County compact drug supply chain management system, method, medium and equipment

Abstract

The invention provides a county compact drug supply chain management system, method, medium and equipment, a cloud-side-end three-layer architecture is adopted, a cloud end is provided with a block chain data middle table, and a cross-unit dependency relationship is coordinated with a risk early warning unit, a dynamic routing optimization unit and an intelligent prediction unit through an AI engine; the edge computing module comprises an edge computing gateway, a local AI preprocessing unit and a cold chain abnormity real-time interception unit, the local AI preprocessing unit operates an AI model to process aggregated data of the edge computing gateway in real time to generate a decision, and the cold chain abnormity real-time interception unit can execute an interception action according to the decision; the terminal comprises a medicine identification terminal device, an environment monitoring terminal device, a mobile scanning terminal device and a hospital medicine cabinet / cold chain cabinet terminal device. According to the scheme, county medical supervision data are shared in real time, a drug supply chain is optimized through AI, the whole process is traceable, manual intervention is reduced, management difficulty is lowered, drug supply safety is improved, and cost and efficiency are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to a supply chain management system, in particular a county close type medicine supply chain management system, method, medium and equipment, belonging to the technical field of medicine supply distribution. BACKGROUND

[0002] The medicine supply chain management system is a whole-process digital management platform for medicines from raw material procurement to patient use, which combines Internet of Things (IoT), blockchain, big data and other technologies to monitor the production, procurement, inventory, distribution, logistics and compliance of medicines. By integrating medicine production, circulation, warehousing, distribution and terminal sales, it ensures the traceability, quality control and supply and demand balance of medicines. Therefore, the medicine supply chain management system is a digital solution for managing the whole process of medicine production and consumption in the pharmaceutical industry, and its core goal is to ensure patient safety, reduce operating costs and meet strict pharmaceutical regulatory requirements.

[0003] However, although the existing medicine supply chain management system has improved efficiency and transparency, it still has some key defects that may affect the safety, accessibility and cost control of medicines, such as: hospitals, pharmaceutical companies, logistics and regulatory departments often use independent systems, data formats are not compatible, leading to data information islandization and fragmentation; relying on traditional barcodes or manual records, it is difficult to achieve real-time tracking of the whole chain; and for temperature-controlled medicines, the cold chain logistics is not perfect enough. Therefore, it is necessary to propose an innovative county close type medicine supply chain management system to review prescriptions, medical insurance settlement, increase online medicine ordering and distribution, increase drug supply center cockpit and support county unified control of medicines. SUMMARY

[0004] The purpose of the present application is to solve the above problems and provide a county close type medicine supply chain management system, method, medium and equipment.

[0005] The technical solution of the present application is: a county close type medicine supply chain management system adopts a three-layer architecture, including a cloud, an edge computing module and a terminal, characterized in that: the cloud is provided with a blockchain data platform and a plurality of sub-units, the blockchain data platform is used to store medicine whole chain production, transportation and sales data to ensure non-tamperability and traceability, and to coordinate cross-module / unit dependency relationship between the AI engine and each sub-unit, each sub-unit includes but is not limited to a risk early warning unit, a dynamic routing optimization unit and an intelligent prediction unit, the risk early warning unit monitors medicine quality risk (such as temperature anomaly, fake drug circulation) and supply chain interruption risk (such as inventory shortage, transportation delay) in real time, the dynamic routing optimization unit optimizes medicine distribution path according to real-time traffic, temperature control demand and inventory urgency, the intelligent prediction unit further combines hospital HIS data and epidemiological data to improve prediction accuracy by establishing a Prophet time series model to decompose trend, seasonality and holiday effect, to predict medicine demand, inventory turnover rate and supply chain interruption probability; the edge computing module includes an edge computing gateway, a local AI preprocessing unit and a cold chain anomaly real-time interception unit, the edge computing gateway aggregates multi-source heterogeneous data (such as RFID, temperature control sensor, inventory record) and distributes to the local AI preprocessing unit after uniform format, the local AI preprocessing unit runs a lightweight AI model to process the aggregated data in real time, generates preliminary decisions (such as temperature drift warning) and receives model updates from the cloud AI engine periodically, and the cold chain anomaly real-time interception unit immediately executes interception actions (such as starting backup refrigeration, notifying the driver) according to the decision result of the local AI preprocessing unit; the terminal includes medicine identification terminal equipment, environment monitoring terminal equipment, mobile scanning terminal equipment and hospital medicine cabinet / cold chain cabinet terminal equipment.

