CN115547445B - Prescription task distribution method, device, equipment and storage medium - Google Patents
Prescription task distribution method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN115547445B CN115547445B CN202211228451.1A CN202211228451A CN115547445B CN 115547445 B CN115547445 B CN 115547445B CN 202211228451 A CN202211228451 A CN 202211228451A CN 115547445 B CN115547445 B CN 115547445B
- Authority
- CN
- China
- Prior art keywords
- task
- prescription
- target
- doctor
- time consumption
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
Abstract
The embodiment of the specification provides a prescription task distribution method, device, equipment and storage medium. The method provides a task pool corresponding to a doctor account; the method comprises the following steps: generating a target prescription task corresponding to the received medicine order data; the prescription task is used for issuing a prescription for the medicines represented by the medicine order data; determining the reference time consumption of the doctor account for processing the prescription task according to the history prescription task completed by the doctor account; under the condition that the target prescription task is put into a task pool according to the reference time consumption of the doctor account, the target prescription task is predicted to be completed by the doctor account corresponding to the task pool to process the predicted time consumption; and distributing the target prescription task to a target task pool determined according to the estimated time consumption. The target prescription task is allocated by predicting the predicted time consumption of the completion of the target prescription task by the corresponding doctor account process, which reduces the time consumption of the completion of the prescription task process to a certain extent.
Description
Technical Field
The embodiment in the specification relates to the technical field of computer networks, in particular to a prescription task distribution method, a prescription task distribution device, prescription task distribution equipment and a storage medium.
Background
Currently, a user may place a drug order before purchasing prescription drugs online. After the doctor account has prescribed a drug for the drug order, the user may make a fee payment to purchase the completed prescribed drug. In the prior art, after receiving a drug order, a server may allocate the prescription task generated based on the drug order according to the number of prescription tasks that have not been processed by a doctor account, so as to issue a corresponding prescription. However, there are differences in the efficiency of different doctors in handling prescription tasks. In some cases, when a plurality of prescription tasks are respectively provided for a plurality of doctor accounts, a part of prescription tasks may be allocated to a doctor with very low efficiency, so that the prescription tasks require long waiting time to be processed, and accordingly, a user who purchases medicines needs to spend long waiting time.
Therefore, the technical problem of unreasonable allocation of prescription tasks exists in the prior art.
Disclosure of Invention
In view of this, various embodiments of the present disclosure are directed to providing a prescription task allocation method, apparatus, device, and storage medium, which reduce the time taken for prescription task processing to complete to some extent.
Various embodiments in the present disclosure provide a method for assigning prescription tasks, providing a task pool corresponding to a doctor account; the task pool is used for storing prescription tasks which are not processed by corresponding doctor accounts; the method comprises the following steps: generating a target prescription task corresponding to the received medicine order data; the prescription task is used for issuing a prescription for the medicines represented by the medicine order data; determining the reference time consumption of the doctor account for processing the prescription task according to the history prescription task completed by the doctor account; under the condition that the target prescription task is put into a task pool according to the reference time consumption of the doctor account, the target prescription task is predicted to be completed by the doctor account corresponding to the task pool to process the predicted time consumption; and distributing the target prescription task to a target task pool determined according to the estimated time consumption.
One embodiment of the present specification provides a prescription task allocation device, providing a task pool corresponding to a doctor account; the task pool is used for storing prescription tasks which are not processed by corresponding doctor accounts; the device comprises: the generation module is used for generating a target prescription task corresponding to the received medicine order data; the prescription task is used for issuing a prescription for the medicines represented by the medicine order data; the determining module is used for determining the reference time consumption of the doctor account for processing the prescription task according to the history prescription task completed by the doctor account; the prediction module is used for predicting the predicted time consumption of the target prescription task for completing the processing by the doctor account corresponding to the task pool under the condition that the target prescription task is put into the task pool according to the reference time consumption of the doctor account; and the allocation module is used for allocating the target prescription task to a target task pool determined according to the estimated time consumption.
The present description provides a computer device comprising a memory storing a computer program and a processor implementing the method according to any of the embodiments above when executing the computer program.
The present description provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the method according to any of the above embodiments.
According to the embodiments provided by the specification, the reference time consumption of the prescription task processing by the doctor account is determined according to the historical prescription task of the doctor account, the estimated time consumption of the target prescription task for completing the processing by the doctor account corresponding to the task pool can be predicted further based on the reference time consumption when the target prescription task is put into any task pool, further, the target task pool can be determined and the target prescription task can be allocated to the target task pool based on the estimated time consumption corresponding to different task pools. Accordingly, the target prescription task can be processed through the doctor account corresponding to the target task pool, and time consumption for completing prescription task processing can be reduced.
Drawings
Fig. 1 is a schematic diagram of an application scenario example of a prescription task allocation method according to an embodiment of the present disclosure.
Fig. 2 is a schematic diagram illustrating an application scenario example of a prescription task allocation method according to an embodiment of the present disclosure.
Fig. 3 is a schematic diagram of a flow of a prescription task allocation method according to an embodiment of the present disclosure.
Fig. 4 is a schematic diagram of a prescription task assigning apparatus according to an embodiment of the present disclosure.
Fig. 5 is a schematic diagram of a computer device according to one embodiment of the present description.
Detailed Description
SUMMARY
In the related art, a user may place a drug order before purchasing a prescribed drug online. Further, a doctor account may be prescribed according to the drug order. After the prescription is filled, the user may further pay for the prescription drug to complete the online purchase of the prescription drug.
Thus, the time consuming prescription of a doctor account creates a shopping experience for purchasing prescription drugs online for a user. In the related art, a target doctor account may be selected to assign prescription tasks generated according to a medicine order by the number of prescription tasks for prescribing a prescription that have not been processed by the doctor account. However, the efficiency of prescribing orders by different doctor accounts is different, so that the prescription order generated according to the medicine order is allocated based on the number of the prescription tasks which are not processed and correspond to the doctor accounts in the related art, the prescription order may be allocated to doctor accounts with relatively less number of the prescription tasks which are not processed but lower efficiency, which may result in longer processing time of part of the prescription tasks.
