Data interaction method, system and chip device for bedside payment settlement

Abstract

The application provides a bedside payment settlement data interaction method, system and chip device, and belongs to the technical field of data interaction, and specifically comprises the following steps: pushing bed and patient information needing to pay for discharge or supplement payment of arrears to a server, constructing a to-be-done list based on the bed and patient information needing to pay for discharge or supplement payment of arrears, determining a discharge payment or supplement payment of arrears task by periodically polling the to-be-done list by a robot, determining a recognition management method of the running state of the robot by an interactive control module, performing interactive processing between the robot and the server according to the active data interaction method and whether the robot belongs to an interactive management target, performing recognition processing of the running state by using the recognition management method of the running state, planning a route according to a nearest neighbor strategy, performing identity verification after arriving, and after verification, the system enters a corresponding payment process according to self-payment or medical insurance, and the matching degree of the recognition and analysis processing of the running state is ensured.

Description

Technical Field

[0001] This invention belongs to the field of data interaction technology, and in particular relates to a data interaction method, system and chip device for bedside payment settlement. Background Technology

[0002] In medical institutions, the payment of medical expenses for inpatients is a crucial part of the medical service process. However, the existing inpatient payment and settlement model suffers from cumbersome procedures and a poor user experience. Patients or their families need to queue at the inpatient department's payment window to complete the payment process. For elderly patients with limited mobility, bedridden patients after surgery, or patients in intensive care, this back-and-forth travel is extremely inconvenient. Furthermore, the repeated back-and-forth between the bed and the payment window during the payment process increases the physical burden on patients and their families, seriously affecting the patient's medical experience and the hospital's work efficiency.

[0003] To address the aforementioned technical issues, existing solutions often utilize fixed self-service terminals for payment processing, thereby improving the patient's medical experience. However, these solutions suffer from the following technical problems: Using mobile robots to process payments at the bedside can greatly improve user convenience. However, during the payment process, payment failures may inevitably occur due to external network interference or abnormal operation of the robots themselves. Therefore, determining the appropriate active interaction methods between different robots and servers to eliminate the influence of external network interference, and identifying the robots that need to be identified and analyzed based on the interaction results, thereby ensuring the reliability of the robots' operation, has become an urgent technical problem to be solved.

[0004] In summary, to solve the above-mentioned technical problems, this application provides a data interaction method, system, and chip device for bedside payment settlement. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, in the first aspect, this application provides a data interaction system for bedside payment settlement, which specifically includes: Robot, server, interactive control module; Information on beds and patients requiring discharge payment or outstanding payment is pushed to the server. The server then builds a task list based on this information. The robot periodically polls the task list to determine the discharge payment or outstanding payment task. The interaction control module determines the update strategy for the interaction control target between the robot and the server based on the interaction failure data in different locations and the interaction failure data of the associated robots in all locations. It also identifies reliable robots based on the interaction data in different locations and uses the reliable robot data and the identification results of the interaction data of reliable robots in potentially risky locations to determine the identification and management method of the robot's operating status. The robot interacts with the server based on the active data interaction method and whether it belongs to the interaction control target. It also uses the operation status identification and management method to identify the operation status. It plans a route according to the nearest neighbor strategy. After arriving, it verifies the identity. After the verification is passed, the robot queries the hospitalized patient information, pre-settlement results and pre-payment account information through the server. It displays the settlement list on the screen for the patient to check. After the patient confirms the list, the system enters the corresponding payment process according to self-pay or medical insurance.

[0006] Furthermore, the potential risk locations are locations where interaction failure data exists.

[0007] Furthermore, the method for determining the reliable robot is as follows: Based on the identification results of interaction data in different potential risk locations, the number of robot interaction failures in different potential risk locations is determined. Based on the proportion of the number of robot interaction failures in the potential risk locations to the total number of interactions, the risk association weight value of the robot in the potential risk locations is determined. The basic risk weight value of the robot is determined by using the average risk association weight value of the robot in different locations, and the reliable robot among the robots is determined by using the basic risk weight value.

[0008] Furthermore, the reliable robots in the robot category are robots whose basic risk weight values ​​meet the requirements, that is, robots whose basic risk weight values ​​are less than a certain threshold.

[0009] Furthermore, the interaction between the robot and the server is processed according to the proactive data interaction method and whether it falls under the interaction control target, specifically including: If the robot is an interactive control target, it will interact with the server in real time at different locations; If the robot is not a target of interactive control, it will actively interact with the server in accordance with the active data interaction method in potential risk locations.

[0010] Secondly, this application provides a data interaction method for bedside payment settlement, applied to the aforementioned bedside payment settlement data interaction system, specifically including: S1 uses the payment and settlement data at the bedside to identify different robot-server interaction failure data. When it determines whether interaction risk identification and analysis is needed in different locations based on the different robot interaction failure data, it proceeds to the next step. S2 determines the update strategy for the interaction control target between the robot and the server based on the interaction failure data in different locations and the associated interaction failure data of the robot in all locations. S3 uses the update strategy to determine the interaction control target in the robot, uses the interaction control target to interact with the server in real time at different locations, and determines the active data interaction method in different potential risk locations based on the interaction processing data of the interaction control target in different potential risk locations and the interaction control target data. S4 performs interaction processing between the robot and the server based on the active data interaction method and the interaction control target, and identifies reliable robots in the robot based on the identification results of interaction data in different locations. Using the reliable robot data and the identification results of the reliable robot's interaction data in potentially risky locations, an identification and management method is used to determine the robot's operating status.