[0006] Further, the county close type medicine supply chain management system, wherein: the blockchain data platform is connected with a standardized supervision and audit interface for calling by other modules / units.

[0007] Further, the county close type medicine supply chain management system, wherein: the data flow of the risk early warning unit and the AI cooperative calculation formula are as follows:

[0008] If S a >τ, an alarm is triggered

[0009] In the formula: x i is the current data (such as temperature, logistics delay, etc.); μ i ,σ i are the historical mean and standard deviation; w i is the weight, which is dynamically adjusted by the AI engine.

[0010] Further, the county close type medicine supply chain management system, wherein: the data flow of the dynamic routing optimization unit and the AI collaborative calculation formula are as follows:

[0011] R = a T 节省 + beta C 成本 - gamma R 风险

[0012] In the formula: T 节省 is the time saving amount; R 风险 is the path risk score, provided by the risk early warning unit.

[0013] Further, the county close type medicine supply chain management system, wherein: the data flow of the intelligent prediction unit and the AI collaborative calculation formula are as follows:

[0014]

[0015] In the formula: y t is the actual demand; y^ t is the predicted demand.

[0016] Further, the county close type medicine supply chain management system, wherein: the Prophet time series model of the intelligent prediction unit is:

[0017] y (t) = g (t) + s (t) + h (t) + epsilon t + lambda F AI (X t )

[0018] In the formula: g (t) is the trend item; s (t) is the seasonal item; h (t) is the holiday effect; epsilon t is the noise item; F AI (X t ) is the correction item of the external AI model (for example: LSTM / Attention); lambda is the weight coefficient of the AI correction item, which can be dynamically adjusted through online learning.

[0019] Further, the county close type medicine supply chain management system, wherein: the real-time decision formula of the cold chain abnormal real-time interception unit is as follows:

[0020]

[0021] In the formula: Delta T is dynamically calculated by a sliding window.

[0022] The application provides a county close type medicine supply chain management method, which realizes intelligent management of the medicine supply chain through cloud decision, edge computing module execution and terminal collection closed loop operation, and comprises the following steps:

[0023] Step (one) terminal operation: pharmaceutical enterprises bind RFID / two-dimensional code double tags for each box of medicine on the production line, register identification information including but not limited to unique ID, production batch and expiration date, and record initial temperature and humidity data when packaging medicine, and each identification data is encrypted through an edge computing gateway and then uploaded to a cloud end block chain data platform in real time for storage;

[0024] Step (two) cloud decision: using an intelligent prediction unit to generate demand prediction based on historical data and external factors (such as season, epidemic) by a Prophet time series model, calculating the distribution path of each hospital in the county (optimized by a genetic algorithm) through a dynamic routing optimization unit, and then issuing inventory allocation instructions and routing schemes to an edge computing module and terminal distribution logistics;

[0025] Step (three) edge computing module execution: receiving temperature and humidity sensor data of a logistics vehicle, denoising and feature extraction of received data by a local AI preprocessing unit, collecting key data (such as abnormal event data) and then synchronizing to the cloud end block chain in real time for viewing by pharmaceutical enterprises and regulatory authorities;

[0026] Step (four) terminal feedback: after the hospital scans and accepts the medicine through a mobile scanning terminal (PDA), the data is automatically synchronized to the hospital medicine cabinet terminal device, and the corresponding cold chain cabinet terminal device continuously monitors the medicine storage environment, and when an abnormality occurs, the cold chain abnormality real-time interception unit of the edge computing module is triggered to quickly intercept and notify medical staff; at the same time, the medicine consumption data is fed back to the cloud intelligent prediction unit to update the next cycle demand model;

[0027] Step (five) cloud closed loop: finally, the risk early warning unit analyzes the whole chain data (for example, the temperature control abnormality rate of a batch is >5%, which needs risk early warning), generates a compliance report for the drug regulatory department to audit, and the whole process data cannot be tampered with.

