A method for automatically scheduling a drug delivery

Abstract

The application relates to a medicine and instrument distribution scheduling method which overcomes the defects of the prior art, and the specific technical scheme is as follows: step one, obtaining parameters of a scheduling target and each dimension thereof, and saving parameters of each resource in a database; step two, distinguishing main point positions and general point positions through priority calculation or manual determination; step three, generating a plurality of main lines according to the main point positions, and performing resource balance adjustment on the main lines to finally determine the lines used in step four; step four, determining the used lines to perform adsorption point calculation, and sequentially adsorbing the remaining general point positions on the used lines; step five, determining redundant resources between adjacent lines according to resource conditions of the lines used in step four; and step six, manually confirming or adjusting all the lines to finally complete distribution of medicine and instrument distribution lines, and generating corresponding scheduling information.

Description

Technical Field

[0001] This invention pertains to a distribution scheduling system, specifically an automatic scheduling system and method for pharmaceutical and medical device distribution. Background Technology

[0002] Pharmaceutical and medical device distribution is a part of logistics, but it has its own unique characteristics compared to general logistics. First, compared to general logistics distribution, the customers for pharmaceutical and medical device distribution are relatively fixed. Second, the distribution needs for pharmaceuticals and medical devices are also relatively fixed. Third, non-cold chain pharmaceuticals have long shelf lives, and some pharmaceuticals can be stored in vehicles for relatively long periods. At the same time, it also involves centralized storage at distribution centers, the need to deliver multiple medications, large distribution volumes, wide distribution areas, the need for cold chain support for some pharmaceuticals and medical devices, the requirement for qualified personnel for some pharmaceuticals and medical devices, and the need for scheduled delivery for some pharmaceuticals and medical devices. Therefore, considering the characteristics of pharmaceutical and medical device distribution, in addition to meeting the needs for fast and flexible delivery compared to general logistics, it can adopt relatively fixed delivery methods to reduce delivery costs, and has more room for improvement in cost-effectiveness compared to general logistics distribution.

[0003] Chinese patent CN202010215653.7 discloses a method, device, system, and server for pushing logistics delivery hotspots. This method generates heat maps of hotspot delivery areas based on set time periods and pushes them to relevant logistics delivery servers. This allows the servers to schedule delivery requests based on these heat maps, enabling delivery personnel to understand the actual conditions of each delivery area and make informed decisions, avoiding subjective errors from relying on experience and improving delivery efficiency. However, this logistics scheduling method does not specifically address the unique characteristics of pharmaceutical and medical device delivery; therefore, there is still significant room for improvement in this technical solution. Summary of the Invention

[0004] This invention addresses the problem that existing technologies fail to reflect the unique characteristics of pharmaceutical and medical device distribution and that distribution costs still have room for adjustment, by providing an automatic scheduling system and method for pharmaceutical and medical device distribution.

[0005] The technical solution adopted by this invention to solve its technical problem is: an automatic scheduling method for drug and medical device delivery, which requires the support of a drug and medical device delivery scheduling system. The drug and medical device delivery scheduling system includes an information intranet, an information extranet, and Internet devices. The information intranet performs scheduling calculations for drug and medical device delivery based on the input target and database. There is an internal and external network isolation device between the information intranet and the information extranet. The information extranet is configured with an APP server. The APP server receives the scheduling calculation results of drug and medical device delivery and sends them to the target Internet devices through the Internet. A firewall is configured between the information extranet and the Internet.

[0006] The information intranet performs scheduling calculations for drug and medical device distribution, including the following steps.

[0007] Step 1: Obtain the scheduling target and its parameters for each dimension, and then use the parameters of each resource already saved in the database;

[0008] Step two: Distinguish between primary and general locations through priority calculation or manual determination;

[0009] Step 3: Calculate and generate several main routes based on the main locations, and perform resource balancing adjustments on the main routes to finally determine the routes used in Step 4.

[0010] Step 4: Determine the lines to be used, calculate the adsorption points, and adsorb the remaining general points onto the lines to be used in sequence. After adsorbing a certain number of general points onto each line, re-determine the adsorption points for all current lines and perform resource balancing adjustments until all general points are adsorbed.

[0011] Step 5: Based on the line resources used in Step 4, determine the redundancy resources between adjacent lines.

[0012] Step six involves manually confirming or adjusting all routes to finalize the allocation of drug and medical device delivery routes and generating corresponding scheduling information.

[0013] Compared to conventional express delivery methods, this invention differentiates delivery targets, designating large hospitals and other entities with fixed cycles, times, and requirements, and high demand for medical devices and medicines, as primary delivery points. Furthermore, because medical device and medicine delivery is plannable, early optimization and detailed planning are possible. Therefore, a main route is established based on the primary delivery points, ensuring a minimum delivery volume before resource allocation is balanced. Smaller pharmacies with lower real-time requirements, some flexibility, and longer delivery cycles are designated as general delivery points. A certain quantity of long-term storable medical devices is allocated as redundancy. This allocation results in a more balanced distribution of capacity, manpower, and time, with better redundancy. Compared to conventional express delivery, this invention fully utilizes the characteristics of medical device and medicine delivery, effectively reducing delivery costs while fully considering the needs of medical device and medicine delivery.

