a, and a=1, 2, …, b, where b represents the type of the to-be-recycled department, and the coordinate position of the department is acquired, and scene classification processing is performed based on the number of departments, if the number of departments is greater than a number threshold value, the department is classified as a large-scene situation, otherwise, if the number of departments is less than the number threshold value, the department is classified as a small-scene situation.

[0009] As a further scheme of the present application, the specific way in which the self-adaptive analysis processing module generates small-scene path information is as follows: The department a with the largest number of needle heads to be recycled is obtained and marked as the starting point of the path, and the department to be recycled with the shortest distance and the highest emergency recovery priority is selected as the target according to the nearest neighbor principle, the distance and the emergency recovery priority are quantitatively scored, the priority of the department to be recycled is calculated by comprehensively considering the two factors, and the department to be recycled with the highest priority is selected as the target to generate the corresponding path, and the departments to be recycled are sequentially selected according to the nearest neighbor principle until the needle head recovery of all the departments to be recycled is completed, and the small-scene path information is generated.

[0010] As a further scheme of the present application, the specific way in which the adaptive analysis processing module generates the large-scene path information is: The key positions of each department, the temporary storage center, the elevator / stairway entrance, etc. are defined as nodes of a graph, and their three-dimensional coordinates are labeled to construct a corresponding graph layer model, and the corresponding transportation time is obtained, then the emergency degree of the department to be recycled is obtained, and a heuristic function is designed, and the specific function formula is, wherein is the heuristic value, and specifically, the larger the value is, the greater the attraction from a to b is, and the more inclined to the path d ab is the actual distance from node a to b, and are weight coefficients, which need to be determined through experiments; According to the obtained heuristic function, a dynamic weight distribution model is constructed by combining the path length, the container overflow level and the vehicle load balancing, , and , and are determined by training historical data, and the large-scene path information is generated.

[0011] As a further scheme of the present application, the specific way in which the dynamic monitoring and adjusting module generates the dynamic adjustment signal is: Real-time monitoring information is obtained, and it is determined whether there is a sudden situation, if there is, a dynamic adjustment signal is generated, otherwise, a normal monitoring signal is generated, and the signal is transmitted to the recovery management information output module.

[0012] As a further scheme of the present application, the specific way in which the dynamic monitoring and adjusting module generates the dynamic adjustment information is: For the small-scene case, the corresponding sudden situation is obtained, and the sudden situation is inserted, and the path of the analyzed department to be recycled after insertion is planned to generate the corresponding dynamic adjustment information, for the large-scene case, the corresponding sudden situation is obtained, and the corresponding sudden situation is inserted and analyzed, specifically, the remaining path is inserted and analyzed based on the generated large-scene path information, and the corresponding dynamic adjustment information is generated, then the generated dynamic adjustment information is transmitted to the recovery management information output module.

[0013] The application provides a needle recycling management platform based on Internet of Things. The application covers special needle treatment requirements, reduces infection risk, supports mobile terminal uploading of new needle samples, automatically updates identification model, adapts to hospital consumable iteration, solves the problem of invalidation of traditional system category update, distinguishes small scenes and large scenes, adapts to complex physical structure of hospitals, inserts nodes along the way, gives priority to high floors, reduces invalid detours, integrates temporary storage center pressure and traffic hub constraints, global coarse planning and local fine optimization, and guarantees cross-building efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 The system schematic diagram of the application is shown in the figure. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0016] Embodiment one Please refer to Figure 1 The application provides a needle recycling management platform based on Internet of Things, which comprises a needle information acquisition module, a recycling management supervision module, a self-adaptive analysis processing module, a dynamic monitoring and adjusting module and a recycling management information output module, and is combined with the accompanying drawings. Figure 1 It can be known that the information between the above function modules is unidirectional transmission.

[0017] The needle information acquisition module is used for acquiring the basic information of the needle, and the basic information comprises the needle type, the needle source and the recycling time, and the basic information is transmitted to the recycling management supervision module.