[0028] In addition, the present application provides a computer readable storage medium having a computer program stored thereon, which controls the county close type medicine supply chain management system when executed by a processor.

[0029] In addition, the present application also provides an electronic device comprising a processor and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the county close type medicine supply chain management system by executing the executable instructions.

[0030] The application adopts the technical scheme, through the cloud synchronization center pharmacy data, based on the cloud block chain data center, a three-layer architecture medicine guarantee system is constructed. Based on the AI decision closed loop medicine supply chain with traceability supervision medicine ability, multi-dimensional data integration production, circulation, terminal data, for the transportation supply of special medicine, real-time temperature control sensing (low-cost IoT sensor can be used, combined with blockchain technology, to ensure that the temperature control data is real and traceable, for example: all-round cold chain monitoring equipment), data is automatically uploaded to the cloud; then insert AI prediction transportation environment risk (such as weather, traffic and warehouse load and other factors), in order to optimize the route.

[0031] Compared with the prior art, after adopting the technical scheme of the application, the supervision data of hospitals, pharmaceutical enterprises and logistics can be interchanged and shared in real time, the medicine transportation route, inventory surplus and quality control can be optimized by AI, which can effectively reduce manual intervention and reduce management difficulty, the medicine is traceable throughout the whole process, the specific flow is transparent, and the problem medicine can be quickly recalled; moreover, the cold chain logistics management of temperature-controlled medicine is monitored and controlled throughout the whole process by real-time temperature control sensing and introduction of intelligent algorithm technology, the quality risk is reduced, the supply chain safety of temperature-controlled medicine (such as vaccines, biological agents, insulin and other temperature-controlled sensitive medicines) is significantly improved, and the cost is reduced and the efficiency is increased. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The system framework of the application is shown in the figure;

[0033] Figure 2 The running schematic diagram of the system data flow and AI coordination mechanism of the application is shown in the figure;

[0034] Figure 3 The structure schematic diagram of the cloud of the application is shown in the figure;

[0035] Figure 4 The structure schematic diagram of the edge computing module of the application is shown in the figure;

[0036] Figure 5 The structure schematic diagram of the terminal of the application is shown in the figure;

[0037] Figure 6 The running schematic diagram of the county medicine supply emergency allocation of the application is shown in the figure;

[0038] Figure 7 The supervision audit interface principle diagram of the ESB mode / platform mode of the application is shown in the figure;

[0039] Figure 8 The supervision audit interface principle diagram of the direct connection mode / no platform of the application is shown in the figure;

[0040] Figure 9 The intelligent health network topology diagram of the county medicine supply of the application is shown in the figure. Detailed Implementation

[0041] The technical solutions of the present invention will be further described below with reference to the accompanying drawings to make them easier to understand and master. The technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] like Figures 1-5 As shown, the present invention provides a county-level integrated pharmaceutical supply chain management system, which adopts a three-tier architecture, including cloud, edge computing module and terminal.

[0043] According to the technical solution of the present invention, the cloud platform is equipped with a blockchain data platform and several sub-units (modular design supports the addition of new units, such as special management of special drugs). The blockchain data platform is used to store data on the entire drug production, transportation and sales chain to ensure immutability and traceability. It coordinates cross-module / unit dependencies with each sub-unit through an AI engine. Each sub-unit includes, but is not limited to, a risk warning unit, a dynamic routing optimization unit and an intelligent prediction unit. The risk warning unit monitors drug quality risks (e.g., abnormal temperature, counterfeit drug circulation) and supply chain disruption risks (e.g., inventory shortage, transportation delay) in real time. The dynamic routing optimization unit optimizes drug delivery routes based on real-time traffic, temperature control requirements, and inventory urgency to achieve precise service. This enables unified procurement, unified distribution and unified supervision of drugs, ensuring the safety, effectiveness, convenience and affordability of medications used by medical institutions. Combined with a network information platform, it can further establish a "three-in-one" drug security system integrating drug supply, use and reimbursement payment. The intelligent prediction unit... The system decomposes trend, seasonality, and holiday effects by establishing a Prophet time series model, and further improves prediction accuracy by combining hospital HIS data and epidemiological data to predict drug demand, inventory turnover rate, and supply chain disruption probability. The edge computing module includes an edge computing gateway, a local AI preprocessing unit, and a cold chain anomaly real-time interception unit. The edge computing gateway aggregates multi-source heterogeneous data (e.g., RFID, temperature control sensors, inventory records) and distributes it to the local AI preprocessing unit after unifying the format. The local AI preprocessing unit runs a lightweight AI model to process the aggregated data in real time and generates preliminary decisions (e.g., temperature drift warnings). It also periodically receives model updates in collaboration with the cloud AI engine. The cold chain anomaly real-time interception unit immediately executes interception actions (e.g., activating backup refrigeration, notifying drivers) based on the decision results of the local AI preprocessing unit. The terminals include drug identification terminal devices, environmental monitoring terminal devices, mobile scanning terminal devices, and hospital medicine cabinet / cold chain cabinet terminal devices.