[0014] Preferably, when the information intranet performs scheduling calculations for drug and medical device distribution, the scheduling of all drugs and medical devices is divided into two calculations: the first calculation is for cold chain drug and medical device distribution, and the second calculation is for general drug and medical device distribution. The target of the cold chain drug and medical device distribution calculation is only cold chain drugs and medical devices, and the resources involved in the allocation are only cold chain adaptation resources. When performing the cold chain drug and medical device distribution calculation, after executing steps one to six, the allocation of cold chain drug and medical device distribution routes is determined, and corresponding cold chain drug and medical device scheduling information is generated accordingly. The remaining unallocated cold chain adaptation resources are used as general drug and medical device adaptation resources in the calculation of all drug and medical device distribution. When performing the calculation of all drug and medical device distribution, the target of the scheduling includes general drugs and medical devices, and the resources involved in the allocation include general drug and medical device adaptation resources. When performing the general drug and medical device distribution calculation, after executing steps one to six, the allocation of general drug and medical device distribution routes is determined, and corresponding general drug and medical device scheduling information is generated accordingly. Because cold chain transportation is a highly specialized aspect of pharmaceutical and medical device distribution, the applicant can independently divide the distribution scheduling of all pharmaceuticals and medical devices into two forms: cold chain distribution and general distribution. This separation ensures that cold chain distribution and general distribution are completely independent of each other. This differs from general express delivery, where, although a direct classification method is used, there is no connection between the cold chain component and general cargo. In this application, the applicant can allocate the distribution scheduling of all pharmaceuticals and medical devices in a connected manner. Prioritizing cold chain distribution, and where conditions permit, surplus cold chain distribution resources can be used as general distribution resources. "Where conditions permit" refers to situations where redundancy is met and resource consumption is minimal. For example, if only a few cold chain distribution points exist in remote areas along the route, general pharmaceutical and medical device distribution can be carried out on the return trip from cold chain distribution through other distribution points. This application fully utilizes the spare capacity in cold chain transportation and leverages the backward compatibility between cold chain and general pharmaceutical and medical device distribution to facilitate general transportation.

[0015] Preferably, the scheduling objectives include the name of the target item, the quantity of the target item, the arrival time of the target item, the arrival location of the target item, the delivery recipient of the target item, and the time required for unloading the target item. The target parameters include the volume occupied by the target item, the weight occupied by the target item, and the storage period of the target item. The database already stores parameters for various resources, including vehicle data, road data, and personnel data. The vehicle data includes at least vehicle restriction information, vehicle carrying volume, and vehicle carrying weight; the road data includes at least vehicle traffic information and road direction information; and the personnel data includes at least personnel scheduling information and personnel qualification information. In this invention, the above-mentioned data, after being formatted, becomes the parameter data that can be called in the pharmaceutical and medical device distribution data, including target dimensions, resource parameters, etc. All data is confidential; therefore, its creation and updating are completed within the information intranet. In this application, the higher the data collection rate, the more beneficial it is for subsequent data processing, providing a large amount of data for calculation and constraint, resulting in better calculation and constraint effects that better reflect the actual situation.

[0016] Preferably, in step two, when determining the primary locations through priority calculation, statistics are performed based on the delivery objects of the target items as the scheduling targets. Data for each dimension of the scheduling targets is statistically analyzed, including the required target item name, quantity, volume, weight, and storage period. Values ​​are assigned to each dimension, with adjustments made according to priority and emphasis. A comprehensive weighted calculation is performed based on the assigned values ​​for each dimension, and the results are prioritized. The delivery objects of the top-ranked targets are selected as primary locations, establishing data sets for each primary location and general location. These data sets include parameters for each dimension and necessary resource parameters. This invention utilizes data sets to calculate resource consumption and dimension parameters. These data are adjusted according to priority and emphasis. Adjustments can be made manually based on experience, providing the calculation results of the first iteration to improve accuracy. Alternatively, the results can be derived by computer using a backpropagation algorithm based on the ranking of existing successful cases.

[0017] Preferably, in step three, when automatically calculating and generating several main routes based on the main locations, the following steps are included: Step S31, setting the number of vehicles to be dispatched, B.

[0018] Step S32: Based on the location of the main points and the constraints of the resources required by the main points, divide all the main points into B independent connection lines to form a connection scheme.

[0019] Step S33: Repeat the above steps to form several main point connection schemes;

[0020] Step S34: Evaluate the resource consumption of the above main point connection schemes, and select the one with the least resource consumption as the main line in step S35 based on the evaluation of resource consumption.

[0021] Step S35, and perform resource balancing adjustment on the main line to determine the line used in step four.

