Intelligent recommendation and distribution method for hospital code scanning hospital guide platform based on diagnosis and treatment information retrieval

Abstract

The invention discloses an intelligent recommendation and distribution method for a hospital code scanning hospital guide platform based on diagnosis and treatment information retrieval, and relates to the technical field of electronic digital data processing. The intelligent recommendation and distribution method for the hospital code scanning hospital guide platform based on diagnosis and treatment information retrieval comprises the following steps of bag taking timeliness judgment, data retrieval and bag taking recommendation. Whether bag taking recommendation and distribution are directly carried out or not is judged through the bag taking aging parameters, if yes, user diagnosis and treatment data are directly retrieved, if not, evaluation for measuring the data synchronization degree between the platforms is carried out, code scanning optimization measures are selected to improve the code scanning real-time performance after the hospital guide platform scans the two-dimensional code of the user, and the user diagnosis and treatment efficiency is improved. Meanwhile, user diagnosis and treatment data are retrieved from a platform authorized by the user, and finally whether bag taking recommendation optimization is carried out or not is judged based on bag taking parameters to improve the effectiveness of bag taking recommendation, so that the intelligent recommendation and distribution real-time performance of the hospital code scanning hospital guide platform is improved; the problem that in the prior art, a hospital code scanning hospital guide platform is poor in intelligent recommendation and distribution real-time performance is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic digital data processing, in particular to a hospital code scanning guide platform intelligent recommendation and distribution method based on diagnosis and treatment information retrieval. BACKGROUND

[0002] The guide platform is integrated in a bag taking machine, a film taking machine or a guide machine. A hospital information system (HIS) is a core system of hospital informatization construction, and bears the tasks of user diagnosis and treatment data collection, storage and management. The HIS system integrates an electronic medical record system (EMR), a laboratory information system (LIS), a picture archiving and communication system (PACS) and other subsystems to build a data hub within the hospital. The data integration process includes data collection, standardized processing and storage to form a clinical data lake (CDL), which provides a data basis for subsequent intelligent recommendation and distribution.

[0003] In terms of data docking, the system realizes two-way data interaction with the HIS through WebService, an application programming interface (API) or an intermediate library (such as MySQL / Oracle), and key data fields include user basic information, a department for treatment, examination items, drug prescriptions and the like. These data provide a basis for building a user demand tag library, and the named entity recognition technology (NER) in the natural language processing technology (NLP) is used to extract key entities such as “medicine” “imaging examination” “drug name” “examination type” and the like from diagnosis and treatment records. According to the extracted key information, the user demand is classified as “medicine bag” or “examination report bag”.

[0004] For example, the product recommendation method, device, computer equipment and storage medium disclosed in Chinese patent application No. CN115757958B include: obtaining sample portrait data of a plurality of sample objects, each sample portrait data covering a plurality of object attribute types, and the coverage of different sample portrait data corresponding to the object attribute types being the same; determining the repetition degree corresponding to each object attribute type; obtaining target portrait data of a target object, and determining the distribution proportion of each product recommendation algorithm according to the repetition degree corresponding to each object attribute type and the target object attribute type corresponding to the effective data contained in the target portrait data; and determining the target virtual product recommended to the target object from all virtual products according to the distribution proportion of each product recommendation algorithm through each product recommendation algorithm.

[0005] For example, the auxiliary use method, device and equipment of learning electronic products and medium disclosed in Chinese patent application No. CN120470184A include: in the case that the use data of a user in a learning electronic product is obtained, based on the obtained use data, the predicted learning time information of the user for each learning module is determined, the predicted learning time information can be used to indicate the time required by the user to learn a to-be-learned content item in a learning module, based on the predicted learning time information of the user for each learning module, the time allocation recommendation information is determined, and the user is recommended to allocate learning time on different learning modules, and the content of each learning module is learned in a targeted manner.

[0006] The above-mentioned technology at least has the following technical problems: In the prior art, the data synchronization technology between the hospital information system and the code scanning and bag taking system may have limitations. For example, some synchronization methods may be based on timing batch processing rather than real-time triggering update, resulting in that the code scanning and bag taking system cannot obtain the latest information in time within the timing period and can only wait for the next synchronization period to come, thereby causing data synchronization delay. Moreover, if the hospital network bandwidth is limited and cannot meet the demand of real-time transmission of a large amount of data, data congestion and transmission delay may occur. Especially during the peak period of hospital business (such as the peak period of seasonal influenza), the network bandwidth is occupied by a large amount of data, and the speed of the code scanning and bag taking system to obtain data will be affected, resulting in that the user cannot obtain the latest information in time when scanning the code.

[0007] In addition, the intelligent recommendation algorithm does not fully consider the actual needs of the user and the real-time state of the guidance platform, resulting in that the recommended bag type or quantity does not meet the user's expectation, and the user may need to operate multiple times to obtain the appropriate bag, and there is a problem of poor real-time performance of the intelligent recommendation and distribution of the hospital code scanning and guidance platform. SUMMARY

[0008] In order to solve the technical problem of poor real-time intelligent recommendation and distribution of the hospital code scanning guidance platform in the prior art, the embodiment of the present application provides a hospital code scanning guidance platform intelligent recommendation and distribution method based on diagnosis and treatment information retrieval. In one aspect, a hospital code scanning guidance platform intelligent recommendation and distribution method based on diagnosis and treatment information retrieval is provided, which comprises the following steps: S1, determining whether to directly perform bag taking recommendation and distribution based on a bag taking timeliness parameter reflecting the bag taking timeliness state, if yes, retrieving user diagnosis and treatment data containing diagnosis and treatment information from a platform authorized by the user after the user's two-dimensional code is scanned on the guidance platform, otherwise, performing S2; S2, performing an evaluation of the degree of data synchronization between the platform authorized by the user and the platform for bag taking recommendation and distribution, and selecting a code scanning optimization measure for improving code scanning real-time according to the synchronization evaluation result and the code scanning parameter after the user's two-dimensional code is scanned on the guidance platform, and retrieving user diagnosis and treatment data from the platform authorized by the user; S3, performing bag taking recommendation according to the user diagnosis and treatment data, and determining whether to perform bag taking recommendation optimization for improving the effectiveness of bag taking recommendation based on a bag taking parameter reflecting the bag taking accuracy.

