Special medicine purchasing and warehousing management system

Abstract

The invention relates to the technical field of supply chain intelligent management, in particular to a special medicine purchasing and warehousing management system. The invention discloses a special medicine purchasing and warehousing management system, and the system comprises the following parts: a qualification auditing preprocessing module which is used for obtaining qualification auditing data and purchasing auditing data of various types of special medicines, and obtaining N first division results according to the qualification auditing data; according to the N first division results, extracting an auditing change record in the qualification auditing data, and according to the auditing change record, obtaining the number of reauditing change hierarchies; and the auditing process optimization module is used for sorting the N first division results from large to small to obtain a first sorting result. According to the system, through quantifying the qualification auditing stability and the transportation plan reliability, the Pearson correlation analysis is utilized to trigger the purchase decision, the drug rejection rate is reduced, the risk is avoided, and the medical resource safety is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supply chain intelligent management, and particularly relates to a special medicine procurement and warehouse management system. BACKGROUND

[0002] Special medicines (such as biological agents, radioactive drugs and high-activity raw medicines) have strict requirements for storage environment, transportation time limit and qualification compliance. The current industry generally adopts segmented management: qualification review relies on manual progressive process, procurement approval and transportation plan are executed separately, and replenishment is triggered by fixed inventory threshold. Although the existing system can record basic data, it does not establish a dynamic correlation mechanism for qualification change and transportation time limit.

[0003] The traditional method has three shortcomings: rigid review process: when the regulations change trigger the qualification review, the system cannot dynamically adjust the priority according to the remaining review level, resulting in backlog of high-time-limit medicines; transportation plan is disconnected: after the approval is rejected, the system does not recalculate the transportation path time, resulting in that the risk of expiration of the medicine is not included in the time limit index update logic; decision isolation: the stability evaluation of the qualification (such as level change) and the transportation reliability (such as time limit deviation) are analyzed separately, and there is no collaborative risk warning capability.

[0004] With the expansion of the clinical application of special medicines and the upgrading of supervision requirements, the above defects lead to two types of dominant risks: compliance failure: qualification review delay causes legal disputes (such as expired medicine inflow); resource loss: transportation plan and expiration date inconsistency cause medicine scrap rate to rise. It is urgent to build an intelligent management system that integrates dynamic optimization of qualification and transportation time limit coordination to ensure medical safety and reduce operating costs. SUMMARY

[0005] In order to overcome the shortcomings of qualification and transportation coordination out of control, the present application provides a special medicine procurement and warehouse management system.

[0006] The technical implementation scheme of the present application is: a special medicine procurement and warehouse management system, comprising the following parts: A qualification review preprocessing module is used to obtain qualification review data and procurement approval data of various special medicines, and obtain N first division results according to the qualification review data; according to the N first division results, extract the review change records in the qualification review data, and obtain the number of re-review change levels according to the review change records; An audit process optimization module is used to sort the N first division results from large to small to obtain a first sorting result, and obtain an updated first sorting result according to the first sorting result; calculate the deviation degree of each qualification approval data meeting the preset update sorting threshold in the updated first sorting result; The transport timeliness calculation module is configured to divide the procurement approval data into approval-passed data and approval-rejected data according to approval results, and obtain a transport timeliness index and a drug expiration date according to the approval-passed data and the approval-rejected data. The procurement decision generation module is configured to use the deviation degree and the transport timeliness effectiveness as a joint evaluation index, calculate a correlation intensity coefficient of the joint evaluation index of all special drugs by using a Pearson correlation coefficient, and determine a procurement and warehouse management method according to the correlation intensity coefficient.

[0007] Preferably, the qualification audit preprocessing module is configured to obtain qualification audit data and procurement approval data of various special drugs, and obtain N first division results according to the qualification audit data, including: extracting each audit level in the qualification audit data according to the qualification audit data; dividing the qualification audit data with the same audit level into the same qualification audit data; dividing the remaining audit levels in all the special drugs according to the number of audit levels to obtain the N first division results.

[0008] Preferably, the qualification audit preprocessing module is configured to extract an audit change record in the qualification audit data according to the N first division results, and obtain a re-audit change level number according to the audit change record, including: The audit change record refers to a record of re-auditing due to a change in regulations or invalidation of materials; extracting an audit level corresponding to the audit change record according to the audit change record and defining it as an audit change level; taking the audit change level as a starting point, counting the number of remaining levels that need to be audited and defining it as a re-audit change level number.

[0009] Preferably, the audit flow optimization module is configured to sort the N first division results from large to small to obtain a first sorting result, and obtain an updated first sorting result according to the first sorting result, including: connecting the re-audit change level number to the back of the corresponding first division result to obtain the updated first sorting result; if the sum of the re-audit change level number and the original audit level number of the current qualification audit data is greater than the sum of the re-audit change level number and the original audit level number of the previous adjacent qualification audit data in the updated first sorting result, the order of the current qualification audit data in the approval sequence is advanced; if the sum of the re-audit change level quantity and the original audit level quantity corresponding to the current qualification audit data is less than the sum of the re-audit change level quantity and the original audit level quantity corresponding to the subsequent adjacent qualification audit data in the updated first sorting result, the order of the current qualification audit data in the approval sequence is delayed; The operation process of updating the first sorting result is repeatedly performed until the updated first sorting result meets a preset updating sorting threshold.