Therefore, it is necessary to provide a prescription task allocation method, which can allocate a target prescription task by predicting the predicted time spent for processing the target prescription task by a corresponding doctor account, so as to solve the technical problem that the processing time spent for the prescription task is long.
Scene example
Referring to fig. 1 and 2, an example of an application scenario of a prescription task allocation system is provided in this specification. The prescription task allocation system may include a server and a plurality of clients. Wherein, the plurality of clients can log in with doctor accounts respectively. The doctor account may receive prescription tasks and process the prescription tasks according to a doctor for which the doctor account corresponds.
In this scenario example, a consumer may place drug order data for purchasing a specified drug at an e-commerce platform. Wherein the prescribed drug belongs to the prescription drug.
After receiving the drug order data, the server may generate a corresponding target prescription task. The target prescription task may be used to prescribe a prescription for purchasing a specified drug for the consumer.
The server may determine the number of prescription tasks processed by the plurality of doctor accounts, respectively, in a specified time. Specifically, doctor account 1 handles 400 prescription tasks in a week. Doctor account 2 handles 150 prescription tasks in a week. The number of treatments of doctor account 1 is greater than 200 pieces of the first threshold value. The number of treatments of doctor account 2 is less than 200 pieces of the first threshold value.
Further, corresponding to the doctor account 1, the server may obtain 5 historical prescription tasks closest to the current time from the completed historical prescription tasks processed by the doctor account 1. The server may determine the benchmark time consumption of doctor account 1 based on the median of the processing time consumption of the historical prescription tasks. The reference time consumption can be determined according to equation 1. Wherein x' 1 The benchmark time consumption may be represented. X is x 1 The processing time of the historical prescription tasks may be represented.
Specifically, according to equation 1, 180s may be used as the median after the median exceeds 180 s. Otherwise, the median may be time consuming as a benchmark for doctor account 1. The processing time of the 5 historic prescription tasks that are currently closest to the doctor account 1 may be 30s, 60s, 50s, 40s, and 60s. Accordingly, doctor account 1 benchmarks take 50s.
x′ 1 ＝min(x 1 180) equation 1
Corresponding to doctor account 2, the server may obtain, as the predicted time consumption, a reference time consumption of the history prescription task with the smallest time consumption among the completed history prescription tasks processed by doctor account 2. Accordingly, doctor account 2 benchmarks for 20s.
Further, the server may determine, according to the number of unprocessed prescription tasks corresponding to different doctor accounts, to put the target prescription into a task pool corresponding to different doctor accounts, where the corresponding doctor accounts process predicted time consumption for completing the target prescription task. Specifically, the number of prescription tasks that have not been processed is increased by 1, indicating a target prescription task that has not been processed. Then, the number of prescription tasks that have not been processed after adding 1 is multiplied by the order benchmark time consumption may result in an estimated time consumption.
Specifically, the number of prescription tasks that the doctor account 1 has not processed is 2, and the number of prescription tasks that the doctor account 2 has not processed is 6. As can be calculated from equation 2, the estimated time taken for doctor account 1 is 150s, and doctor account 2 processes the completion of the processThe predicted time consumption of the target prescription task is 140s. In equation 2, u may represent a first threshold value. num (num) i The number of prescription tasks processed by the doctor account over a specified period of time may be represented. X is x 2 The number of prescription tasks that have not been processed in the task pool corresponding to the doctor account may be represented. X' 1 May represent a minimum processing time in the historical prescription task.
Further, the server may calculate an efficiency value corresponding to the doctor account according to the estimated time consumption. Wherein the efficiency value may be between 0 and 1. Specifically, the efficiency value can be calculated by the disclosure 3. In equation 3, it is assumed that the expected time consumption conforms to the normal distribution. μ may represent the average of the expected time consumption. Sigma may represent the standard deviation of the expected time consumption. Score efficiency The efficiency value may be represented.
Illustratively, the expected time-consuming mean μmay be 140. The standard deviation sigma may be 10. Accordingly, doctor account 1 has an efficiency value of 0.7 and doctor account 2 has an efficiency value of 0.95.
Further, the server may calculate attenuation values corresponding to the doctor account. Wherein the attenuation value may be between 0 and 1. In case the number of treatments of the prescription task of the doctor account within the specified period of time is not smaller than a second threshold, an attenuation value may be calculated by said number of treatments and said second threshold. In the case where the number of processes is smaller than the second threshold value, the attenuation value may be set to 1. Specifically, the attenuation value can be calculated by equation 4. In equation 4, score balance Representing the attenuation values. threshold (threshold) i Representing a second threshold corresponding to a different doctor account. m is a parameter, and can be configured. l is a smoothing factor.
Illustratively, m may be 1 and l may be 0. The second threshold for doctor account 1 and doctor account 2 are both 200. The attenuation score e of the doctor account 1 can be calculated by equation 4 ―2 The attenuation of doctor account 2 is divided into 1.
Further, the server may calculate an efficiency value and a decay value, and calculate an acceptance index that indicates the ability of the doctor account corresponding to the task pool to handle the target prescription task. Specifically, the acceptance index may be obtained by multiplying the attenuation value and the efficiency value as described in equation 5. In equation 5, score total The acceptance index may be represented.
Score total ＝Score efficicncy *SCore balance Equation 5
The acceptance index of doctor account 1 may be 0.7 xe according to equation 5 ―2 The acceptance index of doctor account 2 may be 0.95. The acceptance index of the doctor account 2 is larger than that of the doctor account 2. Thus, the server may assign the target prescription task to the corresponding task pool of doctor account 2.
The doctor account 2 can acquire prescription tasks from the task pool in turn for processing under the condition of completing the current prescription tasks. After processing the target prescription task, the doctor account 2 may return a prescription form for the specified drug.
System architecture
Embodiments of the present disclosure provide a prescription task dispensing system. The prescription task distribution system may include a client and a server.