[0009] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects: 1. The bag taking timeliness parameter is used to determine whether to directly perform bag taking recommendation and distribution, which can flexibly adjust the process according to the actual situation, avoid the failure of the user's multiple attempts to take a bag due to equipment or network problems, reduce invalid recommendations, directly retrieve user diagnosis and treatment data, reduce unnecessary resource waste, improve the operation efficiency of the guidance platform bag taking recommendation and distribution, provide a basis for subsequent accurate bag taking recommendation, and then if not directly recommended, perform data real-time synchronization evaluation to ensure the timeliness of the user diagnosis and treatment data update, improve the accuracy of the bag taking intelligent recommendation, select a code scanning optimization measure based on the synchronization evaluation result and the code scanning parameter, effectively solve the bag taking recommendation delay or error problem that may be caused by different data synchronization or poor code scanning real-time, improve the efficiency and accuracy of the code scanning process, make the user diagnosis and treatment data timely and accurately acquired, provide timely and effective data support for subsequent bag taking recommendation, and finally perform bag taking recommendation according to the user diagnosis and treatment data, which can provide personalized bag taking suggestions according to the actual diagnosis and treatment situation of the user, meet the different needs of the user, determine whether to perform bag taking recommendation optimization for improving the effectiveness of bag taking recommendation based on the bag taking parameter reflecting the bag taking accuracy, further improve the accuracy and effectiveness of the bag taking recommendation, reduce the situation that the user takes an unsuitable bag, and improve the utilization efficiency of the hospital guidance platform resources.

[0010] 2. By implementing different measures based on real-time data synchronization, the adaptability and stability of bag removal recommendations are improved. When the data synchronization level is high, bag removal recommendations are made directly without optimizing user medical data retrieval, which improves the efficiency of bag removal recommendations. When the data synchronization level is average, bag removal recommendations are made after optimizing user medical data retrieval, which improves the accuracy of bag removal recommendations. When the data synchronization level is low, anomalies are promptly indicated, and users are guided to use another triage platform instead of this one to avoid incorrect recommendations. The bag removal recommendation result is then confirmed with the user, giving them the right to choose. Finally, the appropriate message middleware is selected based on the data throughput to better transmit user medical data, improve the performance and response speed of bag removal recommendations, and reasonably store user medical data with expiration times to avoid excessive storage space consumption by user medical data while ensuring the timeliness and accuracy of user medical data. Network status is monitored in a timely manner, and appropriate measures are taken when network anomalies occur. To avoid data retrieval failures or incorrect recommendations due to network issues, the system dynamically adjusts the interface call frequency based on platform load and data synchronization levels. This prevents performance issues caused by excessively frequent or insufficient interface calls, thus improving resource utilization. User medical data is then retrieved according to the obtained interface call frequency to ensure its timeliness and accuracy. A circuit breaker is triggered when the response time is too long, pausing data retrieval to prevent overall performance degradation due to slow responses from individual requests and to prevent cache avalanche effects. Different measures are taken based on the circuit breaker duration to prevent further deterioration of the recommendation system. Finally, the version number is used to determine if the user medical data is synchronized, ensuring that the data used for recommendation is up-to-date and improving its accuracy and reliability.

[0011] 3. By comparing the bag-retrieving recommendation accuracy, it is possible to quickly determine whether the bag-retrieving recommendation needs optimization. If no optimization is needed, unnecessary optimization operations are avoided. Otherwise, through a reinforcement learning mechanism, the bag-retrieving recommendation strategy can be automatically adjusted according to the actual bag-retrieving recommendation accuracy, continuously optimizing the bag-retrieving recommendation to better meet user needs. By mapping the initial reward value and penalty intensity, the direction of bag-retrieving recommendation optimization can be provided. The initial reward value reflects the positive incentive level corresponding to the current bag-retrieving recommendation accuracy, and the penalty intensity reflects the negative adjustment level required due to inaccurate bag-retrieving recommendations, accelerating the convergence speed of bag-retrieving recommendation optimization and improving the accuracy and effectiveness of bag-retrieving recommendations. Then, when the bag-retrieving recommendation accuracy meets the requirements, by promptly detecting bag-retrieving time anomalies and associating them with inventory, it is possible to prevent users from having difficulty retrieving bags due to insufficient inventory. Finally, by selecting a prompting strategy based on inventory levels, it is possible to ensure that users can retrieve bags smoothly and promptly remind preset personnel to replenish inventory, avoiding the situation where subsequent users cannot retrieve bags due to further inventory reduction, thus reducing the impact of insufficient inventory on users. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 One of the flowcharts for intelligent recommendation and allocation in a hospital QR code-based triage platform provided in this application embodiment; Figure 2 The second flowchart of the intelligent recommendation and allocation process of the hospital QR code-based triage platform provided in this application embodiment; Figure 3 A flowchart illustrating the intelligent recommendation and allocation method for a hospital QR code-based triage platform based on medical information retrieval provided in this application embodiment; Figure 4 A flowchart illustrating the optimization of user medical data retrieval in Embodiment 1 of this application is provided. Figure 5 The flowchart for the optimization of user diagnosis and treatment data retrieval in Embodiment 2 provided in this application is shown. Detailed Implementation

[0014] This application provides an intelligent recommendation and allocation method for hospital QR code-based triage platforms based on medical information retrieval. This solves the problem of poor real-time performance in existing hospital QR code-based triage platforms. The method uses a bag-removal timeliness parameter to determine whether to directly recommend and allocate bags. If so, it directly retrieves the user's medical data; otherwise, it assesses the degree of data synchronization between platforms. After the triage platform scans the user's QR code, it selects scanning optimization measures to improve scanning real-time performance. Simultaneously, it retrieves the user's medical data from the user's authorized platforms. Finally, based on bag-removal parameters, it determines whether to optimize bag-removal recommendations to improve their effectiveness, thus improving the real-time performance of intelligent recommendation and allocation in hospital QR code-based triage platforms.