[0010] Preferably, the calculation of the deviation degree of each of the qualification approval data meeting the preset updating sorting threshold each time in the updated first sorting result comprises: extracting the original sorting position and the current sorting position of each of the qualification approval data in the updated first sorting result, calculating the absolute value of the difference between the two to generate a position offset; synchronously obtaining the re-audit change level quantity corresponding to the qualification approval data as a level change amplitude; combining the position offset and the level change amplitude by a preset ratio into a comprehensive evaluation value, defined as a deviation degree; The deviation degree fuses the sorting change intensity and the audit depth change amount. An increase in the numerical value represents an increase in process instability, and a decrease in the numerical value represents an increase in process stability.

[0011] Preferably, the qualification approval data is divided into approved data and rejected data according to the approval result, and the transportation time efficiency index and the drug effective period are obtained according to the approved data and the rejected data, comprising: The approval result includes the final approval state of the first submission; According to the approved data and the rejected data, special drug transportation time efficiency data is obtained; According to the special drug transportation time efficiency data, the transportation time efficiency index and the drug effective period are obtained; The transportation time efficiency index includes the in-transit transportation time, the transit stay time, and the customs clearance processing time. The drug effective period includes a stability factor determined by environmental sensitivity and a decay factor determined by material characteristics.

[0012] Preferably, the transportation time efficiency calculation module is configured to calculate an initial transportation effective period according to the transportation time efficiency index and the drug effective period, obtain an updated transportation effective period according to the initial transportation effective period, and obtain transportation time efficiency effectiveness according to the initial transportation effective period and the updated transportation effective period, comprising: The initial transportation effective period refers to the transportation effective period corresponding to the approved data. According to the rejected data, the corresponding approval node in the rejected data is extracted; According to the approval node, the transportation validity period is recalculated and defined as an updated transportation validity period; According to the initial transportation validity period and the updated transportation validity period, the transportation time limit validity is obtained.

[0013] Preferably, the initial transportation validity period is calculated according to the transportation time limit index and the drug validity period, and the updated transportation validity period is obtained according to the initial transportation validity period, comprising: Extract the transportation time limit index, including the in-transit transportation time, the transit stay time and the customs clearance processing time; Synchronously extract the drug validity period including stability factors and decay factors; According to the comparison calculation of the total duration of the transportation time limit index and the remaining duration of the drug validity period, the initial transportation validity period is generated; Extract the approval node in the approval rejection data; According to the approval node, the individual duration components of the transportation time limit index are readjusted; Combined with the current state of the drug validity period, the updated transportation validity period is dynamically recalculated.

[0014] Preferably, the transportation time limit validity is obtained according to the initial transportation validity period and the updated transportation validity period, comprising: Extract the initial transportation validity period and the updated transportation validity period, calculate the absolute difference between them and define it as the variation amplitude; The variation amplitude is taken as the transportation time limit validity, and the value increase represents the increase of transportation plan instability, and the value decrease represents the improvement of transportation plan stability.

[0015] Preferably, the procurement decision generation module: for taking the deviation degree and the transportation time limit validity as joint evaluation indexes, using Pearson correlation coefficient to calculate the correlation intensity coefficient of the joint evaluation indexes of all special drugs, and determining the procurement and warehouse management method according to the correlation intensity coefficient, comprising: According to the correlation intensity coefficient, all special drug batches are sorted in ascending order to obtain a correlation degree sorting result, and the drug batches with the correlation intensity coefficient reaching a preset safety threshold in the correlation degree sorting result are preferentially selected to activate the automatic procurement and warehousing process; For the drug batches with the correlation intensity coefficient exceeding the risk threshold in the correlation degree sorting result, the artificial review is forcibly started and the logistics operation is frozen, and through the correlation intensity coefficient sorting and risk threshold determination, the risk hierarchical management is realized.

[0016] Beneficial effects: the system divides the qualification audit level and extracts the audit change record to generate the re-audit change level quantity, drives the dynamic sorting to generate the updated first sorting result, calculates the deviation degree based on the position offset and the level change amplitude, accurately quantifies the stability of the qualification audit process, synchronously fuses the transportation time limit index and the drug effective period to calculate the initial transportation effective period and update the transportation effective period, generates the transportation time limit effectiveness through the change amplitude, objectively reflects the reliability of the transportation plan, finally performs the Pearson correlation analysis on the deviation degree and the transportation time limit effectiveness to generate the intensity coefficient, triggers the automatic procurement or manual review instruction according to the double-index joint sorting result and the preset safety threshold, realizes the deep collaborative control of the qualification audit fluctuation and the transportation plan deviation, significantly reduces the special drug scrap rate and effectively avoids the compliance risk, and comprehensively improves the medical resource safety guarantee capability. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The special drug procurement and warehouse management system structure diagram of the present application; Figure 2 The qualification audit level division and change record extraction flowchart of the present application; Figure 3 The audit process dynamic sorting optimization flowchart of the present application; Figure 4 The deviation degree calculation flowchart of the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0019] As known from the background art, the prior art has the problems of high-time-efficiency drug backlog caused by rigid audit process, risk of effective period decay not being taken into account due to the fact that the transportation plan is not recalculated in real time, and lack of qualification stability and transportation reliability collaborative risk warning capability due to decision isolation.

