Drug scheduling method, system, device and equipment

Abstract

The invention relates to the technical field of artificial intelligence Internet of Things, and discloses a medicine scheduling method, system, device and equipment, the method is applied to a server of a medicine scheduling system, the medicine scheduling system further comprises a request end in communication connection with the server, and the method comprises the following steps: receiving a scheduling request sent by the request end; according to the identification information of the target drug, carrying out inventory query to obtain a target scheduling position in which the target drug is stored; under the condition that the scheduling distance meets a distance threshold condition, predicting the traffic state of each candidate road section in the target drug scheduling process to obtain predicted traffic state data of each candidate road section, and optimizing the initial weight of each candidate road section according to the predicted traffic state data to obtain a target weight; and performing path planning according to the target weight through a target path planning algorithm matched with the scheduling distance to obtain a target scheduling path. The method has the beneficial effects that the scheduling path planning capability is improved, so that the drug scheduling efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence Internet of Things technology, and in particular to a drug scheduling method, system, device and equipment. Background Art

[0002] In many large hospitals, due to the large number of emergency patients they need to receive every day, there is often a shortage of emergency medicines. In the process of treating emergency patients, medical staff often cannot quickly understand the storage location of the required medicines and the path to obtain the required medicines, so they cannot quickly replenish the required medicines, resulting in the treatment process not being able to proceed smoothly. In addition, many medicines need to be allocated from outside, including from different campuses and different hospitals, making the scheduling process affected by many factors. Among the related technologies used to schedule medicines, the planning ability of scheduling paths still needs to be improved. Summary of the invention

[0003] The present application provides a drug scheduling method, system, device and equipment, which determines a matching target path planning algorithm according to the scheduling distance, and uses the target path planning algorithm to perform path planning based on optimized weights, thereby achieving the technical effect of improving the scheduling path planning capability and thus improving the drug scheduling efficiency.

[0004] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0005] In a first aspect, an embodiment of the present application provides a drug scheduling method, which is applied to a server of a drug scheduling system, wherein the drug scheduling system further includes a requesting end in communication with the server; the method includes:

[0006] Receiving a scheduling request sent by the requesting end; wherein the scheduling request carries identification information of a target drug to be scheduled;

[0007] An inventory query is performed according to the identification information of the target drug to obtain a target dispatching location where the target drug is stored; wherein the distance between the current location of the requesting end and the target dispatching location is recorded as a dispatching distance, and there are multiple candidate sections between the current location of the requesting end and the target dispatching location;

[0008] When the dispatch distance satisfies the distance threshold condition, predicting the traffic state of each candidate road section during the dispatch process of the target drug to obtain predicted traffic state data of each candidate road section, optimizing the initial weight of each candidate road section according to the predicted traffic state data to obtain the target weight of each candidate road section;

[0009] By using a target path planning algorithm that matches the scheduling distance, path planning is performed according to the target weight of each candidate road section to obtain a target scheduling path to schedule the target drug.

[0010] The drug dispatching method proposed in the embodiment of the present application responds to the dispatching request of the requesting end, determines the target dispatching position according to the identification information of the target drug in the dispatching request, and calls the target path planning algorithm that matches the dispatching distance between the target dispatching position and the current position of the requesting end to perform path planning, and obtains the target dispatching path. Compared with the related art, the present method determines the matching target path planning algorithm according to the dispatching distance, so that the target path planning algorithm can be used to perform corresponding path planning according to the actual environment that may be contained in different dispatching distances, improve the adaptability of the obtained target dispatching path to the actual environment, and improve the efficiency of drug dispatching. In addition, the present method can also predict the traffic status of the candidate road section during the dispatching process, and optimize the initial weight of the candidate road section according to the predicted traffic status data, so that the traffic status of the candidate road section can be combined to obtain a target dispatching path that is more in line with the actual environment, further improving the efficiency of drug dispatching.

[0011] Optionally, the target path planning algorithm is determined in the following manner:

[0012] Determine a comparison result between the dispatch distance and a preset distance threshold;

[0013] Among a plurality of pre-constructed candidate path planning algorithms, a target path planning algorithm matching the comparison result is determined.

[0014] Optionally, the multiple path planning algorithms to be selected include a static path planning algorithm, a dynamic path planning algorithm and a real-time traffic optimization algorithm;

[0015] Determining a target path planning algorithm that matches the comparison result from among the pre-constructed plurality of candidate path planning algorithms comprises:

[0016] When the scheduling distance does not exceed the first distance threshold, the target path planning algorithm adopts the static path planning algorithm;

[0017] When the scheduling distance exceeds the first distance threshold and does not exceed the second distance threshold, the target path planning algorithm adopts the dynamic path planning algorithm;

[0018] When the scheduling distance exceeds the second distance threshold, the target path planning algorithm adopts a combination algorithm of the dynamic path planning algorithm and the real-time traffic optimization algorithm; wherein the second distance threshold is greater than the first distance threshold.