The client may log in with a doctor account for receiving prescription tasks assigned by the server. The client may be an electronic device with network access capabilities. Specifically, for example, the client may be a desktop computer, a tablet computer, a notebook computer, a smart phone, a digital assistant, a smart wearable device, a shopping guide terminal, a television, a smart speaker, a microphone, and the like. Wherein, intelligent wearable equipment includes but is not limited to intelligent bracelet, intelligent wrist-watch, intelligent glasses, intelligent helmet, intelligent necklace etc.. Alternatively, the client may be software capable of running in the electronic device.
The server may assign prescription tasks generated from the received drug order data to a task pool of corresponding doctor accounts. The server may be an electronic device with some arithmetic processing capability. Which may have a network communication module, a processor, memory, and the like. Of course, the server may also refer to software running in the electronic device. The server may also be a distributed server, and may be a system having a plurality of processors, memories, network communication modules, etc. operating in concert. Alternatively, the server may be a server cluster formed for several servers. Or, with the development of science and technology, the server may also be a new technical means capable of realizing the corresponding functions of the embodiment of the specification. For example, a new form of "server" based on quantum computing implementation may be possible.
Example method
Referring to fig. 3, an embodiment of the present disclosure provides a method for assigning prescription tasks. The prescription task allocation method can be applied to a server. The method may provide a task pool corresponding to a doctor account. The task pool may be used to store prescription tasks that have not been processed by the corresponding doctor account. The prescription task allocation method may include the following steps.
Step S110: generating a target prescription task corresponding to the received medicine order data; the target prescription task is used for giving a prescription for the medicine represented by the medicine order data.
In some cases, the server may receive the drug order data to generate a target prescription task based on the drug order data. Further, the server may assign the target prescription task to a target task pool to reduce to some extent the time required for the processing of the target prescription task to complete.
In this embodiment, the target prescription task may represent providing drug order data for a doctor account to receive processing matters of a prescription form fed back by the doctor account. The target prescription task may be processed by a doctor account. Wherein the time consuming processing of the target prescription task by different doctor accounts may be different.
In this embodiment, corresponding task pools may be provided for different doctor accounts. The task pool may be used to store prescription tasks that have not been processed by the corresponding doctor account. Further, in the case that the doctor account is in an idle state, the prescription task may be extracted from the corresponding task pool and sent to the doctor account, so as to process the prescription task. In some embodiments, prescription tasks in the task pool may be arranged in a sequence. And according to the sequence among the prescription tasks in the task pool, the prescription tasks in the task pool can be sequentially sent to a doctor account for processing.
In this embodiment, the drug order data may represent an order placed by a consumer to purchase prescription drugs. Specifically, for example, a consumer may place an order to purchase a specified drug in an e-commerce platform. The order data of the order for purchasing the specified medicine may be taken as the medicine order data. Specifically, the medicine order data may include the name, number, manufacturer and other medicine information of the prescription medicine, the name, age, medical history and other related information of the medicine person taking the prescription medicine, and the like.
In this embodiment, the target prescription task may represent a prescription task of prescribing a prescription for a prescription drug represented by the drug order data.
In the present embodiment, the step of generating the target prescription task in response to the received medicine order data may represent a task of generating a prescription order for the prescription medicine included in the medicine order data based on the medicine order data. In some embodiments, an initial prescription may be generated from information including medication persons, prescription drugs, etc. included in the medication order data for reference and prescription by a doctor. Accordingly, the target prescription task generated corresponding to the received drug order data may represent an prescription based on the initial prescription. Accordingly, a method of generating a target prescription task corresponding to received drug order data may include generating an initial prescription. Of course, the generation target prescription task corresponding to the received medicine order data may also represent a task of providing medicine order data to a doctor account to receive a prescription form fed back by the doctor account. Correspondingly, the method for generating the target prescription task corresponding to the received medicine order data can also comprise packaging the data related to the target prescription task.
Step S120: and determining the reference time consumption of the doctor account for processing the prescription task according to the historical prescription task completed by the doctor account.
In some cases, the efficiency of processing prescription tasks may be different for different doctor accounts. For example, the time consumed for processing prescription tasks is different without the doctor for which the doctor account corresponds. Thus, the benchmark time consumption of a doctor account to process a prescription task may be determined from a historical prescription task for the doctor account. Further, an estimated processing time for the doctor account to process the target prescription task may be determined based on the benchmark time. To further select a target task pool based on the predicted time consumption, and assign the target prescription task to the target task pool to enable the corresponding physician to process the target prescription task.
In this embodiment, the historical prescription task may represent the completion of the prescription task by the doctor account process. Accordingly, processing a completed historical prescription task may have processing time consuming from beginning processing the prescription task to processing the completion of the prescription task. The historical prescription tasks may be a plurality of prescription tasks that the doctor account has recently completed processing. Of course, the historical prescription tasks may also represent that historically corresponding doctor accounts complete all prescription tasks.
In this embodiment, the reference time consumption may be used to determine the estimated time consumption of a doctor's account process to complete the target prescription task.
In this embodiment, the method for determining the reference time consumption of the doctor account for processing the prescription task according to the history prescription task completed by the doctor account may be to determine the reference time consumption based on the processing time consumption of the history prescription task. Specifically, for example, the average processing time of the history prescription task may be taken as the reference time. Of course, the median of the processing time consumption of the randomly selected part of the history prescription task may also be used as the reference time consumption. In some embodiments, the minimum processing time consumption of the historical prescription task may also be selected as the reference time consumption.
In some embodiments, the length of time that the same doctor account is prescribing different classes of drugs may be different. For example, prescriptions for drugs for treating more serious diseases take a relatively longer time. Therefore, according to the historical prescription task completed by the doctor account, the method for determining the reference time consumption of the doctor account for processing the prescription task can also classify the historical prescription task according to the category of the medicine related to the historical prescription task. Different benchmark time periods may be determined for different categories of historical prescription tasks.
Step S130: and predicting the predicted time consumption of the target prescription task for completing the processing by the doctor account corresponding to the task pool under the condition that the target prescription task is put into the task pool according to the reference time consumption of the doctor account.
In some cases, an estimated time consumption for the target prescription task to be completed by the corresponding doctor account of the task pool may be determined based on the benchmark time consumption. By predicting the time consumption, a doctor account capable of completing the target prescription task in a shorter time can be selected for the prescription processing, and the waiting time of a consumer waiting for the completion of the target prescription task processing can be reduced to a certain extent.