[0015] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0016] like Figure 1 One of the flowcharts shown is the intelligent recommendation and allocation process of the hospital's QR code-based triage platform. Figure 2The flowchart shown is the second part of the intelligent recommendation and allocation process for the hospital's QR code-based triage platform. In this flowchart, '1' represents retrieving user medical data. The specific logic is as follows: If the number of users with a duration longer than the reference bag removal time within a preset time period is greater than the number of users with abnormal bag removal (first reference), then a pre-set notification of platform abnormality is issued to the pre-set personnel at a first notification frequency. If the number of users with a duration longer than the reference bag removal time within a preset time period is greater than the number of users with abnormal bag removal (second reference), then a pre-set notification of platform abnormality is issued to the pre-set personnel at a second notification frequency. Otherwise, if the average total bag removal time for users within the preset time period is greater than the reference bag removal time, then the real-time data synchronization index is obtained; otherwise, user medical data is retrieved. After obtaining the real-time data synchronization index, the triage platform scans the user's... If the real-time data synchronization index after scanning the QR code is not greater than the maximum real-time data synchronization index threshold, and the scanning time is greater than the reference scanning time, then the QR code information is processed in parallel. If the real-time data synchronization index is greater than the maximum real-time data synchronization index threshold, and the scanning time is greater than the reference scanning time, then the user is prompted to scan the code again and re-acquire the QR code information. If the scanning time is not greater than the reference scanning time, then no scanning optimization measures are taken. If the optimized scanning time is still greater than the reference scanning time, and the obtained QR code information is incorrect, then the current bag retrieval process ends; otherwise, the abnormal scanning result is reported to the preset personnel at the first prompt frequency. At the same time, the user's medical data is retrieved from the user-authorized platform. If the data is real-time synchronized... If the real-time synchronization index is greater than the maximum real-time data synchronization index threshold, then without optimizing the user's medical data retrieval, bag removal is directly recommended and the user is informed of the recommendation. If the real-time data synchronization index is greater than the minimum real-time data synchronization index threshold, then the user's medical data retrieval is optimized before recommending bag removal, and the user is informed of the recommendation. Otherwise, an abnormal bag removal recommendation result is sent to the preset personnel at the first prompt frequency, and the user is prompted not to use this guidance platform but to use another guidance platform. If the real-time data synchronization index after optimization is not greater than the maximum real-time data synchronization index threshold, then an abnormal bag removal recommendation result is sent to the preset personnel at the second prompt frequency, and the user is prompted... Users who do not use this triage platform will use another one. Bag retrieval recommendations will be made based on the user's medical data. If the accuracy rate of the bag retrieval recommendation is lower than the reference accuracy rate, a reinforcement learning mechanism will be used to optimize the recommendations. If the accuracy rate is not lower than the reference accuracy rate, and the bag retrieval time is longer than the reference time, the inventory of the bag type required by the user will be obtained. If the inventory is lower than the first inventory alarm threshold, the user will be prompted to retrieve the bag, and a preset staff member will be prompted to replenish the inventory at a first prompt frequency. If the inventory is lower than the second inventory alarm threshold, the user will be prompted that there is no inventory and a different triage platform will be used. A preset staff member will be prompted to replenish the inventory at a second prompt frequency. Otherwise, the user will be prompted to retrieve the bag.

[0017] This application is used in the bag retrieval machine of a hospital's QR code-based patient guidance platform. Embodiments of this invention provide an intelligent recommendation and allocation method for a hospital's QR code-based patient guidance platform based on medical information retrieval. For example... Figure 3 The flowchart shown is for an intelligent recommendation and allocation method for a hospital QR code-based triage platform based on medical information retrieval. The processing flow of this method may include the following steps: S1 determines whether to directly recommend and allocate bag collection based on the bag collection timeliness parameter reflecting the bag collection timeliness status. This allows for flexible adjustment of the process according to the actual situation, avoiding invalid operations when direct recommendation and allocation are not suitable, thus improving the operational efficiency of bag collection recommendation and allocation and reducing unnecessary resource waste. If direct allocation is to be performed, the system scans the user's QR code on the triage platform and retrieves the user's medical data containing medical information from the user's authorized platform for subsequent bag collection recommendations based on the user's medical data. Otherwise, S2 is executed. The bag collection timeliness parameter includes the number of users whose bag collection time exceeds the reference bag collection time within a preset time period and the average total bag collection time of users within the preset time period. The user's medical data includes, but is not limited to, the user's department, laboratory examination information, and drug prescription information.

[0018] S2 assesses the degree of data synchronization between the user-authorized platform (such as a hospital information system) and the platform used for bag retrieval recommendation and allocation (such as a triage platform). After the triage platform scans the user's QR code, it selects scanning optimization measures to improve the real-time performance of the scan based on the synchronization assessment results and scanning parameters (i.e., scanning time). This effectively solves the problem of delayed or incorrect bag retrieval recommendations that may be caused by data asynchrony or poor scanning real-time performance. At the same time, regardless of whether scanning optimization is performed, the user's medical data is retrieved from the user-authorized platform. This improves the efficiency and accuracy of the scanning process, enabling the timely and accurate acquisition of user medical data.

[0019] S3 recommends bag removal based on user medical data, providing personalized bag removal suggestions to meet different user needs. If medication prescription information is retrieved from the medical information, a medication bag is recommended; if laboratory test information is retrieved, a test report bag is recommended. Based on bag removal parameters reflecting accuracy (i.e., bag removal recommendation accuracy rate and bag removal time), it determines whether to optimize bag removal recommendations to improve their effectiveness, further improving the accuracy and effectiveness of bag removal recommendations and reducing the occurrence of users receiving unsuitable bags.

[0020] In this embodiment, the above steps enable intelligent management of the entire process from bag retrieval condition judgment and data acquisition optimization to bag retrieval recommendation optimization. This improves the efficiency, accuracy, and real-time performance of bag retrieval recommendation and allocation. The process can be flexibly adjusted according to the user's actual situation and system status to ensure that the user can quickly and accurately obtain a bag that meets their needs. At the same time, it also optimizes the resource management and utilization of the hospital's QR code scanning and guidance platform.

[0021] Furthermore, based on the bag-taking timeliness parameter reflecting the bag-taking timeliness status, a decision is made on whether to directly recommend and allocate bags. The specific process is as follows: Step 1: If the number of users whose bag retrieval time exceeds the reference retrieval time within the preset time period is greater than the first reference number of users with abnormal bag retrieval, it indicates a serious bag retrieval problem. The preset personnel will be notified of the platform's abnormality at the first notification frequency. If the number of users whose bag retrieval time exceeds the reference retrieval time within the preset time period is greater than the second reference number of users with abnormal bag retrieval, it indicates a minor but somewhat abnormal bag retrieval problem. The preset personnel will be notified of the platform's abnormality at the second notification frequency. Otherwise, the bag retrieval problem is not significant, and Step 2 will proceed, with the first notification frequency exceeding the second notification frequency. The reference retrieval time, the first reference number of users with abnormal bag retrieval, the second reference number of users with abnormal bag retrieval, the initial first notification frequency, and the initial second notification frequency are set by the preset personnel.

[0022] If, within each set adjustment observation period (set by preset personnel), the number of users whose bag-retrieving time exceeds the second reference number of abnormal bag-retrieving users, then the first and second prompt frequencies are gradually increased by combining the first backoff factor and the exponential backoff method to make the adjustment of the prompt frequency more reasonable and avoid excessively frequent prompts. Otherwise, the second step is executed. The first backoff factor represents the result obtained by mapping the number of users whose bag-retrieving time exceeds the reference within the preset time period into the first backoff factor mapping set. The first backoff factor mapping set is a set obtained from the preset database that represents the mapping relationship between the number of users whose bag-retrieving time exceeds the reference within the preset time period and the first backoff factor.

[0023] It's important to understand that the exponential backoff method controls the adjustment speed through an exponential function, while the backoff factor controls the magnitude of each adjustment. In the formula, F 1.n+1 F represents the first cue frequency obtained during the (n+1)th adjustment. 1.n Let β represent the first alert frequency obtained in the nth adjustment, β represent the first retreat factor, n=1,2,3,...,N, n represents the number of times the first alert frequency has been adjusted, and N represents the total number of times the first alert frequency has been adjusted.

