Intelligent risk control management method for medicine supply chain

Abstract

The invention relates to the technical field of data management, in particular to an intelligent risk control management method for a medicine supply chain, and the method comprises the steps: collecting medicine circulation data in the medicine supply chain, and sequentially carrying out the cleaning, denoising, weight reduction, formatting and feature extraction of the medicine circulation data, so as to output medicine circulation features, training an initial model based on the medicine circulation characteristics to construct a risk identification model; identifying and predicting potential risks in the medicine supply chain based on the medicine circulation data and the risk identification model so as to output a prediction result, and uploading the prediction result to a medicine supply chain sharing platform for sharing; determining whether the information sharing cooperation coefficient of the medicine supply chain needs to be increased; determining whether a scanning timeout fusing threshold value of the medicine circulation data needs to be increased or not; and determining the section depth ratio of medicine tracing based on the correlation rate of the medicine circulation data. According to the invention, the management real-time performance of the medicine supply chain risk is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, in particular to an intelligent risk control management method for a pharmaceutical supply chain. BACKGROUND

[0002] Under the background of deepening market-oriented reform in the pharmaceutical industry and tightening supervision of the whole life cycle of drugs, the data of the pharmaceutical supply chain presents complex characteristics of multi-source heterogeneity and multi-subject diversity. The core businesses such as drug quality control and compliance operation supervision across subjects require intelligent, accurate and real-time full-link risk control. The core value of the intelligent risk control management method for the pharmaceutical supply chain lies in the fusion of multi-source key information such as drug production batch data, cold chain temperature and humidity data, logistics track data and inventory turnover data. Through big data correlation analysis, artificial intelligence risk feature extraction and real-time dynamic monitoring, the method realizes early warning, accurate identification and rapid response of the risk of the pharmaceutical supply chain, effectively solves the pain points of serious information silos and manual verification lag in the traditional risk control mode. However, the existing methods still have problems such as low fusion efficiency caused by non-uniformity of multi-source data format standards and insufficient adaptability of AI models to cross-link coupled risks, which cannot meet the actual needs of millions of drug batch turnover, multi-subject collaborative linkage and accurate risk control in the whole life cycle.

[0003] Chinese Patent Publication No. CN114707771A discloses a pharmaceutical supply chain management method and platform based on big data, which includes a sales volume information determination module, a warehouse quantity analysis module, a warehouse optimization module and a supplier and distributor processing module. The sales volume information determination module is used to obtain historical sales information, and according to the historical sales information and warehouse information, the first distributor's purchase quantity of medicines to the supplier is predicted. The warehouse quantity analysis module is used to compare the quantity of medicines in the warehouse with the quantity of medicines in the first warehouse to obtain comparison information, and to process the quantity of medicines in the warehouse according to the comparison information. The warehouse optimization module is used to obtain the trajectory information of target users purchasing medicines, decompose the original matrix formed by the trajectory information, and select corresponding medicines from the warehouse according to the decomposed matrix to provide to the target users. The supplier and distributor processing module is used to obtain the medicine quantity information uploaded to the platform by the suppliers and distributors in the platform, provide medicines to the first distributor according to the medicine quantity information, and optimize the departure time of the medicine distribution to the first distributor. The sales volume information determination module is connected with the warehouse quantity analysis module, the warehouse optimization module and the supplier and distributor processing module. It can be seen that the pharmaceutical supply chain management method and platform based on big data have the problem of insufficient real-time management of pharmaceutical supply chain risks due to the use of offline batch processing architecture and the lack of event-driven real-time feedback loop in each module. SUMMARY

[0004] To address this, the present invention provides an intelligent risk control management method for the pharmaceutical supply chain, which overcomes the problem of insufficient real-time management of pharmaceutical supply chain risks in the prior art due to the offline batch processing architecture of each module and the lack of an event-driven real-time feedback closed loop.

[0005] To achieve the above objectives, the present invention provides an intelligent risk control management method for the pharmaceutical supply chain, comprising: Collect pharmaceutical distribution data in the pharmaceutical supply chain, and sequentially clean, denoise, reduce, format, and extract features from the pharmaceutical distribution data to output pharmaceutical distribution features. Based on the pharmaceutical distribution features, train an initial model to build a risk identification model. Based on the pharmaceutical distribution data and the risk identification model, potential risks in the pharmaceutical supply chain are identified and predicted to output prediction results, which are then uploaded to the pharmaceutical supply chain sharing platform for sharing. Obtain the response time of potential risk prediction, and determine whether the real-time management of pharmaceutical supply chain risks meets the requirements based on the response time of the potential risk prediction; If the real-time management of the aforementioned pharmaceutical supply chain risks does not meet the requirements, then it is necessary to determine whether the information sharing and collaboration coefficient of the pharmaceutical supply chain needs to be increased. If it is not necessary to increase the information sharing and collaboration coefficient of the pharmaceutical supply chain, then obtain the potential risk occurrence rate in the pharmaceutical supply chain per unit time to determine whether the risk identification coverage of the pharmaceutical supply chain meets the requirements. If the risk identification coverage of the pharmaceutical supply chain does not meet the requirements, it will be determined whether it is necessary to increase the scanning timeout threshold for pharmaceutical distribution data. If it is not necessary to increase the scanning timeout threshold for pharmaceutical distribution data, the depth ratio of pharmaceutical traceability should be determined based on the correlation rate of pharmaceutical distribution data.

[0006] Furthermore, the prediction results are uploaded to a pharmaceutical supply chain sharing platform for sharing, characterized by including: The prediction results are sequentially formatted and compressed to output a risk prediction result set; The risk prediction result set is digitally signed and uploaded to the pharmaceutical supply chain sharing platform to achieve information sharing among all participants in the pharmaceutical supply chain.

[0007] Furthermore, based on the response time of the predicted potential risks, it is determined whether the real-time management of pharmaceutical supply chain risks meets the requirements, including: Compare the response time for potential risk prediction with the preset first response time; If the response time of the potential risk prediction is less than or equal to the preset first response time, then the real-time management of pharmaceutical supply chain risks is determined to meet the requirements. If the response time of the potential risk prediction is longer than the preset first response time, it is determined that the real-time management of pharmaceutical supply chain risks does not meet the requirements, and it is determined whether the information sharing and collaboration coefficient of the pharmaceutical supply chain needs to be increased based on the response time of the potential risk prediction.