[0020] The present application divides the qualification audit level and extracts the audit change record to generate the re-audit change level quantity, drives the dynamic sorting to generate the updated first sorting result, calculates the deviation degree based on the position offset and the level change amplitude, synchronously fuses the transportation time limit index and the drug effective period to calculate the initial transportation effective period and update the transportation effective period, generates the transportation time limit effectiveness through the change amplitude, finally performs the Pearson correlation analysis on the deviation degree and the transportation time limit effectiveness to generate the intensity coefficient, and triggers the automatic procurement or manual review instruction according to the double-index joint sorting result.

[0021] Example 1: A special drug procurement and warehousing management system, such as Figure 1 As shown, it includes the following parts: Qualification review preprocessing module: used to acquire qualification review data and procurement approval data for various special drugs, and obtain N first classification results based on the qualification review data; It should be noted that the qualification review data for special drugs is highly specialized, differing from the standardized filing process for ordinary drugs. Special drugs require the submission of multi-level regulatory certification documents, including stability verification reports for the active ingredient of the targeted drug, permits for the safe disposal of radioactive isotopes, compliance certificates for cold chain storage of biological agents, and special customs clearance permits for cross-border transportation. This data is dynamically updated with regulations; failure at any stage triggers a complete re-review of the entire process.

[0022] The procurement approval data includes supplier compliance audit results, real-time temperature and humidity monitoring records of the transportation route, historical data on customs clearance timeliness, and traceability information of rejection stages. For ordinary drug procurement, only batch certificates and inventory thresholds need to be verified, while the approval of special drugs requires simultaneous verification of the real-time matching degree between the transportation environment and the expiration date. For example, a two-hour delay in the transportation of biological agents necessitates a reassessment of the risk of efficacy degradation. This dynamic data directly affects the decision to release and store the drugs.

[0023] Based on the qualification review data, extract each review level from the qualification review data; The qualification review data with the same review level are divided into the same qualification review data; The remaining review levels of all the special drugs are divided according to the number of review levels to obtain N first division results.

[0024] It should be noted that, as Figure 2 As shown, the special drug qualification review level refers to the depth of the regulatory agency's classification of certification documents. For example, basic production licenses and cross-border biosafety certifications belong to different regulatory tiers. Because special drugs involve multiple compliance dimensions such as the control of radioactive substances and the continued existence of temperature-sensitive active ingredients, qualification review data often includes dozens of cross-certification documents. Manual classification can easily confuse hierarchical attributes and delay the approval process for time-sensitive drugs.

[0025] It should be noted that the aforementioned identical qualification review data refers to grouping qualification documents with the same review level (such as cold chain certification documents for biological agents and safety permits for radiopharmaceuticals) into the same group. The core function is to eliminate errors in manual classification, ensuring that in-depth certification materials for high-time-sensitivity drugs (such as Level 6 review documents for gene therapy drugs) are strictly separated from basic-level materials (such as Level 3 review documents for routine vaccines). This provides an accurate basis for subsequent dynamic sorting, avoiding delays and misalignments in the approval sequence caused by mixed documents.

[0026] This step aggregates the same level files into the first type of qualification audit data by identifying the inherent level label of the audit data; the remaining files are grouped according to the number of levels to form N division results. This technology realizes automatic clustering of multi-dimensional regulatory requirements, ensures that gene therapy drugs are strictly separated from cell preparation high-level approval materials, solves the sequence misplacement caused by manual classification errors, and significantly improves the accuracy and timeliness of qualification pre-audit.

[0027] According to the N first division results, extract the audit change records in the qualification audit data, and obtain the re-audit change level number according to the audit change records; It should be noted that the special drug approval change record is triggered by the mandatory re-audit requirement of regulatory update or material invalidation. The system automatically extracts the audit change level corresponding to such records, and counts the remaining step number from this level to the final approval to generate the re-audit change level number. This data directly determines the depth of the approval path reset of high timeliness drugs and the priority of emergency scheduling.

[0028] The audit change record refers to a record that needs to be re-audited due to regulatory changes or material invalidation; According to the audit change record, extract the audit level corresponding to the audit change record and define it as the audit change level; Taking the audit change level as the starting point, count the number of remaining levels that need to be audited and define it as the re-audit change level number.

[0029] It should be noted that the special drug audit change record specifically refers to the mandatory re-audit event triggered by regulatory revision or expired declaration materials. The system locates the original audit step position corresponding to the record, for example, the third level transportation qualification of a biological preparation is invalid due to the update of international cold chain standards, and this level is defined as the audit change level.

[0030] Taking this level as the reference point, count the remaining step number from the change level to the final approval, for example, when the third level is invalid in the original five-level approval, the remaining re-audit level number is 3, and this value is the re-audit change level number. This quantitative indicator directly drives the opening of the emergency approval channel for high timeliness drugs, avoiding manual statistical delay.

[0031] Audit process optimization module: for sorting the N first division results from large to small to obtain a first sorting result, and obtaining an updated first sorting result according to the first sorting result; It should be noted that the audit process optimization module generates an initial sequence by arranging N sets of qualification audit data in descending order of the number of levels, and the larger the number of levels, the higher the approval complexity. For example, single-antibody drugs often require six-level deep review, while conventional inactivated vaccines only require three levels. The descending arrangement ensures that high-complexity approvals have priority in processing resources.

[0032] When the re-audit change level number is associated, the system dynamically adjusts the sequence order: if a cell preparation adds a three-level re-audit requirement to the original five-level audit, and the total level number exceeds the adjacent drug, it is automatically moved forward. This mechanism forces the insertion of urgent change tasks at the front of the queue, avoiding the loss of activity of high-timeliness drugs due to process congestion.

[0033] Connect the re-audit change level number to the back of the corresponding first division result to obtain an updated first sorting result. If the sum of the re-audit change level number and the original audit level number corresponding to the current qualification audit data is greater than the sum of the re-audit change level number and the original audit level number corresponding to the previous adjacent qualification audit data in the updated first sorting result, the order of the current qualification audit data in the approval sequence is advanced. If the sum of the re-audit change level number and the original audit level number corresponding to the current qualification audit data is less than the sum of the re-audit change level number and the original audit level number corresponding to the subsequent adjacent qualification audit data in the updated first sorting result, the order of the current qualification audit data in the approval sequence is delayed. Repeat the operation process of updating the first sorting result until the updated first sorting result meets the preset update sorting threshold.

[0034] It should be noted that, as shown in Figure 3 The audit process optimization module associates the re-audit change level number as a new attribute value to the end of the original qualification audit data to form a composite data structure containing the original level and the new re-audit level. This connection operation essentially gives each drug approval item a dynamic weight tag, for example, a gene therapy drug with original five-level audit plus three-level re-audit requirement, the composite level value is updated to eight levels.

[0035] The system compares the composite level values of adjacent drugs in real time: if the current drug composite value exceeds the previous drug, for example, a radiopharmaceutical with a composite value of nine levels is higher than a monoclonal antibody drug with a composite value of seven levels, the approval sequence is immediately moved forward; otherwise, if the composite value is lower than the subsequent drug, the sorting position is delayed. This comparison mechanism is executed in a loop until the composite level values of all drugs in the queue are arranged in strictly descending order. At this time, the preset update sorting threshold is reached, which is defined as no position change in three consecutive iterations of the queue, ensuring that high-urgency change tasks occupy priority processing positions stably.

[0036] Calculate the deviation degree of each said qualification examination data in said update first ranking result meeting the preset update ranking threshold; It should be noted that the deviation degree quantifies the volatility of the qualification review process. Frequent changes in the approval sequence of special drugs will cause priority confusion, for example, a monoclonal antibody drug suddenly rises from the fifth to the first due to emergency re-examination, and the difference between the original position and the current ranking combined with the re-examination level increment collectively represents the abnormal strength of the process. This indicator directly warns of the risk of out-of-control approval of high-value drugs.

[0037] Extract the original ranking position and the current ranking position of each said qualification examination data in said update first ranking result, and calculate the absolute value of the difference between the two to generate a position offset; Synchronously acquire the number of re-examination change levels corresponding to said qualification examination data as the level change amplitude; Combine said position offset and said level change amplitude by a preset ratio to form a comprehensive evaluation value, defined as the deviation degree; The deviation degree combines the ranking change strength and the review depth change amount. An increasing value represents an increasing process instability, and a decreasing value represents an increasing process stability.

[0038] It should be noted that, as shown in Figure 4 The deviation degree calculation quantifies the approval process stability by combining the position offset and the level change amplitude. The position offset represents the ranking change strength, that is, the absolute displacement difference of four between the original fifth ranking of a monoclonal antibody drug and the first ranking after dynamic adjustment. The level change amplitude represents the review depth increment, for example, a new three-level re-examination task is added due to material failure. The two are combined according to the business rule preset weight: formula: Parameter description: (reflects the priority adjustment amplitude, the larger the value, the stronger the process mutation), (directly related to the complexity increment of the approval), : preset ratio coefficient (typical value =0.6, =0.4), when the value exceeds the threshold, an early warning is triggered, for example, the deviation degree of a radioactive drug is seven point two, indicating that the approval process is out of control and needs human intervention.