In this embodiment, in the case where the target prescription task is predicted to be placed in the task pool according to the reference time consumption of the doctor account, the method for predicting the time consumption of the target prescription task for completing the processing by the doctor account corresponding to the task pool may be to determine the time consumption required for completing the prescription task in any one task pool by the corresponding doctor account according to the number of prescription tasks in the task pool and the reference time consumption. Further, the predicted time consumption can be predicted according to the time consumption of the doctor account corresponding to the task pool for processing the target prescription task and the time consumption required by the prescription task in the task pool for being processed and completed by the corresponding doctor account.
In some embodiments, in the case that the target prescription task is put into a task pool, the prescription tasks in the task pool may be divided into a plurality of categories according to the categories of medicines related to the prescription task. According to the standard time consumption of the doctor account corresponding to the task pool for processing the prescription tasks of different categories, the predicted time consumption of the target prescription task for completing the processing by the doctor account corresponding to the task pool can be predicted when the target prescription task is put into the task pool. Further, a target task pool may be determined to assign the target prescription task to the target task pool based on the expected time consumption corresponding to the different task pools.
Specifically, for example, the target prescription task may be to prescribe an influenza-like drug. The task pool 1 includes 2 prescription tasks for anticancer drugs and 2 prescription tasks for influenza drugs. In the case where the target prescription task is put into the task pool 1, 2 prescription tasks for anticancer drugs and 2 prescription tasks for influenza drugs may be included in the task pool 1. The reference time taken for the doctor account 1 corresponding to the task pool 1 to process the prescription task related to the anticancer drug may be 5 minutes. Doctor account 1 takes 2 minutes to process benchmarks for prescription tasks for influenza-like drugs. Accordingly, in the case that the target prescription task is put into the task pool 1, the estimated time taken for the target prescription task to be completed by the doctor account 1 corresponding to the task pool 1 may be 14 minutes.
Step S140: and distributing the target prescription task to a target task pool determined according to the estimated time consumption.
In some cases, a target task pool that is less time consuming to process the target prescription task may be determined based on the estimated time consumption. Further, the target prescription task may be assigned to a target task pool for further processing. The target task pool is selected from the task pools according to the estimated time consumption, so that the efficiency of processing the prescription task by the doctor account corresponding to the task pool and the idleness of the doctor account in the current time period can be considered to a certain extent, a proper task pool can be selected so as to put the target prescription task into the task pool for processing, and the time consumption of processing the target prescription task can be reduced to a certain extent.
In this embodiment, the target task pool determined according to the predicted time consumption may represent a task pool with the smallest predicted time consumption. In some embodiments, according to the reference time consumption of the doctor account corresponding to the different task pools, the predicted time consumption of the target prescription task for completing the processing by the doctor account corresponding to the corresponding task pool can be predicted when the target prescription task is put into the task pool. The expected time consumption for different task pools may be different. The method for determining the target task pool based on the estimated time consumption can also be used for generating the acceptance index according to the estimated time consumption. The acceptance index may represent the capability of a doctor account corresponding to the task pool to process the target prescription task. Further, a target task pool can be determined according to the acceptance index. In the case of a determined target task pool, the target prescription task may be assigned to the target task pool to process the target prescription task through a doctor account corresponding to the target task pool.
In this embodiment, the historical prescription tasks completed by different doctor accounts may determine the reference time consumption corresponding to the corresponding doctor accounts. Further, the estimated time consumption of the corresponding doctor account process to complete the target prescription task may be determined based on the benchmark time consumption and the number of prescription tasks in the task pool corresponding to the doctor account. Further, a target task pool can be determined through time consumption prediction, so that a target prescription task is put into the target task pool and is delivered to a doctor account corresponding to the target task pool for processing. The historical prescription task completed through the processing of the doctor account determines the reference time consumption, so that the efficiency of processing the prescription task by different doctor accounts can be well characterized. And under the condition that the target prescription task is put into the task pool, the predicted time consumption for completing the target prescription task can be further predicted through the reference time consumption by processing doctor accounts corresponding to different task pools. The target task pool is determined through time consumption prediction, so that the efficiency of processing prescription tasks by a doctor account and the idle degree of the doctor account can be considered to a certain extent. Thus, in the case where the target prescription task is put in the target task pool determined by the predicted time consumption, it is possible to process the completion of the target prescription task in a relatively short time and provide the corresponding prescription. To a certain extent, the purchasing experience of the consumer can be improved, and the success probability of purchasing the medicine by the consumer is improved.
In some embodiments, the historical prescription task corresponds to a historical processing time; the method further comprises the steps of: according to the time difference between the historical processing time and the current time of the historical prescription task, selecting the historical prescription task as a target historical prescription task in the historical prescription task; accordingly, according to the historical prescription task completed by the doctor account, determining the reference time consumption of the doctor account for processing the prescription task comprises the following steps: and determining the reference time consumption of the doctor account for processing the prescription task according to the processing time consumption of the target historical prescription task.
In some cases, the doctor for which the doctor account corresponds may have some variance in the time taken to process the prescription task due to experience growth or self-status. Thus, recently processed historical prescription tasks may be selected among the historical prescription tasks to determine the benchmark time consumption. Under the condition that the target prescription task is put into the task pool, the target prescription task is predicted to be time-consuming by the doctor account corresponding to the task pool to finish processing to a certain extent.
The historical prescription task corresponds to historical processing time. The historical processing time may represent the time that the historical prescription task was processed by a doctor account. In particular, the historical processing time may represent a start time of the doctor account processing the historical prescription task. Of course, the historical processing time may also represent the end time of the doctor's account processing the historical prescription tasks. The historical prescription tasks may be ordered according to historical processing time.
The method for selecting the historical prescription task as the target historical prescription task according to the time difference between the historical processing time and the current time of the historical prescription task can be to select the specified number of the historical prescription tasks with smaller time difference as the target historical prescription task based on the time difference. Specifically, for example, according to the time difference, 5 history prescription tasks closest to the current time may be selected as target history prescription tasks.