[0008] Furthermore, determine whether it is necessary to increase the information sharing and collaboration coefficient of the pharmaceutical supply chain, including: The response time of the potential risk prediction is compared with the preset first response time and the preset second response time, respectively; If the response time of the potential risk prediction is longer than the preset second response time, it is determined that the information sharing and collaboration coefficient of the pharmaceutical supply chain needs to be increased. If the response time of the potential risk prediction is greater than the preset first response time and less than or equal to the preset second response time, then it is determined that there is no need to increase the information sharing and collaboration coefficient of the pharmaceutical supply chain.

[0009] Furthermore, the increase in the information sharing and collaboration coefficient of the pharmaceutical supply chain is determined by the difference between the response time of potential risk prediction and the preset second response time.

[0010] Furthermore, the risk identification coverage of the pharmaceutical supply chain is determined based on the potential risk occurrence rate within a unit of time, including: The potential risk incidence rate in the pharmaceutical supply chain within the unit time period is compared with a preset first incidence rate; If the incidence rate of potential risks in the pharmaceutical supply chain per unit time is less than or equal to the preset first incidence rate, then it is determined that the risk identification coverage of the pharmaceutical supply chain meets the requirements, and it is determined whether the information sharing and collaboration coefficient of the pharmaceutical supply chain meets the requirements. If the incidence rate of potential risks in the pharmaceutical supply chain within a unit of time is greater than the preset first incidence rate, it is determined that the risk identification coverage of the pharmaceutical supply chain does not meet the requirements, and it is determined whether the scanning timeout threshold of pharmaceutical circulation data needs to be increased based on the incidence rate of potential risks in the pharmaceutical supply chain within a unit of time.

[0011] Further, determine whether it is necessary to increase the scanning timeout threshold for pharmaceutical distribution data, including: The potential risk occurrence rate in the pharmaceutical supply chain within the unit time period is compared with the preset first occurrence rate and the preset second occurrence rate, respectively. If the incidence rate of potential risks in the pharmaceutical supply chain per unit time is greater than the preset first incidence rate and less than or equal to the preset second incidence rate, then it is determined that the scanning timeout circuit breaker threshold for pharmaceutical circulation data needs to be increased. If the incidence rate of potential risks in the pharmaceutical supply chain within a unit of time is greater than the preset second incidence rate, then it is determined that there is no need to increase the scanning timeout threshold for pharmaceutical circulation data.

[0012] Furthermore, the increase in the scanning timeout threshold of the pharmaceutical distribution data is determined by the difference between the potential risk occurrence rate in the pharmaceutical supply chain per unit time and the preset first occurrence rate.

[0013] Furthermore, the correlation rate of pharmaceutical distribution data determines the depth ratio of pharmaceutical traceability, including: The correlation rate of the pharmaceutical distribution data is compared with the preset correlation rate; If the correlation rate of the pharmaceutical distribution data is greater than the preset correlation rate, it is determined that the reliability of the pharmaceutical distribution data collection and verification meets the requirements, and there is no need to increase the cross-section depth ratio of pharmaceutical traceability. It is also determined whether the scanning timeout circuit breaker threshold of the pharmaceutical distribution data meets the requirements. If the correlation rate of the pharmaceutical distribution data is less than or equal to the preset correlation rate, it is determined that the reliability of the pharmaceutical distribution data collection and verification does not meet the requirements, and the depth ratio of the pharmaceutical traceability needs to be increased.

[0014] Furthermore, the increase in the depth ratio of the pharmaceutical traceability is determined by the difference between the preset correlation rate and the correlation rate of pharmaceutical circulation data.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: The method of this invention adjusts the information sharing and collaboration coefficient of the pharmaceutical supply chain based on the response time of potential risk prediction. Due to the delay in information sharing among various participants in the supply chain, the cross-entity transmission and synchronization of core risk control data are not timely, resulting in the inability to quickly link and trace the source after a risk event is triggered. By increasing the information sharing and collaboration coefficient, a compliant real-time data interoperability channel can be built, enhancing the synchronization efficiency of data throughout the entire process, shortening the delay in risk information transmission, and improving the collaborative response speed of various participants. The scanning timeout threshold of pharmaceutical distribution data is adjusted based on the potential risk occurrence rate in the pharmaceutical supply chain per unit time. Because pharmaceutical distribution data has a non-uniform format and is collected from different stages, the delayed arrival of pharmaceutical distribution data may cause processing and identification tasks to time out. The failure to complete the task in a timely manner resulted in some potential risks going unidentified. Increasing the scan timeout threshold can extend the data processing time, allowing more time to complete risk identification when dealing with complex and large-scale data, thereby improving overall risk coverage and ensuring that potential risks can be identified and addressed promptly. Adjusting the depth ratio of pharmaceutical traceability based on the correlation rate of pharmaceutical distribution data is also crucial. Because pharmaceutical distribution data collection is conducted independently at each stage, the flow data across multiple stages lacks unified correlation identifiers, resulting in a low correlation rate between data from different stages. Increasing the depth ratio of pharmaceutical traceability can strengthen the correlation mapping and synchronous collection of data across stages, improving the correlation rate between data from different stages. This provides a high-quality data foundation for comprehensively extracting risk characteristics and improving risk identification coverage, thus enhancing the real-time management of pharmaceutical supply chain risks.

[0016] Furthermore, the method described in this invention adjusts the information sharing and collaboration coefficient of the pharmaceutical supply chain by setting a preset first response time and a preset second response time. Due to the delay in information sharing among the participants in the supply chain, the cross-entity transmission and synchronization of core risk control data are not timely, resulting in the inability to quickly link and trace the source after a risk event is triggered. By increasing the information sharing and collaboration coefficient, a compliant real-time data interoperability channel can be built, the synchronization efficiency of data in all stages can be strengthened, the delay in risk information transmission can be shortened, the collaborative response speed of the participants can be improved, and the real-time management of pharmaceutical supply chain risks can be further improved.