[0039] It should be noted that in the prior art, the rigid audit process leads to a backlog of high-timeliness drugs, the transportation plan is not recalculated in real time, the risk of expiration decay is not included, and the decision-making is isolated, lacking the ability to cooperate with the risk warning of the stability of the qualification and the reliability of the transportation. Embodiment 1 divides the qualification audit level and extracts the audit change record to generate the number of re-audit change levels, drives dynamic sorting to generate an updated first sorting result, calculates the deviation degree based on the position offset and the level change amplitude to accurately quantify the stability of the qualification audit process, thereby dynamically optimizing the approval sequence and warning the process fluctuation risk.

[0040] In embodiment 1, on the basis of optimizing the qualification audit process, embodiment 2 further evaluates the reliability of the transportation plan by dividing the approval data and calculating the transportation timeliness effectiveness, and the specific implementation is as follows: The transportation timeliness calculation module is used for dividing the procurement approval data into approval pass data and approval rejection data according to the approval result, and obtaining transportation timeliness indicators and drug expiration dates according to the approval pass data and the approval rejection data. It should be noted that the special drug procurement approval data includes supplier qualification certificates, real-time temperature control logs, and special customs clearance documents. Because such drugs are highly sensitive to transportation environment fluctuations, for example, a two-hour interruption of the cold chain for monoclonal antibody drugs will cause activity decay, and the failure of radioactivity isolation for radioactive drugs will cause radiation leakage risk, so it is necessary to ensure full-link compliance through approval.

[0041] For approval rejection data, the system focuses on tracing back the rejection nodes to identify timeliness interruption points, such as a biological preparation being rejected due to transit temperature control qualification, which requires re-calculation of the transportation path. Subsequently, the transportation timeliness indicators and the drug expiration dates are analyzed simultaneously: the transportation timeliness indicators include transit time and customs clearance detention time, and the drug expiration dates include environmental sensitivity decay rate and material half-life, both of which are used to calculate and quantify transportation loss risk, avoiding the inflow of ineffective drugs into the warehouse.

[0042] The approval result includes the final approval status of the first submission; According to the approval pass data and the approval rejection data, obtain special drug transportation timeliness data; According to the special drug transportation timeliness data, obtain transportation timeliness indicators and drug expiration dates; The transportation timeliness indicators include transit transportation time, transit stay time, and customs clearance processing time; The drug expiration date includes stability factors determined by environmental sensitivity and decay factors determined by material characteristics.

[0043] It should be noted that the final approval status in the special drug approval result is explicitly marked as pass or rejection. The system automatically retrieves the real-time positioning information of the logistics company in the approval pass data and the node retention record in the approval rejection data, which directly determines the feasibility of the transportation path and the need for emergency adjustment.

[0044] In the transportation time index: the in-transit transportation time reflects the actual movement time of the logistics vehicle; the transit stay time includes the warehouse sorting and transfer interface time; the customs processing time covers the customs inspection and document verification period. The drug expiration date includes the stability factor of protein denaturation rate caused by temperature fluctuation and the material decay factor of natural decay rate of radioisotope, which together constitute the critical judgment criterion of drug survival period.

[0045] The initial transportation validity period refers to the transportation validity period corresponding to the approval pass data; According to the approval rejection data, the corresponding approval node in the approval rejection data is extracted; According to the approval node, the transportation validity period is recalculated and defined as the updated transportation validity period; According to the initial transportation validity period and the updated transportation validity period, the transportation time effectiveness is obtained.

[0046] It should be noted that the initial transportation validity period of special drugs refers to the survival period of drugs calculated based on the transportation plan approved for the first time, for example, the initial validity period of a certain radioactive drug is two hours (which needs to be adjusted according to the specific nuclide). The approval node in the approval rejection data specifically refers to the specific location of the rejection link in the transportation path, such as the failure of the temperature control audit in the transit warehouse.

[0047] The system recalculates the time loss of the remaining transportation links according to the node, for example, due to the addition of eight hours of retention caused by transit rejection, and generates an updated transportation validity period of sixty-four hours combined with the real-time decay rate of the drug. The transportation time effectiveness is quantified by the absolute difference between the initial value and the updated value, which reflects the stability of the transportation chain, and when the index exceeds the threshold, the high-risk batch is automatically frozen.

[0048] Extract the transportation time index: including in-transit transportation time, transit stay time and customs processing time; Synchronously extract the drug expiration date including stability factor and decay factor; According to the comparison calculation of the total length of the transportation time index and the remaining length of the drug expiration date, the initial transportation validity period is generated; Extract the approval node in the approval rejection data; According to the approval node, the length of each component of the transportation time index is adjusted; The dynamic recalculation is combined with the current state of the drug expiration date to generate an updated transportation expiration date.