Of course, a history prescription task whose time difference is within a preset range may be selected as the target history prescription task according to the time difference. For example, a doctor account may be selected to complete a historical prescription task within a week as a target historical prescription task. Accordingly, the range of time differences may represent 0 to 7 days.
Accordingly, the method for determining the reference time consumption of the doctor account for processing the prescription task according to the processing time consumption of the target historical prescription task may be to determine the reference time consumption based on the statistical data such as the average, the median, the mode and the like of the processing time consumption of the target historical prescription task.
In some implementations, prescription tasks in the task pool are arranged into a task sequence; and under the condition that the target prescription task is predicted to be put into the task pool according to the reference time consumption of the doctor account, the predicted time consumption step of completing the processing of the target prescription task by the doctor account corresponding to the task pool comprises the following steps: determining the number of tasks arranged before the target prescription task under the condition that the target prescription task is put into a task pool; and predicting the estimated time consumption according to the task number and the reference time consumption of the corresponding doctor account.
In some cases, prescription tasks in a task pool may be arranged into a task sequence. The prescription tasks can be sequentially provided to the doctor account for processing in the server according to the order of the prescription tasks in the task pool. Thus, the predicted time consumption may be predicted by determining the number of tasks that are ordered before the target prescription task with the target prescription task placed in the task pool. The number of tasks arranged before the target prescription task may represent the number of prescription tasks that need to wait for processing to complete before processing the target prescription task.
The method for predicting the predicted time consumption according to the task number and the reference time consumption of the corresponding doctor account can be to directly multiply the task number by the time length obtained by the reference time consumption as the predicted time consumption. Of course, in some embodiments, the number of tasks, benchmark time consumption, and drug order data may be used as inputs to the predictive model by constructing the predictive model of predicted time consumption. Accordingly, the output of the predictive model may be taken as the predicted time consumption.
In some cases, different prescription tasks may have different priority levels. For example, prescription tasks prescribed for prescription drugs for treating acute disease may be prioritized over prescription tasks involving prescription drugs for treating chronic disease. Thus, in the process of placing the target prescription task into the task pool, the target prescription task can be placed into a designated position of the task sequence in the task pool according to the priority level of the target prescription task. Accordingly, before processing the target prescription task, it may be necessary to wait for the corresponding doctor account to process the task arranged before the target prescription task. Thus, the estimated time consumption may be predicted based on the number of tasks ordered before the target prescription task and the reference time consumption according to the number of tasks and the corresponding doctor account.
In some embodiments, the method for assigning prescription tasks may further include: determining the processing quantity of prescription tasks of a doctor account in a specified time period; correspondingly, the step of predicting the time consumption of the target prescription task to be processed by the doctor account corresponding to the task pool is performed only when the processing number is not smaller than the first threshold value and the step of predicting the time consumption of the target prescription task to be processed by the doctor account corresponding to the task pool is performed when the target prescription task is predicted to be put into the task pool.
In some cases, a more efficient doctor may handle more prescription tasks with a doctor's account. Accordingly, the number of prescription tasks handled by the doctor account corresponding to the less efficient doctor may be less. To ensure that doctors can form benign competition under the condition of certain workload. Therefore, the step of predicting the predicted time consumption of the target prescription task to be processed by the doctor account corresponding to the task pool can be performed only when the processing number of the prescription tasks in the designated time period of the doctor account is not less than the first threshold value, and the step of predicting the predicted time consumption of the target prescription task to be processed by the doctor account corresponding to the task pool is performed when the target prescription task is predicted to be put into the task pool. Accordingly, in the case where the processing task is less than the first threshold, the target task pool may be determined by other policies.
The number of prescription tasks processed by the doctor account over a specified period of time may represent the number of prescription tasks that the doctor account has accumulated to complete over the specified period of time. Specifically, for example, the specified period of time may refer to monday to friday. The number of tasks may represent the number of prescription tasks that the doctor account completed within the workday of the week. In particular, the current time may be monday, and the number of tasks may represent the number of tasks that are cumulatively completed from monday to monday.
The first threshold may represent a reference amount of a preset number of tasks. For example, the first threshold may represent a baseline number of treatments the doctor account needs to complete a prescription task within a specified period of time.
In some embodiments, determining the benchmark time consuming for the doctor account to process the prescription task based on the historical prescription task completed by the doctor account comprises: when the processing quantity is smaller than the first threshold value, selecting a designated quantity of historical prescription tasks from the historical prescription tasks of the doctor account according to the sequence from small processing time to large processing time; and determining the reference time consumption of the doctor account for processing the prescription tasks according to the appointed number of historical prescription tasks.
In some cases, a more efficient doctor may handle more prescription tasks with a doctor's account. Accordingly, the number of prescription tasks handled by the doctor account corresponding to the less efficient doctor may be less. A less efficient doctor may be a relatively inexperienced doctor. This may result in less efficient doctors being assigned a smaller number of prescription tasks and not able to achieve exercise growth. In addition, more efficient doctors can also take more prescription tasks and create more stress. Thus, a target prescription task may be preferentially assigned to doctor accounts having a process number less than a first threshold.
And under the condition that the processing quantity is smaller than the first threshold value, selecting a specified quantity of historical prescription tasks with the smallest processing time according to the processing time of the historical prescription tasks, and determining the reference time of the doctor account for processing the prescription tasks. For example, the server may select a historical prescription task that has the least processing time consumption, and determine a benchmark time consumption for the doctor account to process the prescription task. Of course, the server may also order the historical prescription tasks by processing time. Then, 10 historical prescription tasks are selected in order of time consumption from small to large to determine the reference time consumption of the doctor account to process the prescription tasks.
In the case that the history prescription task may be a prescription task with the minimum processing time, the method of determining the reference time consumption of the doctor account for processing the prescription task according to the specified number of history prescription tasks with the minimum processing time may be to take the minimum processing time as the reference time consumption. Accordingly, when the target prescription task is predicted to be placed in the task pool according to the reference time consumption of the doctor account, the predicted time consumption of the target prescription task for completing the processing by the doctor account corresponding to the task pool may represent the smaller predicted time consumption according to the smaller processing time consumption in the historical prescription task. Accordingly, the probability that the task pool corresponding to the doctor account with the number of processing tasks smaller than the first threshold may be allocated to the prescription task is greater.