[0022] This invention employs a resource sorting approach to select the optimal route when forming the main routes from the main points. There are multiple selection methods for establishing each route. For example, based on the location of the main points and the constraints of their required resources, several return points are randomly selected, and then the frog-jump algorithm is used to divide all main points into B independent connecting routes, forming a connection scheme. Alternatively, gradient descent can be used, where after determining a main point, the next main point with the minimum resource consumption is found for connection, with each route randomly sorted after determining a main point. Another approach is to establish the main routes under stricter constraints, such as distance constraints, and then perform a fully random selection. Therefore, a suitable constraint is essential, and the corresponding algorithm is selected based on the different constraints to achieve the effect of establishing several connecting routes. Furthermore, since the establishment of the main routes involves a certain degree of randomness, adjustments to the main routes are necessary. During adjustments, the resource consumption of the main routes can be balanced. Therefore, under relatively lenient constraints, there are more possible line connection schemes and fewer possible missing schemes. Subsequent adjustments require higher computational requirements and consume more time. Conversely, under more stringent constraints, there are fewer possible line connection schemes and less computational requirements and consume less time. However, more possible missing schemes may occur, and even disconnections may happen. Therefore, the constraints can be set manually and intervened according to the actual situation.

[0023] When using constraint algorithms, they can be set according to different stages of the calculation (iteration). For example, in the initial stage, the constraints are set to be more stringent, while in the subsequent stages, the constraints need to be set to be more lenient. The stringency and leniency of the constraints change as the calculation rounds progress.

[0024] Preferably, the density of scheduling targets in the gaps between all adjacent main lines is calculated. If the density of scheduling targets is less than a set threshold, step four is executed; otherwise, the number of cluster centers is determined based on the numerical value of the scheduling targets. Initial cluster centers are manually determined. Based on the dimensional data of the scheduling targets and the initial cluster centers, a clustering algorithm is used for one round of clustering. After clustering, the cluster centers are re-determined, and then a second round of clustering is performed. After the second round of clustering, the cluster centers are re-determined. After repeating several rounds of clustering, the determined cluster centers are used as principal points, and step three is repeated. If the density of scheduling targets in the gaps between adjacent main lines is greater than a certain threshold, it indicates that there are a large number of points that need to be attracted between adjacent main lines. In this case, the center point of the high-density area can be selected as the principal point. This method can further rationally allocate resources. The density of scheduling targets in the gaps in this invention can refer to the density in two-dimensional geographic space or the density space under selected multi-dimensional parameters. Furthermore, after repeating several rounds of clustering, in addition to using the determined cluster centers as principal points, the point with the highest resource consumption in the cluster circle can also be selected as the principal point.

[0025] Preferably, in steps S35 and four, the resource balancing adjustment includes the following steps: arranging the points in the route according to resource consumption evaluation, then transferring the points that meet the criteria to the nearest points on adjacent routes, recalculating the resource consumption evaluation of the two related routes, and updating the corresponding routes if the resource consumption evaluation of the two related routes is more balanced; otherwise, not updating the routes. The criteria in this invention can be reasonably selected based on manual requirements. For example, the requirement can be set to compare the average resource consumption of all current routes with the resource consumption of the current target route, calculating that points in the current route whose resource consumption exceeds or falls below the average can participate in resource balancing. Furthermore, the sorting of all points participating in resource balancing can be done by balancing from highest to lowest resource consumption, or from lowest to highest, or by random selection. Alternatively, the sorting can be based on the adsorption force of the points participating in adsorption, and then balancing can be done by balancing from highest to lowest, lowest to highest, or by random selection of the adsorption force of the points participating in adsorption. In this application, the magnitude of the attraction force differs significantly from that of general express delivery. While general express delivery can determine the approximate quantity of goods to be delivered to the target point, this quantity is unstable, unpredictable, and highly variable, and the delivery resources between different points cannot be adjusted. In contrast, in this application, the determination of the main point has a large lead time. Based on this, by allocating the same resources at various resource points, the attraction force is ultimately formed using these shared resources, thereby enabling the free adjustment of various resources during the delivery process and reducing the ability to withstand unplanned events during delivery.

[0026] Preferably, the arrival locations of the target, i.e., the main points and general points, utilize commercial map interface data. The adsorption order of general points is sorted in descending order of the unloading time required for the target. For general points, under the constraint of required resources, adjacent main points and general points already included in the route within a set radius are found and adsorbed to form a new route. If several main points and general points already included in the route exist within the set radius, the route with the larger remaining resource is determined based on the remaining resource amount. The adsorption order of general points refers to scattered general points that have not been adsorbed onto the route. When adsorbing such points, a specific order is required, determined by sorting the unloading time of the target in descending order. A reasonable order arrangement allows for better adsorption, prioritizing resources with larger sequence numbers for better performance.