[0024] It should be added that by setting different thresholds for the number of users with abnormal bag retrieval and corresponding notification frequencies, the system can promptly and effectively notify pre-set personnel based on the severity of the abnormality. This allows pre-set personnel to take appropriate measures based on the abnormal situation. By gradually increasing the notification frequency using a first backoff factor and an exponential backoff method, the system avoids information overload caused by frequent notifications in the early stages of an abnormality, allowing pre-set personnel to focus on handling the current problem. As the abnormality persists, the notification frequency is gradually increased to ensure that pre-set personnel do not overlook serious abnormalities, thus improving the overall stability and reliability of intelligent bag retrieval recommendation and allocation. This achieves timely and effective monitoring and handling of abnormal situations on the triage platform, while ensuring the efficiency and accuracy of bag retrieval recommendation and allocation under normal circumstances.

[0025] The second step involves assessing the data synchronization between the user's authorized platform and the platform used for bag removal recommendation and allocation. This helps identify potential data inconsistencies or data transmission delays, ensuring that the user's authorized medical information is accurately and promptly used for bag removal recommendation and allocation, thus improving the accuracy and effectiveness of bag removal recommendation and allocation. Otherwise, bag removal recommendation and allocation are performed directly, reducing unnecessary assessment steps and improving the response speed and efficiency of intelligent bag removal recommendation and allocation, providing users with a smoother bag removal experience. The average total bag removal time represents the average of the total bag removal time, which is the total time spent by the user from starting the bag removal operation to completing the entire process.

[0026] Furthermore, an assessment was conducted on the degree of data synchronization between the platform used to measure user authorization and the platform used for bag recommendation and allocation. The specific methods are as follows: The end-to-end data update duration is obtained by calculating the difference between the timestamp of the data update completed by the platform used for bag recommendation and allocation and the timestamp of the data change in the platform authorized by the user within a preset time period. The end-to-end data update duration is then compared with a reference end-to-end data update duration to obtain a data update duration comparison coefficient, which reflects the degree of difference between the actual data update duration and the reference end-to-end data update duration. The comparison processing in this application refers to ratio calculation. The reference end-to-end data update duration and the reference offline cache coefficient are set by preset personnel. An increase in the end-to-end data update duration may lead to an increase in the data change duration, thereby reducing the success rate of data synchronization changes, and may also lead to an increase in the offline cache coefficient.

[0027] The success rate of data synchronization changes is calculated by comparing the number of successful data changes within a preset time period with the total number of data changes. This results in a data synchronization change success coefficient, which reflects the success rate of change operations during data synchronization. The number of successful data changes represents the number of times data changes were successfully completed from the user-authorized platform to the platform used for bag recommendation and allocation within the preset time period. The total number of data changes represents the total number of times data changes were initiated from the user-authorized platform to the platform used for bag recommendation and allocation within the preset time period. A decrease in the data synchronization change success rate may lead to the offline cache data not being updated in a timely manner, thereby increasing the offline cache coefficient.

[0028] The offline cache coefficient is obtained by calculating the difference between the timestamp of the latest data in the user-authorized platform and the timestamp of the offline cache data in the platform used for bag retrieval recommendation and allocation within a preset time period. The offline cache coefficient is then compared with the reference offline cache coefficient to obtain the offline cache latency comparison coefficient, which reflects the degree of latency of the offline cache data relative to the latest data.

[0029] By introducing a synchronous evaluation balance parameter, weights are assigned to the data update duration comparison coefficient, the data synchronization change success coefficient, and the offline cache latency comparison coefficient. Then, the weighted data update duration comparison coefficient and the weighted offline cache latency comparison coefficient are inversely proportionalized and coupled with the weighted data synchronization change success coefficient to obtain the real-time data synchronization index. The specific constraint expression for the real-time data synchronization index is as follows: In the formula, T represents the real-time data synchronization index, T1 represents the data update time comparison coefficient, T2 represents the data synchronization change success coefficient, T3 represents the offline cache latency comparison coefficient, α1 represents the first synchronization evaluation balance parameter, α2 represents the second synchronization evaluation balance parameter, and α3 represents the third synchronization evaluation balance parameter.

[0030] The synchronization evaluation balance parameters involved are obtained from a preset database and set by preset personnel. Specifically, they include a first synchronization evaluation balance parameter, a second synchronization evaluation balance parameter, and a third synchronization evaluation balance parameter, the sum of which is 1. For example, the data update duration comparison coefficient and the first synchronization evaluation balance parameter form a corresponding first synchronization evaluation balance parameter mapping set. The real-time data update duration comparison coefficient is input into the first synchronization evaluation balance parameter mapping set to obtain the first synchronization evaluation balance parameter. The first synchronization evaluation balance parameter represents the degree of influence of the data update duration comparison coefficient on the real-time data synchronization index. The second synchronization evaluation balance parameter represents the degree of influence of the data synchronization change success coefficient on the real-time data synchronization index. The third synchronization evaluation balance parameter represents the degree of influence of the offline cache latency comparison coefficient on the real-time data synchronization index.

[0031] In this embodiment, the above steps comprehensively and quantitatively evaluate the degree of data synchronization between the user-authorized platform and the platform used for bag recommendation and allocation. This helps to promptly identify problems in the data synchronization process, such as data update delays, thereby enabling targeted optimization and improvement of user medical data acquisition and enhancing the real-time performance of user medical data acquisition.

[0032] Furthermore, based on the synchronous evaluation results and scanning parameters, optimization measures to improve the real-time performance of scanning are selected. The specific process is as follows: If the real-time data synchronization index is not greater than the maximum real-time data synchronization index threshold, and the scanning time is greater than the reference scanning time, then the QR code information is processed in parallel. Parallel processing means splitting the QR code into a preset number of regions and then decoding each region's QR code in parallel. The preset number of regions represents the result obtained by mapping the real-time data synchronization index and scanning time into a QR code splitting number mapping set. The maximum real-time data synchronization index threshold, the minimum real-time data synchronization index threshold, and the reference scanning time are set by preset personnel. The scanning time represents the time elapsed from the start of scanning the QR code to successfully obtaining the QR code information. The QR code splitting number mapping set is a set obtained from a preset database representing the mapping relationship between the real-time data synchronization index, scanning time, and the number of QR code splits. By splitting the QR code into multiple regions for parallel decoding, multi-threaded processing capabilities are fully utilized, significantly shortening the overall decoding time. When the real-time data synchronization index is low and the scanning time is long, parallel processing can effectively improve scanning real-time performance, reduce user waiting time, and enhance user experience.