In the case where the history prescription task may be a plurality of prescription tasks whose processing time consumption is smallest, an average value of the processing time consumption of the plurality of history prescription tasks may be taken as the reference time consumption. In one aspect, a smaller, relatively smaller benchmark time consumption may be set for a doctor account to increase the probability of being assigned to the prescription task if the number of treatments in a given time period is less than a first threshold. On the other hand, the efficiency of processing prescription tasks by the doctor account can be considered to a certain extent, and benign competition can be formed among different doctors to a certain extent.
In some embodiments, the method for assigning prescription tasks may further include: calculating an excess number of the processing number exceeding a second threshold value in the case where the processing number is greater than the second threshold value; wherein the second threshold is not less than the first threshold; accordingly, the step of assigning the target prescription task to a target task pool determined based on the estimated time consumption includes: assigning the target prescription task to a target task pool determined based on the estimated time consumption and the excess amount.
In some cases, a doctor account corresponding to a more efficient doctor may receive more prescription tasks, and thus may increase the pressure on the doctor account. And to some extent, the accuracy of prescription making is also affected. Thus, in the event that the number of treatments is greater than a second threshold, the target task pool may be determined based on the projected time consumption for the doctor account and the excess number of treatments exceeding the second threshold. In particular, in the case where the excess number is large, the number of prescription tasks assigned to the task pool corresponding to the corresponding doctor account can be reduced.
The excess number may represent a number of tasks that the doctor account processes in a specified period of time exceeding the second threshold. For example, the doctor account may process 100 tasks in a given time period. The second threshold is 90 pieces. The corresponding excess number is 10 pieces.
The second threshold may be preset. The second threshold may be greater than the first threshold. The second threshold may represent a constraint threshold to constrain allocation of prescription tasks to doctor accounts. In some embodiments, the constraint thresholds may be different for different doctors. Accordingly, in the case where the number of tasks processed by different doctor accounts within a specified time period is the same, the excess number corresponding to different constraint thresholds may be different. Specifically, for example, the second threshold value corresponding to different rated doctors may be different according to the ratings of the doctors. For example, the second threshold of the primary practitioner may be set to 100. The secondary primary physician's second threshold may be set to 80. The specific setting of the second threshold value can be determined according to the actual application scene.
The method for determining the target task pool according to the estimated time consumption and the excess amount may be to perform weighted calculation on the estimated time consumption and the excess amount to obtain a corresponding score. A target task pool may be determined in accordance with the score. For example, the expected time consumption and excess amount corresponding to the doctor account may be added to obtain the corresponding score. Further, a task pool corresponding to the doctor account with a smaller score can be selected as the target task pool. In particular, the probability of selecting a task pool that is the target task pool is relatively small, with the task pool corresponding to a doctor account that is expected to be time consuming or that has a large excess number. Of course, in some embodiments, an extension coefficient may be calculated based on the exponential function and the excess number. The larger the excess number, the larger the extension coefficient may be. The method for allocating the target prescription task to the target task pool determined according to the estimated time consumption and the excess amount can select the task pool corresponding to the corrected estimated time consumption with smaller value as the target task pool according to the corrected estimated time consumption obtained by multiplying the extension coefficient by the estimated time consumption.
In some embodiments, the method for assigning prescription tasks may further include: mapping the estimated time consumption to a designated interval to obtain an efficiency value; calculating an attenuation value based on the excess number; wherein the attenuation value is in the specified interval; determining a carrying index of the task pool according to the efficiency value and the attenuation value; wherein, the acceptance index represents the capability of a doctor account corresponding to a task pool to process the target prescription task; accordingly, the step of assigning the target prescription task to a target task pool determined based on the estimated time consumption includes: and distributing the target prescription task to a target task pool determined according to the acceptance index.
In some cases, the efficiency of processing prescription tasks by a doctor account and the balance of prescription task allocation can be considered to a certain extent by determining the target task pool through the processing quantity of the prescription tasks by the doctor account in a specified time period and the estimated time spent by the doctor account corresponding to the task pool to complete processing in the case that the target prescription tasks are put into the task pool. The cultivation of doctors corresponding to doctor accounts can be considered while the prescription is provided for consumers in a short time. However, the units are expected to be different in time consuming and excessive amounts. Calculating the score based on a fixed weight or coefficient may result in different predicted time consumption and different excess amounts, which may be of different contribution rates to the score. Thus, the projected time consumption and excess amount can be mapped to the same specified interval using a specified function, and an acceptance index representing the ability of the doctor account corresponding to the task pool to handle the target prescription task is calculated to determine the target task pool.
The specified interval may represent a specified range of values. Specifically, for example, the specified interval may be between 0 and 1. Of course, the specified interval may be between 0 and 100. The method for mapping the estimated time consumption to the specified interval to obtain the efficiency value can map the estimated time consumption to the specified interval based on an inverse normalization method. Specifically, the inverse normalization can be performed by equation 3. Of course, in some embodiments, the inverse normalization may also be performed based on the specific gravity of the difference between the estimated time and the minimum estimated time to the maximum estimated time and the minimum estimated time.
Based on the excess number, the method of calculating the attenuation value may be calculated according to a specified function such that the larger the excess number, the smaller the attenuation value. Specifically, based on the excess amount, the method of calculating the attenuation value may be calculated by equation 4. In some embodiments, the attenuation value may also be calculated using the inverted sigmod function. In some embodiments, the excess amount may be represented by a ratio of the number of prescription tasks completed to a second threshold. Accordingly, a smoothing factor may be added to the ratio.
The acceptance index may represent the ability of a doctor account corresponding to the task pool to handle the target prescription task. The acceptance index can be calculated by an efficiency value and a decay value.
According to the efficiency value and the attenuation value, the method for determining the acceptance index of the task pool can carry out weighted summation on the efficiency value and the attenuation value to obtain the acceptance index. Of course, the method of determining the acceptance index of the task pool according to the efficiency value and the attenuation value may also be obtained by multiplying the efficiency value and the attenuation value according to formula 5.