[0044] Preferably, such as Figure 6 As shown in the above structure, when a public health emergency occurs (such as a special illness or natural disaster), the system achieves rapid emergency response through the following steps: Real-time analysis of data from the CDC, social media sentiment, and hospitalization demand via an AI engine triggers an early warning; automated scanning of real-time inventory at all nodes (hospitals, pharmacies, warehouses) within the county, marking shortages of medicines, optimizing transportation routes based on drug source (pharmacy or regional central warehouse), drug characteristics (such as cold chain requirements), traffic conditions, and delivery vehicle location, prioritizing speed and accuracy to improve supply efficiency; real-time tracking of delivery status via IoT temperature and humidity sensors, triggering AI for dynamic adjustments in case of anomalies; and, if necessary, activating onboard backup power and dispatching nearby replacement medicines; and initiating drone relay transportation (i.e., activating backup transportation nodes, with drones delivering to remote village clinics, etc.). All of the above operations are recorded in the blockchain data platform for post-event auditing and are tamper-proof.

[0045] like Figure 7 and Figure 8 As shown, the blockchain data platform is connected to a standardized regulatory audit interface, which includes ESB mode / platform mode and direct connection mode / no platform, for use by other modules / units.

[0046] like Figure 9 As shown, the Smart Health Connect network topology is a digital neural network connecting drug production, warehousing, logistics, hospitals, and regulatory authorities, making the drug supply chain fully transparent and visible. It optimizes data routing based on network load, ensuring priority transmission of critical tasks. Through digital integration and process optimization, it significantly improves the efficiency and quality of drug supply.

[0047] Preferably, in the above structure, the data flow and AI collaborative calculation formula of the risk warning unit are as follows:

[0048] If S a >τ will trigger an alarm.

[0049] In the formula: x i For current data (e.g., temperature, logistics delays, etc.); μ i ,σ i For historical mean and standard deviation; w i The weights are dynamically adjusted by the AI ​​engine.

[0050] Preferably, in the above structure: the data flow and AI collaborative calculation formula of the dynamic routing optimization unit are as follows:

[0051] R = α·T 节省 +β·C 成本 -γ·R风险

[0052] wherein: T 节省 is the time saving amount; R 风险 is the path risk score, provided by the risk warning unit.

[0053] Further, the above-mentioned county close-type drug supply chain management system, wherein: the data flow of the intelligent prediction unit and the AI collaborative calculation formula are as follows:

[0054]

[0055] wherein: y t is the actual demand; y^ t is the predicted demand.

[0056] Preferably, in the above structure: the Prophet time series model of the intelligent prediction unit is:

[0057] y(t) = g(t) + s(t) + h(t) + ∈ t + λ · f AI (X t )

[0058] wherein: g(t) is the trend item; s(t) is the seasonal item; h(t) is the holiday effect; ∈t is the noise item; f AI (X t ) is the correction item of the external AI model (such as LSTM / Attention); λ is the weight coefficient of the AI correction item, which can be dynamically adjusted through online learning.

[0059] When the monthly demand (unit: ten thousand boxes) of antibiotics (such as cephalosporins) in a certain county's drug supply chain is as shown in Table 1 below, and external variables (such as influenza outbreak index, holiday marker) are added.