[0033] If the real-time data synchronization index is greater than the maximum real-time data synchronization index threshold, and the scanning time is greater than the reference scanning time, the user will be prompted to scan the code again and obtain the QR code information again. When the real-time data synchronization index is high and the scanning time is long, there may be problems such as data synchronization delay or interference affecting the scanning. Prompting the user to scan the code again can rebuild the scanning environment, which may bypass the current problem, obtain the correct QR code information, improve the scanning success rate, and ensure the smooth progress of subsequent processes.

[0034] If the scanning time is not greater than the reference scanning time, no scanning optimization measures will be taken to avoid unnecessary consumption of system resources and increase in operational complexity.

[0035] If the optimized scanning time is still longer than the reference scanning time, and the obtained QR code information is incorrect, it may mean that the current user is maliciously scanning the code. The current bag retrieval process will end, and the problem of obtaining incorrect QR code information will persist until the next user scans the code. In this case, the preset personnel will be notified of the QR code information acquisition error. Otherwise, the abnormal scanning result will be reported to the preset personnel at the first notification frequency. This allows the preset personnel to understand the operation status of the triage platform in a timely manner, investigate the cause of the problem, and ensure the high efficiency of the intelligent bag retrieval recommendation.

[0036] The QR code scanning optimization measures include parallel processing of QR code information and re-acquiring QR code information.

[0037] Furthermore, after evaluating the degree of data synchronization between the platform used to measure user authorization and the platform used for bag retrieval recommendation and allocation, the process also includes determining whether to take user medical data retrieval optimization measures to improve the effectiveness of bag retrieval recommendations. The specific process is as follows: If the real-time data synchronization index is greater than the maximum real-time data synchronization index threshold, user medical data retrieval optimization is not performed; instead, bag removal recommendation is made directly and the user is informed of the recommendation result, thus improving the efficiency of bag removal recommendation. If the real-time data synchronization index is greater than the minimum real-time data synchronization index threshold, user medical data retrieval optimization is performed before bag removal recommendation, and the user is informed of the recommendation result, thus improving the accuracy of bag removal recommendation. Otherwise, a bag removal recommendation error prompt is sent to the preset personnel at the first prompt frequency, instructing the user not to use this bag removal machine but to use another one, avoiding bag removal recommendation errors caused by data asynchrony between the user-authorized platform and the platform used for bag removal recommendation and allocation. The maximum real-time data synchronization index threshold is greater than the minimum real-time data synchronization index threshold. Different measures are taken based on different real-time data synchronization indices to make bag removal recommendations more accurate.

[0038] If the real-time synchronization index of the optimized user medical data retrieval is not greater than the maximum real-time synchronization index threshold, then a bag-removal recommendation error message will be sent to the preset personnel at the second prompt frequency, and the user will be prompted not to use the bag-removal machine here, but to use another bag-removal machine.

[0039] The process of confirming the bag recommendation result with the user means that if the user agrees with the bag recommendation result after it is shown to the user, a bag will be assigned to the user according to the recommendation result; otherwise, the user can choose the bag type, giving the user the right to choose and ensuring the accuracy of bag assignment.

[0040] like Figure 4The flowchart of the user medical data retrieval optimization in Embodiment 1 is shown below. The specific logic is as follows: If the data throughput is greater than the reference throughput, the first message middleware is used in combination with the number of partitions; otherwise, the second message middleware is used. User medical data within a preset storage time is stored. If the storage time of user medical data is greater than the preset storage time, the corresponding user medical data automatically expires. A set data packet is sent to the user-authorized platform. After receiving the set data packet, the user-authorized platform returns a response information. If the platform used for bag retrieval recommendation and allocation receives the response information within the preset time period, it indicates that the network status is normal; otherwise, it indicates that the network status is abnormal. When the network recovers, the unfinished bag retrieval process is completed. If the network connection is successful within the maximum number of retries, a network recovery strategy is adopted; otherwise, the retry interval is gradually increased. When the network status abnormal time reaches the network status abnormal time threshold, feedback is given to the preset personnel.

[0041] To further clarify, the user's medical data retrieval has been optimized, specifically as follows: First, if the data throughput exceeds the reference throughput, the first message middleware (Kafka) is used based on the number of partitions; otherwise, the second message middleware (RabbitMQ) is used. The number of partitions represents the result of mapping the data throughput and the real-time data synchronization index into a partition number mapping set. This partition number mapping set is a collection obtained from a preset database representing the mapping relationship between data throughput, the real-time data synchronization index, and the number of partitions. The reference throughput, preset storage time, maximum number of retries, and network status anomaly time thresholds are set by preset personnel. Selecting the message middleware based on data throughput can better optimize the data transmission process of user medical data and improve the performance and response speed of user medical data retrieval.

[0042] Secondly, user medical data is stored for a preset storage period. User medical data stored for a period longer than the preset storage period will automatically expire. The expiration time of the corresponding user medical data will be reset after each access to the user medical data. For example, the cache time of user medical data will be extended after the user scans the code. This avoids excessive user medical data occupying storage space and ensures the timeliness of user medical data.

[0043] Finally, according to the network monitoring frequency set by the preset personnel, the system sends the set data packets to the user-authorized platform. After receiving the set data packets, the user-authorized platform returns a response message. If the platform used for bag retrieval recommendation and allocation receives the response message within the preset time period, it indicates that the network status is normal; otherwise, it indicates that the network status is abnormal, and a retry strategy is adopted, and the user is informed that "network connection is in progress." By monitoring the network status in a timely manner and adopting a retry strategy when the network is abnormal, the system ensures the normal operation of user medical data retrieval and avoids failure to obtain user medical data or incorrect recommendations due to network problems.

[0044] The retry strategy specifically involves reconnecting to the network according to a pre-set fast retry interval (i.e., the time interval for reconnecting to the network). If the network connection is successful within the maximum number of retries (i.e., the maximum number of allowed fast retries), a network recovery strategy is adopted. This involves completing the unfinished bag retrieval process based on the recorded operation status and scanning information, enabling the connection to be restored as quickly as possible during brief network anomalies to ensure the normal operation of user medical data retrieval. Otherwise, the retry interval is gradually increased by combining the second backoff factor and the exponential backoff method (the specific process is consistent with the first prompt frequency adjustment process mentioned above) to avoid network congestion caused by frequent retries. When the network status anomaly time reaches the network status anomaly time threshold, a "network connection anomaly" message is sent to the pre-set personnel to facilitate timely handling of network problems.

[0045] The second backoff factor represents the result obtained by mapping the real-time data synchronization index into the second backoff factor mapping set; the second backoff factor mapping set is a set obtained from a preset database that represents the mapping relationship between the real-time data synchronization index and the second backoff factor.