[0018] The recycling management and monitoring module is used to establish a corresponding needle identification database based on the acquired basic information. The main function of this database is to classify needles, for example, by use: infusion needles, injection needles, blood collection needles, suture needles, etc. (different needles have different infection risks and handling requirements; for example, suture needles require separate puncture prevention treatment); by contamination level: ordinary infectious needles (such as routine injections), highly pathogenic needles (such as those used in infectious disease departments); and by size: 0.5mm, 0.7mm, etc. (this affects container space occupancy). When hospitals introduce... When using new types of needles (such as anti-puncture safety needles), the module allows medical staff to upload sample images via mobile devices, automatically updating the recognition library algorithm model to avoid recognition failures due to product category updates. Simultaneously, it retrieves the corresponding department based on the needle's origin and labels it as i, where i = 1, 2, ..., j, and j represents the department type. Next, it retrieves the recycling container corresponding to department i, configuring 1-3 smart containers per department (based on needle production volume). Each container is bound to a department code via a unique ID (e.g., container ID "R-N1-01" represents container number 1 in Internal Medicine Ward 1). The module synchronizes the "container ID-department code-location coordinates" mapping in real time (e.g., "R-N1-01" corresponds to "Treatment Room of Internal Medicine Ward 1, East Side, 5th Floor, Inpatient Department"), and obtains the number of needles in the recycling container. It then compares this number with a recycling threshold, the specific value of which is set by the operator based on historical data, department attributes, and space limitations. For example, historical data might include 70% of the department's average daily needle production over the past three months (e.g., if Internal Medicine Ward 1 produces an average of 100 needles per day, a recommended threshold of 70 needles). Attributes: The threshold for high-risk departments (such as infectious disease wards and emergency departments) is lowered by 20% (the threshold is set to 64 out of an average of 80 needles per day in the emergency department), and the threshold for low-frequency departments (such as health check-up centers) is increased by 30%; Space restrictions: 80% of the actual container capacity (for a container with a capacity of 100 needles, the threshold is no more than 80 to avoid overflow). If the number of needles exceeds the recycling threshold, it means that the corresponding department needs to recycle them and will be marked as a department to be recycled. Conversely, if the number of needles is less than the recycling threshold, it means that the corresponding department has not met the recycling standard and will be marked as a normal monitoring department. For the departments identified as needing needle recycling, corresponding department information is generated, including the number of needles and the department location. This information is then transmitted to the adaptive analysis and processing module. For the departments identified as being under normal monitoring, the number of needles is monitored at a time interval of T and compared with the recycling threshold. The specific value of time T is set by the operator.

[0019] The adaptive analysis and processing module analyzes the acquired information on departments to be recycled, obtains all departments to be recycled and labels them as 'a', where a = 1, 2, ..., b, and b represents the type of department to be recycled. It also obtains the coordinates of the departments and performs scene classification based on the number of departments. If the number of departments is greater than a threshold, it is classified as a large scene; otherwise, if the number of departments is less than the threshold, it is classified as a small scene. The specific value of the threshold is set by the operator. For example, if the number of departments is 30 and the preset number is 45, the corresponding scene is further classified as a small scene. If the number of departments exceeds 45, it is classified as a large scene. Analyzing the small-scale scenarios obtained from the classification, the department 'a' with the largest number of needles to be recycled was identified and marked as the starting point of the path (temporary storage center). Simultaneously, based on the nearest neighbor principle, priority was given to selecting the nearest department requiring urgent recycling. Specifically, distance and urgency were quantified and scored, with urgency determined by the number of needles: highly urgent (exceeding the threshold by 30%+) was assigned 100 points; moderately urgent (exceeding the threshold by 10%-30%) was assigned 70 points; and near overflow (≥80% of the threshold but not exceeding it) was assigned 40 points. Regarding distance... The scoring is calculated as follows: distance between a department and the current path endpoint ÷ maximum possible distance in the small scene. Score = 100 - (normalized value × 100) (the closer the distance, the higher the score). The priority of the departments to be retrieved is calculated by combining the two factors. Specifically, the priority is calculated according to the formula: priority = (distance score × weight 1) + (retrieval urgency score × weight 2). The department with the highest priority is selected as the target to generate the corresponding path. After selection, the nearest neighbor principle is followed to select the needles in turn until all departments to be retrieved have retrieved the needles, and the small scene path information is generated. For example, starting from the starting point, a preliminary path is generated from high to low priority scores (e.g., W1→N1→E1→...). If two consecutive departments need to detour (e.g., a straight-line distance of 100 meters but an actual detour of 300 meters), then a "middle-line department" is inserted (e.g., W1→A→N1, where A is a department that is on the same line and has a higher priority). If a department is located on a high floor (e.g., the 5th floor) and subsequent departments are all on lower floors (1st-2nd floors), then the department on the higher floor is given priority (to avoid repeated ups and downs).