[0060]

[0061] 1) Trend item g(t) (linear growth):

[0062] g(t) = 10.0 + 0.15 · t

[0063] g(18) = 10.0 + 0.15 × 18 = 12.7 (ten thousand boxes);

[0064] 2) Seasonal item s(t) (monthly cycle):

[0065]

[0066] s(18) = 0.5cos(2πx18 / 12) + 0.3sin(2πx18 / 12) = -0.2;

[0067] 3) Holiday term h(t) (Spring Festival effect):

[0068]

[0069] h(18) = 0;

[0070] 4) AI correction term:

[0071] Wherein the LSTM input:

[0072] X t = [X flu = 0.5, X holiday = 0]

[0073] LSTM output:

[0074] f LSTM (X t ) = 1.8

[0075] Dynamic weight λ = 0.6.

[0076] 5) Noise term ∈t:

[0077]

[0078] Therefore, the final antibiotic demand prediction value of 2025-07 is:

[0079] y(18) = 12.7 (trend) - 0.2 (season) + 0 (holiday) + 0.6x1.8 (AI correction) + ∈t ≈ 13.6 (ten thousand boxes)

[0080] As can be seen, the rophet time series model described above has smaller errors and higher prediction accuracy than the traditional rophet model. The AI correction term reduces the prediction error by 65% by capturing sudden factors, and the dynamic weight λ can be automatically adjusted with data updates to adapt to long-term evolution.

[0081] Preferably, in the above structure: the real-time decision formula of the cold chain anomaly real-time interception unit is as follows:

[0082]

[0083] In the formula, ΔT is dynamically calculated by a sliding window.

[0084] The application provides a county close type medicine supply chain management method, which realizes intelligent management of the medicine supply chain through cloud decision, edge computing module execution and terminal collection closed loop operation, and comprises the following steps:

[0085] (1) Terminal operation: the pharmaceutical enterprises bind RFID / two-dimensional code double labels for each box of medicine on the production line, register identification information including but not limited to unique ID, production batch and valid period, and record the initial temperature and humidity data synchronously when the medicine is packaged, and each identification data is encrypted through the edge computing gateway and then uploaded to the cloud end block chain data platform in real time.

[0086] (2) Cloud decision: the intelligent prediction unit generates demand prediction based on historical data and external factors (such as season, epidemic) by using the Prophet time series model, calculates the distribution path of each hospital in the county (optimized by genetic algorithm) through the dynamic routing optimization unit, and then issues the inventory allocation instructions and routing scheme to the edge computing module and terminal distribution logistics.

[0087] (3) Edge computing module execution: receiving the temperature and humidity sensor data of the logistics vehicle, the received data is denoised and feature extracted by the local AI preprocessing unit, and after the collection of key data (such as abnormal event data) is completed, it is synchronized to the cloud end block chain in real time for the pharmaceutical enterprises and regulatory authorities to check.

[0088] (4) Terminal feedback: after the hospital scans and accepts the medicine through the mobile scanning terminal (PDA), the data is automatically synchronized to the hospital medicine cabinet terminal device, the corresponding cold chain cabinet terminal device continuously monitors the medicine storage environment, and when an abnormality occurs, the cold chain abnormal real-time interception unit of the edge computing module will quickly intercept and notify the medical staff; at the same time, the medicine consumption data is fed back to the cloud intelligent prediction unit to update the next cycle demand model.

[0089] Step (5) cloud closed loop: finally, the risk early warning unit analyzes the whole chain data (for example: if the temperature control abnormal rate of a batch is >5%, risk early warning is needed; if the temperature control abnormal rate of a batch is <5%, no risk early warning is needed, and real-time monitoring continues), generates a compliance report for the drug supervision department to audit, and the whole process data cannot be tampered with.

[0090] In addition, the application provides a computer readable storage medium, the readable medium comprises a computer storage medium and a communication medium, wherein the communication medium adopts a time series database (for example, InfluxDB) of distributed cloud storage, can efficiently process time series numbers, and is suitable for temperature control sensor data and inventory change records.