[0019] Optionally, the target path planning algorithm adopts a combination algorithm of a dynamic path planning algorithm and a real-time traffic optimization algorithm; the target path planning algorithm matched with the scheduling distance performs path planning according to the target weight of each candidate road section to obtain a target scheduling path, including:

[0020] In the process of scheduling the target drug, the dynamic path planning algorithm is used to perform path planning according to the target weight of each candidate road section to obtain an initial scheduling path;

[0021] The real-time traffic optimization algorithm is used to optimize the initial scheduling path according to the real-time traffic status of the initial scheduling path to obtain the target scheduling path.

[0022] Optionally, performing inventory query according to the identification information of the target drug to obtain a target dispatch location where the target drug is stored includes:

[0023] Performing an inventory type query according to the drug type of the target drug to determine a plurality of storage locations storing the target drug;

[0024] The plurality of storage locations are arranged in ascending order according to the distance between each storage location and the current location, and the storage location with the smallest distance is used as the target scheduling location.

[0025] Optionally, predicting the traffic status of each candidate road section during the dispatching process of the target drug to obtain predicted traffic status data of each candidate road section includes:

[0026] Acquire historical traffic status data of each candidate road section within a specified time period; wherein the scheduling duration period of the scheduling request is within the specified time period;

[0027] Based on the historical traffic status data, traffic status prediction is performed on each candidate road section to obtain traffic status data to be screened within the specified time period;

[0028] According to the scheduling duration period, the predicted traffic status data corresponding to the scheduling duration period is extracted from the traffic status data to be filtered within the specified time period.

[0029] Optionally, the scheduling request also carries quantity information of the target drug; the method further includes:

[0030] Generate a scheduling plan for the target drug according to the quantity information, the target scheduling location, and the target scheduling path;

[0031] The scheduling scheme is sent to the requesting end so that the requesting end displays the scheduling scheme.

[0032] In a second aspect, an embodiment of the present application provides a drug dispatching system, the system comprising a service end and a request end that are communicatively connected to each other; wherein:

[0033] The requesting end is used to send a scheduling request to the service end; wherein the scheduling request carries identification information of the target drug to be scheduled;

[0034] The service end is used to receive the dispatch request, perform inventory query according to the identification information of the target drug, and obtain the target dispatch location where the target drug is stored; wherein the distance between the current position of the request end and the target dispatch location is recorded as the dispatch distance, and there are multiple candidate sections between the current position of the request end and the target dispatch location; when the dispatch distance meets the distance threshold condition, the traffic status of each candidate section during the dispatch of the target drug is predicted to obtain the predicted traffic status data of each candidate section, and the initial weight of each candidate section is optimized according to the predicted traffic status data to obtain the target weight of each candidate section; through the target path planning algorithm matching the dispatch distance, the path planning is performed according to the target weight of each candidate section to obtain the target dispatch path, so as to dispatch the target drug.

[0035] In a third aspect, an embodiment of the present application provides a drug dispatching device, which is applied to a server of a drug dispatching system, wherein the drug dispatching system further includes a requesting end in communication with the server; the device includes:

[0036] A scheduling request receiving module, used to receive the scheduling request sent by the requesting end; wherein the scheduling request carries the identification information of the target drug to be scheduled;

[0037] A target location determination module, configured to perform inventory query according to the identification information of the target drug, and obtain a target dispatch location where the target drug is stored; wherein the distance between the current location of the requesting end and the target dispatch location is recorded as the dispatch distance, and there are multiple candidate sections between the current location of the requesting end and the target dispatch location;

[0038] A candidate road section optimization module is used to predict the traffic state of each candidate road section during the dispatching process of the target drug when the dispatching distance meets the distance threshold condition, obtain the predicted traffic state data of each candidate road section, optimize the initial weight of each candidate road section according to the predicted traffic state data, and obtain the target weight of each candidate road section;

[0039] The target path planning module is used to perform path planning according to the target weight of each candidate road section through a target path planning algorithm that matches the scheduling distance, so as to obtain a target scheduling path to schedule the target drug.

[0040] In a fourth aspect, an embodiment of the present application provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method described in any one of the above embodiments by executing the computer instructions.

[0041] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute any one of the methods in the above embodiments.

[0042] In a sixth aspect, an embodiment of the present application provides a computer program product, including computer instructions, wherein the computer instructions are used to enable a computer to execute any one of the methods described in the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 A step diagram of a drug scheduling method provided in an embodiment of the present application;

[0045] Figure 2 A diagram showing the steps of determining a target path planning algorithm in an embodiment of the present application;

[0046] Figure 3 This is a step diagram of a planning algorithm for matching different target paths according to scheduling distances in an embodiment of the present application;

[0047] Figure 4 A diagram showing the steps for obtaining a target scheduling path in an embodiment of the present application;

[0048] Figure 5 A diagram showing the steps for obtaining predicted traffic status data in an embodiment of the present application;

[0049] Figure 6 A framework diagram of a drug scheduling system provided in an embodiment of the present application;

[0050] Figure 7A module diagram of a drug scheduling device provided in an embodiment of the present application;

[0051] Figure 8 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0053] In many large hospitals, due to the need to receive a large number of emergency patients every day, emergency medicines are often insufficient when the inventory management of medicines is not in place. In the process of treating emergency patients, medical staff often need to do their best to treat patients, but they cannot quickly understand the storage location of the required medicines and the path to obtain the required medicines, so they cannot quickly replenish the required medicines, resulting in the treatment process not being able to proceed smoothly. At the same time, some medicines need to be dispatched from outside, including from different campuses and different hospitals, due to storage environment or inventory status. When dispatching medicines from outside, the dispatch path of the medicines will include urban roads, and the complexity of urban roads makes the dispatch process affected by multiple factors, resulting in the medicines not being able to reach the required location quickly.

[0054] In the related technologies for dispatching medicines, a specific path planning algorithm is usually used to plan the dispatch path. In actual situations, depending on the dispatch distance, the dispatch path may contain a variety of different environments. The technical solution of using only one path planning algorithm has low adaptability to the environment and cannot cope with the various environmental changes in the dispatch path. In addition, the related technologies also lack technical solutions that can optimize the dispatch path according to the traffic conditions of the candidate sections.

[0055] Based on the above problems, the present application provides a drug scheduling method, system, device and equipment, wherein the method is applied to the server end of the drug scheduling system, and the drug scheduling system also includes a request end that is communicatively connected to the server end; the method includes: receiving a scheduling request sent by the request end; wherein the scheduling request carries the identification information of the target drug to be scheduled; performing an inventory query based on the identification information of the target drug to obtain a target scheduling location where the target drug is stored; wherein the distance between the current position of the request end and the target scheduling location is recorded as the scheduling distance, and there are multiple candidate sections between the current position of the request end and the target scheduling location; when the scheduling distance meets the distance threshold condition, predicting the traffic status of each candidate section during the scheduling process of the target drug to obtain the predicted traffic status data of each candidate section, optimizing the initial weight of each candidate section based on the predicted traffic status data to obtain the target weight of each candidate section; using a target path planning algorithm that matches the scheduling distance, performing path planning based on the target weight of each candidate section to obtain a target scheduling path to schedule the target drug.

[0056] The drug scheduling method provided in the present application responds to a scheduling request from a requesting end, determines a target scheduling position according to the identification information of the target drug in the scheduling request, and based on the target scheduling position and the current position of the requesting end, calls a target path planning algorithm that matches the scheduling distance between the two to perform path planning, thereby obtaining a target scheduling path.

[0057] Compared with related technologies, this method determines the matching target path planning algorithm according to the scheduling distance, so that the target path planning algorithm can be used to perform corresponding path planning according to the actual environment that may be contained in different scheduling distances, thereby improving the adaptability of the obtained target scheduling path to the actual environment and improving the efficiency of drug scheduling.

[0058] In addition, this method can also predict the traffic status of candidate sections during the scheduling process, and optimize the initial weights of candidate sections based on the predicted traffic status data, so that the traffic status of candidate sections can be combined to obtain a target scheduling path that is more in line with the actual environment, further improving the efficiency of drug scheduling.

[0059] The drug dispatch method provided in this specification can be applied to the server of the drug dispatch system, wherein the server can be a data platform, application system, computing platform, etc. capable of receiving and processing data. It is understandable that this method can also be applied to the dispatch path planning of different types of products after adaptive modification.

[0060] According to an embodiment of the present application, an embodiment of a drug scheduling method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0061] In this embodiment, a drug scheduling method is provided, which is applied to a server of a drug scheduling system. The drug scheduling system also includes a requesting end that is in communication with the server. Figure 1 As shown, the method includes:

[0062] S100. Receive a scheduling request sent by a requesting end; wherein the scheduling request carries identification information of a target drug to be scheduled.

[0063] S200. Perform an inventory query based on the identification information of the target drug to obtain the target scheduling location where the target drug is stored; wherein the distance between the current location of the requesting end and the target scheduling location is recorded as the scheduling distance, and there are multiple candidate sections between the current location of the requesting end and the target scheduling location.

[0064] S300. When the dispatch distance meets the distance threshold condition, the traffic status of each candidate road section during the dispatch process of the target drug is predicted to obtain the predicted traffic status data of each candidate road section, and the initial weight of each candidate road section is optimized according to the predicted traffic status data to obtain the target weight of each candidate road section.

[0065] S400. Using a target path planning algorithm that matches the dispatching distance, path planning is performed according to the target weight of each candidate road section to obtain a target dispatching path to dispatch the target drugs.

[0066] Among them, the dispatch request can be actively sent by the medical staff through the requesting end after discovering that the medicine is insufficient, or it can be automatically sent by the requesting end when managing its own medicine inventory when the inventory quantity of the medicine does not meet the required quantity. The dispatch request carries the identification information of the target medicine that needs to be dispatched. The identification information can be the name, number or type of the target medicine, etc., which can identify the specific medicine, so that the service end can perform an inventory query based on the identification information to determine the target dispatching location where the target medicine is stored. In some embodiments, the dispatch request can also carry dispatch time information. When the purpose of the dispatch is to replenish the medicine inventory of the requesting end, the priority of the dispatch process is lower, so the appropriate time can be selected according to the dispatch time information for drug dispatch.