[0027] Preferably, the storage conditions and storage time of the medicines and medical devices are used as constraints. Based on the overall target names and quantities in the route, as well as the ratio of main points to general points, the adjustable redundant resources within the route are determined. Simultaneously, the available redundant resources between adjacent routes are determined based on the target names and quantities between adjacent routes. After determining the redundant resources, if the use of redundant resources occurs, the target point is treated as a general point. Under the constraint of the required resources, adjacent main points and general points within a set radius that are already included in routes containing redundant resources are found and merged to form a new route. The setting of redundant resources is necessary, but due to unforeseen circumstances, the activation of redundant resources is also possible. Therefore, this invention designs a method for forming new routes in conjunction with redundant resources.

[0028] As a preferred method, when manually confirming or adjusting all routes, the resource status of the current route is calculated. If the resource status of the current route exceeds the upper limit, adjustment is not allowed. If the resource balance of the current route exceeds the set threshold, an alarm is triggered and manual confirmation is required. After the allocation of the medicine and medical device delivery routes is completed, corresponding scheduling information is generated. The scheduling information includes the vehicle data, personnel data, name of the target item, amount of the target item, arrival time of the target item, arrival location of the target item, and the recipient of the target item.

[0029] The substantial effect of this invention is that it differentiates the delivery targets, designating large hospitals and other entities with fixed cycles, fixed times, fixed requirements, and large quantities of medicines and medical devices as primary delivery points. Main routes are established based on these primary points to ensure a minimum delivery limit for medicines and medical devices before resource allocation is balanced. Small pharmacies with lower real-time requirements, some leeway, and longer delivery cycles, and other delivery points with smaller needs, are designated as general delivery points. A certain amount of medicines and medical devices that can be stored for extended periods are used as redundancy for scheduling. This scheduling results in a more balanced allocation of transportation capacity, manpower, and time, with better redundancy. Under the premise of fully considering medicine and medical device delivery, it can effectively reduce delivery costs. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the overall system structure of the Chinese medicine and medical device delivery and dispatching system of the present invention;

[0031] Figure 2 This is a schematic diagram of an overall process of the present invention;

[0032] Figure 3 This is a schematic diagram showing one possible process of step S2 in this invention;

[0033] Figure 4 This is a schematic diagram showing one possible process of step S3 in this invention;

[0034] Figure 5 This is an additional flowchart illustrating step S3 in this invention.

[0035] In the diagram: 1. Intranet, 2. Intranet / Extranet isolation device, 3. Extranet, 4. Firewall, 5. Internet, 6. Internet device. Detailed Implementation

[0036] The technical solution of the present invention will be further described in detail below through specific embodiments.

[0037] Example 1:

[0038] An automated scheduling method for drug and medical device delivery requires a drug and medical device delivery scheduling system (see appendix). Figure 1 The drug and medical device delivery scheduling system includes an intranet 1, an extranet 3, and an internet device 6. The intranet performs scheduling calculations for drug and medical device delivery based on the input target and database. An intranet-extranet isolation device 2 separates the intranet and extranet. The extranet is equipped with an APP server. The APP server receives the scheduling calculation results of drug and medical device delivery and sends them to the target internet device via the internet 5. A firewall 4 separates the extranet from the internet.

[0039] Therefore, the scheduling objectives defined here include the name of the target item, the quantity of the target item, the arrival time of the target item, the arrival location of the target item, the delivery recipient of the target item, and the time required for unloading the target item. The target parameters include the volume occupied by the target item, the weight occupied by the target item, and the storage period of the target item. The database already stores parameters for various resources, including vehicle data, road data, and personnel data. The vehicle data includes at least vehicle traffic restriction information, vehicle carrying volume, and vehicle carrying weight; the road data includes at least vehicle traffic information and road direction information; and the personnel data includes at least personnel scheduling information and personnel qualification information. After formatting, the above data becomes the parameter data that can be called in the pharmaceutical and medical device distribution data, including target dimensions, resource parameters, etc. All data is confidential; therefore, its creation and updating are completed within the internal information network.

[0040] The information intranet performs scheduling calculations for drug and medical device distribution, including the following steps.

[0041] Step 1: Obtain the scheduling target and its parameters for each dimension, and based on the parameters of each resource already stored in the database, i.e., append... Figure 1 Step S1 in the process;

[0042] Step two: Differentiate between primary and secondary points through priority calculation or manual determination, i.e., supplementary points. Figure 1 Step S2 in the process;

[0043] Step 3: Calculate and generate several main routes based on the main locations, and perform resource balancing adjustments on these main routes to finally determine the routes used in Step 4, i.e., the attached routes. Figure 1 Step S3 in the process;

[0044] Step four: Determine the lines to be used, calculate the adsorption points, and then sequentially adsorb the remaining general points onto the lines being used. After adsorbing a certain number of general points onto each line, recalculate the adsorption points for all current lines and perform resource balancing adjustments until all general points have been adsorbed, i.e., attached. Figure 1 Step S4 in the process;

[0045] Step 5: Based on the line resources used in Step 4, determine the redundant resources between adjacent lines, i.e., the supplementary... Figure 1 Step S5 in the process;

[0046] Step six involves manually confirming or adjusting all routes to finalize the allocation of drug and medical device delivery routes, and generating corresponding scheduling information, i.e., appendix. Figure 1 Step S6 in the process.