[0020] The large-scale scenario obtained from the classification is analyzed. Key locations such as departments, temporary storage centers, and elevator / staircase entrances are defined as nodes in the graph, and their 3D coordinates are labeled (e.g., room 302 on the 3rd floor of the outpatient building is (3,302)). A corresponding layer model is constructed, and the corresponding transportation time is obtained. Then, the urgency level of the department to be recycled is obtained, and a heuristic function is designed. The specific function formula is as follows: ,in As a heuristic value, a larger value indicates a greater attraction from a to b, and a stronger inclination towards that path. ab This represents the actual distance from node a to node b. and The weighting coefficients need to be determined through experiments. The overflow coefficient indicates that if the container status of the department to be recycled is abnormal, the coefficient is 10, and if it is normal, the coefficient is 1. The time window coefficient indicates that if the current time is within the allowed recycling time of department a, the coefficient is 1, and if not, the coefficient is 0.5. Based on the obtained heuristic function, a dynamic weight allocation model is constructed by combining path length, container overflow level, and vehicle load balancing. ,and , and Path information for large scenes is determined and generated through training with historical data; For example, in a large-scale recycling scenario at a top-tier hospital, there are 62 departments awaiting recycling (distributed across the outpatient building, inpatient building, and emergency building), of which 12 are highly urgent (the infectious disease department and the emergency department are overflowing). The constraints are: recycling vehicles are prohibited from using the passenger elevators in the outpatient building from 7:00 to 9:00; the temporary storage center in the inpatient department is already 85% full (pressure coefficient 0.85), so these should be prioritized for bypassing.

[0021] The Infectious Diseases Department (K-Hospital-5-503, Risk Level 3, Overloaded) is marked as a high-risk node; the Inpatient Temporary Storage Center (S-Hospital-1, Pressure Coefficient 0.85) is a node that needs to be bypassed; the Outpatient Building Passenger Elevator (T-Outpatient-3-Passenger Elevator) is unavailable from 7:00 to 9:00.

[0022] The overall risk coefficient of the infectious disease department node is 10×0.7+3×0.3=7.9, the time window coefficient is 1 (within the recovery period), the transportation cost coefficient is 0 (no waiting when using the freight elevator), and the heuristic value is significantly higher than that of the regular node.

[0023] Overall rough planning: Prioritize the main route of "inpatient building freight elevator → infectious disease department → emergency department", bypassing the inpatient temporary storage center; Localized optimizations: Tasks within the outpatient building are assigned to "stairs + idle vehicles" to avoid peak passenger elevator hours; Multi-vehicle collaboration: High-risk tasks are assigned to the nearest vehicle A with 60% remaining capacity, while routine tasks are assigned to vehicles B and C to avoid uneven load distribution.

[0024] Next, the generated small scene path information and large scene path information are transmitted to the recycling management information output module.

[0025] The recycling management information output module is used to display the acquired small scene path information and large scene path information to the corresponding management personnel.

[0026] Example 2 As a second embodiment of the present invention, it is implemented based on the first embodiment, and the difference from the first embodiment is as follows: The adaptive analysis and processing module transmits the generated small-scene path information and large-scene path information to the dynamic monitoring and adjustment module. Simultaneously, it combines the acquired real-time monitoring information to dynamically adjust both, and the specific adjustment methods are as follows: The system acquires real-time monitoring information and determines whether there are any emergencies. Emergencies include the addition of new departments to be analyzed and the change in the priority of the departments to be analyzed and recycled. If such emergencies exist, a dynamic adjustment signal is generated; otherwise, a normal monitoring signal is generated and transmitted to the recycling management information output module. The generated dynamic adjustment signals are analyzed. For small-scale scenarios, the corresponding emergencies are obtained and inserted. Simultaneously, path planning is performed on the departments to be analyzed after insertion, and corresponding dynamic adjustment information is generated. For large-scale scenarios, the corresponding emergencies are obtained and inserted for analysis. Specifically, the insertion is performed on the remaining paths based on the generated large-scale path information, and corresponding dynamic adjustment information is generated. Then, the generated dynamic adjustment information is transmitted to the recycling management information output module.