[0011] Furthermore, the data on failed interactions between the robot and the server is determined based on the number of failed interactions between the robot and the server during payment settlement.

[0012] Furthermore, it was determined that interaction risks in different locations needed to be identified and analyzed, specifically including: S11 uses the interaction failure data between different robots and the server to determine the number of interaction failures between different robots and the server; S12 determines the interaction risk robots among the robots based on the number of interaction failures, and uses the interaction risk robot data to determine whether it is necessary to perform interaction risk identification and analysis processing in different locations.

[0013] Thirdly, this application provides a chip device for use in a robot. The chip device includes: a data acquisition module, a data processing module, and a control module. The data acquisition module is responsible for acquiring user data, the data processing module is responsible for executing the aforementioned data interaction method for bedside payment settlement and payment processing, and the control module is responsible for data interaction with the server for settlement information and user information.

[0014] The beneficial effects of this invention are as follows: By using interaction failure data from different locations and the associated interaction failure data of robots in all locations, the probability of interaction recognition risks due to external interference in different locations is determined. Based on the probability of interaction recognition risks due to external interference in different locations, the update strategy for the interaction control target between the robot and the server is determined, thus laying the foundation for accurately determining the real interference risks in different locations.

[0015] By utilizing reliable robot data and the identification results of reliable robots' interaction data in potential risk locations, this method determines the reliability of identification in potential risk locations from the perspective of the number of reliable robots, and identifies locations with high interference risk. Using the number of locations with high interference risk and the identification results of locations with high interference risk among potential risk locations, it determines the efficiency and reliability of robot operation and maintenance after excluding locations with high interference risk. Based on the efficiency and reliability of robot operation and maintenance after excluding locations with high interference risk, it determines a method for identifying and managing the robot's operational status, enabling differentiated operation and maintenance of robots and ensuring the reliability of their operational status.

[0016] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0019] Figure 1 This is a framework diagram of a data interaction system for bedside payment and settlement. Figure 2 This is a flowchart of a data interaction method for bedside payment settlement; Figure 3 This is a flowchart illustrating the methods for identifying and analyzing interactive risks in different locations. Figure 4 This is a flowchart illustrating the method for determining the update strategy of the interaction control target between the robot and the server. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0021] Example 1 like Figure 1 As shown, this application provides a data interaction system for bedside payment settlement, specifically including: Robot, server, interactive control module; Information on beds and patients requiring discharge payment or outstanding payment is pushed to the server. The server then builds a task list based on this information. The robot periodically polls the task list to determine the discharge payment or outstanding payment task. The interaction control module determines the update strategy for the interaction control target between the robot and the server based on the interaction failure data in different locations and the interaction failure data of the associated robots in all locations. It also identifies reliable robots based on the interaction data in different locations and uses the reliable robot data and the identification results of the interaction data of reliable robots in potentially risky locations to determine the identification and management method of the robot's operating status. The robot actively interacts with the server based on the active data interaction method and whether it belongs to the interaction control target. It also uses the operation status identification and management method to identify the operation status, plans the route according to the nearest neighbor strategy, and performs identity verification upon arrival. After the verification is passed, the robot queries the inpatient information, pre-settlement results and pre-payment account information through the server, and displays the settlement list on the screen for the patient to check. After the patient confirms the list, the system enters the corresponding payment process according to self-pay or medical insurance.

[0022] The robot plans its route using the nearest neighbor strategy. Upon arrival, it verifies the patient's identity. After successful verification, the robot queries the server for inpatient information, pre-settlement results, and pre-payment account information. It then displays the settlement list on the screen for the patient to check. After the patient confirms the list, the system proceeds to the corresponding payment process based on whether the patient pays out of pocket or is covered by medical insurance.

[0023] The specific task acquisition, confirmation, and issuance process is as follows: The HIS (Hospital Information System) pushes information on beds requiring discharge payment or outstanding payment to the cloud server. A chatbot periodically polls for the task list, and nurses select the corresponding task on the chatbot's end, issuing the discharge payment or outstanding payment task. Nurses can also manually select beds on the bed selection interface. When a bed is selected, the system pre-queries patient information and payment status from the HIS; after verification, the nurse confirms and issues the task. The system differentiates task types based on the patient's hospital status: those still hospitalized with outstanding payments are assigned outstanding payment, while those scheduled for discharge are assigned discharge settlement. Simultaneously, the system identifies whether the patient is self-paying or covered by medical insurance based on the HIS payment category and issues this information along with the task.

[0024] After the task is assigned, the robot plans its route using a nearest neighbor strategy, prioritizing the nearest bed. Upon arrival, it first performs biometric identification via camera to confirm the presence of someone next to the bed, then guides the patient to choose an identity verification method: scanning the hospital wristband code or manually entering their name and the last six digits of their ID number, which are then compared with the patient information provided in the task. Once verification is successful, the robot queries the HIS (Hospital Information System) via the server for inpatient information, pre-settlement results, and pre-payment account information, displaying a settlement list on the screen for the patient to review. If there are any refundable charges, the patient is prompted to proceed to the nurses' station to process the refund and the process is terminated. After the patient confirms the list, the system proceeds to the corresponding payment process based on whether the payment is out-of-pocket or covered by medical insurance.