[0047] To account for the differences between general and cold chain medical devices, the information intranet performs two separate calculations for scheduling medical device distribution: the first for cold chain distribution and the second for general distribution. The cold chain distribution calculation targets only cold chain medical devices, and the allocated resources include only cold chain compatible resources. During the cold chain distribution calculation, steps one through six are executed to determine the allocation of cold chain distribution routes, generating corresponding cold chain distribution scheduling information. The remaining unallocated cold chain compatible resources are treated as general medical device compatible resources and included in the overall medical device distribution calculation. During the overall medical device distribution calculation, the scheduling targets include general medical devices, and the allocated resources include general medical device compatible resources. During the general medical device distribution calculation, steps one through six are executed to determine the allocation of general medical device distribution routes, generating corresponding general medical device distribution scheduling information.

[0048] The distribution and scheduling of all medicines and medical devices are completely and independently divided into two forms: cold chain distribution and general distribution. That is, cold chain distribution and general distribution are completely separated and independent of each other. At the same time, the distribution and scheduling of all medicines and medical devices can also be allocated in a coordinated manner. After prioritizing cold chain distribution, when conditions permit, a portion of the surplus cold chain distribution resources can be used as general distribution resources. The so-called "when conditions permit" means that the redundancy is met and the resource consumption itself is small. For example, if there are only a few cold chain distribution points in remote areas along the route, then when the return trip of the cold chain distribution passes through other distribution points, general medicines and medical devices can be distributed. In this embodiment, determining the use of a route for adsorption point calculation refers to the outermost points of the route. When a route is first determined, all points are adsorption points. Once an adsorption point has adsorbed other points, it will no longer participate in subsequent calculations except for the main point. The arrival location of the target, i.e., the location of the main point and general points, calls commercial map interface data. The adsorption order of general points is sorted in descending order of the time required to unload the target. For general points, under the constraint of required resources, adjacent main points and general points already included in the route within a set radius are found and adsorbed to form a new route. In this embodiment, the adsorption point calculation is allocated according to the magnitude of the adsorption force.

[0049] If several main and general points already included in the route exist within the set radius, they can generally be determined based on the remaining resources of the route, and the point will be attracted to the route with the larger remaining resources. Besides this approach, other options exist. A more specific example is: Point C4 is a general point. In this case, points L1A and L13 in route L1 and L24 in route L2 are all capable of attracting point C4. The resource consumption required for point C4 to attract all participating points is calculated, and the various dimensions of resource consumption are weighted to form a comprehensive calculation value. The weighted values ​​include the transit time TZ for transporting medical supplies, the medical supply disassembly time TC, and the waiting time TD. These weighted values ​​are then used to form the comprehensive calculation value M. Lij :

[0050] M Lij＝ 1 / (J TZ ×TZ Lij +J TC ×TC Lij +J TD ×TD Lij )

[0051] In the above formula, J TZ J TC J TD All weighted values ​​are manually set, M Lij It is also considered to be the adsorption force at the current point, and M is calculated according to the above formula. L1A M L13 M L24 Then sort or select according to probability. If sorting is used, select according to M. L1A M L13 M L24 If the values ​​are sorted by size, and the selection is based on probability, then the probability of selecting point L1A is M. L1A / (M L1A +M L13 +M L24 And so on. The significance of making a selection is that, given sufficient computing power and time, it can maintain adequate diversity and select the one with the lowest overall resource consumption from a sufficient number of line samples, compared to selecting according to M... L1A M L13 M L24 Sort by numerical value to find a better route.

[0052] In this embodiment, location differentiation is a crucial step and one of the main differences from general delivery and express delivery. Therefore, in step two (see Appendix), Figure 3First, when determining the primary locations through priority calculation, statistics are performed based on the delivery objects of the target items as the scheduling targets. Data for each dimension of the scheduling targets is statistically analyzed (S21). Each dimension includes the name of the target item, the quantity of the target item, the volume occupied by the target item, the weight occupied by the target item, and the storage period of the target item. Values ​​are assigned to each dimension (S22), with adjustments made according to priority and emphasis. A comprehensive weighted calculation is performed based on the assigned values ​​for each dimension (S23). The calculation results are then prioritized (S24), and the delivery objects of the top-ranked targets are selected as primary locations (S25). Data sets corresponding to each primary location and general location (S26) are established, including parameters for each dimension of the primary and general locations, as well as necessary resource parameters. This invention utilizes a dataset to calculate corresponding resource consumption and dimensional parameters. When assigning these data values, adjustments are made according to priority and emphasis. These adjustments can be made manually based on experience, and the calculation results of the first iteration can be provided as needed to improve the accuracy of manual intervention. Alternatively, the results can be derived by computer based on the ranking of existing successful cases and the backpropagation algorithm.