[0091] In addition, the application further provides an electronic device comprising a processor and a memory for storing executable instructions of the processor (wherein the memory can be used independently or integrated with the processor), and the processor is configured to execute the county close type medicine supply chain management system by executing the executable instructions.

[0092] In the technical scheme of the application, the AI+blockchain+edge computing system architecture is the technical key of the application, and the data flow and AI coordination mechanism are the technical key of the application, Figure 2 The key point of the application is the system data flow and AI coordination mechanism management framework and cooperation process, and the real-time, safety and adaptability of the medicine supply chain are realized through the three-layer architecture coordination of cloud-edge-end and the AI decision closed loop.

[0093] In this way, by using the technical scheme of the application, the pharmacy data is synchronized through the cloud synchronization center, the cloud blockchain data platform is used as the basis, the three-layer architecture medicine guarantee system of cloud-edge-end is constructed, the AI decision closed loop medicine supply chain with the ability of tracing and supervising medicines is used as the basis, the production, circulation and terminal data are integrated in multiple dimensions, the real-time temperature control sensing is carried out for the transportation and supply of special medicines (low-cost IoT sensors can be used, combined with blockchain technology, to ensure that the temperature control data is real and traceable, for example, all-around cold chain monitoring equipment), and the data is automatically uploaded to the cloud; and the AI is used to predict the transportation environment risk (such as weather, traffic and warehouse load), so as to optimize and adjust the route.

[0094] It can be found from the above description that, compared with the prior art, after the technical scheme of the present application is adopted, the supervision data of each hospital, pharmaceutical enterprise and logistics is interchanged and shared in real time, the system comprehensively realizes the construction of regional cloud drug warehouse based on hospital drug warehouse, realizes full coverage of counties, towns and villages, and synchronizes the data (specifically including drug directory, dictionary, code, etc.) of the central pharmacy of the grassroots units, and forcibly interchanges the data of each link of the supply chain; through AI optimization of the drug transportation route, inventory surplus and quality monitoring, manual intervention can be effectively reduced, the management difficulty can be reduced, the drug can be traced throughout the whole process, the specific flow is transparent, and the problem drug can be quickly recalled; moreover, the cold chain logistics management of the temperature-controlled drug is monitored and controlled throughout the whole process through real-time temperature control sensing and introduction of intelligent algorithm technical means, the quality risk is reduced, the safety of the supply chain of the temperature-controlled drug (such as vaccine, biological preparation, insulin and other temperature-controlled sensitive drugs) is significantly improved, and the cost is reduced and the efficiency is increased.

[0095] The technical scheme, working process and implementation effect of the present application are described in detail above, and it should be noted that the described is only a typical example of the present application, in addition to this, the present application can have other various specific implementation manners, and any technical scheme formed by equivalent replacement or equivalent transformation falls within the scope of the present application.

Claims

1. A county-level integrated pharmaceutical supply chain management system, employing a three-tier architecture including cloud, edge computing modules, and terminals, characterized in that: The cloud platform is equipped with a blockchain data platform and several sub-units. The blockchain data platform is used to store data on the entire pharmaceutical production, transportation and sales chain, and coordinates cross-module / unit dependencies with each sub-unit through an AI engine. Each sub-unit includes, but is not limited to, a risk warning unit, a dynamic routing optimization unit and an intelligent prediction unit. The risk warning unit monitors pharmaceutical quality risks and supply chain disruption risks in real time. The dynamic routing optimization unit optimizes pharmaceutical delivery routes based on real-time traffic, temperature control requirements and inventory urgency. The intelligent prediction unit predicts pharmaceutical demand, inventory turnover rate and supply chain disruption probability by establishing a Prophet time series model. The edge computing module includes an edge computing gateway, a local AI preprocessing unit, and a cold chain anomaly real-time interception unit. The edge computing gateway aggregates multi-source heterogeneous data and distributes it to the local AI preprocessing unit after unifying the format. The local AI preprocessing unit runs a lightweight AI model to process the aggregated data in real time and generates preliminary decisions. It also periodically receives model updates in collaboration with the cloud AI engine. The cold chain anomaly real-time interception unit immediately executes interception actions based on the decision results of the local AI preprocessing unit. The terminals include drug labeling terminal devices, environmental monitoring terminal devices, mobile scanning terminal devices, and hospital medicine cabinet / cold chain cabinet terminal devices.