[0046] Furthermore, based on the bag-picking parameters that reflect the accuracy of bag picking, it is determined whether to perform bag-picking recommendation optimization to improve the effectiveness of bag picking recommendations. The specific process is as follows: If the bag-removal recommendation accuracy is lower than the reference bag-removal recommendation accuracy (set by preset personnel), a reinforcement learning mechanism is used to optimize the bag-removal recommendation by combining the initial reward value and the penalty intensity. Specifically, based on the current bag-removal recommendation accuracy and bag-removal time, an action (such as increasing the reward or increasing the penalty) is selected. After executing the action (such as increasing the reward or increasing the penalty), a reward is assigned based on the bag-removal recommendation accuracy and bag-removal time. For example, if the bag-removal time is greater than the reference bag-removal time, the timeout minutes and the penalty intensity are multiplied to obtain the penalty score. Then, the experience (state, action, reward) is stored in the experience pool. Finally, the training model is sampled from the experience pool. The system updates its bag recommendation strategy based on bag type. Bag recommendation accuracy is represented by the ratio of recommended bag types to the user's actual bag needs. By comparing bag recommendation accuracy, it's easy to quickly determine if the system needs optimization. A low accuracy indicates poor recommendation performance, requiring improvement measures; otherwise, it proceeds to the next step, avoiding unnecessary optimization and improving efficiency. Through reinforcement learning, the system automatically adjusts initial reward and penalty levels based on actual recommendation performance, continuously optimizing bag recommendations to better meet user needs and improve accuracy.

[0047] The initial reward value represents the result obtained by mapping the bag-picking recommendation accuracy and the current initial reward value into the initial reward value mapping set. The initial reward value mapping set is a set obtained from a preset database that represents the mapping relationship between the bag-picking recommendation accuracy and the current initial reward value and the mapped initial reward value. The penalty intensity represents the result obtained by mapping the bag-picking recommendation accuracy and the current penalty intensity into the penalty intensity mapping set. The penalty intensity mapping set is a set obtained from a preset database that represents the mapping relationship between the bag-picking recommendation accuracy and the current penalty intensity and the mapped penalty intensity.

[0048] If the bag-recommendation accuracy rate is not less than the reference bag-recommendation accuracy rate, and the bag-recommendation time is greater than the reference bag-recommendation time, then the inventory quantity of the bag type required by the user is obtained. If the inventory quantity is less than the first inventory quantity alarm threshold, the user is prompted to retrieve the bag, and the preset personnel are prompted to replenish the inventory at the first prompt frequency. The first and second inventory quantity alarm thresholds are set by the preset personnel. When the bag-recommendation accuracy rate meets the requirements, the user's bag-recommendation time is further checked. If the bag-recommendation time exceeds the reference value, it indicates a possible inventory-related problem. Therefore, the inventory quantity of the bag type required by the user needs to be obtained, linking the bag-recommendation time with the inventory quantity. This provides a basis for subsequent prompts and handling based on the inventory situation. When the inventory quantity is lower than the first inventory quantity alarm threshold, the user is prompted to retrieve the bag, and the preset personnel are prompted to replenish the inventory at the first prompt frequency. This ensures that the user can retrieve the bag smoothly at the current time, and also promptly reminds the preset personnel to replenish the inventory, preventing further inventory reduction that could lead to the user being unable to retrieve the bag later.

[0049] If the inventory level is less than the second inventory alarm threshold, the user is notified that there is no stock and the bag-retrieving machine should be replaced. Pre-set personnel are prompted to replenish the inventory at the second notification frequency. Otherwise, the user is prompted to retrieve a bag. When the inventory level is below the second inventory alarm threshold, it indicates that the inventory is very tight. In this case, the user is notified that there is no stock and it is recommended to replace the bag-retrieving machine. Pre-set personnel are prompted to replenish the inventory at the second notification frequency, promptly informing the user of the inventory status and preventing the user from wasting time waiting to retrieve a bag without their knowledge. The higher notification frequency also encourages pre-set personnel to replenish the inventory as quickly as possible, reducing the impact of insufficient inventory on the user. When the inventory level is not less than the first inventory alarm threshold, it indicates that the inventory is sufficient. The user only needs to be prompted to retrieve a bag, simplifying the operation process and avoiding unnecessary prompts and interference.

[0050] The first inventory level alarm threshold is greater than the second inventory level alarm threshold.

[0051] Example 2: Figure 5The flowchart for optimizing user medical data retrieval in Embodiment 2 shows the following specific logic: User medical data is retrieved from the user-authorized platform according to the interface call frequency; if the response time for retrieving user medical data is greater than the reference response time, a circuit breaker is automatically triggered; otherwise, the retrieval of user medical data continues; if the circuit breaker time is less than the reference circuit breaker time after triggering, the retrieval of user medical data is paused, and feedback is given to the user; otherwise, user medical data is retrieved again according to the interface call frequency, and the user is prompted to retrieve the bag at the manual window, and the information query error is reported to the preset personnel; if a version number is not present in the user medical data after retrieval, a version number is added to the user medical data, and the version number of the user medical data is then judged; otherwise, the version number of the user medical data is judged directly; specifically, judging the version number of the user medical data involves: if the version number of the user medical data in the platform used for bag retrieval recommendation and allocation is lower than the version number of the user medical data in the user-authorized platform, the user medical data with the corresponding version number is retrieved from the user-authorized platform; otherwise, it indicates that the user medical data is synchronized.

[0052] If there is a sudden surge in traffic as described in Example 1 (such as during a seasonal flu peak), it can lead to message backlog, increased latency, and even queue crashes. Without a proper caching strategy, cache penetration or cache avalanche may occur. Optimization of user medical data retrieval, specifically: The interface call frequency is obtained by mapping the load and real-time data synchronization index of the platform used for bag retrieval recommendation and allocation into the interface call frequency mapping set. This avoids system performance problems caused by too frequent or too few interface calls and improves the resource utilization of user medical data retrieval. The interface call frequency mapping set is a collection obtained from a preset database that represents the mapping relationship between the load and real-time data synchronization index of the platform used for bag retrieval recommendation and allocation and the interface call frequency.

[0053] By retrieving user medical data from the user-authorized platform according to the frequency of API calls, the timeliness and accuracy of user medical data are ensured, while avoiding system performance issues caused by too frequent or too infrequent API calls, thus improving the system's resource utilization.

[0054] If the response time for obtaining user medical data is longer than the reference response time, the circuit breaker will be automatically triggered to avoid a decline in overall system performance due to slow response of individual requests and to prevent cache avalanche effect; otherwise, the acquisition of user medical data will continue. The reference response time and reference circuit breaker time are set by preset personnel.

[0055] Different measures are taken depending on the circuit breaker time. If the circuit breaker time is less than the reference circuit breaker time, the acquisition of user medical data is suspended and the user is informed that "information query is in progress" to avoid the avalanche effect. Otherwise, the user medical data is reacquired according to the interface call frequency, and the user is prompted to pick up the bag at the manual window. The preset personnel are informed that "information query error" to ensure the availability of user medical data.