[0053] After determining the primary and secondary points, these points need to be further processed. In step three, several main lines are automatically calculated and generated based on the primary points (see appendix). Figure 4 This includes the following steps:

[0054] Step S31, set the number of vehicles to be dispatched, B.

[0055] Step S32: Based on the location of the main points and the constraints of the resources required by the main points, divide all the main points into B independent connection lines to form a connection scheme.

[0056] Step S33: Repeat the above steps to form several main point connection schemes;

[0057] Step S34: Evaluate the resource consumption of the above main point connection schemes, and select the one with the least resource consumption as the main line in step S35 based on the evaluation of resource consumption.

[0058] Step S35, and perform resource balancing adjustment on the main line to determine the line used in step four.

[0059] This invention employs a resource sorting approach to select the optimal route when forming the main routes from the main points. There are multiple selection methods for establishing each route. For example, based on the location of the main points and the constraints of their required resources, several return points are randomly selected, and then the frog-jump algorithm is used to divide all main points into B independent connecting routes, forming a connection scheme. Alternatively, gradient descent can be used, where after determining a main point, the next main point with the minimum resource consumption is found for connection, with each route randomly sorted after determining a main point. Another approach is to establish the main routes under stricter constraints, such as distance constraints, and then perform a fully random selection. Therefore, a suitable constraint is essential, and the corresponding algorithm is selected based on the different constraints to achieve the effect of establishing several connecting routes. Furthermore, since the establishment of the main routes involves a certain degree of randomness, adjustments to the main routes are necessary. During adjustments, the resource consumption of the main routes can be balanced. Therefore, under relatively lenient constraints, there are more possible route connections, fewer potential lost connections, and subsequent adjustments require higher computational demands and consume more time. Conversely, under stricter constraints, there are fewer possible route connections, less computational demands and less time consumption during subsequent adjustments, but more potential lost connections, and even connection breaks. Therefore, constraints must be set manually and intervened upon based on the actual situation. In practical application, constraint algorithms can be set according to different stages of computation (iteration). For example, in the initial stage, constraints are set more strictly, while in subsequent stages, constraints need to be set more leniently. The stringency and leniency of constraints change as the computation progresses. The stringency and leniency of constraints can be reflected in the numerical selection of constraints or in the number of constraints themselves.

[0060] Further (see appendix) Figure 5The following steps are required: 1) Calculate the density of scheduling targets in the gaps between all adjacent main lines (S36). If the density of scheduling targets is less than the set threshold (S37), proceed to step four (S3D). Otherwise, determine the number of cluster centers based on the value of the scheduling targets (S38). Manually determine the initial cluster centers (S39). Perform a first round of clustering using a clustering algorithm based on the dimensional data of the scheduling targets and the initial cluster centers (S3A). After clustering, redetermine the cluster centers (S3B). Then perform a second round of clustering. After the second round of clustering, redetermine the cluster centers. Repeat this process several times. The determined cluster centers are then used as principal points (S3C), and step three is repeated. If the density of scheduling targets in the gaps between adjacent main lines is greater than a certain threshold, it indicates that there are a large number of points that need to be attracted between adjacent main lines. At this time, the center point of the high-density area can be selected as the main point. In this way, resources can be allocated more rationally. The density of scheduling targets in the gaps in this invention can refer to the density in two-dimensional geographic space or the density space under selected multi-dimensional parameters. Furthermore, after repeating several rounds of clustering, in addition to taking the determined cluster center point as the main point, the point with the highest resource consumption in the cluster circle can also be selected as the main point.

[0061] In steps S35 and four, the resource balancing adjustment includes the following steps: Ranking the points in the route according to resource consumption evaluation, then transferring the points that meet the criteria to the nearest points on adjacent routes, recalculating the resource consumption evaluation of the two related routes, and updating the corresponding routes if the resource consumption evaluation of the two related routes is more balanced; otherwise, not updating the routes. The criteria in this invention can be reasonably selected based on manual requirements. For example, the requirement can be set by comparing the average resource consumption of all current routes with the resource consumption of the current target route, calculating that points in the current route whose resource consumption exceeds or falls below the average can participate in resource balancing. Furthermore, the sorting of all points participating in resource balancing can be done by balancing from highest to lowest resource consumption, from lowest to highest, or by random selection. Alternatively, the sorting can be based on the adsorption force of the points participating in adsorption, and then balancing can be done by balancing from highest to lowest, lowest to highest, or by random selection of the adsorption force of the points participating in adsorption. Using the storage conditions and storage time of medicines and medical devices as constraints, and based on the overall target names and quantities in the route, as well as the ratio of main points to general points, the adjustable redundant resources within the route are determined. Simultaneously, based on the target names and quantities between adjacent routes, the available redundant resources between adjacent routes are determined. After determining the redundant resources, if the use of redundant resources occurs, the target point is treated as a general point. Under the constraints of the required resources, adjacent main points and general points within a set radius that are already included in routes containing redundant resources are found and merged to form a new route. The setting of redundant resources is necessary, but due to unforeseen circumstances, the activation of redundant resources is also possible. Therefore, this embodiment designs a method for forming new routes by coordinating redundant resources. When manually confirming or adjusting all routes, the resource status of the current route is calculated. If the resource status of the current route exceeds the upper limit, adjustment is not allowed. If the resource balance of the current route exceeds the set threshold, an alarm is triggered, and manual confirmation is required. After the allocation of medicine and medical device delivery routes is completed, corresponding scheduling information is generated. The scheduling information includes vehicle data, personnel data, target name, target quantity, target arrival time, target arrival location, and target recipient. Manual setting serves as the final determination, ensuring the final feasibility of this embodiment. Therefore, the information intranet can provide several corresponding scheduling schemes under each constraint. The scheduling scheme includes various data such as routes and consumption comparisons, which are manually verified and selected.