And determining a target task pool according to the acceptance index. Specifically, the target task pool may be determined according to the size of the acceptance index. For example, a task pool corresponding to a doctor account for which the acceptance index satisfies a specified condition may be set as the target task pool. For example, the specified condition may be that the acceptance index is maximum.
Example apparatus, electronic device, storage Medium, and software
Referring to fig. 4, a prescription task distributing apparatus is disclosed. The device may provide a task pool corresponding to the doctor account. The task pool is used for storing prescription tasks which are not processed by corresponding doctor accounts. The apparatus may include: the system comprises a generating module, a determining module, a predicting module and an allocating module.
The generation module is used for generating a target prescription task corresponding to the received medicine order data; the target prescription task is used for giving a prescription for the medicine represented by the medicine order data.
And the determining module is used for determining the reference time consumption of the doctor account for processing the prescription task according to the history prescription task completed by the doctor account.
And the prediction module is used for predicting the predicted time consumption of the target prescription task for completing the processing by the doctor account corresponding to the task pool under the condition that the target prescription task is put into the task pool according to the reference time consumption of the doctor account.
And the allocation module is used for allocating the target prescription task to a target task pool determined according to the estimated time consumption.
The specific functions and effects achieved by the prescription task allocation device can be explained in reference to other embodiments of the present specification, and will not be described herein. The individual modules in the prescription task dispensing device may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in hardware or independent of a processor in the computer equipment, and can also be stored in a memory in the computer equipment in a software mode, so that the processor can call and execute the operations corresponding to the modules.
Please refer to fig. 5. In some embodiments, a computer device may be provided, including a memory having a computer program stored therein, and a processor, which when executing the computer program, implements the prescription task allocation method of the embodiments.
The present specification embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a computer, causes the computer to perform the prescription task allocation method in any of the above embodiments.
The present description also provides a computer program product comprising instructions which, when executed by a computer, cause the computer to perform the prescription task allocation method of any of the above embodiments.
It will be appreciated that the specific examples herein are intended only to assist those skilled in the art in better understanding the embodiments of the present disclosure and are not intended to limit the scope of the present invention.
It should be understood that, in various embodiments of the present disclosure, the sequence number of each process does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
It will be appreciated that the various embodiments described in this specification may be implemented either alone or in combination, and are not limited in this regard.
Unless defined otherwise, all technical and scientific terms used in the embodiments of this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this specification belongs. The terminology used in the description is for the purpose of describing particular embodiments only and is not intended to limit the scope of the description. The term "and/or" as used in this specification includes any and all combinations of one or more of the associated listed items. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It will be appreciated that the processor of the embodiments of the present description may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by integrated logic circuits of hardware in a processor or instructions in software form. The processor may be a general purpose processor, a Digital signal processor (Digital SignalProcessor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), an off-the-shelf programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The methods, steps and logic blocks disclosed in the embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present specification may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
It will be appreciated that the memory in the embodiments of this specification may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable Programmable ROM (EPROM), an Electrically Erasable Programmable ROM (EEPROM), or a flash memory, among others. The volatile memory may be Random Access Memory (RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present specification.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system, apparatus and unit may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional unit in each embodiment of the present specification may be integrated into one processing unit, each unit may exist alone physically, or two or more units may be integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present specification may be essentially or portions contributing to the prior art or portions of the technical solutions may be embodied in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present specification. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a read-only memory (ROM), a random-access memory (RAM), a magnetic disk, or an optical disk, etc.
The foregoing is merely specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope disclosed in the present disclosure, and should be covered by the scope of the present disclosure. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A prescription task allocation method is characterized in that a task pool corresponding to a doctor account is provided; the task pool is used for storing prescription tasks which are not processed by corresponding doctor accounts; the method comprises the following steps:
generating a target prescription task corresponding to the received medicine order data; the target prescription task is used for giving a prescription for the medicine represented by the medicine order data;
determining the reference time consumption of the doctor account for processing the prescription task according to the history prescription task completed by the doctor account;
under the condition that the target prescription task is put into a task pool according to the reference time consumption of the doctor account, the target prescription task is predicted to be completed by the doctor account corresponding to the task pool to process the predicted time consumption;
mapping the estimated time consumption to a designated interval to obtain an efficiency value corresponding to the doctor account; wherein the efficiency value is inversely proportional to the estimated time consumption;
calculating an excess number of the processing number exceeding a second threshold value in the case that the processing number is larger than the second threshold value; wherein the processing number represents the number of prescription tasks that the doctor account has accumulated to complete in a specified time period;
calculating attenuation values corresponding to doctor accounts based on the excess quantity; wherein the attenuation value is in the specified interval; wherein the excess amount is inversely proportional to the decay value;
Determining a carrying index of the task pool according to the efficiency value and the attenuation value; wherein, the acceptance index represents the capability of a doctor account corresponding to a task pool to process the target prescription task; wherein the acceptance index is proportional to the efficiency value and the acceptance index is proportional to the attenuation value;
and distributing the target prescription task to a target task pool determined according to the acceptance index.
2. The method of claim 1, wherein the historical prescription task corresponds to a historical processing time; the method further comprises the steps of:
according to the time difference between the historical processing time and the current time of the historical prescription task, selecting the historical prescription task as a target historical prescription task in the historical prescription task;
accordingly, according to the historical prescription task completed by the doctor account, determining the reference time consumption of the doctor account for processing the prescription task comprises the following steps:
and determining the reference time consumption of the doctor account for processing the prescription task according to the processing time consumption of the target historical prescription task.
3. The method of claim 1, wherein prescription tasks in the task pool are arranged into a task sequence; and under the condition that the target prescription task is predicted to be put into the task pool according to the reference time consumption of the doctor account, the predicted time consumption step of completing the processing of the target prescription task by the doctor account corresponding to the task pool comprises the following steps:
Determining the number of tasks arranged before the target prescription task under the condition that the target prescription task is put into a task pool;
and predicting the estimated time consumption according to the task number and the reference time consumption of the corresponding doctor account.