[0017] Furthermore, the method of the present invention adjusts the scanning timeout threshold of pharmaceutical distribution data by setting a preset first occurrence rate and a preset second occurrence rate. Since pharmaceutical distribution data is not uniform in format and is collected from different links, the delayed arrival of pharmaceutical distribution data may cause processing and identification tasks to fail to be completed in time due to timeout, resulting in some potential risks not being identified. By increasing the scanning timeout threshold, the data processing time can be extended, allowing more time to complete risk identification when facing complex and large-scale data, improving the overall risk coverage, ensuring that potential risks can be identified in time and corresponding measures can be taken, and further improving the real-time management of pharmaceutical supply chain risks.

[0018] Furthermore, the method described in this invention adjusts the depth ratio of pharmaceutical traceability by setting a preset correlation rate. Since pharmaceutical distribution data collection is carried out independently at each stage, the flow data of multiple stages lacks a unified correlation identifier, and the correlation rate of data at different stages is low. By increasing the depth ratio of pharmaceutical traceability, the correlation mapping and synchronous collection of cross-stage data can be strengthened, the correlation rate of data at different stages can be improved, and a high-quality data foundation can be provided for comprehensively extracting risk characteristics and improving the coverage of risk identification, thereby further improving the real-time management of pharmaceutical supply chain risks. Attached Figure Description

[0019] Figure 1 This is an overall flowchart of the intelligent risk control management method for the pharmaceutical supply chain according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the process of determining whether to increase the information sharing and collaboration coefficient of the pharmaceutical supply chain in the intelligent risk control management method of the pharmaceutical supply chain, as described in this embodiment of the invention. Figure 3 This is a flowchart illustrating the process of determining whether to increase the scanning timeout threshold for pharmaceutical distribution data in the intelligent risk control management method for the pharmaceutical supply chain, as described in this embodiment of the invention. Figure 4 This is a flowchart illustrating the logic of determining the depth ratio of pharmaceutical traceability in an intelligent risk control management method for the pharmaceutical supply chain, as described in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0021] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0022] Please see Figure 1As shown, it is an overall flowchart of the intelligent risk control management method for the pharmaceutical supply chain according to an embodiment of the present invention.

[0023] This invention provides an intelligent risk control management method for the pharmaceutical supply chain, comprising: Step S1: Collect pharmaceutical distribution data in the pharmaceutical supply chain, and sequentially clean, denoise, reduce, format, and extract features from the pharmaceutical distribution data to output pharmaceutical distribution features. Based on the pharmaceutical distribution features, train the initial model to build a risk identification model. Step S2: Based on the pharmaceutical distribution data and the risk identification model, identify and predict potential risks in the pharmaceutical supply chain to output prediction results, and upload the prediction results to the pharmaceutical supply chain sharing platform for sharing. Step S3: Obtain the response time of potential risk prediction, and determine whether the real-time management of pharmaceutical supply chain risks meets the requirements based on the response time of potential risk prediction. Step S4: If the real-time management of the pharmaceutical supply chain risk does not meet the requirements, determine whether it is necessary to increase the information sharing and collaboration coefficient of the pharmaceutical supply chain. Step S5: If it is not necessary to increase the information sharing and collaboration coefficient of the pharmaceutical supply chain, then obtain the potential risk occurrence rate in the pharmaceutical supply chain per unit time to determine whether the risk identification coverage of the pharmaceutical supply chain meets the requirements. Step S6: If the risk identification coverage of the pharmaceutical supply chain does not meet the requirements, determine whether it is necessary to increase the scanning timeout threshold of pharmaceutical circulation data. Step S7: If it is not necessary to increase the scanning timeout threshold of pharmaceutical distribution data, then determine the depth ratio of pharmaceutical traceability based on the correlation rate of pharmaceutical distribution data.

[0024] Specifically, pharmaceutical distribution data includes production batch information, transportation data, and procurement data.

[0025] Specifically, the characteristics of pharmaceutical distribution include the production batch qualification rate, the drug loss rate during transportation, and the volatility of procurement prices.

[0026] Specifically, the process of training the initial model based on the characteristics of pharmaceutical distribution to construct a risk identification model involves selecting and reducing the dimensionality of pharmaceutical distribution characteristics, screening out features related to risk identification, and constructing training, testing, and validation sets respectively. The initial model is trained based on the training set, and the model parameters are adjusted using methods such as cross-validation. The model performance is evaluated using the validation set, and the model's generalization ability on unseen data is detected. Finally, the model is evaluated using the testing set to obtain a stable and reliable risk identification model.

[0027] Specifically, the initial model is a machine learning framework with risk prediction and classification capabilities.

[0028] Specifically, the risk identification model can be a random forest, a support vector machine, or a Bayesian model, with a preferred embodiment being a random forest.

[0029] Specifically, the process of identifying and predicting potential risks in the pharmaceutical supply chain based on pharmaceutical distribution data and the aforementioned risk identification model, and outputting prediction results, involves collecting and processing relevant data from the pharmaceutical supply chain, using a trained risk identification model to make predictions, and outputting prediction results for potential risks.

[0030] Specifically, potential risks include the risk of drug cross-selling, the risk of unsold drugs nearing their expiration date, and the risk of drugs becoming ineffective due to improper storage.

[0031] Specifically, the forecast results include forecasts of supplier default risk, drug quality problem risk, and transportation delay risk.

[0032] Specifically, the pharmaceutical supply chain sharing platform is a pharmaceutical distribution data collaborative exchange system jointly built by all participants in the pharmaceutical supply chain based on consortium blockchain technology.

[0033] Specifically, the information sharing and collaboration coefficient of the pharmaceutical supply chain is a key parameter for quantifying the degree of information sharing and collaborative processing among supply chain entities.

[0034] Specifically, the scanning timeout circuit breaker threshold for pharmaceutical distribution data is the maximum scanning time during which the risk identification model interrupts the scanning process when identifying data from a single data node in the process of identifying and predicting potential risks in the pharmaceutical supply chain.

[0035] Specifically, the depth ratio of pharmaceutical traceability is the ratio of the number of pharmaceutical traceability data dimensions that are actually collected and linked in different observational sections of pharmaceutical traceability to the total number of complete traceability data dimensions that should be collected.

[0036] Specifically, the observation sections include the drug time section, the drug space section, and the drug quality section.

[0037] Specifically, pharmaceutical traceability data dimensions include inventory cycle, supplier location, drug production batch, and customer demand.

[0038] In implementation, the method of this invention adjusts the information sharing and collaboration coefficient of the pharmaceutical supply chain based on the response time of potential risk prediction. Due to information sharing delays among various participants in the supply chain, the cross-entity transmission and synchronization of core risk control data are not timely, resulting in the inability to quickly link and trace the source after a risk event is triggered. By increasing the information sharing and collaboration coefficient, a compliant real-time data interoperability channel can be built, enhancing the synchronization efficiency of data throughout the entire process, shortening the delay in risk information transmission, and improving the collaborative response speed of various participants. The scanning timeout threshold for pharmaceutical distribution data is adjusted based on the potential risk occurrence rate in the pharmaceutical supply chain per unit time. Because pharmaceutical distribution data has inconsistent formats and is collected from different stages, the delayed arrival of pharmaceutical distribution data may cause processing and identification tasks to fail to be completed in a timely manner due to timeouts. This leads to some potential risks going unidentified. Increasing the scan timeout threshold can extend data processing time, allowing more time to complete risk identification when dealing with complex and large-scale data, improving overall risk coverage, and ensuring that potential risks can be identified in a timely manner and corresponding measures can be taken. The depth ratio of pharmaceutical traceability is adjusted based on the correlation rate of pharmaceutical distribution data. Because pharmaceutical distribution data collection is carried out independently at each stage, the flow data of multiple stages lacks a unified correlation identifier, and the correlation rate of data between different stages is low. By increasing the depth ratio of pharmaceutical traceability, the correlation mapping and synchronous collection of cross-stage data can be strengthened, improving the correlation rate of data between different stages. This provides a high-quality data foundation for comprehensively extracting risk characteristics and improving risk identification coverage, thereby improving the real-time management of pharmaceutical supply chain risks.

[0039] Specifically, uploading the prediction results to a pharmaceutical supply chain sharing platform for sharing is characterized by including: The prediction results are sequentially formatted and compressed to output a risk prediction result set; The risk prediction result set is digitally signed and uploaded to the pharmaceutical supply chain sharing platform to achieve information sharing among all participants in the pharmaceutical supply chain.

[0040] Specifically, digital signature is a technical means of generating a unique identifier code for verifying data integrity based on an asymmetric cryptographic system. It is a technical means well known to those skilled in the art and will not be elaborated on further here.

[0041] Please continue reading. Figure 2 The diagram shown is a logical flowchart illustrating the process of determining whether to increase the information sharing and collaboration coefficient of the pharmaceutical supply chain in an intelligent risk control management method according to an embodiment of the present invention.

[0042] Specifically, determining whether the real-time management of pharmaceutical supply chain risks meets requirements based on the response time of the predicted potential risks includes: Compare the response time for potential risk prediction with the preset first response time; If the response time of the potential risk prediction is less than or equal to the preset first response time, then the real-time management of pharmaceutical supply chain risks is determined to meet the requirements. If the response time of the potential risk prediction is longer than the preset first response time, it is determined that the real-time management of pharmaceutical supply chain risks does not meet the requirements, and it is determined whether the information sharing and collaboration coefficient of the pharmaceutical supply chain needs to be increased based on the response time of the potential risk prediction.

[0043] The reasons why the real-time management of pharmaceutical supply chain risks may not meet requirements include inadequate risk identification coverage or insufficient information sharing and collaboration coefficients within the pharmaceutical supply chain. The next step is to determine the specific cause, which is also the process of determining whether to increase the information sharing and collaboration coefficients of the pharmaceutical supply chain.

[0044] Specifically, determining whether it is necessary to increase the information sharing and collaboration coefficient of the pharmaceutical supply chain includes: The response time of the potential risk prediction is compared with the preset first response time and the preset second response time, respectively; If the response time of the potential risk prediction is longer than the preset second response time, it is determined that the information sharing and collaboration coefficient of the pharmaceutical supply chain needs to be increased. If the response time of the potential risk prediction is greater than the preset first response time and less than or equal to the preset second response time, then it is determined that there is no need to increase the information sharing and collaboration coefficient of the pharmaceutical supply chain.

[0045] Specifically, when the response time for potential risk prediction exceeds the preset second response time, it indicates that the reason for the non-compliance of real-time management of pharmaceutical supply chain risks is that the information sharing and coordination coefficient of the pharmaceutical supply chain is not up to standard, thus requiring an increase in the information sharing and coordination coefficient. When the response time for potential risk prediction exceeds the preset first response time but is less than or equal to the preset second response time, it can be preliminarily determined that the risk identification coverage of the pharmaceutical supply chain is not up to standard. The next step is to determine whether the risk identification coverage of the pharmaceutical supply chain meets the requirements based on the occurrence rate of potential risks within the pharmaceutical supply chain per unit time, i.e., to determine whether the non-compliance of real-time management of pharmaceutical supply chain risks is due to the non-compliance of risk identification coverage.

[0046] It is understandable that the preset first response time is shorter than the preset second response time, and the three intervals divided by the preset first and second response times correspond to three different scenarios: The first interval is when the response time for potential risk prediction is less than or equal to the preset first response time. The corresponding situation is that the real-time management of pharmaceutical supply chain risks meets the requirements, and no adjustment is needed. The second interval is the response time for potential risk prediction that is greater than the preset first response time and less than or equal to the preset second response time. The corresponding situation is: due to the inconsistent format of pharmaceutical distribution data and the fact that the data is collected from different links, the delayed arrival of pharmaceutical distribution data may cause the processing and identification tasks to fail to be completed in time due to timeouts, resulting in some potential risks not being identified. In this case, it is necessary to further determine whether the risk identification coverage of the pharmaceutical supply chain meets the requirements. The third interval is when the response time for potential risk prediction is longer than the preset second response time. The corresponding situation is that due to the delay in information sharing among the participants in the supply chain, the cross-entity transmission and synchronization of core risk control data is not timely, which makes it impossible to quickly link and trace the source after the risk event is triggered. At this time, it is necessary to adjust the information sharing and coordination coefficient of the pharmaceutical supply chain.

[0047] Understandably, in the process of managing pharmaceutical supply chain risks, using preset first and second response times to characterize the real-time nature of risk management essentially transforms abstract risk response timeliness requirements into quantifiable response time ranges by linking risk event response time with the real-time nature of supply chain risk control. The preset first response time serves as a dividing line between whether the real-time nature of pharmaceutical supply chain risk control is met; its core function is to determine whether the system meets the basic timeliness standards for drug quality traceability and risk event handling. The preset second response time serves as a dividing line between the severity and cause of delays; its core function is to determine the root cause of non-compliance with real-time requirements. The preset first and second response times can be set according to actual operating conditions. The setting of these preset first and second response times aims to ensure the real-time nature and practicality of pharmaceutical supply chain risk management. Optionally, the preset first response duration and second response duration are determined through a limited number of trials by evaluating the effectiveness of different response durations in managing pharmaceutical supply chain risks. The determined preset first response duration and second response duration should be neither too short nor too disruptive to the pharmaceutical supply chain risk management process. For example, the preset first response duration is generally selected in the range of [1s, 3s], and the preset second response duration is generally selected in the range of [7s, 9s].

[0048] Preferably, the preferred embodiment of the preset first response duration is 2s, and the preferred embodiment of the preset second response duration is 8s.

[0049] Specifically, the response time for potential risk prediction is the interval between the moment the risk identification model receives the pharmaceutical distribution data used for prediction and the moment the prediction result is generated.

[0050] Specifically, the increase in the information sharing and collaboration coefficient of the pharmaceutical supply chain is determined by the difference between the response time of potential risk prediction and the preset second response time.

[0051] Specifically, when the difference between the predicted response time of potential risks and the preset second response time is within 2 seconds, the information sharing and collaboration coefficient of the pharmaceutical supply chain increases to 1.1 times the original value. When the difference exceeds 2 seconds, in addition to increasing to 1.1 times the original value, the information sharing and collaboration coefficient of the pharmaceutical supply chain increases by 0.03 for every additional second. For example, when the difference between the predicted response time of potential risks and the preset second response time is 4 seconds, the current information sharing and collaboration coefficient of the pharmaceutical supply chain is 0.6, and the increased information sharing and collaboration coefficient of the pharmaceutical supply chain is 0.6×1.1+0.03×2=0.72.

[0052] In practice, the method described in this invention adjusts the information sharing and collaboration coefficient of the pharmaceutical supply chain by setting a preset first response time and a preset second response time. Due to the delay in information sharing among the participants in the supply chain, the cross-entity transmission and synchronization of core risk control data are not timely, resulting in the inability to quickly link and trace the source after a risk event is triggered. By increasing the information sharing and collaboration coefficient, a compliant real-time data exchange channel can be built, the synchronization efficiency of data in all stages can be strengthened, the delay in risk information transmission can be shortened, the collaborative response speed of the participants can be improved, and the real-time management of risks in the pharmaceutical supply chain can be further improved.

[0053] Please continue reading. Figure 3 As shown, it is a logical flowchart of the process of determining whether to increase the scanning timeout circuit breaker threshold of pharmaceutical circulation data in the intelligent risk control management method for the pharmaceutical supply chain according to an embodiment of the present invention.

[0054] Specifically, determining whether the risk identification coverage of the pharmaceutical supply chain meets the requirements is based on the potential risk occurrence rate within the pharmaceutical supply chain per unit of time, including: The potential risk incidence rate in the pharmaceutical supply chain within the unit time period is compared with a preset first incidence rate; If the incidence rate of potential risks in the pharmaceutical supply chain per unit time is less than or equal to the preset first incidence rate, then it is determined that the risk identification coverage of the pharmaceutical supply chain meets the requirements, and it is determined whether the information sharing and collaboration coefficient of the pharmaceutical supply chain meets the requirements. If the incidence rate of potential risks in the pharmaceutical supply chain per unit time is greater than the preset first incidence rate, then the risk identification coverage of the pharmaceutical supply chain is determined to be unsatisfactory.

[0055] Specifically, if the incidence rate of potential risks in the pharmaceutical supply chain per unit time is less than or equal to the preset first incidence rate, it is determined that the risk identification coverage of the pharmaceutical supply chain meets the requirements. However, if the real-time management of previously determined pharmaceutical supply chain risks does not meet the requirements, it is necessary to further determine whether the information sharing and collaboration coefficient of the pharmaceutical supply chain meets the requirements.

[0056] In implementation, the information sharing and collaboration coefficient of the actual pharmaceutical supply chain is compared with the predetermined collaboration coefficient threshold to determine whether the information sharing and collaboration coefficient of the pharmaceutical supply chain meets the requirements. If the actual information sharing and collaboration coefficient of the pharmaceutical supply chain is less than the predetermined collaboration coefficient threshold, the information sharing and collaboration coefficient of the pharmaceutical supply chain is determined to be non-compliant. The predetermined collaboration coefficient threshold is the average value of the information sharing and collaboration coefficient of the pharmaceutical supply chain monitored in the previous three months of the historical period.

[0057] If the information sharing and collaboration coefficient of the pharmaceutical supply chain does not meet the requirements, then the information sharing and collaboration coefficient of the pharmaceutical supply chain should be increased; if the information sharing and collaboration coefficient of the pharmaceutical supply chain meets the requirements, then the response time of potential risk prediction should be re-collected, and the real-time management of pharmaceutical supply chain risks should be reassessed to determine whether it meets the requirements.

[0058] When the incidence rate of potential risks in the pharmaceutical supply chain per unit time exceeds the preset first incidence rate, the cause of the failure to meet the real-time requirements for pharmaceutical supply chain risk management can be identified as insufficient risk identification coverage. This insufficient coverage could be due to either a failure to meet the scanning timeout threshold for pharmaceutical distribution data, or insufficient reliability of pharmaceutical distribution data collection and verification. The next step is to determine the specific cause, which involves deciding whether to increase the scanning timeout threshold for pharmaceutical distribution data.

[0059] Specifically, determining whether to increase the timeout threshold for scanning pharmaceutical distribution data includes: The potential risk occurrence rate in the pharmaceutical supply chain within the unit time period is compared with the preset first occurrence rate and the preset second occurrence rate, respectively. If the incidence rate of potential risks in the pharmaceutical supply chain per unit time is greater than the preset first incidence rate and less than or equal to the preset second incidence rate, then it is determined that the scanning timeout circuit breaker threshold for pharmaceutical circulation data needs to be increased. If the incidence rate of potential risks in the pharmaceutical supply chain within a unit of time is greater than the preset second incidence rate, then it is determined that there is no need to increase the scanning timeout threshold for pharmaceutical circulation data.

[0060] Specifically, if the incidence rate of potential risks in the pharmaceutical supply chain per unit time is greater than a preset first incidence rate but less than or equal to a preset second incidence rate, it is determined that the reason for the non-compliance of the risk identification coverage of the pharmaceutical supply chain is that the scanning timeout threshold for pharmaceutical distribution data does not meet the requirements. Therefore, it is necessary to increase the scanning timeout threshold for pharmaceutical distribution data. If the incidence rate of potential risks in the pharmaceutical supply chain per unit time is greater than the preset second incidence rate, it can be preliminarily determined that the reliability of pharmaceutical distribution data collection and verification does not meet the requirements. Next, it is necessary to make a final determination based on the correlation rate of pharmaceutical distribution data to determine whether the reliability of pharmaceutical distribution data collection and verification meets the requirements, that is, to determine whether the reason for the non-compliance of the risk identification coverage of the pharmaceutical supply chain is the non-compliance of the reliability of pharmaceutical distribution data collection and verification.

[0061] It is understandable that the preset first incidence rate is lower than the preset second incidence rate, and the three intervals divided by the preset first and second incidence rates correspond to three different scenarios: The first interval is when the potential risk occurrence rate in the pharmaceutical supply chain per unit time is less than or equal to the preset first occurrence rate. The corresponding situation is: the risk identification coverage of the pharmaceutical supply chain meets the requirements. At this time, it is necessary to further determine whether the information sharing and collaboration coefficient of the pharmaceutical supply chain meets the requirements. The second interval is when the incidence rate of potential risks in the pharmaceutical supply chain per unit time is greater than the preset first incidence rate and less than or equal to the preset second incidence rate. The corresponding situation is: due to the inconsistent format of pharmaceutical distribution data and the fact that the data is collected from different links, the delayed arrival of pharmaceutical distribution data may cause the processing and identification tasks to fail to be completed in time due to timeout, resulting in some potential risks not being identified. In this case, it is necessary to adjust the scanning timeout circuit breaker threshold of pharmaceutical distribution data. The third interval is when the potential risk occurrence rate in the pharmaceutical supply chain per unit time is greater than the preset second occurrence rate. The corresponding situation is: because the pharmaceutical distribution data collection is carried out independently on a single link basis, the flow data of multiple links lacks a unified association identifier, and the data association rate of different links is low. At this time, it is necessary to further judge whether the reliability of the pharmaceutical distribution data collection and verification meets the requirements.

[0062] Understandably, in the process of managing pharmaceutical supply chain risks, introducing preset first and second occurrence rates to characterize the risk identification coverage of the pharmaceutical supply chain essentially transforms the abstract risk identification coverage requirements into quantifiable occurrence rate ranges by correlating the potential risk occurrence rate with the risk identification coverage. The preset first occurrence rate serves as a dividing line to distinguish whether the pharmaceutical supply chain risk identification coverage meets the standards; its core function is to determine whether the system meets the basic coverage standards for drug distribution risk warning and hidden danger identification. The preset second occurrence rate serves as a dividing line to distinguish the severity and cause of risk identification problems; its core function is to determine the root cause of non-compliance with coverage requirements. The preset first and second occurrence rates can be set according to actual operating conditions. The setting of the preset first and second occurrence rates aims to ensure the real-time nature and practicality of pharmaceutical supply chain risk management. Optionally, the preset first and second occurrence rates are determined through a limited number of trials by evaluating the management effect of different time-delayed occurrence rates on pharmaceutical supply chain risks. The determined preset first and second occurrence rates should be neither too low nor excessively disruptive to the pharmaceutical supply chain risk management process. For example, the preset first incidence rate is generally selected in the range of [2%, 4%], and the preset second incidence rate is generally selected in the range of [6%, 8%].

[0063] Preferably, the first occurrence rate is 3% in the preferred embodiment, and the second occurrence rate is 7% in the preferred embodiment.

[0064] Specifically, the potential risk occurrence rate in the pharmaceutical supply chain per unit time is the ratio of the number of risk events that actually occur within a unit time without prior risk identification results to the total number of all actual risk events.

[0065] Specifically, the increase in the scanning timeout threshold of the pharmaceutical distribution data is determined by the difference between the potential risk occurrence rate in the pharmaceutical supply chain per unit time and a preset first occurrence rate.

[0066] Specifically, when the difference between the potential risk occurrence rate in the pharmaceutical supply chain and the preset first occurrence rate within a unit time is within 2%, the scanning timeout threshold for pharmaceutical distribution data is increased to 1.1 times the original value. When the difference between the potential risk occurrence rate in the pharmaceutical supply chain and the preset first occurrence rate within a unit time exceeds 2%, the scanning timeout threshold for pharmaceutical distribution data is increased by 1 second for every 1% exceeding the original value, in addition to the increase to 1.1 times the original value. For example, when the difference between the potential risk occurrence rate in the pharmaceutical supply chain and the preset first occurrence rate within a unit time is 4%, the current scanning timeout threshold for pharmaceutical distribution data is 10 seconds. The increased scanning timeout threshold for pharmaceutical distribution data is 10 × 1.1 + 1 × 2 = 13 seconds.

[0067] In practice, the method described in this invention adjusts the scanning timeout threshold for pharmaceutical distribution data by setting a preset first occurrence rate and a preset second occurrence rate. Due to the inconsistent format of pharmaceutical distribution data and its collection from different stages, the delayed arrival of pharmaceutical distribution data may cause processing and identification tasks to fail to be completed in a timely manner due to timeouts, resulting in some potential risks not being identified. By increasing the scanning timeout threshold, the data processing time can be extended, allowing more time to complete risk identification when facing complex and large-scale data, improving the overall risk coverage, ensuring that potential risks can be identified in a timely manner and corresponding measures can be taken, and further improving the real-time management of pharmaceutical supply chain risks.

[0068] Please continue reading. Figure 4 As shown, it is a logical flowchart of the method for determining the depth ratio of pharmaceutical traceability in the intelligent risk control management method for the pharmaceutical supply chain according to an embodiment of the present invention.

[0069] Specifically, the correlation rate of pharmaceutical distribution data determines the depth ratio of pharmaceutical traceability, including: The correlation rate of the pharmaceutical distribution data is compared with the preset correlation rate; If the correlation rate of the pharmaceutical distribution data is greater than the preset correlation rate, it is determined that the reliability of the pharmaceutical distribution data collection and verification meets the requirements, and there is no need to increase the cross-section depth ratio of pharmaceutical traceability. It is also determined whether the scanning timeout circuit breaker threshold of the pharmaceutical distribution data meets the requirements. If the correlation rate of the pharmaceutical distribution data is less than or equal to the preset correlation rate, it is determined that the reliability of the pharmaceutical distribution data collection and verification does not meet the requirements, and the depth ratio of the pharmaceutical traceability needs to be increased.

[0070] Specifically, when the correlation rate of pharmaceutical distribution data is greater than the preset correlation rate, it is determined that the reliability of pharmaceutical distribution data collection and verification meets the requirements. However, if it has been previously determined that the risk identification coverage of the pharmaceutical supply chain does not meet the requirements, then it is necessary to further determine whether the scanning timeout circuit breaker threshold of pharmaceutical distribution data meets the requirements.

[0071] In practice, the scanning timeout threshold of the actual pharmaceutical distribution data is compared with the predetermined timeout threshold to determine whether the scanning timeout threshold of the pharmaceutical distribution data meets the requirements. If the scanning timeout threshold of the actual pharmaceutical distribution data is less than the predetermined timeout threshold, the scanning timeout threshold of the pharmaceutical distribution data is determined to be non-compliant. The predetermined timeout threshold is the average value of the scanning timeout threshold of the pharmaceutical distribution data monitored in the previous three months of the historical period.

[0072] If the actual pharmaceutical distribution data scanning timeout threshold does not meet the requirements, the actual pharmaceutical distribution data scanning timeout threshold will be increased; if the actual pharmaceutical distribution data scanning timeout threshold meets the requirements, the potential risk occurrence rate in the pharmaceutical supply chain per unit time will be re-collected, and the risk identification coverage of the pharmaceutical supply chain will be re-evaluated to determine whether it meets the requirements.

[0073] When the correlation rate of pharmaceutical distribution data is less than or equal to the preset correlation rate, it can be determined that the reason why the risk identification coverage of the pharmaceutical supply chain does not meet the requirements is that the reliability of pharmaceutical distribution data collection and verification does not meet the requirements. Therefore, it is necessary to increase the depth ratio of pharmaceutical traceability.

[0074] It is understandable that the two preset correlation rate intervals correspond to two different scenarios: The first interval is when the correlation rate of pharmaceutical distribution data is less than or equal to the preset correlation rate. The corresponding situation is: because the collection of pharmaceutical distribution data is carried out independently on a single link, the flow data of multiple links lacks a unified correlation identifier, and the correlation rate of data in different links is low. At this time, it is necessary to adjust the scanning timeout circuit breaker threshold of pharmaceutical distribution data. The second interval is when the correlation rate of pharmaceutical distribution data is greater than the preset correlation rate. The corresponding situation is: the reliability of pharmaceutical distribution data collection and verification meets the requirements. At this time, it is necessary to further determine whether the scanning timeout circuit breaker threshold of the pharmaceutical distribution data meets the requirements.

[0075] Understandably, in the process of managing pharmaceutical supply chain risks, introducing a preset correlation rate to characterize the reliability of pharmaceutical distribution data collection and verification is essentially because the preset correlation rate can be set according to actual working conditions. The purpose of setting the preset correlation rate is to ensure the real-time nature and practicality of pharmaceutical supply chain risk management. Optionally, the preset correlation rate is determined through a limited number of trials by evaluating the management effect of different correlation rates of lockout failures on pharmaceutical supply chain risks. The determined preset correlation rate should be neither too low nor too high, and should not cause excessive interference to the pharmaceutical supply chain risk management process. For example, the preset correlation rate is generally selected within the range of [96%, 98%].

[0076] Preferably, the preset correlation rate in the preferred embodiment is 97%.

[0077] Specifically, the correlation rate of the pharmaceutical distribution data is the ratio of the number of data items that have successfully established effective correlations to the total number of data items that should theoretically have correlations.

[0078] Specifically, the increase in the depth ratio of the pharmaceutical traceability is determined by the difference between the preset correlation rate and the correlation rate of pharmaceutical circulation data.

[0079] Specifically, when the difference between the preset correlation rate and the correlation rate of pharmaceutical distribution data is within 2%, the depth ratio of pharmaceutical traceability is increased to 1.1 times the original value. When the difference between the preset correlation rate and the correlation rate of pharmaceutical distribution data exceeds 2%, in addition to increasing to 1.1 times the original value, the depth ratio of pharmaceutical traceability increases by 1% for every 1% exceeding the original value. For example, when the difference between the preset correlation rate and the correlation rate of pharmaceutical distribution data is 4%, and the current depth ratio of pharmaceutical traceability is 80%, the increased depth ratio of pharmaceutical traceability is 80×1.1+1×2=90%.

[0080] In practice, the method described in this invention adjusts the depth ratio of pharmaceutical traceability by setting a preset correlation rate. Since pharmaceutical distribution data collection is carried out independently at each stage, the flow data of multiple stages lacks a unified correlation identifier, and the correlation rate of data at different stages is low. By increasing the depth ratio of pharmaceutical traceability, the correlation mapping and synchronous collection of cross-stage data can be strengthened, the correlation rate of data at different stages can be improved, and a high-quality data foundation can be provided for the comprehensive extraction of risk characteristics and the improvement of risk identification coverage, thereby further improving the real-time management of pharmaceutical supply chain risks.

[0081] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A smart risk control management method for the pharmaceutical supply chain, characterized in that, include: Collect pharmaceutical distribution data in the pharmaceutical supply chain, and sequentially clean, denoise, reduce, format, and extract features from the pharmaceutical distribution data to output pharmaceutical distribution features. Based on the pharmaceutical distribution features, train an initial model to build a risk identification model. Based on the pharmaceutical distribution data and the risk identification model, potential risks in the pharmaceutical supply chain are identified and predicted to output prediction results, which are then uploaded to the pharmaceutical supply chain sharing platform for sharing. Obtain the response time of potential risk prediction, and determine whether the real-time management of pharmaceutical supply chain risks meets the requirements based on the response time of the potential risk prediction; If the real-time management of the aforementioned pharmaceutical supply chain risks does not meet the requirements, then it is necessary to determine whether the information sharing and collaboration coefficient of the pharmaceutical supply chain needs to be increased. If it is not necessary to increase the information sharing and collaboration coefficient of the pharmaceutical supply chain, then obtain the potential risk occurrence rate in the pharmaceutical supply chain per unit time to determine whether the risk identification coverage of the pharmaceutical supply chain meets the requirements. If the risk identification coverage of the pharmaceutical supply chain does not meet the requirements, it will be determined whether it is necessary to increase the scanning timeout threshold for pharmaceutical distribution data. If it is not necessary to increase the scanning timeout threshold for pharmaceutical distribution data, the depth ratio of pharmaceutical traceability should be determined based on the correlation rate of pharmaceutical distribution data.

2. The intelligent risk control management method for the pharmaceutical supply chain according to claim 1, wherein the prediction results are uploaded to a pharmaceutical supply chain sharing platform for sharing, characterized in that, include: The prediction results are sequentially formatted and compressed to output a risk prediction result set; The risk prediction result set is digitally signed and uploaded to the pharmaceutical supply chain sharing platform to achieve information sharing among all participants in the pharmaceutical supply chain.

3. The intelligent risk control management method for the pharmaceutical supply chain according to claim 2, characterized in that, The response time based on the predicted potential risks determines whether the real-time management of pharmaceutical supply chain risks meets the requirements, including: Compare the response time for potential risk prediction with the preset first response time; If the response time of the potential risk prediction is less than or equal to the preset first response time, then the real-time management of pharmaceutical supply chain risks is determined to meet the requirements. If the response time of the potential risk prediction is longer than the preset first response time, it is determined that the real-time management of pharmaceutical supply chain risks does not meet the requirements, and it is determined whether the information sharing and collaboration coefficient of the pharmaceutical supply chain needs to be increased based on the response time of the potential risk prediction.

4. The intelligent risk control management method for the pharmaceutical supply chain according to claim 3, characterized in that, Determine whether it is necessary to increase the information sharing and collaboration coefficient in the pharmaceutical supply chain, including: The response time of the potential risk prediction is compared with the preset first response time and the preset second response time, respectively; If the response time of the potential risk prediction is longer than the preset second response time, it is determined that the information sharing and collaboration coefficient of the pharmaceutical supply chain needs to be increased. If the response time of the potential risk prediction is greater than the preset first response time and less than or equal to the preset second response time, then it is determined that there is no need to increase the information sharing and collaboration coefficient of the pharmaceutical supply chain.

5. The intelligent risk control management method for the pharmaceutical supply chain according to claim 4, characterized in that, The increase in the information sharing and collaboration coefficient of the pharmaceutical supply chain is determined by the difference between the response time of potential risk prediction and the preset second response time.

6. The intelligent risk control management method for the pharmaceutical supply chain according to claim 5, characterized in that, Determine whether the risk identification coverage of the pharmaceutical supply chain meets the requirements based on the potential risk occurrence rate in the pharmaceutical supply chain per unit time, including: The potential risk incidence rate in the pharmaceutical supply chain within the unit time period is compared with a preset first incidence rate; If the incidence rate of potential risks in the pharmaceutical supply chain per unit time is less than or equal to the preset first incidence rate, then it is determined that the risk identification coverage of the pharmaceutical supply chain meets the requirements, and it is determined whether the information sharing and collaboration coefficient of the pharmaceutical supply chain meets the requirements. If the incidence rate of potential risks in the pharmaceutical supply chain within a unit of time is greater than the preset first incidence rate, it is determined that the risk identification coverage of the pharmaceutical supply chain does not meet the requirements, and it is determined whether the scanning timeout threshold of pharmaceutical circulation data needs to be increased based on the incidence rate of potential risks in the pharmaceutical supply chain within a unit of time.

7. The intelligent risk control management method for the pharmaceutical supply chain according to claim 6, characterized in that, Determine whether it is necessary to increase the scanning timeout threshold for pharmaceutical distribution data, including: The potential risk occurrence rate in the pharmaceutical supply chain within the unit time period is compared with the preset first occurrence rate and the preset second occurrence rate, respectively. If the incidence rate of potential risks in the pharmaceutical supply chain per unit time is greater than the preset first incidence rate and less than or equal to the preset second incidence rate, then it is determined that the scanning timeout circuit breaker threshold for pharmaceutical circulation data needs to be increased. If the incidence rate of potential risks in the pharmaceutical supply chain within a unit of time is greater than the preset second incidence rate, then it is determined that there is no need to increase the scanning timeout threshold for pharmaceutical circulation data.

8. The intelligent risk control management method for the pharmaceutical supply chain according to claim 7, characterized in that, The increase in the scanning timeout threshold of the pharmaceutical distribution data is determined by the difference between the potential risk occurrence rate in the pharmaceutical supply chain per unit time and the preset first occurrence rate.

9. The intelligent risk control management method for the pharmaceutical supply chain according to claim 8, characterized in that, The depth ratio of pharmaceutical traceability is determined based on the correlation rate of pharmaceutical distribution data, including: The correlation rate of the pharmaceutical distribution data is compared with the preset correlation rate; If the correlation rate of the pharmaceutical distribution data is greater than the preset correlation rate, it is determined that the reliability of the pharmaceutical distribution data collection and verification meets the requirements, and there is no need to increase the cross-section depth ratio of pharmaceutical traceability. It is also determined whether the scanning timeout circuit breaker threshold of the pharmaceutical distribution data meets the requirements. If the correlation rate of the pharmaceutical distribution data is less than or equal to the preset correlation rate, it is determined that the reliability of the pharmaceutical distribution data collection and verification does not meet the requirements, and the depth ratio of the pharmaceutical traceability needs to be increased.

10. The intelligent risk control management method for the pharmaceutical supply chain according to claim 9, characterized in that, The increase in the depth ratio of the pharmaceutical traceability is determined by the difference between the preset correlation rate and the correlation rate of pharmaceutical circulation data.

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