[0062] Compared to typical door-to-door courier and food delivery services, this embodiment is more suitable for businesses with pharmaceutical and medical device needs. Furthermore, compared to typical distribution points, this embodiment takes into account the actual needs of each pharmaceutical and medical device delivery, needs that are not considered in typical courier delivery distribution points. Therefore, this embodiment is more suitable for the pharmaceutical and medical device delivery field. In summary, the method of this embodiment, compared with general express delivery, adds differentiation of delivery targets. Large hospitals and other targets with fixed cycles, fixed times, fixed requirements, and large quantities of medicines and medical devices are designated as primary delivery points. Furthermore, because medicine and medical device delivery is plannable, early optimization can be performed, allowing sufficient time for detailed pre-planning. Therefore, a main route is set based on the primary delivery points, ensuring a minimum delivery volume before resource allocation is balanced. Small pharmacies with lower real-time requirements, some leeway, and longer delivery cycles are designated as general delivery points. A certain amount of medicines and medical devices that can be stored for a long time are used as redundancy for scheduling. The resulting allocation of transport capacity, manpower, and time is more balanced, and redundancy is better. Compared with general express delivery, this method fully utilizes the characteristics of medicine and medical device delivery and effectively reduces delivery costs while fully considering the needs of medicine and medical device delivery. In this application, the magnitude of the attraction force differs significantly from that of general express delivery. While general express delivery can determine the approximate quantity of goods to be delivered to the target point, this quantity is unstable, unpredictable, and highly variable, and the delivery resources between different points cannot be adjusted. In contrast, in this application, the determination of the main point has a large lead time. Based on this, by allocating the same resources at various resource points, the attraction force is ultimately formed using these shared resources, thereby enabling the free adjustment of various resources during the delivery process and reducing the ability to withstand unplanned events during delivery.

[0063] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Other variations and modifications are possible without departing from the technical solutions described in the claims.

Claims

1. A method for automatically scheduling a drug delivery procedure, comprising: This requires a pharmaceutical and medical device delivery scheduling system, which includes an intranet, an extranet, and internet devices. The intranet performs scheduling calculations for pharmaceutical and medical device delivery based on the input targets and a database. There are internal and external network isolation devices between the intranet and the extranet. The extranet is configured with an APP server, which receives the scheduling calculation results and sends them to the target internet devices via the internet. A firewall is configured between the extranet and the internet. The intranet's scheduling calculations for pharmaceutical and medical device delivery include the following steps: Step 1, obtaining the scheduling targets and their parameters, and using the parameters of various resources already stored in the database; Step 2, distinguishing between primary and secondary locations through priority calculation or manual determination. Step 3: Calculate and generate several main routes based on the main locations, and perform resource balancing adjustments on the main routes to finally determine the routes used in Step 4. When automatically calculating and generating several main routes based on the main locations, the process includes the following steps: Step S31, setting the number of vehicles to be dispatched, B. Step S32: Based on the location of the main points and the constraints of the resources required by the main points, divide all the main points into B independent connection lines to form a connection scheme; Step S33: Repeat the above steps to form several main point connection schemes. Step S34: Evaluate the resource consumption of the above main point connection schemes, and select the one with the least resource consumption as the main line in step S35 based on the evaluation of resource consumption. Step S35, and perform resource balancing adjustment on the main line to determine the line used in step four; Step 4: Determine the lines to be used, calculate the adsorption points, and adsorb the remaining general points onto the lines to be used in sequence. After adsorbing a certain number of general points onto each line, re-determine the adsorption points for all current lines and perform resource balancing adjustments until all general points have been adsorbed. Step 5: Based on the line resources used in Step 4, determine the redundancy resources between adjacent lines. Step six: Manually confirm or adjust all routes to finalize the allocation of drug and medical device delivery routes and generate corresponding scheduling information accordingly. When the information intranet performs scheduling calculations for pharmaceutical and medical device distribution, the scheduling of all pharmaceutical and medical device distribution is divided into two calculations. The first calculation is for cold chain pharmaceutical and medical device distribution, and the second calculation is for general pharmaceutical and medical device distribution. The target of the cold chain pharmaceutical and medical device distribution calculation is only cold chain pharmaceutical and medical devices, and the resources involved in the allocation are only cold chain adaptation resources. After executing steps one to six during the cold chain pharmaceutical and medical device distribution calculation, the allocation of cold chain pharmaceutical and medical device distribution routes is determined, and corresponding cold chain pharmaceutical and medical device scheduling information is generated accordingly. The remaining unallocated cold chain adaptation resources are used as general pharmaceutical and medical device adaptation resources in the calculation of all pharmaceutical and medical device distribution. During the calculation of all pharmaceutical and medical device distribution, the target of the scheduling includes general pharmaceutical and medical devices, and the resources involved in the allocation include general pharmaceutical and medical device adaptation resources. After executing steps one to six during the general pharmaceutical and medical device distribution calculation, the allocation of general pharmaceutical and medical device distribution routes is determined, and corresponding general pharmaceutical and medical device scheduling information is generated accordingly. In steps S35 and four, the resource balancing adjustment includes the following steps: arranging the points in the line according to the comprehensive resource consumption evaluation, then transferring the points that meet the conditions to the nearest points of the adjacent lines, recalculating the resource consumption evaluation of the two related lines, and updating the corresponding lines if the resource consumption evaluation of the two related lines is more balanced; otherwise, not updating the lines. The adsorption order of general points is sorted in descending order of the time required to unload the target. For general points, under the constraint of required resources, adjacent main points and general points already included in the line within the set radius are found and adsorbed to form a new line. If there are several main points and general points already included in the line within the set radius, the adsorption is determined according to the remaining amount of resources in the line, and the line with the larger remaining amount of resources is adsorbed.