[0027] For unexpected situations in small scenarios, the optimized response strategy focuses more on global optimization of the path structure rather than local insertion. It quickly adjusts the path with the principle of minimum disturbance and prioritizes the use of existing resources. If the distance from the current path is ≤150 meters, the path is directly inserted (e.g., if the original path is A→B→C, and the new path D is only 80 meters away from B, the path is adjusted to A→B→D→C). If the distance is greater than 150 meters, determine whether it can be handled by other nearby recycling vehicles (e.g., if there is a vehicle with a remaining capacity of ≥50% and a distance of <100 meters). Otherwise, generate a side quest (turn back after completing the current path).

[0028] If the distance is greater than 150 meters, determine whether it can be handled by other nearby recycling vehicles (e.g., if there is a vehicle with ≥50% remaining capacity and a distance of <100 meters), otherwise generate a side quest (turn back after completing the current path). If the urgency level of a department is upgraded (e.g., from routine to high), its priority score is recalculated. If the score enters the top 30%, it will be moved to the first node after the starting point of the path (e.g., if the original path is A→B→C, and B is upgraded, it will be adjusted to A→B→C).

[0029] If the urgency level of a department is upgraded (e.g., from routine to high), its priority score is recalculated. If the score enters the top 30%, it will be moved to the first node after the starting point of the path (e.g., if the original path is A→B→C, and B is upgraded, it will be adjusted to A→B→C).

[0030] In response to sudden situations in large-scale scenarios, with the goal of optimal resource allocation, tasks and routes are reallocated. If the task belongs to a high-risk department (such as an infectious disease ward), route replanning is immediately triggered, and the task is prioritized to the recycling vehicle that is "closest to the location and has the same protection level". If it belongs to a regular department → calculate its impact on the load balancing of the current route (e.g., after adding it, the load of a certain vehicle exceeds 80%, while the load of other vehicles is <50%). If the impact is greater than the threshold (e.g., 15%) → reassign the task.

[0031] If it belongs to a regular department → calculate its impact on the load balancing of the current route (e.g., after adding it, the load of one vehicle exceeds 80%, while other vehicles are <50%). If the impact is greater than the threshold (e.g., 15%) → reassign tasks. If the urgency level of a department is upgraded → determine whether a separate vehicle needs to be dispatched (e.g., if the vehicle is overloaded by more than 50% and is more than 300 meters away from the main route), otherwise remove the department from the original route and insert it into a higher priority route (e.g., change it from a regular vehicle route to a dedicated infectious disease vehicle route).

[0032] If the urgency level of a department is upgraded → determine whether a separate vehicle needs to be dispatched (e.g., if the vehicle is overloaded by more than 50% and is more than 300 meters away from the main route), otherwise remove the department from the original route and insert it into a higher priority route (e.g., change it from a regular vehicle route to a dedicated infectious disease vehicle route).

[0033] The recycling management information output module is used to display the acquired dynamic adjustment information to the corresponding management personnel.

[0034] Example 3, as Example 3 of the present invention, focuses on combining the implementation processes of Example 1 and Example 2.

[0035] The data in the above formulas are all calculated using numerical values, without substituting the units of the parameters. In addition, the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0036] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A needle recycling management platform based on the Internet of Things, characterized in that, include: The recycling management and supervision module is used to establish a needle identification library based on the basic information transmitted by the needle information collection module, classify needles, obtain and label the corresponding department based on the source of the needle, obtain the recycling container corresponding to the department, synchronize the container ID-department code-location coordinate mapping relationship in real time, compare the number of needles in the recycling container with the recycling threshold, mark the department to be recycled, and transmit the information of the department to be recycled to the adaptive analysis and processing module. The adaptive analysis and processing module is used to analyze the acquired information on departments to be recycled, identify the departments to be recycled, classify the scenarios based on the number of departments to obtain large-scale scenarios and small-scale scenarios. For small-scale scenarios, the department with the most needles to be recycled is used as the starting point of the path, and the priority is determined by quantifying and scoring the distance and the urgency of recycling, and small-scale path information is generated. For large-scale scenarios, construct a layer model, design heuristic functions and dynamic weight allocation models, and generate large-scale path information; The dynamic monitoring and adjustment module is used to acquire real-time monitoring information, determine whether there are any emergencies, and generate dynamic adjustment signals if there are. The dynamic adjustment signals are analyzed. In small-scale scenarios, emergencies are inserted according to the principle of minimum disturbance and the path is replanned. In large-scale scenarios, tasks and paths are reallocated with the goal of optimal resource allocation. Dynamic adjustment information is generated and transmitted to the recycling management information output module.