[0067] The server is also connected to multiple external systems, which correspond to warehouses for storing drugs to manage the inventory of drugs in the warehouses. The server uses the drug inventory information provided by the external system to perform inventory query according to the identification information of the target drug to determine the target dispatch location where the target drug is stored.

[0068] The distance threshold condition may be that there is a candidate road section between the current location of the requesting end and the target dispatching location that is an urban road. When the drug dispatching process passes through an urban road, the traffic status of the urban road will affect the time required for drug dispatching. Therefore, it is necessary to predict the traffic status of each candidate road section during the dispatching process of the target drug, so as to improve the timeliness of the target dispatching path and the efficiency of drug dispatching.

[0069] Specifically, the requesting end sends a dispatching request to the server end. After receiving the dispatching request, the server end parses the dispatching request to obtain the identification information of the target drug to be dispatched and other related information, thereby determining the target drug to be dispatched. The server end performs an inventory query in multiple connected external systems based on the target drug, thereby determining the target dispatching location where the target drug is stored. The method of determining the target dispatching location in order to replenish the drug inventory of the requesting end is exemplified. At this time, the dispatching request also carries the quantity information of the target drug. If it is found that a single warehouse cannot meet the dispatching request after the inventory query, multiple target dispatching locations are determined based on the distance between different warehouses and the current location of the requesting end, and the quantity of the target drugs stored in the warehouse, so that the requesting end can dispatch the target drugs from multiple target dispatching locations.

[0070] Furthermore, there are multiple candidate road sections between the current position of the requesting end and the target dispatching position. When the dispatching distance does not meet the distance threshold condition, it means that the distance between the requesting end and the target dispatching position is relatively close, and the candidate roads only include the internal roads of the hospital. Since the internal roads are closed and the traffic flow is relatively small, the initial weight of the candidate roads at this time is mainly related to the length of the candidate roads. When the dispatching distance meets the distance threshold condition, it means that the distance between the requesting end and the target dispatching position is relatively far, and the candidate roads include the internal roads of the hospital and the urban roads outside the hospital. Since the traffic flow on urban roads is large and there are many interference factors, the initial weight of the candidate roads at this time is related to multiple convenient factors, including: the length of the candidate roads, traffic flow, traffic status and speed limit, etc. The candidate roads have different traffic status in different time periods, so it is necessary to predict the traffic status of the candidate roads during the dispatching process. For example, if the dispatching process occurs during peak hours in the morning and evening, some candidate roads may be congested, resulting in the dispatching process being affected, so it is necessary to adjust and optimize the initial weights of the above candidate roads.

[0071] Furthermore, similar to the distance threshold condition, according to different dispatch distances, the candidate roads may include roads corresponding to different actual environments, including internal roads and urban roads, etc. Different types of target path planning algorithms need to be used for roads corresponding to different actual environments, so as to improve the efficiency and accuracy of path planning, and further improve the efficiency of drug dispatch.

[0072] In some embodiments, after obtaining the target dispatch path, the drug transport vehicle is searched according to the target dispatch position, the optimal transport vehicle that meets the requirements is determined, and the target dispatch path is sent to the optimal transport vehicle to control the optimal transport vehicle to dispatch the target drug. In other embodiments, the target dispatch path can also be sent to the requesting end to guide medical staff to dispatch the target drug.

[0073] The drug scheduling method provided in this embodiment responds to the scheduling request of the requesting end, determines the target scheduling position according to the identification information of the target drug in the scheduling request, and calls the target path planning algorithm that matches the scheduling distance between the target scheduling position and the current position of the requesting end to perform path planning to obtain the target scheduling path.

[0074] Compared with related technologies, this method determines the matching target path planning algorithm according to the scheduling distance, so that the target path planning algorithm can be used to perform corresponding path planning according to the actual environment that may be contained in different scheduling distances, thereby improving the adaptability of the obtained target scheduling path to the actual environment and improving the efficiency of drug scheduling.

[0075] In addition, this method can also predict the traffic status of candidate sections during the scheduling process, and optimize the initial weights of candidate sections based on the predicted traffic status data, so that the traffic status of candidate sections can be combined to obtain a target scheduling path that is more in line with the actual environment, further improving the efficiency of drug scheduling.

[0076] Reference Figure 2 As shown, as an embodiment of the present application, the target path planning algorithm is determined in the following manner:

[0077] S402. Determine a comparison result between the dispatch distance and a preset distance threshold.

[0078] S404. Determine a target path planning algorithm that matches the comparison result from among the multiple pre-constructed candidate path planning algorithms.