[0049] It should be noted that the initial transportation expiration date of special drugs is calculated based on the difference between the total duration of transportation time and the remaining duration of the drug, to ensure the feasibility of the transportation plan. The formula is: , wherein, : initial transportation expiration date (hours), : remaining expiration date of the drug (hours), determined by stability factors (environmental sensitivity) and decay factors (material characteristics), : transportation duration (hours), : transit duration (hours), : customs processing duration (hours), integrating temperature-sensitive stability factors and isotope decay factors, reflecting the survival limit of the drug. This logic prevents the expiration of transportation from causing the drug to fail. The approval node in the approval rejection data refers to the specific location of the rejection link in the transportation chain, such as the customs rejection point. The system adjusts the time limit component accordingly (such as increasing the transit duration) and updates the real-time decay state of the drug. The updated transportation expiration date formula is: , wherein, : updated transportation expiration date (hours), : real-time remaining expiration date of the drug at the time of rejection (hours), : transportation time limit component adjusted according to the rejection node (hours). Dynamic recalculation responds to additional loss risks caused by path changes.

[0050] Extract the initial transportation expiration date and the updated transportation expiration date, calculate the absolute difference between the two and define it as the variation amplitude; The variation amplitude is used as the transportation time limit effectiveness. An increase in this value indicates an increase in the instability of the transportation plan, and a decrease in the value indicates an improvement in the stability of the transportation plan.

[0051] It should be noted that the transportation time limit effectiveness quantifies the plan execution deviation through the absolute difference between the initial transportation expiration date and the updated transportation expiration date, and the formula is: , with the output value being the absolute difference (hours). This variation amplitude directly reflects the anti-interference ability of the transportation link: for example, a certain biological agent has an initial expiration date of forty-eight hours, and due to the rejection of the transit station, the updated expiration date is reduced to forty hours, an eight-hour variation amplitude indicates that the transportation plan is severely impacted by unexpected factors, and the stability is significantly reduced.

[0052] This index is designed based on the zero tolerance of special drugs to time limit deviation, such as the risk of radiation dose out of control caused by a two-hour delay in the transportation of radioactive drugs. The system automatically classifies and responds based on the variation amplitude: low amplitude fluctuations maintain automated transportation, and high amplitude fluctuations force path optimization with intervention, blocking failed drugs from entering the warehouse from the data layer.

[0053] In the prior art, the disconnection of the transportation plan causes the transportation path length to be not recalculated in real time after the approval is rejected, and the risk of expiration of the drug is not included in the time limit update logic, which causes radioactive drug radiation leakage or loss of resources due to loss of biological activity. Embodiment 2 divides the approved data and the rejected data, extracts the transportation time limit index, and dynamically generates the initial transportation expiration date of the drug expiration date; for the rejected data, the approval node is located to recalculate the time limit component, and the updated transportation expiration date is generated in combination with the real-time decay state; finally, the absolute difference between the two defines the transportation time limit effectiveness, quantifies the anti-interference ability of the transportation chain, and realizes the precise control of the survival period of the high-sensitivity drug.

[0054] In embodiment 1, the stability of the qualification audit is quantified, and the transportation reliability is evaluated. Embodiment 3 needs to use the deviation degree and the transportation time limit effectiveness as joint evaluation indexes to trigger the risk stratification decision, and the specific implementation is as follows: The procurement decision generation module is used to use the deviation degree and the transportation time limit effectiveness as joint evaluation indexes, calculate the correlation strength coefficient of all special drugs by using the Pearson correlation coefficient, and determine the procurement and warehouse management method according to the correlation strength coefficient.

[0055] It should be noted that the value range of the deviation degree is always lower bounded by zero, which corresponds to the completely stable state of the qualification audit process, indicating that there is no sorting change and no need for re-audit; the upper limit is determined by the dynamic scale of the approval sequence and the maximum allowed depth of the re-audit level, and the actual value domain is adaptively expanded with the business scene. The value range of the transportation time limit effectiveness is also lower bounded by zero, which represents that the transportation plan has not deviated in time limit; the upper limit is constrained by the inherent expiration date attribute of the drug, and the specific upper limit value is dynamically defined by the environmental sensitivity and material decay characteristics of the drug.

[0056] When performing Pearson correlation calculation, the deviation degree and the transportation time limit effectiveness have different dimensions, and must be standardized. The deviation degree needs to be scaled to the standardized interval based on the current approval sequence characteristics, and the transportation time limit effectiveness needs to be converted to the standardized interval according to the drug expiration date attribute. This operation ensures that the correlation strength coefficient strictly follows the mathematical definition, and its output value is always in the closed interval of negative one to positive one, objectively representing the inherent correlation strength between the qualification audit stability and the transportation plan reliability. Finally, according to the preset risk threshold, the grading response mechanism is triggered to realize the risk cooperative control.