2. The IoT-based needle recycling management platform according to claim 1, characterized in that, It also includes a needle information acquisition module and a retrieval management information output module; The needle information collection module is used to collect basic information about the needles, including needle type, needle source, and recycling time, and transmit the collected basic information to the recycling management and monitoring module. The recycling management information output module is used to display the acquired small scene path information, large scene path information, or dynamic adjustment information to the corresponding management personnel.

3. The IoT-based needle recycling management platform according to claim 1, characterized in that, The specific method by which the recycling management and monitoring module marks the departments to be recycled is as follows: Based on the source of the needle, obtain the corresponding department and label it as i, where i = 1, 2, ..., j, and j represents the type of department. Then, obtain the recycling container corresponding to department i, and obtain the number of needles in the recycling container, and compare it with the recycling threshold. If the number of needles exceeds the recycling threshold, the corresponding department needs to recycle them and will be marked as a department to be recycled. Conversely, if the number of needles is less than the recycling threshold, the corresponding department has not met the recycling standard and will be marked as a normal monitoring department.

4. The IoT-based needle recycling management platform according to claim 1, characterized in that, The adaptive analysis and processing module analyzes the acquired information on departments to be recovered in the following specific way: Obtain all departments to be recycled and label them as a, where a = 1, 2, ..., b, and b represents the type of department to be recycled. At the same time, obtain the coordinates of the departments and perform scene classification based on the number of all departments. If the number of departments is greater than the number threshold, it is classified as a large scene. Conversely, if the number of departments is less than the number threshold, it is classified as a small scene.

5. The IoT-based needle recycling management platform according to claim 1, characterized in that, The adaptive analysis and processing module generates small scene path information in the following specific way: The system retrieves the department 'a' with the most needles to be collected and marks it as the starting point of the path. It then prioritizes selecting the nearest department that urgently needs needle collection, based on the nearest proximity principle. Specifically, it quantifies and scores distance and urgency, combining both to calculate the priority of each department. The system then selects the department with the highest priority and generates the corresponding path. This process continues sequentially according to the nearest proximity principle until all needles in all departments have been collected, generating small-scene path information.

6. The IoT-based needle recycling management platform according to claim 5, characterized in that, The specific method by which the adaptive analysis and processing module generates large-scene path information is as follows: Key locations such as departments, temporary storage centers, and elevator / staircase entrances are defined as nodes in the graph, and their 3D coordinates are labeled to construct corresponding layer models. Simultaneously, the corresponding transportation times are obtained. Next, the urgency level of the department awaiting recycling is determined, and a heuristic function is designed. The specific function formula is as follows: ,in As a heuristic value, a larger value indicates a greater attraction from a to b, and a stronger inclination towards that path. ab This represents the actual distance from node a to node b. and These are weighting coefficients and need to be determined experimentally. Based on the obtained heuristic function, a dynamic weight allocation model is constructed by combining path length, container overflow level, and vehicle load balancing. ,and , and The path information for large scenes is determined and generated through training with historical data.

7. The IoT-based needle recycling management platform according to claim 1, characterized in that, The specific method by which the dynamic monitoring and adjustment module generates dynamic adjustment signals is as follows: The system acquires real-time monitoring information and determines whether there are any emergencies. If so, it generates a dynamic adjustment signal; otherwise, it generates a normal monitoring signal and transmits it to the recycling management information output module.

8. The IoT-based needle recycling management platform according to claim 1, characterized in that, The specific method by which the dynamic monitoring and adjustment module generates dynamic adjustment information is as follows: For small-scale scenarios, the corresponding emergencies are acquired and inserted. Simultaneously, path planning is performed on the departments to be analyzed after insertion, and corresponding dynamic adjustment information is generated. For large-scale scenarios, the corresponding emergencies are acquired and inserted for analysis. Specifically, the insertion is performed on the remaining paths based on the generated large-scale scenario path information, and corresponding dynamic adjustment information is generated. Then, the generated dynamic adjustment information is transmitted to the recycling management information output module.

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