[0079] Specifically, when the dispatch distance is different, the candidate roads may include roads corresponding to different actual environments. For example, when the dispatch distance is short, the candidate roads may only include internal roads; as the dispatch distance increases, urban roads will be added as candidate roads on the basis of internal roads, and as the number of urban roads increases, the complexity of the dispatch path increases. Therefore, for candidate roads corresponding to different actual environments, it is necessary to adopt a target path planning algorithm that is adapted to the candidate roads, so as to improve the efficiency and accuracy of path planning, and then improve the efficiency of drug dispatch.

[0080] Furthermore, the dispatch distance is compared with a preset distance threshold, and a matching target path planning algorithm is determined from a plurality of pre-constructed candidate path planning algorithms according to the comparison result.

[0081] Reference Figure 3 As shown, as an embodiment of the present application, a plurality of path planning algorithms to be selected include a static path planning algorithm, a dynamic path planning algorithm and a real-time traffic optimization algorithm;

[0082] Among the pre-built multiple candidate path planning algorithms, determine the target path planning algorithm that matches the comparison result, including:

[0083] S410. When the scheduling distance does not exceed the first distance threshold, the target path planning algorithm adopts a static path planning algorithm.

[0084] S412. When the scheduling distance exceeds the first distance threshold and does not exceed the second distance threshold, the target path planning algorithm adopts a dynamic path planning algorithm.

[0085] S414. When the dispatch distance exceeds the second distance threshold, the target path planning algorithm adopts a combination of a dynamic path planning algorithm and a real-time traffic optimization algorithm; wherein the second distance threshold is greater than the first distance threshold.

[0086] Specifically, when the dispatch distance does not exceed the first distance threshold, it means that the requesting end is close to the target dispatching position, and the candidate roads only include the internal roads of the hospital. Since the internal roads are closed and have less traffic, they belong to a static environment. At this time, the target path planning algorithm that matches the dispatch distance is a static path planning algorithm. Exemplarily, the static path planning algorithm can be an A* algorithm.

[0087] Furthermore, when the dispatch distance exceeds the first distance threshold and does not exceed the second distance threshold, it indicates that the distance between the requesting end and the target dispatching position is medium, and the candidate roads include the internal roads of the hospital and the urban roads outside the hospital, and the number of urban roads among the candidate roads is small. Since the urban roads have a large traffic volume and many interference factors, they belong to a dynamic environment. At this time, the target path planning algorithm that matches the dispatch distance is a dynamic path planning algorithm. Exemplarily, the dynamic path planning algorithm can be a Dijkstra algorithm.

[0088] Furthermore, when the dispatch distance exceeds the second distance threshold, it indicates that the requesting end is far away from the target dispatching location, and the candidate roads include the internal roads of the hospital and the urban roads outside the hospital, and the number of urban roads among the candidate roads is relatively large. At this time, a real-time traffic optimization algorithm is added on the basis of the dynamic path planning algorithm, and the target dispatching path is optimized in real time according to the traffic conditions of the urban roads during the dispatching process to improve the efficiency of drug dispatching.

[0089] Reference Figure 4 As shown, as an embodiment of the present application, the target path planning algorithm adopts a combination algorithm of a dynamic path planning algorithm and a real-time traffic optimization algorithm; through the target path planning algorithm matching the scheduling distance, the path planning is performed according to the target weight of each candidate road section to obtain the target scheduling path, including:

[0090] S420. During the scheduling of target drugs, a dynamic path planning algorithm is used to perform path planning according to the target weight of each candidate road section to obtain an initial scheduling path.

[0091] S422. Utilize the real-time traffic optimization algorithm to optimize the initial scheduling path according to the real-time traffic status of the initial scheduling path to obtain the target scheduling path.

[0092] Specifically, when the scheduling distance exceeds the second distance threshold, a dynamic path planning algorithm is used to perform path planning according to the target weight of each candidate road section to obtain an initial scheduling path, and the scheduling process is performed according to the initial scheduling path.

[0093] Furthermore, during the dispatching process, the server also obtains the real-time traffic status of the candidate roads in real time, and makes judgments based on the real-time traffic status of the candidate roads. If the real-time traffic status of the candidate road indicates that the traffic flow on the candidate road is large or a traffic accident occurs, which will affect the dispatching process, the weight of the candidate road is reduced to obtain the optimized weight of the candidate road; if the real-time traffic status of the candidate road indicates that the traffic status of the candidate road is good and can improve the speed of the dispatching process, the weight of the candidate road is increased to obtain the optimized weight of the candidate road.

[0094] Furthermore, after obtaining the optimized weights of the candidate roads, a combination algorithm of a dynamic path planning algorithm and a real-time traffic optimization algorithm is used to perform path planning according to the optimized weights, thereby optimizing the initial scheduling path to obtain a target scheduling path.

[0095] As an embodiment of the present application, performing inventory query according to identification information of a target drug to obtain a target dispatch location where the target drug is stored includes:

[0096] S210. Perform an inventory type query based on the drug type of the target drug to determine multiple storage locations where the target drug is stored.

[0097] S220. Arrange the multiple storage locations in ascending order according to the distance between each storage location and the current location, and use the storage location with the shortest distance as the target scheduling location.

[0098] Specifically, the identification information of the target drug includes the drug type of the target drug. The server determines the specific target drug based on the drug type of the target drug, and performs an inventory type query in a connected external system based on the drug type of the target drug to determine multiple warehouses storing the target drugs as multiple storage locations.

[0099] Furthermore, the nearest storage location is determined from the multiple storage locations according to the distance as the target scheduling location, thereby improving the efficiency of drug scheduling. In some embodiments, if the number of target drugs stored in a single target scheduling location cannot meet the scheduling request, multiple target scheduling locations can be obtained, and the target scheduling path is determined according to the scheduling distance between each of the multiple target scheduling locations and the current location of the requesting end.

[0100] Reference Figure 5 As shown, as an embodiment of the present application, the traffic state of each candidate road section during the dispatching process of the target drug is predicted to obtain the predicted traffic state data of each candidate road section, including:

[0101] S310. Obtain historical traffic status data of each candidate road section within a specified time period; wherein the scheduling duration period of the scheduling request is within the specified time period.

[0102] S320. Predict the traffic status of each candidate road section based on the historical traffic status data to obtain the traffic status data to be screened within a specified time period.

[0103] S330. According to the scheduling duration period, extract the predicted traffic status data corresponding to the scheduling duration period from the traffic status data to be filtered within the specified time period.

[0104] Specifically, the time period that the scheduling process is expected to last is determined according to the scheduling request as the designated time period. When determining the designated time period, problems that occur during the scheduling process are also considered, so the length of the designated time period is greater than the scheduling duration period of the scheduling request.

[0105] Furthermore, historical traffic status data of each candidate road section within a specified time period is obtained, and the historical traffic status data is input into a time series data prediction model. The traffic status of each candidate road within a specified time period is predicted by the time series data prediction model to obtain traffic status data to be screened within the specified time period. The traffic status data to be screened includes traffic status data of each candidate road within and outside the scheduling duration period.

[0106] Furthermore, the scheduling duration period is determined according to the scheduling distance, and the corresponding predicted traffic state data is extracted from the traffic state data to be screened according to the scheduling duration period as a basis for optimizing the initial weight.

[0107] As an embodiment of the present application, the scheduling request also carries quantity information of the target drug; the method further includes:

[0108] S510. Generate a scheduling plan for the target drug based on the quantity information, the target scheduling location and the target scheduling path.

[0109] S520. Send the scheduling plan to the requesting end, so that the requesting end displays the scheduling plan.

[0110] Specifically, a scheduling plan for the target drug is generated based on the quantity information, the target scheduling location, and the target scheduling path, and the scheduling plan is sent to the requesting end to display the scheduling plan on the requesting end, so that the requesting end can grasp the scheduling process of the target drug in real time and determine the current location and arrival time of the target drug. In addition, by displaying the scheduling plan, the requesting end can intervene in time when problems occur in the scheduling process to ensure the smooth progress of the scheduling process.

[0111] Reference Figure 6 As shown, the embodiment of the present application also provides a drug scheduling system, which includes a service end and a request end that are connected to each other in communication; wherein:

[0112] The requesting end is used to send a dispatching request to the server end; wherein the dispatching request carries the identification information of the target drug to be dispatched;

[0113] The server is used to receive a dispatch request, perform an inventory query based on the identification information of the target drug, and obtain a target dispatch location where the target drug is stored; wherein the distance between the current location of the requester and the target dispatch location is recorded as the dispatch distance, and there are multiple candidate sections between the current location of the requester and the target dispatch location; when the dispatch distance meets the distance threshold condition, the traffic state of each candidate section during the dispatch of the target drug is predicted to obtain the predicted traffic state data of each candidate section, and the initial weight of each candidate section is optimized based on the predicted traffic state data to obtain the target weight of each candidate section; through a target path planning algorithm that matches the dispatch distance, path planning is performed based on the target weight of each candidate section to obtain a target dispatch path to dispatch the target drug.

[0114] Accordingly, please refer to Figure 7 The embodiment of the present application provides a drug dispatching device, which is applied to a server of a drug dispatching system. The drug dispatching system also includes a requesting end that is in communication with the server. The device includes:

[0115] The scheduling request receiving module 100 is used to receive a scheduling request sent by a requesting end; wherein the scheduling request carries identification information of a target drug to be scheduled.

[0116] The target location determination module 200 is used to perform inventory query based on the identification information of the target drug to obtain the target scheduling location where the target drug is stored; wherein the distance between the current location of the requesting end and the target scheduling location is recorded as the scheduling distance, and there are multiple candidate sections between the current location of the requesting end and the target scheduling location.

[0117] The candidate road section optimization module 300 is used to predict the traffic status of each candidate road section during the scheduling process of the target drug when the scheduling distance meets the distance threshold condition, obtain the predicted traffic status data of each candidate road section, and optimize the initial weight of each candidate road section according to the predicted traffic status data to obtain the target weight of each candidate road section.

[0118] The target path planning module 400 is used to perform path planning according to the target weight of each candidate road section through a target path planning algorithm that matches the scheduling distance, and obtain a target scheduling path to schedule the target drugs.

[0119] In some optional implementations, the target path planning module 400 includes:

[0120] The distance comparison unit is used to determine the comparison result between the dispatch distance and the preset distance threshold.

[0121] The algorithm determination unit is used to determine a target path planning algorithm that matches the comparison result from among a plurality of pre-constructed candidate path planning algorithms.

[0122] In some optional implementations, the plurality of path planning algorithms to be selected include a static path planning algorithm, a dynamic path planning algorithm, and a real-time traffic optimization algorithm; and the algorithm determination unit includes:

[0123] The first algorithm determination subunit is used to determine that the target path planning algorithm adopts a static path planning algorithm when the scheduling distance does not exceed a first distance threshold.

[0124] The second algorithm determination subunit is used to determine that the target path planning algorithm adopts a dynamic path planning algorithm when the scheduling distance exceeds the first distance threshold and does not exceed the second distance threshold.

[0125] The third algorithm determines the subunit, which is used to determine that when the scheduling distance exceeds a second distance threshold, the target path planning algorithm adopts a combination of a dynamic path planning algorithm and a real-time traffic optimization algorithm; wherein the second distance threshold is greater than the first distance threshold.

[0126] In some optional implementations, the target path planning algorithm adopts a combination of a dynamic path planning algorithm and a real-time traffic optimization algorithm; the target path planning module 400 also includes:

[0127] The path planning unit is used to perform path planning according to the target weight of each candidate section using a dynamic path planning algorithm during the scheduling process of the target drug to obtain an initial scheduling path.

[0128] The path optimization unit is used to optimize the initial scheduling path according to the real-time traffic status of the initial scheduling path by using the real-time traffic optimization algorithm to obtain the target scheduling path.

[0129] In some optional implementations, the target location determination module 200 includes:

[0130] The inventory query unit is used to query the inventory type according to the drug type of the target drug and determine multiple storage locations where the target drug is stored.

[0131] The distance sorting unit is used to sort the multiple storage locations in ascending order according to the distance between each storage location and the current location, and use the storage location with the smallest distance as the target scheduling location.

[0132] In some optional implementations, the candidate road segment optimization module 300 includes:

[0133] The traffic status data acquisition unit is used to acquire the historical traffic status data of each candidate road section within a specified time period; wherein the scheduling duration period of the scheduling request is within the specified time period.

[0134] The traffic status data prediction unit is used to predict the traffic status of each candidate road section based on the historical traffic status data to obtain the traffic status data to be screened within a specified time period.

[0135] The traffic status data extraction unit is used to extract the predicted traffic status data corresponding to the scheduling duration period from the traffic status data to be filtered within a specified time period according to the scheduling duration period.

[0136] In some optional implementations, the scheduling request also carries quantity information of the target drug; the device also includes a scheduling plan display module, including:

[0137] The scheduling plan generating unit is used to generate a scheduling plan for the target drug according to the quantity information, the target scheduling position and the target scheduling path.

[0138] The scheduling scheme display unit is used to send the scheduling scheme to the requesting end so that the requesting end displays the scheduling scheme.

[0139] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0140] The drug scheduling device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0141] See also Figure 8 , Figure 8It is a structural diagram of a computer device provided by an embodiment of the present application. As shown in the figure, the computer device includes: one or more processors 10, a memory 20, and an interface for connecting various components, including a high-speed interface and a low-speed interface. Each component uses different buses to communicate with each other, and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 8 A processor 10 is taken as an example.

[0142] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0143] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0144] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0145] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0146] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0147] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0148] The embodiment of the present application provides a computer program product, which includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method of any embodiment of the present application.

[0149] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

[0150] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0151] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0152] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0153] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0154] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0155] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0156] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0157] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0158] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0159] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A drug scheduling method, characterized in that: The method is applied to a server end of a drug dispatching system, wherein the drug dispatching system further comprises a requesting end in communication connection with the server end; the method comprises: Receiving a scheduling request sent by the requesting end; wherein the scheduling request carries identification information of a target drug to be scheduled; An inventory query is performed according to the identification information of the target drug to obtain a target dispatching location where the target drug is stored; wherein the distance between the current location of the requesting end and the target dispatching location is recorded as a dispatching distance, and there are multiple candidate sections between the current location of the requesting end and the target dispatching location; When the dispatch distance satisfies the distance threshold condition, predicting the traffic state of each candidate road section during the dispatch process of the target drug to obtain predicted traffic state data of each candidate road section, optimizing the initial weight of each candidate road section according to the predicted traffic state data to obtain the target weight of each candidate road section; By using a target path planning algorithm that matches the scheduling distance, path planning is performed according to the target weight of each candidate road section to obtain a target scheduling path to schedule the target drug.