[0025] The settlement statement for out-of-pocket patients mainly displays the total cost, discount amount, and actual payment amount. If the actual payment amount is greater than zero, the robot generates a payment QR code, which the patient scans to pay. The system continuously checks the payment result until the funds are received, and the funds are automatically credited to the inpatient prepayment account. The system then checks the prepayment account again to confirm that the costs have been settled. Patients can also increase the recharge amount as needed and generate a new QR code. After the costs are settled, the system processes the tasks according to their type: for outstanding payment tasks, the task is completed, and the robot displays a payment success message; for discharge settlement tasks, the system continues to call the HIS discharge settlement interface, using the hospital number as proof to formally complete the hospitalization procedures. After all settlements are completed, the patient is guided to download the electronic invoice and provide service feedback. The execution record is then reported to the server, marking the current bed's task as completed.

[0026] In addition to the total cost, the settlement list for patients covered by medical insurance also displays the amount already paid by medical insurance and the amount the patient still needs to pay out of pocket. If the out-of-pocket amount is greater than zero, the process is the same as for out-of-pocket payments: a QR code is generated for the patient to scan, and the funds are credited to the pre-payment account and the payment is confirmed. If the out-of-pocket amount is zero, the scanning step is skipped. Then, the patient proceeds to medical insurance discharge settlement: the robot guides the patient to scan their electronic medical insurance voucher. The national medical insurance electronic voucher platform verifies the insured information, and after successful verification, the medical insurance card information is used to call the HIS discharge settlement interface to complete the medical insurance discharge settlement. The task of paying outstanding fees is completed when the out-of-pocket portion is successfully recharged; the discharge settlement task is only considered complete after the above medical insurance discharge settlement is finished. After all is completed, the patient is guided to download the electronic invoice, provide service feedback, and report the execution record. If there are other beds to be processed in the batch, the robot proceeds to the next bed according to the planned sequence, and returns to the standby point after all is completed.

[0027] Furthermore, the potential risk locations are locations where interaction failure data exists.

[0028] Specifically, the method for determining the reliable robot is as follows: Based on the identification results of interaction data in different potential risk locations, the number of robot interaction failures in different potential risk locations is determined. Based on the proportion of the number of robot interaction failures in the potential risk locations to the total number of interactions, the risk association weight value of the robot in the potential risk locations is determined. The basic risk weight value of the robot is determined by using the average risk association weight value of the robot in different locations, and the reliable robot among the robots is determined by using the basic risk weight value.

[0029] It should be noted that the reliable robots mentioned are robots whose basic risk weight values ​​meet the requirements, that is, robots whose basic risk weight values ​​are less than a certain threshold.

[0030] Specifically, based on the proactive data interaction method and whether it falls under the interaction control target, proactive interaction processing between the robot and the server is carried out, including: If the robot is an interactive control target, it will interact with the server in real time at different locations; If the robot is not a target of interactive control, it will actively interact with the server in accordance with the active data interaction method in potential risk locations.

[0031] Example 2 Secondly, such as Figure 2 As shown, this application provides a data interaction method for bedside payment settlement, applied to the aforementioned bedside payment settlement data interaction system, specifically including: S1 uses the payment and settlement data at the bedside to identify different robot-server interaction failure data. When it determines whether interaction risk identification and analysis is needed in different locations based on the different robot interaction failure data, it proceeds to the next step. Furthermore, the data on failed interactions between the robot and the server is determined based on the number of failed interactions between the robot and the server during payment settlement.

[0032] Specifically, such as Figure 3 As shown, it is determined that interaction risk identification and analysis are required in different locations, specifically including: In this embodiment, based on different robot-server interaction failure data, the degree of need for abnormal identification and processing of robot operation status is determined, and based on the degree of need, it is determined whether it is necessary to identify and analyze interaction risks in different locations, thereby laying the foundation for differentiated identification and maintenance of robot operation status.

[0033] Interaction failure data refers to data records showing that the robot failed to successfully complete the data interaction with the server during the bedside payment and settlement process, including specific manifestations such as communication timeout, data verification failure, and connection interruption. The degree of need for anomaly identification and handling refers to the urgency of determining whether further investigation into the cause of the interaction failure is necessary based on the robot's interaction failure status, focusing on the location.

[0034] This paragraph explains the overall design intent of S1: by first assessing the severity of interaction failures before deciding whether to proceed with location-based analysis, complex location-based analysis can be avoided for all robots, thereby saving analysis resources while ensuring differentiated operation and maintenance.

[0035] A hospital has 20 bedside payment robots. In the past period, the total number of failed interactions with the server has been relatively high, and the need for anomaly identification and processing is high. Therefore, it was determined that interaction risk identification and analysis should be carried out in different locations to identify whether there are situations where multiple robots fail to interact due to external interference in certain locations.

[0036] S11 uses the interaction failure data between different robots and the server to determine the number of interaction failures between different robots and the server; The number of interaction failures refers to the cumulative number of times a single robot fails to interact with the server within a statistical period. It reflects the reliability of the robot's interaction and quantifies the interaction failure data into a comparable frequency indicator, providing a unified quantitative benchmark for subsequent determination of whether each robot is an interaction risk robot.

[0037] The number of times the 20 robots failed to interact with the server in the past 24 hours were as follows: R1=3 times, R2=5 times, R3=15 times, R4=2 times, R5=20 times, and R6 to R20 were all ≤4 times.

[0038] S12 determines the interaction risk robots among the robots based on the number of interaction failures, and uses the interaction risk robot data to determine whether it is necessary to perform interaction risk identification and analysis processing in different locations.

[0039] Interaction risk robots refer to robots whose number of interaction failures reaches or exceeds a preset threshold (i.e., "does not meet requirements"). This indicates that the interaction reliability of these robots is low and they need to be closely monitored. The number of interaction risk robots reflects the overall severity of interaction failures and provides a basis for decision-making on whether further location-based risk analysis is needed. When the proportion of interaction risk robots is high, there is a greater need to investigate interference factors from the location dimension.

[0040] If robot R3 = 15 times, which is greater than the threshold, then R3 is determined to be an interactive risk robot.

[0041] Yes, the interaction risk robot in the robot is a robot whose number of interaction failures does not meet the requirements. Specifically, a robot whose number of interaction failures exceeds a certain threshold is considered an interaction risk robot.

[0042] "The number of interaction failures does not meet the requirements" in this embodiment means that the number of interaction failures is greater than the preset threshold for the number of interaction failures.

[0043] Specifically, based on the interactive risk robot data, determining whether interactive risk identification and analysis processing is needed in different locations includes: Based on the proportion of interactive risk robots in the total number of robots, an interactive risk weight value is determined. When the interactive risk weight value does not meet the requirements, the risk of interaction failure is relatively high. It is difficult to use the number of interaction failures to perform robot operation and maintenance. Therefore, it is necessary to eliminate interference from external communication anomalies and carry out targeted operation and maintenance. Thus, it is determined that interactive risk identification, analysis and processing in different locations are required.

[0044] The interaction risk weight value refers to the proportion of interaction risk robots among all robots, reflecting the severity of overall interaction failure. When the proportion is greater than the threshold, the interaction risk weight value is considered to be unsatisfactory.

[0045] By assessing the proportion of overall interaction failures, we can determine the degree of concentration. When the proportion of interaction risk robots is high, the operation and maintenance effect from the robot dimension alone is limited. It is necessary to investigate whether there are common external interference factors from the location dimension, so as to achieve differentiated operation and maintenance.

[0046] S2 determines the update strategy for the interaction control target between the robot and the server based on the interaction failure data in different locations and the associated interaction failure data of the robot in all locations. Specifically, such as Figure 4 As shown, the method for determining the update strategy of the interaction control target between the robot and the server is as follows: In this implementation, by using interaction failure data in different locations and the associated interaction failure data of robots in all locations, the probability of interaction identification risk due to external interference in different locations is determined. Based on the probability of interaction identification risk due to external interference in different locations, the update strategy for the interaction control target between the robot and the server is determined, thus laying the foundation for accurately determining the real interference risk in different locations.

[0047] Associated robots refer to a group of robots that have experienced interaction failures at a certain location. Interaction recognition risk caused by external interference refers to the risk of robot interaction failures caused by external factors such as the network environment, electromagnetic environment, and signal shielding at that location, which is distinct from hardware or software malfunctions of the robot itself.

[0048] This paragraph explains the overall design intent of S2: by comparing the robot's interaction failures at different locations, it can identify whether there are external interference factors, eliminate internal interference caused by the robot's own malfunctions, and accurately locate the real high-risk locations of external interference.

[0049] Robot R7 experienced interaction failures in both ward A and ward B, but never failed in ward C. Therefore, it can be preliminarily inferred that R7 may have experienced external interference in wards A and B.

[0050] S21 uses interaction failure data in different locations to identify locations with interaction failure data as potential risk locations; Potential risk locations refer to locations where robot interaction has failed at least once, which may be due to external interference factors. By selecting locations where interaction failures have occurred as a candidate set of potential risk locations, the scope of subsequent analysis can be focused on locations with failure records, thus narrowing the scope of investigation.

[0051] If interaction failures have occurred in the past period, the following locations are considered as potential risk locations: Ward A, Ward B, Ward C, Outpatient Department D, and Emergency Department E.

[0052] In the above steps, the number of potential risk locations is obtained, and it is determined whether the number of potential risk locations is greater than the preset risk location number threshold. If so, the update strategy of the interaction control target between the robot and the server is determined to be to take all robots that do not belong to the interaction risk robot as the interaction control target. If not, proceed to step S22. Interactive control targets refer to robots that require enhanced interactive control, which interact with the server in real time during the control period. The distribution range of interference is determined by the number of potential risk locations. Its significance lies in the fact that when there are many potential risk locations, interference may be prevalent in multiple locations. Therefore, all non-interactive risk robots should be subject to enhanced control to cope with the risk of widespread external interference.

[0053] Assuming there are 5 potential risk locations that exceed the threshold, all robots that are not considered interaction risk robots (a total of 16 robots, R1 / R2 / R4 / R6 / R7 / R9 / R10 / R12 / R13 / R15 / R16 / R17 / R18 / R19 / R20) are identified as interaction control targets.

[0054] S22 uses the interaction failure data of the robot in all locations to identify the robots that are excluded from the interaction risk robots and treats them as normal interaction robots. Based on the associated robots with interaction failures in the potential risk locations, the number of interaction failures of normal interaction robots in the potential risk locations is determined. Based on the number of interaction failures of different normal interaction robots in the potential risk locations, the interaction risk weight coefficient of the potential risk locations is determined. Normal interactive robots refer to robots other than those with interaction risks. The interaction risk weight coefficient is the sum of preset weight values ​​corresponding to the number of interaction failures of different normal interactive robots in a potential risk location. It reflects the severity of the interaction risk caused by external interference at that location. The interaction risk weight coefficient is constructed by the number of failures of normal interactive robots in potential risk locations, eliminating the interference of the interaction risk robot's own malfunctions and purely reflecting the impact of external interference factors on normal robots.

[0055] The associated normal robots for ward A, a potential risk location, are R1, R4, R6, and R7, with 1, 0, 1, and 2 failures respectively. The preset weight values ​​are 0.5, 0, 0.5, and 1.0 respectively. Therefore, the interaction risk weight coefficient = 0.5 + 0 + 0.5 + 1.0 = 2.0.

[0056] It should be noted that in the above steps, the interaction risk weight coefficient of the potential risk location is obtained, and it is determined whether there is a potential risk location whose interaction risk weight coefficient is greater than the preset weight coefficient threshold. If yes, then proceed to step S23; otherwise, the update strategy of the interaction control target between the robot and the server is determined to be to take all robots that have not had any interaction failures in the most recent preset time period among the robots that are not interaction risk robots as interaction control targets. To determine whether there are high-risk locations for external interference, if the interaction risk weight coefficients of all potential risk locations are not significant (≤ threshold), it indicates that there are no prominent high-risk locations for external interference, and it is only necessary to strengthen the control of normal robots that have not failed recently.

[0057] The interaction risk weight coefficients of potential risk locations Ward A (coefficient 2.0), Ward B (coefficient 1.5), and Outpatient D (coefficient 0.5) are all no greater than the threshold. There are no potential risk locations with interaction risk weight coefficients greater than the threshold. Strategy B is determined: Robots that have not had any interaction failures in the past week (such as R4, R9, R16, R19, and R20) that are not considered interaction risk robots are all taken as interaction control targets.

[0058] S23 uses the potential risk locations and the interaction risk weight coefficients of different potential risk locations to determine the update strategy for the interaction control target between the robot and the server.

[0059] It should be noted that S23, based on S22, further refines the classification of scenarios with high-risk locations, and determines the specific scope of interactive control targets based on the number of risk locations screened and the identification requirement value.

[0060] This section is the final decision-making stage of S2. By jointly evaluating the number of risky locations and the overall identification requirements, the scope of interactive control objectives is further refined, so that the strategy selection takes into account both the density of high-risk locations and the overall identification requirements.

[0061] S231 uses potential risk locations with an interaction risk weight coefficient greater than a preset weight coefficient as screening risk locations, and determines whether the number of screening risk locations meets the requirements. If not, it is determined that the number of screening risk locations is too large. Therefore, it is determined that the update strategy for the interaction control target between the robot and the server is to use all robots that do not belong to the interaction risk robot as the interaction control target. If not, proceed to step S232. This step determines whether the number of risk locations screened is too large. When the number of risk locations screened is large (not meeting the requirement of ≤ threshold), the scope of external interference is wide, and it is necessary to strengthen the control of all non-interactive risk robots. For example, if there are 4 risk locations screened, the number does not meet the requirement, so determine strategy A: treat all robots that are not interactive risk robots as interactive control targets.

[0062] S232 determines the identification requirement value based on the number of screened risk locations and the interaction risk weight coefficients of different potential risk locations. It then determines whether the identification requirement value is greater than a preset requirement threshold. If so, the update strategy for the interaction control target between the robot and the server is to include all robots that are not considered interaction risk robots and whose number of interaction failures within the most recent preset time period is less than a preset failure number as interaction control targets. If not, the update strategy for the interaction control target between the robot and the server is to include all robots that are not considered interaction risk robots and whose number of interaction failures within the most recent preset time period as interaction control targets.

[0063] Specifically, in one possible embodiment, the identification demand value = number of risk locations screened × adjustment coefficient + sum of the interaction risk weight coefficients of each potential risk location. The overall identification pressure is judged by the identification demand value. Its significance is that when the identification demand is high (> threshold), the control scope is expanded to cope with the identification pressure, including robots with a small number of interaction failures; when the identification demand is low, only robots that have not failed recently are controlled to save resources.

[0064] S3 uses the update strategy to determine the interaction control target in the robot, uses the interaction control target to interact with the server in real time at different locations, and determines the active data interaction method in different potential risk locations based on the interaction processing data of the interaction control target in different potential risk locations and the interaction control target data. Specifically, the method for determining the proactive data interaction method in the potential risk locations is as follows: In this embodiment, based on the interaction processing data of the interaction control target in different potential risk locations and the interaction control target data, the reliability of identifying interference risks in potential risk locations using the interaction control target is determined. The active data interaction method in potential risk locations is determined using the identification reliability, thereby ensuring the efficiency of identifying interference risks in potential risk locations while ensuring unnecessary interaction data.

[0065] S31 uses the interaction processing data of interaction control targets in different potential risk locations to determine the number of interaction processing times for different interaction control targets in potential risk locations; The number of interaction processing times refers to the cumulative number of interactions completed by each interaction control target at each potential risk location. The interaction density of each interaction control target at each potential risk location is calculated to provide raw data for subsequent calculation of interaction matching weight values.

[0066] In ward B, a potential risk location, the interaction count for interactive control target R4 is 20, and for R9 it is 15, for a total of 35 interactions; in emergency room E, R4 is 10, and for R9 it is 12, for a total of 22 interactions.

[0067] S32 determines the proportion of interactive control targets in the robot based on the interactive control target data, and uses it as the proportion of interactive control targets. The percentage of interactive control targets = number of interactive control targets ÷ number of robots. This reflects the size of the control scope, quantifies the control scope, determines whether it is possible to effectively identify locations where there are network anomalies, and provides input for subsequent judgment on whether other robots need to supplement active data interaction. For example, if there are 5 interactive control targets and 20 robots, the percentage of interactive control targets = 5 ÷ 20 = 0.25.

[0068] It should be noted that in step S32, based on the proportion of the number of interactions controlled, it is determined whether the proportion of the number of interactions controlled is greater than the preset control proportion threshold. If so, it is determined that the active data interaction method in the potential risk location is that the robot does not need to perform active data interaction processing except for the interaction control target. If not, proceed to step S33. To determine whether the control coverage is sufficient, when the proportion of interactive control is large enough (>0.30), the control target has covered most robots, the recognition reliability is high, and there is no need for other robots to actively supplement the interaction.

[0069] S33 determines the proactive data interaction method in the potential risk location based on the number of interaction processing and the proportion of interaction control quantity for different interaction control objectives in different potential risk locations.

[0070] Specifically, step S33 further assesses the interaction density of each potential risk location when control coverage is insufficient. Its significance lies in differentiating which potential risk locations require proactive data interaction for supplementation. This includes the following: S331 determines the interaction matching weight value of the potential risk location based on the number of interaction processing of different interaction control targets in the potential risk location, and determines whether there is a potential risk location with an interaction matching weight value less than a preset matching weight threshold. If so, it is determined that the active data interaction method in the potential risk location is that the robot, excluding the interaction control target, needs to perform active data interaction processing in all potential risk locations. If not, proceed to step S332. The interaction matching weight value is the sum of the preset matching weight values ​​corresponding to the number of interaction processing times of each interaction control target. It determines whether there are potential risk locations with excessively low interaction density. When there are locations with interaction matching weight values ​​less than a threshold, the identification reliability of these locations is severely insufficient, requiring non-interaction control targets to actively interact with data at all potential risk locations to fully supplement the data.

[0071] The interaction matching weight value of the potential risk location outpatient clinic D is 8 + 12 = 20, which is less than the threshold. There are potential risk locations with a value less than the threshold. Method 2: Non-interactive control targets need to undergo proactive data interaction processing at all potential risk locations.

[0072] The interaction risk weight value of the potential risk location is determined by the sum of the preset matching weight values ​​corresponding to the number of interaction processing times of different interaction control targets in the potential risk location.

[0073] It should be noted that the preset matching weight value is divided according to the number of interaction processing: ≤10 times corresponds to 5, 11-20 times corresponds to 15, 21-30 times corresponds to 25, and >30 times corresponds to 40. For example, in ward B, R4 interacts 20 times → 15, R9 interacts 15 times → 15, so the interaction matching weight value = 15 + 15 = 30.

[0074] S332 determines the control matching value by using the interaction matching weight value in different potential risk locations and the proportion of the number of interaction control measures. It then determines whether the control matching value is greater than the preset matching value. If so, it determines that the active data interaction method in the potential risk location is that the robot excluding the interaction control target does not need to perform active data interaction processing. If not, it uses the robot excluding the interaction control target to perform active data interaction processing in potential risk locations where the interaction matching weight value is less than the preset matching weight threshold.

[0075] Control matching value = interaction matching weight value × interaction control quantity ratio. It comprehensively judges whether the overall proactive interaction capability is sufficient. The overall recognition capability is judged by the matching value. When the control matching value > the threshold, the overall recognition capability is sufficient and no additional proactive interaction is required; otherwise, proactive data interaction is only added at potential risk locations with low interaction density.

[0076] It is understandable that the higher the interaction matching weight value in different potential risk locations, the larger the proportion of interaction control, and the higher the control matching value.

[0077] S4 performs interaction processing between the robot and the server based on the active data interaction method and the interaction control target, and identifies reliable robots in the robot based on the identification results of interaction data in different locations. Using the reliable robot data and the identification results of the reliable robot's interaction data in potentially risky locations, an identification and management method is used to determine the robot's operating status.

[0078] Specifically, the method for determining the robot's operating status identification and management method is as follows: In this embodiment, by utilizing reliable robot data and the identification results of reliable robot interaction data in potential risk locations, the reliability of identification in potential risk locations is determined from the perspective of the number of reliable robots, and locations with high interference risk are identified among potential risk locations. By using the number of locations with high interference risk and the identification results of locations with high interference risk among potential risk locations, the efficiency and reliability of robot operation and maintenance after excluding locations with high interference risk are determined. Based on the efficiency and reliability of robot operation and maintenance after excluding locations with high interference risk, a method for identifying and managing the robot's operating status is determined, realizing differentiated operation and maintenance of robots and ensuring the reliability of the operating status.

[0079] S41 uses the identification results of interaction data in different potential risk locations to determine the number of robot interaction failures in different potential risk locations, and determines the risk association weight value of the robot in the potential risk locations based on the proportion of the number of robot interaction failures in the number of interactions. Risk association weight value = number of failed interactions ÷ number of interactions, reflecting the robot's interaction failure rate at that location, quantifying the robot's failure rate at each potential risk location, and providing basic data for subsequent determination of reliable robots and risk identification robots.

[0080] For example, if robot R7 interacts with ward B 80 times and fails to interact 8 times, the risk association weight value is 8 ÷ 80 = 0.10.

[0081] S42 uses the average risk association weight value of the robot in different locations to determine the basic risk weight value of the robot, and uses the basic risk weight value to determine the reliable robot among the robots; The basic risk weight value is the average of the risk-related weight values ​​of multiple potential risk locations. The overall performance of the robot in multiple locations is quantified by the basic risk weight value. The average value reflects the overall reliability of the robot and serves as the basis for judging the reliability of the robot.

[0082] For example, the average risk association weight value of R7 in ward A (0.05), ward B (0.10), and outpatient D (0.02) is 0.057 < the threshold, so R7 is a reliable robot.

[0083] S43 determines the robots that need to undergo operational status identification processing based on the risk association weight values ​​of reliable robots in different potential risk locations and reliable robot data.

[0084] It should be noted that in step S43, the robot that needs to undergo operational status identification processing is determined through a joint judgment based on factors such as the risk identification robot, the risk location, and the number of reliable robots. Specifically, this includes the following: The reliable robot in the robot is a robot whose basic risk weight value meets the requirements, that is, a robot whose basic risk weight value is less than a certain threshold. In this embodiment, "meets the requirements" means that the basic risk weight value is less than the preset reliable robot threshold.

[0085] S431. Reliable robots in potential risk locations whose risk association weight values ​​are above a preset risk weight threshold and whose interaction counts meet the requirements (i.e., are greater than a certain threshold) are identified as risk identification robots. Potential risk locations where the number of risk identification robots is above a preset risk robot number threshold are identified as risk locations. It is determined whether the number of risk locations is greater than the preset risk location number threshold. If so, the robot is identified as a robot that needs to undergo operational status identification processing as long as the risk association weight value of the robot in any location that is not a risk location is above the preset risk weight threshold. If not, proceed to step S432. In the above steps, risk locations are identified by the interaction of reliable robots, which are locations where the interaction failure rate of reliable robots is significantly high. When the number of risk locations is large (>threshold), external interference is common. At this time, when performing state identification, the number of risk locations with external interference that need to be eliminated is large. When the risk association weight value of any location that is not a risk location is large, operation and maintenance processing is required, that is, strict identification and judgment are performed on all locations.

[0086] S432 Obtain the number of reliable robots and determine whether the number of reliable robots is greater than a preset reliable robot number threshold. If yes, proceed to step S433. If no, the identification reliability of potential risk locations is poor. Therefore, as long as the risk association weight value of the robot in multiple potential risk locations that are neither risk locations nor second-class target types is above the preset risk weight threshold, the robot is regarded as a robot that needs to be identified in terms of its operating status. To determine whether the number of reliable robots is sufficient, if there are insufficient reliable robots (less than the threshold), the overall reliability of identification is poor. In this case, it is difficult to effectively identify risky locations. It is necessary to expand the identification scope to all non-risky locations and potential risky locations that do not belong to the second type of target. As long as the risk association weight values ​​of multiple locations among the potential risky locations that do not belong to the risky locations and do not belong to the second type of target are above the preset risk weight threshold, then the robot is regarded as a robot that needs to be identified in terms of its operating status.

[0087] S433 uses the number of reliable robots and the number of reliable robots whose interaction frequency meets the requirements in different potential risk locations as a basis to determine the risk identification matching coefficient. It then determines whether the risk identification matching coefficient is greater than a preset matching coefficient threshold. If so, the robot is considered as a robot that needs to undergo operational status identification processing as long as the risk association weight value of the robot in multiple locations that are not in risk locations is above the preset risk weight threshold. If not, the robot is considered as a robot that needs to undergo operational status identification processing as long as the risk association weight value of the robot in multiple locations that are not in risk locations and do not belong to the target type is above the preset risk weight threshold.

[0088] Risk identification matching coefficient = number of reliable robots × second adjustment coefficient + number of reliable robots in each potential risk location whose interaction count meets the requirements (i.e., greater than the threshold) × third adjustment coefficient. The average of the results is obtained, where the second adjustment coefficient and the third adjustment coefficient are preset values ​​used for dimensionless processing, and their values ​​range from 0 to 0.1.

[0089] Potential risk locations of the target type refer to locations where multiple risk identification robots are present. Potential risk locations of the second type refer to locations where risk identification robots are present.

[0090] The significance of judging whether the identification capability is sufficient by using the risk identification matching coefficient is that when the matching coefficient is high, the identification capability is sufficient, so it is only necessary to exclude risky locations; when the matching coefficient is low and the identification reliability is high, it is necessary to exclude potential locations of the target type, that is, there are potential risky locations of multiple risk identification robots, which pose a certain degree of interference risk.

[0091] Example 3 Thirdly, this application provides a chip device for use in a robot. The chip device includes: a data acquisition module, a data processing module, and a control module. The data acquisition module is responsible for acquiring user data, the data processing module is responsible for executing the aforementioned data interaction method for bedside payment settlement and payment processing, and the control module is responsible for data interaction with the server for settlement information and user information.

[0092] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0093] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0094] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A data interaction system for bedside payment and settlement, characterized in that, Specifically, it includes: Robot, server, interactive control module; Information on beds and patients requiring discharge payment or outstanding payment is pushed to the server. The server then builds a task list based on this information. The robot periodically polls the task list to determine the discharge payment or outstanding payment task. The interaction control module determines the update strategy for the interaction control target between the robot and the server based on the interaction failure data in different locations and the interaction failure data of the associated robots in all locations. It also identifies reliable robots based on the interaction data in different locations and uses the reliable robot data and the identification results of the interaction data of reliable robots in potentially risky locations to determine the identification and management method of the robot's operating status. The robot interacts with the server based on the active data interaction method and whether it belongs to the interaction control target. It also uses the operation status identification and management method to identify the operation status. It plans a route according to the nearest neighbor strategy. After arriving, it verifies the identity. After the verification is passed, the robot queries the hospitalized patient information, pre-settlement results and pre-payment account information through the server. It displays the settlement list on the screen for the patient to check. After the patient confirms the list, the system enters the corresponding payment process according to self-pay or medical insurance.

2. The data interaction system for bedside payment and settlement as described in claim 1, characterized in that, The potential risk locations are those where data interaction failures occur.

3. The data interaction system for bedside payment and settlement as described in claim 1, characterized in that, The method for determining the reliable robot is as follows: Based on the identification results of interaction data in different potential risk locations, the number of robot interaction failures in different potential risk locations is determined. Based on the proportion of the number of robot interaction failures in the potential risk locations to the total number of interactions, the risk association weight value of the robot in the potential risk locations is determined. The basic risk weight value of the robot is determined by using the average risk association weight value of the robot in different locations, and the reliable robot among the robots is determined by using the basic risk weight value.

4. The data interaction system for bedside payment and settlement as described in claim 3, characterized in that, The reliable robots in the robot category are those whose basic risk weight values ​​meet the requirements.

5. The data interaction system for bedside payment settlement as described in claim 1, characterized in that, The robot's proactive interaction with the server is processed based on the proactive data interaction method and whether it falls under the interaction control target. Specifically, this includes: If the robot is an interactive control target, it will interact with the server in real time at different locations; If the robot is not a target of interactive control, it will actively interact with the server in accordance with the active data interaction method in potential risk locations.

6. A data interaction method for bedside payment settlement, applied to the data interaction system for bedside payment settlement as described in any one of claims 1-5, characterized in that, Specifically, it includes: Using the payment and settlement data at the bedside, we can identify different interaction failure data between the robot and the server. By utilizing the interaction failure data of different robots, we can determine when to identify and analyze the interaction risks in different locations and then proceed to the next step. Based on the interaction failure data in different locations, and combined with the interaction failure data of the associated robots in all locations, the update strategy for the interaction control target between the robot and the server is determined. The interaction control target in the robot is determined by using an update strategy. The interaction control target interacts with the server in real time at different locations. Based on the interaction processing data of the interaction control target in different potential risk locations and the interaction control target data, the active data interaction method in different potential risk locations is determined. Based on the active data interaction method and interaction control objectives, the robot and server interact and process the interaction data in different locations. The robot is identified as a reliable robot. The robot's operating status is determined by the identification and management method using the reliable robot data and the identification results of the reliable robot's interaction data in potentially risky locations.

7. The data interaction method for bedside payment settlement as described in claim 6, characterized in that, The data on failed interactions between the robot and the server is determined based on the number of failed interactions between the robot and the server during payment settlement.

8. The data interaction method for bedside payment settlement as described in claim 6, characterized in that, The identification and analysis of interaction risks in different locations needs to be determined, specifically including: By using the interaction failure data between different robots and the server, the number of interaction failures between different robots and the server can be determined; Based on the number of interaction failures, identify the interaction risk robots among the robots, and use the interaction risk robot data to determine whether it is necessary to perform interaction risk identification and analysis processing in different locations.

9. The data interaction method for bedside payment settlement as described in claim 6, characterized in that, The method for determining the update strategy of the interaction control target between the robot and the server is as follows: Based on interaction failure data in different locations, locations where interaction failure data exists are identified as potential risk locations. Using the interaction failure data of the robot in all locations, the robots that are removed from the robots with interaction risks are identified and treated as normal interaction robots. Based on the associated robots with interaction failures in the potential risk locations, the number of interaction failures of normal interaction robots in the potential risk locations is determined. Based on the number of interaction failures of different normal interaction robots in the potential risk locations, the interaction risk weight coefficient of the potential risk locations is determined. By utilizing the potential risk locations and the interaction risk weight coefficients of different potential risk locations, an update strategy for the interaction control objectives between the robot and the server is determined.

10. A chip device for use in a robot, the chip device comprising: The invention comprises a data acquisition module, a data processing module, and a control module, characterized in that the data acquisition module is responsible for collecting user data, the data processing module is responsible for executing the data interaction method and payment processing of bedside payment settlement as described in any one of claims 6-9, and the control module is responsible for data interaction with the server for settlement information and user information.

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