2. The county-level integrated pharmaceutical supply chain management system according to claim 1, characterized in that: The blockchain data platform is connected to a standardized regulatory audit interface for use by other modules / units.

3. The county-level integrated pharmaceutical supply chain management system according to claim 1, characterized in that: The data flow and AI collaborative calculation formula of the risk warning unit are as follows: If S a >τ will trigger an alarm. In the formula: x i For current data; μ i ,σ i For historical mean and standard deviation; w i For weights.

4. The county-level integrated pharmaceutical supply chain management system according to claim 1, characterized in that: The data flow and AI collaborative calculation formula of the dynamic routing optimization unit are as follows: R=α·T 节省 +β·C 成本 -γ·R 风险 In the formula: T 节省 Time savings; R 风险 Path risk scoring.

5. The county-level integrated pharmaceutical supply chain management system according to claim 1, characterized in that: The data flow and AI collaborative calculation formula of the intelligent prediction unit are as follows: In the formula: y t For actual needs; y^ t To predict demand.

6. The county-level integrated pharmaceutical supply chain management system according to claim 5, characterized in that: The Prophet time series model of the intelligent prediction unit is: y(t)=g(t)+s(t)+h(t)+∈ t +λ·f AI (X t ) In the formula: g(t) is the trend term; s(t) is the seasonal term; h(t) is the holiday effect; ∈t is the noise term; f AI (X t ) represents the correction term of the external AI model; λ represents the weight coefficient of the AI ​​correction term, which can be dynamically adjusted through online learning.

7. The county-level integrated pharmaceutical supply chain management system according to claim 1, characterized in that: The real-time decision-making formula of the cold chain anomaly real-time interception unit is as follows: In the formula: ΔT is obtained by dynamic statistical calculation using a sliding window.

8. A county-level integrated pharmaceutical supply chain management method, characterized in that... Includes the following steps: Step S1, Terminal Operation: On the production line, pharmaceutical companies bind RFID / QR code dual tags to each box of medicine and write identification information including but not limited to unique ID, production batch, and expiration date. The environmental monitoring terminal records the initial temperature and humidity data simultaneously when the medicine is packaged. After each identification data is encrypted through the edge computing gateway, it is uploaded to the cloud blockchain data platform for storage in real time. Step S2, Cloud Decision-Making: Using the intelligent prediction unit based on historical data and external factors (such as seasons and epidemics), the Prophet model is used to generate demand forecasts. The dynamic routing optimization unit pre-calculates the delivery routes of each hospital in the county (genetic algorithm optimization). Then, the inventory allocation instructions and routing schemes are sent to the edge computing module and terminal distribution logistics. Step S3, the edge computing module executes: receiving temperature and humidity sensor data from logistics vehicles, performing noise reduction and feature extraction by the local AI preprocessing unit, and synchronizing key data (such as abnormal events) to the cloud blockchain in real time for pharmaceutical companies and regulators to view; Step S4, Terminal Feedback: After the hospital scans and accepts medicines using a mobile scanning terminal (PDA), the data is automatically synchronized to the hospital's medicine cabinet terminal equipment. The corresponding cold chain cabinet terminal equipment continuously monitors the medicine storage environment. In case of an anomaly, the edge computing module's cold chain anomaly real-time interception unit will quickly intercept and notify medical staff. At the same time, medicine consumption data is fed back to the cloud intelligent prediction unit to update the demand model for the next cycle. Step S5, Cloud-based Closed Loop: Finally, the risk warning unit analyzes the entire chain of data and generates a compliance report for audit by the drug regulatory authorities. The data throughout the process is tamper-proof.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program controls the county-level integrated pharmaceutical supply chain management system as described in any one of claims 1 to 7 when executed by the processor.

10. An electronic device comprising a processor and a memory for storing processor-executable instructions, characterized in that: The processor is configured to execute the county-level integrated pharmaceutical supply chain management system according to any one of claims 1 to 7 by executing the executable instructions.

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