4. The method according to claim 1, wherein the method further comprises:
determining the processing quantity of prescription tasks of a doctor account in a specified time period;
correspondingly, the step of predicting the time consumption of the target prescription task to be processed by the doctor account corresponding to the task pool is performed only when the processing number is not smaller than the first threshold value and the step of predicting the time consumption of the target prescription task to be processed by the doctor account corresponding to the task pool is performed when the target prescription task is predicted to be put into the task pool.
5. The method of claim 4, wherein the step of determining a benchmark time for the doctor account to process the prescription task based on the historical prescription task completed by the doctor account comprises:
when the processing quantity is smaller than the first threshold value, selecting a designated quantity of historical prescription tasks from the historical prescription tasks of the doctor account according to the sequence from small processing time to large processing time; and determining the reference time consumption of the doctor account for processing the prescription tasks according to the appointed number of historical prescription tasks.
6. A prescription task distribution device, characterized in that a task pool corresponding to a doctor account is provided; the task pool is used for storing prescription tasks which are not processed by corresponding doctor accounts; the device comprises:
the generation module is used for generating a target prescription task corresponding to the received medicine order data; the target prescription task is used for giving a prescription for the medicine represented by the medicine order data;
the determining module is used for determining the reference time consumption of the doctor account for processing the prescription task according to the history prescription task completed by the doctor account;
the prediction module is used for predicting the predicted time consumption of the target prescription task for completing the processing by the doctor account corresponding to the task pool under the condition that the target prescription task is put into the task pool according to the reference time consumption of the doctor account;
the distribution module is used for mapping the estimated time consumption to a designated interval to obtain an efficiency value corresponding to the doctor account; wherein the efficiency value is inversely proportional to the estimated time consumption; calculating an excess number of the processing number exceeding a second threshold value in the case that the processing number is larger than the second threshold value; wherein the processing number represents the number of prescription tasks that the doctor account has accumulated to complete in a specified time period; calculating attenuation values corresponding to doctor accounts based on the excess quantity; wherein the attenuation value is in the specified interval; wherein the excess amount is inversely proportional to the decay value; determining a carrying index of the task pool according to the efficiency value and the attenuation value; wherein, the acceptance index represents the capability of a doctor account corresponding to a task pool to process the target prescription task; wherein the acceptance index is proportional to the efficiency value and the acceptance index is proportional to the attenuation value; and distributing the target prescription task to a target task pool determined according to the acceptance index.
7. A computer device comprising a memory storing a computer program and a processor implementing the method of any one of claims 1 to 5 when the computer program is executed by the processor.
8. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211228451.1A CN115547445B (en) | 2022-10-09 | 2022-10-09 | Prescription task distribution method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211228451.1A CN115547445B (en) | 2022-10-09 | 2022-10-09 | Prescription task distribution method, device, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115547445A CN115547445A (en) | 2022-12-30 |
| CN115547445B true CN115547445B (en) | 2023-09-19 |
Family
ID=84732710
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211228451.1A Active CN115547445B (en) | 2022-10-09 | 2022-10-09 | Prescription task distribution method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115547445B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110223753A (en) * | 2019-06-13 | 2019-09-10 | 广州宝荣科技应用有限公司 | A kind of Checking prescription distribution method and system based on queuing duration |
| CN111785346A (en) * | 2020-05-29 | 2020-10-16 | 北京沃东天骏信息技术有限公司 | Prescription order distribution method, system, device and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7827041B2 (en) * | 2004-03-31 | 2010-11-02 | Cvs Pharmacy, Inc. | System and methods of providing pharmacy services |
-
2022
- 2022-10-09 CN CN202211228451.1A patent/CN115547445B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110223753A (en) * | 2019-06-13 | 2019-09-10 | 广州宝荣科技应用有限公司 | A kind of Checking prescription distribution method and system based on queuing duration |
| CN111785346A (en) * | 2020-05-29 | 2020-10-16 | 北京沃东天骏信息技术有限公司 | Prescription order distribution method, system, device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115547445A (en) | 2022-12-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10686718B1 (en) | Allocating resources | |
| US7581008B2 (en) | System and method for allocating server resources | |
| US20140337532A1 (en) | Method and system for arbitraging computer resources in a cloud computing environment | |
| CN107231264A (en) | For the method and apparatus for the capacity for managing Cloud Server | |
| JP2020074217A (en) | Device and method for placing dynamic peg order | |
| KR102315856B1 (en) | Rider Auto-Allocation System for Food Delivery | |
| WO2004022463A1 (en) | Safe stock amount calculation method, safe stock amount calculation device, order making moment calculation method, order making moment calculation device, and order making amount calculation method | |
| US20110213669A1 (en) | Allocation of Resources | |
| CN110930245A (en) | Data processing method and device, electronic equipment and storage medium | |
| CN115547445B (en) | Prescription task distribution method, device, equipment and storage medium | |
| CN113822722A (en) | Virtual resource distribution control method and device and server | |
| CN108833505B (en) | Data request processing method, server and storage medium | |
| US20220245721A1 (en) | Machine learning based automated savings goals | |
| US20210390481A1 (en) | Allocating Resources | |
| CN109829811A (en) | Loan method and loan device | |
| JP6183942B1 (en) | Point management system and constraint judgment device | |
| JP6640429B1 (en) | Electronic value management system, electronic value management method and program | |
| TW201305948A (en) | Reservation management device, reservation management method, reservation management program, and computer readable recording medium for storing the program | |
| CN111861711A (en) | Resource allocation method and related product | |
| US9537727B2 (en) | Discrete, depleting chips for obtaining desired service level characteristics | |
| CN114677194A (en) | Traffic guidance service processing method and device and electronic equipment | |
| CN112418670A (en) | Case allocation method, device, equipment and medium | |
| CN113222720A (en) | Reputation-based privacy protection incentive mechanism method, device and storage medium | |
| JP6669908B1 (en) | Information processing system, information processing method and information processing program | |
| CN110457355A (en) | A kind of method, apparatus and computer readable storage medium of data aggregate |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |