Method and system for processing medicine flow direction data and electronic equipment

Abstract

The invention provides a medicine flow direction data processing method and system and electronic device.The medicine flow direction data processing method comprises the steps that in response to an input service strategy, medicine flow direction data in a target time period are obtained; determining a risk impact factor corresponding to a verification rule as a target risk impact factor in response to the fact that the medicine flow direction data meets the verification rule of any configuration; wherein the verification rules are in one-to-one correspondence with the risk influence factors; obtaining a dynamic weight of the target risk influence factor under the business strategy; and according to the dynamic weights of all the target risk influence factors and the basic evaluation indexes, calculating to obtain an evaluation result of the credibility of the medicine flow direction data. According to the method and the device, the credibility of the medicine flow direction data can be quantitatively evaluated, and the evaluation result of the credibility of the medicine flow direction data can be ensured to be consistent with the current business strategy, so that the accuracy of evaluating the credibility of the data is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of processing medicine flow data, and in particular to a method and system for processing medicine flow data, and an electronic device. Background Art

[0002] In the pharmaceutical distribution sector, sales, procurement, inventory, and other data generated by pharmaceutical companies, distributors, hospitals, and other stakeholders is a core foundation for market analysis, sales forecasting, channel management, and compliance monitoring. However, the credibility of this data has long faced severe challenges. Traditional data verification methods rely primarily on manual spot checks, financial reconciliation, and simple rule-based early warnings based on fixed thresholds. These methods have the following shortcomings:

[0003] Insufficient data integrity verification: Traditional methods struggle to cover all distribution levels, especially for data from second- and third-tier distributors and small retail outlets. Collection often suffers from blind spots, leading to broken data chains and an inability to form a complete traceability view.

[0004] Single-source authenticity verification methods: Existing technologies often rely on static thresholds. For example, simply setting "monthly sales exceeding 1,000 boxes is considered an anomaly" doesn't adapt to the dynamic changes of different products, regions, and market cycles. This one-size-fits-all approach can severely limit accuracy in complex scenarios, such as salespeople hoarding inventory to meet performance targets, cross-channel arbitrage, and data entry errors or malicious tampering. False positives and missed negatives are common.

[0005] Serious timeliness lags: Data verification is typically conducted a month or even a quarter after a transaction occurs, resulting in delayed risk discovery. For example, by the time a distributor is found to be severely hoarding inventory, months have often passed. This not only wastes pharmaceutical companies' marketing resources but also misses the optimal opportunity for market intervention.

[0006] Lack of quantitative credibility assessment: Traditional methods can usually only output binary conclusions of "normal" or "abnormal", and cannot conduct refined quantitative assessments of data credibility. Decision makers cannot intuitively understand the high or low data credibility of a certain batch, a certain dealer, or a certain region, making it difficult to prioritize risks and accurately allocate resources. Summary of the Invention

[0007] The technical problem to be solved by the present disclosure is to overcome the above-mentioned defects in the prior art and provide a method and system for processing pharmaceutical flow data, and an electronic device that can quantitatively evaluate the credibility of the data.

[0008] The present disclosure solves the above technical problems through the following technical solutions:

[0009] A first aspect of the present disclosure provides a method for processing medicine flow data, comprising the following steps:

[0010] Responding to an inputted business strategy, obtaining medicine flow data in a target time period; wherein the business strategy includes the target time period;

[0011] In response to the pharmaceutical flow data satisfying any configured verification rule, determining the risk impact factor corresponding to the verification rule as a target risk impact factor; wherein the verification rule corresponds to the risk impact factor one-to-one;

[0012] Obtaining the dynamic weight of the target risk impact factor under the business strategy;

[0013] The evaluation result of the credibility of the pharmaceutical flow data is calculated based on the dynamic weights of all target risk influencing factors and basic evaluation indicators.

[0014] Optionally, obtaining the dynamic weight of the target risk impact factor under the business policy specifically includes:

[0015] Obtaining the weighted impact coefficient of the target risk impact factor under the business strategy;

[0016] The dynamic weight of the target risk impact factor is determined according to the initial weight and weight impact coefficient of the target risk impact factor.

[0017] Optionally, the evaluation result of the credibility of the pharmaceutical flow data is calculated based on the dynamic weights of all target risk influencing factors and basic evaluation indicators, specifically including:

[0018] Calculate the current evaluation index of each target risk impact factor based on the dynamic weight and basic evaluation index of each target risk impact factor;

[0019] An evaluation result of the credibility of the pharmaceutical flow data is determined based on the current evaluation indicators of all target risk influencing factors.

[0020] Optionally, the processing method further includes: displaying a current evaluation indicator of the target risk impact factor in response to a triggering operation on the evaluation result.

[0021] Optionally, the business strategy further includes a target strategy type, and obtaining a weight influence coefficient of the target risk impact factor under the business strategy specifically includes:

[0022] According to the correspondence between the strategy type, the risk impact factor and the weight impact coefficient, the weight impact coefficient corresponding to the target strategy type and the target risk impact factor is obtained.

[0023] Optionally, the business strategy further includes target products, target regions and / or target channels;

[0024] The obtaining of the pharmaceutical flow data in the target period specifically includes: obtaining the pharmaceutical flow data of the target product, the target area and / or the target channel in the target period.

[0025] A second aspect of the present disclosure provides a system for processing medicine flow data, comprising:

[0026] a data acquisition module, configured to acquire pharmaceutical flow data in a target time period in response to an input business strategy; wherein the business strategy includes the target time period;

[0027] a target determination module, configured to, in response to the pharmaceutical flow data satisfying any configured verification rule, determine the risk impact factor corresponding to the verification rule as a target risk impact factor; wherein the verification rule corresponds to the risk impact factor in a one-to-one manner;

[0028] A weight acquisition module, configured to acquire the dynamic weight of the target risk impact factor under the business strategy;

[0029] The indicator calculation module is used to calculate the credibility evaluation result of the pharmaceutical flow data based on the dynamic weights of all target risk influencing factors and basic evaluation indicators.

[0030] Optionally, the weight acquisition module is specifically used to obtain the weight influence coefficient of the target risk impact factor under the business strategy, and determine the dynamic weight of the target risk impact factor based on the initial weight and weight influence coefficient of the target risk impact factor.

[0031] Optionally, the indicator calculation module is specifically used to calculate the current evaluation index of the target risk impact factor based on the dynamic weight and basic evaluation index of each target risk impact factor, and to determine the evaluation result of the credibility of the medical flow data based on the current evaluation indicators of all target risk impact factors.

[0032] Optionally, the processing system further includes an indicator display module, configured to display a current evaluation indicator of the target risk impact factor in response to a triggering operation on the evaluation result.

[0033] Optionally, the business strategy also includes a target strategy type, and the weight acquisition module is specifically used to obtain the weight influence coefficient corresponding to the target strategy type and the target risk influence factor according to the correspondence between the strategy type, risk influence factor and weight influence coefficient.

[0034] Optionally, the business strategy further includes a target product, a target region and / or a target channel; the data acquisition module is specifically used to acquire the pharmaceutical flow data of the target product, the target region and / or the target channel in a target time period.

[0035] A third aspect of the present disclosure provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and configured to run on the processor, wherein the processor implements the processing method described in the first aspect when executing the computer program.

[0036] A fourth aspect of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the processing method described in the first aspect when executed by a processor.

[0037] A fifth aspect of the present disclosure provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the processing method as described in the first aspect is implemented.

[0038] On the basis of conforming to the common sense in this field, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present disclosure.

[0039] The positive progress of the present disclosure is that the credibility issue of pharmaceutical flow data is systematically deconstructed into multiple independent, quantifiable risk impact factors, each of which focuses on a specific, precisely defined verification rule. The risk impact factor corresponding to the verification rule satisfied by the pharmaceutical flow data is determined as the target risk impact factor. Based on the dynamic weight of the target risk impact factor under the business strategy and the basic evaluation indicators, an evaluation result of a quantitative evaluation of the credibility of the pharmaceutical flow data can be obtained. Because the dynamic weight of the target risk impact factor can reflect changes in the business strategy, it can ensure that the evaluation result of the credibility of the pharmaceutical flow data is consistent with the current business strategy, thereby improving the accuracy of the evaluation data credibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flowchart of a method for processing medicine flow data provided by an exemplary embodiment of the present disclosure;

[0041] Figure 2 A flowchart of step S13 provided for an exemplary embodiment of the present disclosure;

[0042] Figure 3 A flowchart of step S14 is provided for an exemplary embodiment of the present disclosure;

[0043] Figure 4 A structural block diagram of a system for processing medicine flow data provided by an exemplary embodiment of the present disclosure;

[0044] Figure 5 The present invention provides a structural diagram of an electronic device according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0045] The present disclosure is further illustrated below by way of examples, but the present disclosure is not limited to the scope of the examples.

[0046] In the embodiments of the present disclosure, prefixes such as "first" and "second" are used only to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. In the embodiments of the present disclosure, the use of prefixes such as ordinal numbers to distinguish description objects does not constitute a limitation on the described objects. For the statement of the described objects, please refer to the description in the context of the claims or embodiments, and no unnecessary limitations should be constituted due to the use of such prefixes. In addition, in the description of this embodiment, unless otherwise specified, the meaning of "plurality" is two or more.

[0047] In the embodiments of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0048] The method for processing pharmaceutical flow data provided by the embodiments of the present disclosure can not only realize the quantitative evaluation of the credibility of pharmaceutical flow data, but also ensure that the evaluation results of the credibility of pharmaceutical flow data are consistent with the current business strategy, thereby improving the accuracy of the credibility of the evaluated data, specifically including: in response to the input business strategy, obtaining pharmaceutical flow data in the target time period; in response to the pharmaceutical flow data satisfying any configured verification rule, determining the risk impact factor corresponding to the verification rule as the target risk impact factor; wherein the verification rule corresponds one-to-one to the risk impact factor; obtaining the dynamic weight of the target risk impact factor under the business strategy; and calculating the evaluation result of the credibility of the pharmaceutical flow data based on the dynamic weights of all target risk impact factors and the basic evaluation index.

[0049] Figure 1 This is a flowchart illustrating a method for processing medication flow data according to an exemplary embodiment of the present disclosure. This method can be executed by a medication flow data processing system. This processing system can be implemented in software and / or hardware and can be part or all of an electronic device. The following describes the method for processing medication flow data according to this embodiment, using an electronic device as the execution subject.

[0050] like Figure 1 As shown, the method for processing medicine flow data provided in this embodiment may include the following steps S11 to S14:

[0051] Step S11: Responding to the inputted business policy, obtaining medicine flow data in a target time period, wherein the business policy includes the target time period.

[0052] In specific implementation, business personnel can input business strategies through a visual interface or API interface. In some examples, the target period can be a certain month, a certain year, a certain number of months, etc.

[0053] It should be noted that pharmaceutical flow data includes tracking data from the entire process of pharmaceutical production, distribution, and final purchase. In step S11, pharmaceutical flow data for the target time period can be obtained from different data sources. In some examples, sales orders, delivery notes, and other data are obtained from the pharmaceutical company's ERP (Enterprise Resource Planning) system; sales flow data tables, procurement flow data tables, and inventory flow data tables are obtained from the purchase, sales, and inventory management systems of commercial companies such as first-tier and second-tier distributors; hospital consumption data tables and online procurement data tables are obtained from the HIS (Hospital Information System) system or online procurement platform; and traceability code data tables are obtained from the pharmaceutical traceability code platform.

[0054] In some examples, the business strategy also includes a target strategy type, which may be any one of new product launch promotion, inventory clearance promotion, compliance crackdown period, or regular operation.

[0055] In some examples, the business strategy also includes a target product, a target region and / or a target channel. Accordingly, in step S11, it is necessary to obtain the pharmaceutical flow data of the target product, the target region and / or the target channel in the target time period. Among them, the target product can be a specific drug, the target region can be the southwest, northwest, east China and other regions, and the target channel can be a hospital or pharmacy, etc. In a specific example, the input business strategy is "for the East China region where D001 is located, implement a new product launch promotion strategy in August". The target channel in this business strategy is dealer D001, the target region is the East China region, the target time period is August, and the target strategy type is new product launch promotion.

[0056] Step S12: In response to the pharmaceutical flow data satisfying any configured verification rule, the risk impact factor corresponding to the verification rule is determined as the target risk impact factor, wherein the verification rule corresponds to the risk impact factor one-to-one.

[0057] Abnormal drug inventory can be caused by a variety of factors, including dead stock, slow inventory consumption, and imbalanced inventory levels. Each factor has varying severity and business implications. Based on this, this embodiment systematically deconstructs the credibility of drug flow data into multiple independent, quantifiable risk factors, each of which focuses on a specific, precisely defined verification rule.

[0058] In a specific example, the credibility of pharmaceutical flow data is deconstructed into 17 risk factors. These risk factors and their corresponding verification rules are as follows:

[0059] Risk Impact Factor RA-01: Batch Number Integrity - Traceability Code Mismatch. Its corresponding verification rule is: Check the combination of distributor, terminal (such as hospital, pharmacy), product specification, and batch number in the traceability code data table to see if there is a missing or quantity mismatch in the sales flow data table.

[0060] Risk impact factor RA-02: Batch purchase, sales, and inventory imbalance. The corresponding verification rule is: the absolute difference between the actual inventory at the end of the period and the theoretical inventory at the end of the period exceeds the first preset threshold, where the theoretical inventory at the end of the period = the inventory at the beginning of the period + the purchase quantity - the sales quantity;

[0061] Risk impact factor RA-03: Product purchase, sales, and inventory imbalance. The corresponding verification rule is: check whether there is a difference between the actual inventory at the end of the period and the theoretical inventory at the end of the period;

[0062] Risk Impact Factor RA-04: Upstream and downstream price inconsistency. The corresponding verification rule is: Check the commercial company's purchase flow to see if there is any upstream sales flow within 15 days, and whether the sales price is inconsistent with the purchase price;

[0063] Risk Impact Factor RA-05: Online procurement data is missing corresponding flow direction. The corresponding verification rule is: the hospital's online procurement platform has purchase records, but its upstream distributor has no corresponding sales flow records;

[0064] Risk impact factor RA-06: Hospital consumption volume and sales flow do not match. The corresponding verification rule is: the monthly total sales flow volume is inconsistent with the hospital's monthly consumption volume;

[0065] Risk Impact Factor RA-07: Theoretical purchases without sales. The corresponding verification rule is: Based on the distributor's upstream sales flow, it is determined that the distributor had theoretical purchases last month, but the distributor's sales flow does not show any sales records for the current month.

[0066] Risk Impact Factor RA-08: There was actual purchase but no sales last month. The corresponding verification rule is: Based on the distributor's purchasing flow, it is determined that the distributor actually purchased goods last month, but there is no sales record in the distributor's sales flow for the current month.

[0067] Risk Impact Factor RA-09: The terminal's purchase volume is too large. The corresponding verification rule is: the terminal's total monthly purchase volume exceeds the preset reasonable threshold;

[0068] Risk Impact Factor RA-10: Sales volume exceeds total available volume. The corresponding verification rule is: total sales volume for the month is greater than the sum of the beginning inventory and the total purchase volume for the month.

[0069] Risk Impact Factor RA-11: The number of chain stores covered is abnormally small. The corresponding verification rule is: the number of downstream stores covered by the chain distributor's sales in the current month is less than the second preset threshold;

[0070] Risk Impact Factor RA-12: Circular transactions between businesses of the same batch of goods. The corresponding verification rule is: different dealers buy and sell similar quantities of the same batch of goods within six months.

[0071] Risk Impact Factor RA-13: Dead Inventory. The corresponding verification rule is: for a combination of distributor, product specification, and batch number, the inventory quantity has not changed in the last N months, where N ≥ 1.

[0072] Risk Impact Factor RA-14: Slow Inventory Consumption. Its corresponding verification rule is: For a given combination of distributor, product specification, and batch number, the inventory turnover rate deviates significantly from the inventory turnover rate for that product specification region, for example, by 2 times or more, or there is inventory from M months ago that has not been consumed yet, where M ≥ 1.

[0073] Risk Impact Factor RA-15: Abnormal Sales Distribution. Its corresponding verification rule is: the number of sales items of a distributor is greater than 3,000, and the volatility of the proportion of sales items with a first digit of 1 exceeds the benchmark by 20%, which is 30.1%. For example, a distributor has 10,000 sales flows, of which 6,000 have a first digit of 1. The proportion of sales items with a first digit of 1 is a, a = 6,000 / 10,000 = 60%. The value that exceeds the benchmark by 20% is b, b = 30.1% × (1 + 20%) = 36.12. If the number of sales items is greater than 3,000 and a > b, the above verification rule is met.

[0074] Risk Impact Factor RA-16: Multiple Suppliers for a Single Terminal. The corresponding verification rule is: For the same terminal and the same product specification, more than P distributors supply the same terminal in the same month, where P ≥ 1.

[0075] Risk impact factor RA-17: Cross-channel price consistency. The corresponding verification rule is: for the same distributor and the same product, the price sold to different channel types is exactly the same. Different channel types include hospitals and pharmacies.

[0076] In some examples, SQL statements can be used to implement the validation rules corresponding to the aforementioned risk impact factors. In some examples, streaming computing frameworks such as Apache Flink are used to execute these SQL statements. Streaming computing frameworks support event time processing and exactly-once stateful guarantees, ensuring that risk impact factors requiring stateful calculations (such as RA-02, RA-03, or RA-10) are accurately calculated even in the presence of out-of-order data and system failures. Compared to batch processing frameworks such as SparkBatch, the low latency of streaming computing frameworks can shorten the data credibility assessment cycle from days to minutes.

[0077] In other examples, complex validation rules, such as those corresponding to RA-12, can also be implemented using a graph computing engine (such as Neo4j's Cypher query), which has better performance in multi-hop relationship queries.

[0078] To improve the accuracy of assessing the credibility of pharmaceutical flow data, the pharmaceutical flow data obtained in step S11 can be cleansed before executing step S12. For example, for the prod_qty field in the sales flow data table, apply the "eliminate records where prod_qty <= 0 or prod_qty > 1,000,000" cleansing rule to filter out obvious outliers in the prod_qty field.

[0079] Step S13: Obtain the dynamic weight of the target risk impact factor under the business strategy. The dynamic weight refers to the weight of the target risk impact factor that changes dynamically with the change of the business strategy.

[0080] Step S14: Calculate the credibility evaluation result of the pharmaceutical flow data based on the dynamic weights of all target risk influencing factors and basic evaluation indicators.

[0081] The basic assessment indicator for each risk impact factor can be preset in advance and is used to represent the inherent severity of the risk impact factor. In some examples, the basic assessment indicator is the basic risk score of the risk impact factor. The higher the score, the greater the risk. In some examples, the assessment result of the credibility of the pharmaceutical flow data is the final risk score. The higher the score, the greater the risk, that is, the lower the credibility. Taking the above 17 risk impact factors RA-01 to RA-17 as an example, the corresponding basic risk scores are 80, 90, 85, 75, 70, 65, 50, 55, 60, 95, 40, 85, 60, 50, 70, 65, and 45 respectively.

[0082] In other examples, the credibility of the pharmaceutical flow data is evaluated as a credibility score, where a higher score indicates higher credibility. In a specific example, after obtaining a final risk score, the credibility score can be obtained by subtracting the final risk score from a predetermined score, such as 100.

[0083] Business policies are usually subjective and textual, and cannot be directly understood and executed by computer programs. When business policies change, technical personnel need to manually interpret, communicate, and modify the verification logic and thresholds in the code, resulting in response delays of up to several weeks, which cannot meet the rapidly changing needs of the market. For example, the marketing department launched a one-month promotion and required the temporary relaxation of the monitoring of "excessive terminal purchases". Traditional methods cannot achieve a quick response. In this embodiment, since the dynamic weight of the target risk influencing factor can reflect changes in business policies, it can ensure that the evaluation results of the credibility of the pharmaceutical flow data are consistent with the current business policy, thereby improving the accuracy of the credibility of the evaluation data.

[0084] In some examples, before executing step S14, the dynamic weights of all target risk impact factors are normalized, that is, the sum of all dynamic weights is ensured to be 1.

[0085] In an optional embodiment, as Figure 2 As shown, step S13 specifically includes the following steps S31 to S33:

[0086] Step S31: Obtain the weighted influence coefficient of the target risk impact factor under the business strategy, wherein the weighted influence coefficient represents the influence of the business strategy on the weight of the risk impact factor.

[0087] In some examples, based on the correspondence between the policy type, the risk impact factor, and the weight impact coefficient, a weight impact coefficient corresponding to the target policy type and the target risk impact factor is obtained.

[0088] In specific implementations, a mapping table can be set up to store the correspondence between policy types, risk impact factors, and weight impact coefficients. Taking the 17 risk impact factors mentioned above as an example, a mapping table is set up as shown in Table 1. The business explanations in this mapping table are used to explain the impact of different policy types on the weights of different risk impact factors. A weight impact coefficient greater than zero indicates relaxed monitoring, less than zero indicates tightened monitoring, and equal to zero indicates no impact.

[0089] Table 1

[0090]

[0091] In specific implementations, the above mapping table can be stored in a distributed configuration center. Adjustments to business policies do not require redeployment of code. Business personnel inputting business policies on the interface can be pushed to the distributed configuration center in real time. The distributed configuration center can dynamically perceive configuration changes and immediately apply the new weight influence coefficient in the next calculation window, thereby achieving timely response to business policies.

[0092] Step S32: Obtain the initial weight of the target risk impact factor. The initial weight of each risk impact factor can be preset according to the inherent importance of the risk impact factor, and the initial weight represents the weight distribution of the risk impact factor under normal operating conditions.

[0093] Step S33: Determine the dynamic weight of the target risk impact factor according to the initial weight and weight impact coefficient of the target risk impact factor.

[0094] In this embodiment, the weight of the target risk impact factor can be dynamically adjusted through the weight impact coefficient of the target risk impact factor under the business strategy.

[0095] In some examples, a linear weighting method is used to determine the dynamic weight. In other examples, a nonlinear function or a hierarchy analysis method model is used to determine the dynamic weight.

[0096] In a specific example, the following linear weighting formula is used to calculate the dynamic weight of the target risk impact factor:

[0097] w1_i=w0_i×(1+pi_i);

[0098] Among them, w1_i represents the dynamic weight of the target risk impact factor RA-i, w0_i represents the initial weight of the target risk impact factor RA-i, and pi_i represents the weight influence coefficient of the target risk impact factor RA-i.

[0099] In order to ensure that the sum of all dynamic weights is 1, the above dynamic weights need to be normalized:

[0100] w11_i= w1_i / sum;

[0101] Among them, w11_i represents the dynamic weight of the normalized target risk impact factor RA-i, and sum represents the sum of the dynamic weights of all target risk impact factors.

[0102] Assume that the target risk impact factors include RA-02 and RA-05. The initial weight of RA-02 is 0.6, and its weight coefficient under the new product launch promotion strategy is 0.5. The initial weight of RA-05 is 0.4, and its weight coefficient under the new product launch promotion strategy is -0.2. According to the above linear weighting formula, the dynamic weight of RA-02 is 0.6 × (1 + 0.5) = 0.9, and the dynamic weight of RA-05 is 0.4 × (1 - 0.2) = 0.32. After normalization, the dynamic weight of RA-02 is 0.9 / (0.9 + 0.32) = 0.738, and the dynamic weight of RA-05 is 0.32 / (0.9 + 0.32) = 0.262.

[0103] In an optional embodiment, as Figure 3 As shown, step S14 specifically includes the following steps S41-S42:

[0104] Step S41: Calculate the current evaluation index of the target risk impact factor based on the dynamic weight of each target risk impact factor and the basic evaluation index. In a specific implementation, the product of the dynamic weight of each target risk impact factor and the basic evaluation index can be used as the current evaluation index of the target risk impact factor.

[0105] Step S42: Determine an evaluation result of the credibility of the pharmaceutical flow data based on the current evaluation indicators of all target risk impact factors.

[0106] In a specific implementation, the current evaluation indicators of all target risk influencing factors can be summed to obtain an evaluation result for the credibility of the pharmaceutical flow data. In some examples, the basic evaluation indicator is a basic risk value, the current evaluation indicator is a current risk value, and the evaluation result for the credibility of the pharmaceutical flow data is the sum of the current evaluation indicators, i.e., the final risk value.

[0107] In a specific implementation, the credibility evaluation result of the pharmaceutical flow data can be obtained by subtracting the sum of the current evaluation indicators from a preset score. In some examples, the credibility score is obtained by subtracting the sum of the current evaluation indicators, i.e., the final risk value, from a preset score of 100, and used as the credibility evaluation result of the pharmaceutical flow data.

[0108] Taking the target risk impact factors RA-02 and RA-05 as an example, the dynamic weight of RA-02 is 0.738, and the basic risk value is 90 points. The current risk value is 0.738 × 90 = 66.42 points. The dynamic weight of RA-05 is 0.262, and the basic assessment index is 70 points. The current risk value is 0.262 × 70 = 18.34 points. Summing the current risk values ​​yields a final risk value of 66.42 + 18.34 = 84.76 points. Converting this to a credibility score yields 100 - 84.76 = 15.24 points.

[0109] In an optional embodiment, the above-mentioned processing method further includes the following steps: in response to a trigger operation for the evaluation result, displaying the current evaluation index of the target risk impact factor. In a specific example, the evaluation result for the credibility of the pharmaceutical flow data is a credibility score, specifically 95 points. When the user clicks on the credibility score, the current evaluation index of the target risk impact factor RA-07 is displayed as: 50×0.1=5 points, wherein the basic risk score of the target risk impact factor RA-07 is 50 points, and the dynamic weight under the current business policy is 0.1.

[0110] In this embodiment, users can trace the source of the credibility assessment results by triggering them, that is, they can view the risk factors that affect the credibility of the pharmaceutical flow data, thereby achieving the interpretability of the assessment results. Based on this, targeted improvements can be made subsequently, thus forming a closed loop of data quality management of assessment, discovery, and improvement.

[0111] In some examples, the above-mentioned credibility assessment can also be performed on the pharmaceutical flow data of different distributors. By sorting the quantitative credibility assessment results, distributors with poor credibility assessment results can be screened out. By prioritizing the management of these distributors, the efficiency of risk intervention can be improved.

[0112] Figure 4 This is a structural block diagram of a system for processing medicine flow data provided by an exemplary embodiment of the present disclosure. Figure 4As shown, the processing system for pharmaceutical flow data includes a data acquisition module 41, a target determination module 42, a weight acquisition module 43 and an indicator calculation module 44. The data acquisition module 41 is used to obtain pharmaceutical flow data in a target period in response to an input business policy; wherein the business policy includes a policy type and a target period. The target determination module 42 is used to determine the risk impact factor corresponding to any configured verification rule as a target risk impact factor in response to the pharmaceutical flow data satisfying any configured verification rule; wherein the verification rule corresponds one-to-one to the risk impact factor. The weight acquisition module 43 is used to obtain the dynamic weight of the target risk impact factor under the business policy. The indicator calculation module 44 is used to calculate an evaluation result of the credibility of the pharmaceutical flow data based on the dynamic weights of all target risk impact factors and basic evaluation indicators.

[0113] In an optional embodiment, the weight acquisition module is specifically used to obtain the weight influence coefficient of the target risk impact factor under the business strategy, and to determine the dynamic weight of the target risk impact factor based on the initial weight and weight influence coefficient of the target risk impact factor.

[0114] In an optional embodiment, the indicator calculation module is specifically used to calculate the current evaluation index of the target risk influencing factor based on the dynamic weight and basic evaluation index of each target risk influencing factor, and to determine the evaluation result of the credibility of the medical flow data based on the current evaluation indicators of all target risk influencing factors.

[0115] In an optional embodiment, the processing system further includes an indicator display module, configured to display a current evaluation indicator of the target risk impact factor in response to a triggering operation on the evaluation result.

[0116] Optionally, the business strategy also includes a target strategy type, and the weight acquisition module is specifically used to obtain the weight influence coefficient corresponding to the target strategy type and the target risk influence factor according to the correspondence between the strategy type, risk influence factor and weight influence coefficient.

[0117] In an optional embodiment, the business strategy also includes target products, target regions and / or target channels; the data acquisition module is specifically used to obtain the pharmaceutical flow data of the target products, the target regions and / or the target channels in the target time period.

[0118] It should be noted that the processing system for medicine flow data in this embodiment may be a separate chip, chip module or electronic device, or a chip or chip module integrated into an electronic device.

[0119] The various modules / units included in the medical flow data processing system described in this embodiment may be software modules / units, hardware modules / units, or partly software modules / units and partly hardware modules / units.

[0120] Since the system embodiments generally correspond to the method embodiments, reference will be made to the description of the method embodiments for relevant details. The system embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components of the units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the disclosed solution.

[0121] Figure 5 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of the present disclosure. The electronic device includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the steps of the processing method described above. Figure 5 The electronic device 3 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0122] The components of the electronic device 3 may include, but are not limited to: the at least one processor 4 mentioned above, the at least one memory 5 mentioned above, and a bus 6 connecting different system components (including the memory 5 and the processor 4).

[0123] The bus 6 includes a data bus, an address bus, and a control bus.

[0124] The memory 5 may include a volatile memory, such as a random access memory (RAM) 51 and / or a cache memory 52 , and may further include a read-only memory (ROM) 53 .

[0125] The memory 5 may also include a program tool 55 having a set (at least one) of program modules 54, such program modules 54 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0126] The processor 4 executes various functional applications and data processing, such as the above-mentioned processing method, by running the computer program stored in the memory 5 .

[0127] The electronic device 3 can also communicate with one or more external devices 7 (e.g., keyboard, pointing device, etc.). Such communication can be performed through an input / output (I / O) interface 8. Furthermore, the electronic device 3 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) through a network adapter 9. Figure 5 As shown, the network adapter 9 communicates with other modules of the electronic device 3 via the bus 6. It should be understood that although Figure 5 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 3, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0128] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0129] An exemplary embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned processing method are implemented.

[0130] The readable storage medium may include, but is not limited to, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0131] In a possible implementation manner, the present disclosure may also be implemented in the form of a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the above processing method are implemented.

[0132] Among them, the computer program for executing the present disclosure can be written in any combination of one or more programming languages, and the computer program can be executed completely on the electronic device, partially on the electronic device, as a stand-alone software package, partially on the electronic device and partially on a remote device, or completely on the remote device.

[0133] While specific embodiments of the present disclosure have been described above, those skilled in the art will appreciate that these are merely illustrative and that the scope of protection of the present disclosure is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present disclosure, and such changes and modifications are intended to fall within the scope of protection of the present disclosure.

Claims

1. A method for processing medicine flow data, characterized in that: The following steps are involved: Responding to an inputted business strategy, obtaining medicine flow data in a target time period; wherein the business strategy includes the target time period; In response to the pharmaceutical flow data satisfying any configured verification rule, determining the risk impact factor corresponding to the verification rule as a target risk impact factor; wherein the verification rule corresponds to the risk impact factor one-to-one; Obtaining the dynamic weight of the target risk impact factor under the business strategy; The evaluation result of the credibility of the pharmaceutical flow data is calculated based on the dynamic weights of all target risk influencing factors and basic evaluation indicators.

2. The processing method according to claim 1, characterized in that The obtaining of the dynamic weight of the target risk impact factor under the business strategy specifically includes: Obtaining the weighted impact coefficient of the target risk impact factor under the business strategy; The dynamic weight of the target risk impact factor is determined according to the initial weight and weight impact coefficient of the target risk impact factor.

3. The processing method according to claim 2, characterized in that The evaluation results of the credibility of the pharmaceutical flow data are calculated based on the dynamic weights of all target risk influencing factors and basic evaluation indicators, specifically including: Calculate the current evaluation index of each target risk impact factor based on the dynamic weight and basic evaluation index of each target risk impact factor; An evaluation result of the credibility of the pharmaceutical flow data is determined based on the current evaluation indicators of all target risk influencing factors.

4. The processing method according to claim 3, characterized in that The processing method further comprises: In response to a triggering operation on the evaluation result, a current evaluation indicator of the target risk impact factor is displayed.

5. The processing method according to claim 2, characterized in that The business strategy also includes a target strategy type, and obtaining the weight influence coefficient of the target risk impact factor under the business strategy specifically includes: According to the correspondence between the strategy type, the risk impact factor and the weight impact coefficient, the weight impact coefficient corresponding to the target strategy type and the target risk impact factor is obtained.

6. The processing method according to any one of claims 1 to 5, characterized in that The business strategy may also include target products, target regions and / or target channels; The obtaining of the pharmaceutical flow data in the target period specifically includes: obtaining the pharmaceutical flow data of the target product, the target area and / or the target channel in the target period.

7. A system for processing medicine flow data, characterized in that: include: a data acquisition module, configured to acquire pharmaceutical flow data in a target time period in response to an input business strategy; wherein the business strategy includes the target time period; a target determination module, configured to, in response to the pharmaceutical flow data satisfying any configured verification rule, determine the risk impact factor corresponding to the verification rule as a target risk impact factor; wherein the verification rule corresponds to the risk impact factor in a one-to-one manner; A weight acquisition module, configured to acquire the dynamic weight of the target risk impact factor under the business strategy; The indicator calculation module is used to calculate the credibility evaluation result of the pharmaceutical flow data based on the dynamic weights of all target risk influencing factors and basic evaluation indicators.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and configured to run on the processor, wherein: When the processor executes the computer program, the processing method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processing method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the processing method according to any one of claims 1 to 6 is implemented.

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