2. The method according to claim 1, characterized in that: The target path planning algorithm is determined by: Determine a comparison result between the dispatch distance and a preset distance threshold; Among a plurality of pre-constructed candidate path planning algorithms, a target path planning algorithm matching the comparison result is determined.

3. The method according to claim 2, characterized in that The multiple path planning algorithms to be selected include a static path planning algorithm, a dynamic path planning algorithm and a real-time traffic optimization algorithm; Determining a target path planning algorithm that matches the comparison result from among the pre-constructed plurality of candidate path planning algorithms comprises: When the scheduling distance does not exceed the first distance threshold, the target path planning algorithm adopts the static path planning algorithm; When the scheduling distance exceeds the first distance threshold and does not exceed the second distance threshold, the target path planning algorithm adopts the dynamic path planning algorithm; When the scheduling distance exceeds the second distance threshold, the target path planning algorithm adopts a combination algorithm of the dynamic path planning algorithm and the real-time traffic optimization algorithm; wherein the second distance threshold is greater than the first distance threshold.

4. The method according to claim 1, characterized in that: The target path planning algorithm adopts a combination algorithm of a dynamic path planning algorithm and a real-time traffic optimization algorithm; the target path planning algorithm matched with the scheduling distance performs path planning according to the target weight of each candidate road section to obtain a target scheduling path, including: In the process of scheduling the target drug, the dynamic path planning algorithm is used to perform path planning according to the target weight of each candidate road section to obtain an initial scheduling path; The real-time traffic optimization algorithm is used to optimize the initial scheduling path according to the real-time traffic status of the initial scheduling path to obtain the target scheduling path.

5. The method according to claim 1, characterized in that The performing inventory query according to the identification information of the target drug to obtain the target dispatch location where the target drug is stored includes: Performing an inventory type query according to the drug type of the target drug to determine a plurality of storage locations storing the target drug; The plurality of storage locations are arranged in ascending order according to the distance between each storage location and the current location, and the storage location with the smallest distance is used as the target scheduling location.

6. The method according to claim 1, characterized in that The predicting of the traffic status of each candidate road section during the dispatching process of the target drug to obtain the predicted traffic status data of each candidate road section includes: Acquire historical traffic status data of each candidate road section within a specified time period; wherein the scheduling duration period of the scheduling request is within the specified time period; Based on the historical traffic status data, traffic status prediction is performed on each candidate road section to obtain traffic status data to be screened within the specified time period; According to the scheduling duration period, the predicted traffic status data corresponding to the scheduling duration period is extracted from the traffic status data to be filtered within the specified time period.

7. The method according to any one of claims 1 to 6, characterized in that: The scheduling request also carries the quantity information of the target drug; the method further includes: Generate a scheduling plan for the target drug according to the quantity information, the target scheduling location, and the target scheduling path; The scheduling scheme is sent to the requesting end so that the requesting end displays the scheduling scheme.

8. A drug dispatching system, characterized in that: The system comprises a service end and a request end which are connected to each other for communication; wherein: The requesting end is used to send a scheduling request to the service end; wherein the scheduling request carries identification information of the target drug to be scheduled; The service end is used to receive the dispatch request, perform inventory query according to the identification information of the target drug, and obtain the target dispatch location where the target drug is stored; wherein the distance between the current position of the request end and the target dispatch location is recorded as the dispatch distance, and there are multiple candidate sections between the current position of the request end and the target dispatch location; when the dispatch distance meets the distance threshold condition, the traffic status of each candidate section during the dispatch of the target drug is predicted to obtain the predicted traffic status data of each candidate section, and the initial weight of each candidate section is optimized according to the predicted traffic status data to obtain the target weight of each candidate section; through the target path planning algorithm matching the dispatch distance, the path planning is performed according to the target weight of each candidate section to obtain the target dispatch path, so as to dispatch the target drug.

9. A drug dispatching device, characterized in that: The device is applied to a server end of a drug dispatching system, wherein the drug dispatching system further comprises a request end in communication connection with the server end; the device comprises: A scheduling request receiving module, used to receive the scheduling request sent by the requesting end; wherein the scheduling request carries the identification information of the target drug to be scheduled; A target location determination module, configured to perform inventory query according to the identification information of the target drug, and obtain a target dispatch location where the target drug is stored; wherein the distance between the current location of the requesting end and the target dispatch location is recorded as the dispatch distance, and there are multiple candidate sections between the current location of the requesting end and the target dispatch location; A candidate road section optimization module is used to predict the traffic state of each candidate road section during the dispatching process of the target drug when the dispatching distance meets the distance threshold condition, obtain the predicted traffic state data of each candidate road section, optimize the initial weight of each candidate road section according to the predicted traffic state data, and obtain the target weight of each candidate road section; The target path planning module is used to perform path planning according to the target weight of each candidate road section through a target path planning algorithm that matches the scheduling distance, so as to obtain a target scheduling path to schedule the target drug.

10. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 7 by executing the computer instructions.

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