2. The automatic scheduling method for drug and medical device delivery according to claim 1, characterized in that: The scheduling objectives include the name of the target item, the quantity of the target item, the arrival time of the target item, the arrival location of the target item, the delivery object of the target item, and the time required for unloading the target item. The target parameters include the volume occupied by the target item, the weight occupied by the target item, and the storage period of the target item. The database has stored parameters for various resources, including vehicle data, road data, and personnel data. The vehicle data includes at least vehicle traffic restriction information, vehicle carrying volume, and vehicle carrying weight. The road data includes at least vehicle traffic information and road direction information. The personnel data includes at least personnel scheduling information and personnel qualification information.

3. The automatic scheduling method for drug and medical device delivery according to claim 1, characterized in that: In step two, when determining the primary locations through priority calculation, statistics are performed based on the delivery objects of the target items as the scheduling targets. Data for each dimension of the scheduling targets is statistically analyzed, including the name of the target item, the quantity of the target item, the volume occupied by the target item, the weight occupied by the target item, and the storage period of the target item. Values ​​are assigned to each of these dimensions, with adjustments made according to priority and emphasis. A comprehensive weighted calculation is performed based on the assigned values ​​for each dimension, and the calculation results are prioritized. The delivery objects of the top-ranked targets are selected as primary locations, and a data set is established for each primary location and general location. This data set includes parameters for each dimension of the primary and general locations, as well as necessary resource parameters.

4. The automatic scheduling method for drug and medical device delivery according to claim 1, characterized in that: Calculate the density of scheduling targets in the gaps between all adjacent main lines. If the density of scheduling targets is less than the set threshold, proceed to step four. Otherwise, determine the number of cluster centers based on the value of the scheduling targets. Initial cluster centers are determined manually. Based on the dimensional data of the scheduling targets and the initial cluster centers, perform one round of clustering using a clustering algorithm. After clustering, redetermine the cluster centers. Then perform a second round of clustering. After the second round of clustering, redetermine the cluster centers. Repeat this process for several rounds. The determined cluster centers are then used as principal points, and step three is repeated.

5. The automatic scheduling method for drug and medical device delivery according to claim 2, characterized in that: The destination of the target object, i.e., the location of the main point and general points, is accessed through commercial map interface data.

6. The automatic scheduling method for drug and medical device delivery according to claim 1, characterized in that: Using the storage conditions and storage time of medicines and medical devices as constraints, and based on the overall target name and quantity in the route, as well as the ratio of main points and general points, the adjustable redundant resources within the route are determined. At the same time, based on the target name and quantity between adjacent routes, the available redundant resources between adjacent routes are determined. After determining the redundant resources, if the use of redundant resources occurs, the target point is treated as a general point. Under the constraint of the required resources, the main points and general points within the set radius that are already included in the route containing redundant resources are found and merged to form a new route.

7. The automatic scheduling method for drug and medical device delivery according to claim 6, characterized in that: When manually confirming or adjusting all routes, the resource status of the current route is calculated. If the resource status of the current route exceeds the upper limit, adjustment is not allowed. If the resource balance of the current route exceeds the set threshold, an alarm is triggered and manual confirmation is required. After the allocation of medicine and medical device delivery routes is completed, corresponding scheduling information is generated. The scheduling information includes vehicle data, personnel data, name of the target item, quantity of the target item, arrival time of the target item, arrival location of the target item, and the recipient of the target item.

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