[0057] It should be noted that the degree of deviation and the effectiveness of transportation timeliness are used as joint evaluation indicators because they respectively quantify the volatility of qualification review and the degree of instability in transportation plans, jointly reflecting the risk transmission effect of the two links in the supply chain. For example, a high degree of deviation and a large value for the effectiveness of transportation timeliness for a certain radiopharmaceutical indicate that qualification re-review and transportation delays are worsening simultaneously. The Pearson correlation coefficient formula is: Parameter description: : No. Batch deviation value (stability of qualification review). : No. Batch transportation timeliness validity value (transportation plan reliability). : Mean of two indicators Total number of drug batches; joint evaluation indicators reveal the coupling strength between qualification and transportation risk; correlation strength coefficient. Quantifying the degree of linear correlation between the two: | A high-risk warning was triggered when the value reached 0.8, indicating that qualification fluctuations and transportation instability formed a positive feedback loop.

[0058] All special drug batches are sorted in ascending order based on the correlation strength coefficient to obtain the correlation ranking result. Drug batches whose correlation strength coefficient reaches the preset safety threshold in the correlation ranking result are selected first to activate the automated procurement and warehousing process. For drug batches whose correlation strength coefficient exceeds the risk threshold in the correlation ranking results, manual review is forcibly initiated and logistics operations are frozen. Risk stratification and control are achieved through correlation strength coefficient ranking and risk threshold determination.

[0059] It should be noted that ascending sorting arranges the correlation strength coefficients from low to high, forming a risk gradient sequence. The system prioritizes screening drug batches at the beginning of the sequence whose correlation strength coefficients meet the preset safety threshold. For example, if the deviation and transportation timeliness / effectiveness of a certain monoclonal antibody drug are both in the low-risk range, its correlation coefficient meets the safety threshold requirement, and the procurement process is automatically triggered.

[0060] The preset safety thresholds are dynamically calibrated and generated using historical risk data: the deviation threshold is determined based on operational data regression analysis; the transportation timeliness and effectiveness threshold is related to the critical requirements of drug activity; and the correlation strength coefficient threshold is verified through historical records. The system forms a risk gradient sequence based on the ascending order of the correlation strength coefficients and achieves hierarchical control through risk threshold determination—when the coefficient meets the safety threshold, the automated process is activated; when it is below the risk threshold, logistics are frozen and manual review is triggered, such as in the systemic risk scenario of radiopharmaceuticals.

[0061] Embodiment 3 constructs the key closed loop of special medicine supply chain risk collaborative management and control. The core value lies in that when facing the emergency replenishment of geographically isolated areas or the sudden interruption of complex transportation links, the system dynamically captures the validity of transportation time limit, responds to path changes in real time, and recalculates the survival window by integrating the drug decay characteristics, to ensure that high-sensitivity emergency drugs can still accurately match the activity requirements under the time limit pressure. This significantly optimizes the drug availability of the end node and effectively avoids the risk of resource loss caused by rigid transportation plans.

[0062] Further, the system deeply analyzes the dynamic coupling relationship between the stability of qualification audit (deviation degree) and the reliability of transportation plan (transportation time limit validity) through Pearson correlation analysis. Strong positive correlation signals clearly warn of the amplification effect of the superposition of double-link risks, while negative or weak correlation reveals the system's ability to successfully isolate single-link fluctuations. This intelligent diagnosis based on risk transmission intensity drives a layered decision engine: automatically releasing low-risk and urgently needed drug batches, while implementing fuse verification on high-coupling risk batches, ultimately achieving a global optimal balance between resource safety and response efficiency at the supply chain level.

[0063] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A special medicine purchase and storage management system, characterized by, The application relates to a qualification audit preprocessing module for obtaining qualification audit data and procurement approval data of various special medicines, and obtaining N first division results according to the qualification audit data; extracting audit change records in the qualification audit data according to the N first division results, and obtaining a re-audit change level number according to the audit change records; An audit process optimization module for sorting the N first division results from large to small to obtain a first sorting result, and obtaining an updated first sorting result according to the first sorting result; Calculating the deviation degree of each qualification approval data meeting a preset update sorting threshold in the updated first sorting result; A transport time limit calculation module for dividing the procurement approval data into approval pass data and approval rejection data according to approval results, obtaining a transport time limit index and a medicine validity period according to the approval pass data and the approval rejection data; A procurement decision generation module for taking the deviation degree and the transport time limit effectiveness as joint evaluation indexes, calculating a correlation intensity coefficient of the joint evaluation indexes of all special medicines by using a Pearson correlation coefficient, and determining a procurement and warehouse management method according to the correlation intensity coefficient. The qualification audit preprocessing module is used for obtaining qualification audit data and procurement approval data of various special medicines, and obtaining N first division results according to the qualification audit data, and comprises the following steps:

2. The special medicine purchase and storage management system according to claim 1, wherein extracting each audit level in the qualification audit data according to the qualification audit data; dividing the qualification audit data with the same audit level into the same qualification audit data; dividing the remaining audit levels in all the special medicines according to the number of audit levels to obtain N first division results. The method comprises the following steps:

3. The special medicine purchase and storage management system according to claim 1, wherein The audit change record refers to a record of re-auditing due to regulation change or material invalidation; extracting the audit level corresponding to the audit change record according to the audit change record and defining it as an audit change level; taking the audit change level as a starting point, counting the number of remaining levels that need to be audited and defining it as a re-audit change level number. The audit process optimization module is used for sorting the N first division results from large to small to obtain a first sorting result, and obtaining an updated first sorting result according to the first sorting result, and comprises the following steps:

4. The special medicine purchase and storage management system according to claim 1, wherein connecting the re-audit change level number to the back of the corresponding first division result to obtain an updated first sorting result; if the sum of the re-audit change level number and the original audit level number of the current qualification audit data is greater than the sum of the re-audit change level number and the original audit level number of the previous adjacent qualification audit data in the updated first sorting result, the order of the current qualification audit data in the approval sequence is advanced. ​ If the sum of the re-audit change level quantity and the original audit level quantity corresponding to the current qualification audit data is less than the sum of the re-audit change level quantity and the original audit level quantity corresponding to the subsequent adjacent qualification audit data in the updated first sorting result, the order of the current qualification audit data in the approval sequence is delayed; The operation process of updating the first sorting result is repeatedly performed until the updated first sorting result meets the preset updating sorting threshold.

5. The system for purchase and storage management of special medicines according to claim 1, wherein The calculation of the deviation degree of each qualification audit data in the updated first sorting result that meets the preset updating sorting threshold each time includes: Extract the original sorting position and the current sorting position of each qualification audit data in the updated first sorting result, calculate the absolute value of the difference between the two to generate a position offset; Synchronously obtain the re-audit change level quantity corresponding to the qualification audit data as the level change amplitude; Combine the position offset and the level change amplitude by a preset ratio to form a comprehensive evaluation value, which is defined as the deviation degree; The deviation degree fuses the sorting change intensity and the audit depth change amount, and the value increase represents the increase of process instability, and the value decrease represents the increase of process stability.

6. The special medicine purchase and storage management system according to claim 1, wherein The procurement approval data is divided into approved data and rejected data according to the approval result, and the transportation time limit and the drug validity period are obtained according to the approved data and the rejected data, including: The approval result includes the final approval state of the first submission; According to the approved data and the rejected data, special drug transportation time limit data is obtained; According to the special drug transportation time limit data, the transportation time limit and the drug validity period are obtained; The transportation time limit includes the in-transit transportation time, the transit stay time and the customs processing time; The drug validity period includes stability factors determined by environmental sensitivity and decay factors determined by material characteristics.

7. The system for purchase and storage management of special medicines according to claim 1, wherein The transportation time limit calculation module is configured to calculate an initial transportation validity period according to the transportation time limit and the drug validity period, obtain an updated transportation validity period according to the initial transportation validity period, and obtain transportation time limit effectiveness according to the initial transportation validity period and the updated transportation validity period, including: The initial transportation validity period refers to the transportation validity period corresponding to the approved data; According to the rejected data, the corresponding approval node in the rejected data is extracted; According to the approval node, the transportation validity period is recalculated and defined as the updated transportation validity period; According to the initial transportation validity period and the updated transportation validity period, the transportation time limit effectiveness is obtained.

8. The special medicine purchase and storage management system according to claim 1, wherein According to the transportation time limit and the drug validity period, the initial transportation validity period is calculated, and the updated transportation validity period is obtained according to the initial transportation validity period, including: Extract the transportation time limit: including in-transit transportation time, transit stay time and customs processing time; Synchronously extract the drug validity period including stability factors and decay factors; According to the total duration of the transportation time limit and the remaining duration of the drug validity period, the initial transportation validity period is calculated; Extract the approval node in the rejected data; According to the approval node, the time limit for transportation is adjusted for each time length component; Combined with the current state of the drug expiration date, the dynamic recalculation is performed to generate an updated transportation expiration date.

9. The special medicine purchase and storage management system according to claim 1, wherein According to the initial transportation expiration date and the updated transportation expiration date, the transportation time limit validity is obtained, including: Extracting the initial transportation expiration date and the updated transportation expiration date, calculating the absolute difference and defining it as the variation amplitude; The variation amplitude is used as the transportation time limit validity, and the value increase represents the increase of the transportation plan instability, and the value decrease represents the improvement of the transportation plan stability.

10. The system for purchase and storage management of special medicines according to claim 1, wherein The procurement decision generation module: for the deviation degree and the transportation time limit validity as a joint evaluation index, using Pearson correlation coefficient to calculate the correlation intensity coefficient of the joint evaluation index of all special drugs, and determining the procurement and warehouse management method according to the correlation intensity coefficient, including: According to the correlation intensity coefficient, all special drug batches are sorted in ascending order to obtain the correlation degree sorting result, and the drug batches with the correlation intensity coefficient reaching the preset safety threshold in the correlation degree sorting result are preferentially selected to activate the automatic procurement and warehouse process; For the drug batches with the correlation intensity coefficient exceeding the risk threshold in the correlation degree sorting result, the manual review is forcibly started and the logistics operation is frozen, and the risk stratified control is realized through the correlation intensity coefficient sorting and risk threshold judgment.

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