[0056] If the user's medical data does not contain a version number, then add a version number to the user's medical data and then judge the version number of the user's medical data; otherwise, judge the version number of the user's medical data directly. If the user's medical data is stored in a database, a field specifically for storing the version number can be added to the table containing the user's medical data, such as version_number. The data type of the field can be an integer (such as INT), a string (such as VARCHAR, which can store a version number format like "1.0.0"), etc. The specific choice depends on the version number representation rules and comparison requirements.

[0057] The version number of user medical data is determined as follows: Since user medical data is transferred from the user-authorized platform to the platform used for bag retrieval recommendation and distribution, the version number of the user medical data on the platform used for bag retrieval recommendation and distribution will not be higher than the version number on the user-authorized platform. If the version number of the user medical data on the platform used for bag retrieval recommendation and distribution is lower than the version number on the user-authorized platform, it indicates that the user medical data is out of sync. In this case, the user medical data with the corresponding version number is retrieved from the user-authorized platform. Otherwise, it indicates that the user medical data is synchronized. For example, the version number consists of a major version number, a minor version number, and a revision number, separated by a period. In this case, the version number of 1.2.3 is lower than that of 2.2.4. Determining whether the user medical data is synchronized by using the version number ensures that the user medical data used for bag retrieval recommendation is up-to-date, improving the accuracy and reliability of bag retrieval recommendations.

[0058] In summary, this application embodiment determines whether to directly recommend and allocate bag collection based on the bag collection timeliness parameter. This allows for flexible process adjustments based on actual conditions, avoiding multiple failed bag collection attempts by users due to equipment or network issues, reducing invalid recommendations, and directly retrieving user medical data. This reduces unnecessary resource waste and improves the operational efficiency of bag collection recommendation and allocation on the triage platform, providing a foundation for accurate subsequent bag collection recommendations. If direct recommendation is not performed, real-time data synchronization and evaluation are conducted to ensure the timeliness of updates to the user medical data, improving the accuracy of intelligent bag collection recommendations. Based on the synchronization evaluation results and scanning parameters, scanning optimization measures are selected, effectively solving the problem of data inconsistencies. The poor real-time performance of synchronization or scanning may lead to delays or errors in bag retrieval recommendations. Improving the efficiency and accuracy of the scanning process ensures timely and accurate acquisition of user medical data, providing timely and effective data support for subsequent bag retrieval recommendations. Finally, bag retrieval recommendations based on user medical data provide personalized suggestions according to the user's actual medical condition, meeting different user needs. Based on bag retrieval parameters reflecting accuracy, the system determines whether to optimize bag retrieval recommendations to improve their effectiveness, further enhancing the accuracy and effectiveness of recommendations, reducing the occurrence of users receiving unsuitable bags, and improving the utilization efficiency of hospital triage platform resources.

[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0063] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0064] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0065] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0066] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0067] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0068] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0069] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0070] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0071] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0072] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0073] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0075] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0076] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for intelligent recommendation and allocation of hospital QR code-based triage platforms based on medical information retrieval, characterized in that: The method includes: S1, based on the bag retrieval timeliness parameter reflecting the bag retrieval timeliness status, determine whether to directly recommend and allocate bag retrieval. If to do so, after scanning the user's QR code on the triage platform, retrieve the user's medical data containing medical information from the platform authorized by the user. Otherwise, execute S2. S2, evaluate the degree of data synchronization between the platform authorized by the user and the platform used for bag recommendation and allocation. After scanning the user's QR code on the triage platform, select scanning optimization measures to improve the real-time performance of scanning based on the synchronization evaluation results and scanning parameters. At the same time, retrieve the user's medical data from the platform authorized by the user. S3 makes bag removal recommendations based on user medical data, and determines whether to optimize the bag removal recommendations to improve their effectiveness based on bag removal parameters that reflect the accuracy of bag removal.

2. The intelligent recommendation and allocation method for a hospital QR code-based triage platform based on medical information retrieval as described in claim 1, characterized in that: The specific process for determining whether to directly recommend and allocate bags based on the bag retrieval timeliness parameter reflecting the bag retrieval timeliness status is as follows: Step 1: If the number of users whose bag removal time exceeds the reference bag removal time within the preset time period is greater than the number of users with abnormal bag removal at the first reference time, then the preset personnel guidance platform is notified of an abnormality at the first prompt frequency. If the number of users whose bag removal time exceeds the reference bag removal time within the preset time period is greater than the number of users with abnormal bag removal at the second reference time, then the preset personnel guidance platform is notified of an abnormality at the second prompt frequency. Otherwise, proceed to Step 2, where the first prompt frequency is greater than the second prompt frequency. If the number of users whose bag retrieval time exceeds the reference bag retrieval time exceeds the number of abnormal users whose bag retrieval time exceeds the second reference bag retrieval time within each set adjustment observation period, the first prompt frequency and the second prompt frequency will be gradually increased by combining the first backoff factor and the exponential backoff method; otherwise, the second step will be executed. The first backoff factor represents the result obtained by mapping the number of users whose bag retrieval time exceeds the reference bag retrieval time within the preset time period into the first backoff factor mapping set. The second step involves assessing the data synchronization between the user's average total bag-taking time within the preset time period and the reference bag-taking time. Otherwise, bag-taking recommendation and allocation are performed directly.

3. The intelligent recommendation and allocation method for a hospital QR code-based triage platform based on medical information retrieval according to claim 1, characterized in that: The evaluation of the degree of data synchronization between the platform for measuring user authorization and the platform for bag recommendation and allocation is conducted using the following method: The end-to-end data update duration is output based on the timestamp of data change in the user-authorized platform within a preset time period to the timestamp of data update completed by the platform used for bag recommendation and allocation. The end-to-end data update duration is compared with the reference end-to-end data update duration to obtain the data update duration comparison coefficient. The number of successful data changes within a preset time period is compared with the total number of data changes to obtain the data synchronization change success coefficient. The offline cache coefficient is output based on the timestamp of the offline cache data in the platform used for bag recommendation and allocation within a preset time period and the timestamp of the latest data in the user-authorized platform. The offline cache coefficient is compared with the reference offline cache coefficient to obtain the offline cache latency comparison coefficient. By introducing a synchronous evaluation balance parameter, weights are assigned to the data update duration comparison coefficient, the data synchronization change success coefficient, and the offline cache latency comparison coefficient. The weighted data update duration comparison coefficient and the weighted offline cache latency comparison coefficient are then inversely proportionalized and coupled with the weighted data synchronization change success coefficient to obtain the real-time data synchronization index.

4. The intelligent recommendation and allocation method for a hospital QR code-based triage platform based on medical information retrieval according to claim 3, characterized in that: The specific process for selecting scanning optimization measures to improve scanning real-time performance based on the synchronous evaluation results and scanning parameters is as follows: If the real-time data synchronization index is not greater than the maximum real-time data synchronization index threshold, and the scanning time is greater than the reference scanning time, then the QR code information is processed in parallel. The parallel processing of QR code information means splitting the QR code into a preset number of regions and then decoding the QR code in each region in parallel. The preset number of regions represents the result obtained by mapping the real-time data synchronization index and the scanning time into the QR code splitting number mapping set. If the real-time data synchronization index is greater than the maximum real-time data synchronization index threshold, and the scanning time is greater than the reference scanning time, the user will be prompted to scan the code again and obtain the QR code information again. If the scanning time is not greater than the reference scanning time, no scanning optimization measures will be taken; If the optimized scanning time is still longer than the reference scanning time, and the obtained QR code information is incorrect, then the current bag retrieval process will end; otherwise, the abnormal scanning result will be reported to the preset personnel at the first prompt frequency. The QR code scanning optimization measures include parallel processing of QR code information and re-acquiring QR code information.

5. The intelligent recommendation and allocation method for a hospital QR code-based triage platform based on medical information retrieval according to claim 3, characterized in that: After evaluating the degree of data synchronization between the platform used to measure user authorization and the platform used for bag retrieval recommendation and allocation, the process also includes determining whether to take user medical data retrieval optimization measures to improve the effectiveness of bag retrieval recommendations. The specific process is as follows: If the real-time data synchronization index is greater than the maximum real-time data synchronization index threshold, then the user's medical data retrieval optimization will not be performed, and the bag removal recommendation will be directly made and the bag removal recommendation result will be confirmed to the user. If the real-time data synchronization index is greater than the minimum real-time data synchronization index threshold, the user's medical data retrieval and optimization will be performed before bag removal recommendation is made, and the bag removal recommendation result will be confirmed to the user. Otherwise, the abnormal bag removal recommendation result will be sent to the preset personnel at the first prompt frequency, and the user will be notified. If the real-time synchronization index of the user's medical data after retrieval optimization is not greater than the maximum real-time synchronization index threshold, then send the bag-removal recommendation abnormal result to the preset personnel at the second prompt frequency and prompt the user. The abnormal bag recommendation result indicates that the bag recommendation error is caused by the data asynchrony between the user-authorized platform and the platform used for bag recommendation and allocation; The maximum real-time data synchronization index threshold is greater than the minimum real-time data synchronization index threshold.

6. The intelligent recommendation and allocation method for a hospital QR code-based triage platform based on medical information retrieval according to claim 5, characterized in that: The optimization of user medical data retrieval is as follows: If the data throughput is greater than the reference throughput, the first message middleware is used in combination with the number of partitions; otherwise, the second message middleware is used. The number of partitions represents the result obtained by mapping the data throughput and the real-time data synchronization index into the partition number mapping set. Store user medical data for a preset storage period. If the storage time of user medical data exceeds the preset storage time, the corresponding user medical data will automatically expire, and the expiration time of the corresponding user medical data will be reset after each access to the user medical data. According to the set network monitoring frequency, the set data packets are sent to the user-authorized platform. After receiving the set data packets, the user-authorized platform returns a response message. If the platform used for bag recommendation and allocation receives the response message within the preset time period, it indicates that the network status is normal; otherwise, it indicates that the network status is abnormal, and a retry strategy is adopted.

7. The intelligent recommendation and allocation method for a hospital QR code-based triage platform based on medical information retrieval as described in claim 6, characterized in that: The retry strategy is as follows: Reconnect to the network according to the set fast retry interval. If the network connection is successful within the maximum number of retries, a network recovery strategy is adopted. Otherwise, the retry interval is gradually increased by combining the second backoff factor and the exponential backoff method to avoid network congestion caused by frequent retries. When the network status abnormal time reaches the network status abnormal time threshold, feedback is given to the preset personnel. The second backoff factor represents the result obtained by mapping the real-time data synchronization index into the second backoff factor mapping set; The network recovery strategy refers to completing the unfinished bag retrieval process based on the recorded operation status and scanning information.

8. The intelligent recommendation and allocation method for a hospital QR code-based triage platform based on medical information retrieval according to claim 6, characterized in that: The optimization of user medical data retrieval also includes: The interface call frequency is obtained by mapping the interface call frequency into a set based on the load and real-time data synchronization index of the platform used for bag recommendation and allocation. Retrieve user medical data from the user-authorized platform based on the frequency of API calls; If the response time for acquiring user medical data is longer than the reference response time, the circuit breaker will be automatically triggered; otherwise, the acquisition of user medical data will continue. If the circuit breaker time is less than the reference circuit breaker time, the acquisition of user medical data will be suspended and the user will be notified. Otherwise, the user medical data will be acquired again according to the interface call frequency. At the same time, the user will be prompted to pick up the bag at the manual window and the preset personnel will be notified of "information query error".

9. The intelligent recommendation and allocation method for a hospital QR code-based triage platform based on medical information retrieval as described in claim 8, characterized in that: After obtaining user medical data from the user-authorized platform according to the interface call frequency, the process also includes: If the user's medical data does not have a version number, then add a version number to the user's medical data and then check the version number of the user's medical data; otherwise, check the version number of the user's medical data directly. The determination of the version number of user medical data is as follows: if the version number of user medical data in the platform used for bag recommendation and allocation is lower than the version number of user medical data in the platform authorized by the user, then the user medical data with the corresponding version number is obtained from the platform authorized by the user; otherwise, it indicates that the user medical data is synchronized.

10. The intelligent recommendation and allocation method for a hospital QR code-based triage platform based on medical information retrieval according to claim 1, characterized in that: The process for determining whether to optimize bag-taking recommendations to improve their effectiveness based on bag-taking parameters that reflect accuracy is as follows: If the accuracy of bag-taking recommendations is lower than the reference bag-taking recommendation accuracy, a reinforcement learning mechanism will be used to optimize the bag-taking recommendations, taking into account the initial reward value and the penalty intensity. The initial reward value represents the result obtained by mapping the bag-taking recommendation accuracy and the current initial reward value into the initial reward value mapping set, and the penalty intensity represents the result obtained by mapping the bag-taking recommendation accuracy and the current penalty intensity into the penalty intensity mapping set. If the bag-recommendation accuracy rate is not less than the reference bag-recommendation accuracy rate, and the bag-recommendation time is greater than the reference bag-recommendation time, then obtain the inventory quantity of the bag type required by the user. If the inventory quantity is less than the first inventory quantity alarm threshold, then prompt the user to retrieve the bag, and prompt the preset personnel to replenish the inventory quantity according to the first prompt frequency. If the inventory level is less than the second inventory level alarm threshold, the user will be notified that there is no inventory, and the preset personnel will be prompted to replenish the inventory according to the second prompt frequency; otherwise, the user will be prompted to take the bag. The first inventory level alarm threshold is greater than the second inventory level alarm threshold.

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