Medical consumable terminal replenishment monitoring system

Abstract

The invention relates to the technical field of medical consumables, and discloses a medical consumable terminal replenishment monitoring system which comprises a data acquisition module, a data integration module, a data calculation module, a data analysis module, an execution module and a feedback module. According to the invention, real-time monitoring and analysis of inventory conditions are realized, abnormal replenishment problems can be accurately identified, response can be quickly made, risks in inventory management are effectively reduced while replenishment decision scientificity and accuracy are improved, supply chain management of medical consumables is optimized, management efficiency is improved, effective control of the risks is realized, and the risk management efficiency is improved. And corresponding alarm signals can be generated according to different levels of risks, so that the severity of abnormal replenishment can be more effectively transmitted by the hierarchical alarm mechanism, and a user is helped to quickly identify the emergency degree of a problem and take corresponding treatment measures.

Description

Technical Field

[0001] The present invention relates to the technical field of medical consumables, and in particular to a terminal replenishment monitoring system for medical consumables. Background Art

[0002] In the modern medical field, supply chain management is a key component to ensure the efficient operation of medical services. As a part of supply chain management, medical consumables management covers the inventory control and replenishment management of drugs, medical devices and other consumables.

[0003] Currently, many medical institutions rely on traditional inventory management systems, which are usually based on manual records and regular audits to manage the inventory of medical consumables. This traditional method has several obvious shortcomings, including poor real-time data collection and delayed information updates. Due to the delay and static nature of the data, managers often cannot obtain the current actual demand situation when making replenishment decisions, which leads to misjudgment of inventory status. This information lag makes it difficult for medical institutions to respond quickly when faced with sudden medical needs or supply chain disruptions, thus affecting the efficiency and accuracy of inventory management.

[0004] Therefore, we proposed a terminal replenishment monitoring system for medical consumables to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a terminal replenishment monitoring system for medical consumables to solve the problem of data delay and static state proposed in the above-mentioned background technology. Managers often cannot obtain the current actual demand situation when making replenishment decisions, which leads to the problem of misjudgment of inventory status.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a terminal replenishment monitoring system for medical consumables, comprising a data acquisition module, a data integration module, a data calculation module, a data analysis module, an execution module and a feedback module;

[0007] The data collection module is used to collect the real-time data of the terminal and the quarterly data of the same period of previous years;

[0008] The data integration module is used to process the multi-source data collected by the data collection module and reorganize it into a first data set, a second data set and a third data set;

[0009] The data calculation module is used to integrate and calculate the first data set, the second data set and the third data set, so as to generate a replenishment alarm coefficient BJX;

[0010] The data analysis module is used to compare the replenishment alarm coefficient BJX with the preset first threshold value Y to generate a first comparison result, and determine whether abnormal replenishment of current medical consumables occurs according to the first comparison result;

[0011] If the judgment result is that abnormal replenishment occurs, the replenishment alarm coefficient BJX is integrated and calculated with the preset first threshold value Y to generate a replenishment level value LJX, and the replenishment level value LJX is compared with the preset second threshold value R to generate a second comparison result, and the abnormal replenishment level of the current medical consumables is judged according to the second comparison result;

[0012] The execution module is used to execute sending of a corresponding alarm signal according to the second comparison result;

[0013] The feedback module is used to feed back multiple data to the operation page.

[0014] Preferably, the data acquisition module includes a first acquisition unit, a second acquisition unit and a third acquisition unit;

[0015] The first collection unit and the second collection unit are used to collect the previous quarter information and real-time information of medical consumables, including inventory, fluctuation value, purchase cycle, usage, medical activity frequency and disease transmission frequency;

[0016] The third collection unit is used to collect the transportation information of medical consumables including transportation time, transportation distance, traffic congestion, wind force value and rainfall.

[0017] Preferably, the data integration module includes a data preprocessing unit and a data sorting unit;

[0018] The data preprocessing unit is used to preprocess the multi-source data collected by the data acquisition module;

[0019] The data sorting unit is used to non-dimensionalize the multi-source data after pre-processing, sort the non-dimensionalized data, and reclassify all the data according to the previous quarterly data and real-time data, so as to generate the first data set, the second data set and the third data set.

[0020] Preferably, the first data set includes the inventory volume WCH of the same quarter in the past, the fluctuation value WSB of the same quarter in the past, the purchase cycle WZQ of the same quarter in the past, the usage volume WSY of the same quarter in the past, the medical activity frequency WPL of the same quarter in the past, and the disease transmission rate WYD of the same quarter in the past;

[0021] The second data set includes real-time inventory quantity SCH, real-time fluctuation value SSB, real-time purchase cycle SZQ, real-time usage SSY, real-time medical activity frequency SPL, and real-time disease transmission rate SYD;

[0022] The third data set includes the transportation time WSJ of the same quarter in the past, the transportation distance WLC of the same quarter in the past, the traffic congestion WJT of the same quarter in the past, the wind force value WFL of the same quarter in the past, the rainfall WJY of the same quarter in the past, the real-time transportation time SSJ, the real-time transportation distance SLC, the real-time traffic congestion SJT, the real-time wind force value SFL and the real-time rainfall SJY.

[0023] Preferably, the data calculation module includes a first calculation unit, a second calculation unit and a third calculation unit;

[0024] The first calculation unit is used for integrating and calculating the first data set, the second data set and the third data set, so as to generate the inventory incentive value CHY, the consumption incentive value HHY and the transportation difficulty incentive value YSY;

[0025] The second calculation unit is used for integrating the inventory incentive value CHY, the consumption incentive value HHY and the transportation difficulty incentive value YSY into a first reference coefficient S1 and a second reference coefficient S2;

[0026] The third calculation unit is used to integrate the first reference coefficient S1 and the second reference coefficient S2 to calculate the replenishment alarm coefficient BJX.

[0027] Preferably, the inventory incentive value CHY, the consumption incentive value HHY and the transportation difficulty incentive value YSY are calculated and obtained by the following formulas respectively:

[0028]

[0029] Wherein: a1, a2, b1 and b2 are weight values, ln is a logarithmic function, and the values ​​of a1, a2, b1 and b2 are adjusted and set by the user;

[0030] WCH is the inventory volume in the same quarter of the previous period, WSB is the fluctuation value in the same quarter of the previous period, WZQ is the purchase cycle in the same quarter of the previous period, WSY is the usage volume in the same quarter of the previous period, WPL is the frequency of medical activities in the same quarter of the previous period, WYD is the disease transmission rate in the same quarter of the previous period, WSJ is the transportation time in the same quarter of the previous period, WLC is the transportation distance in the same quarter of the previous period, WJT is the traffic congestion in the same quarter of the previous period, WFL is the wind force value in the same quarter of the previous period, and WJY is the rainfall in the same quarter of the previous period;

[0031] SCH is the real-time inventory, SSB is the real-time fluctuation value, SZQ is the real-time procurement cycle, SSY is the real-time usage, SPL is the real-time frequency of medical activities, SYD is the real-time disease transmission rate, SSJ is the real-time transportation time, SLC is the real-time transportation distance, SJT is the real-time traffic congestion, SFL is the real-time wind value, and SJY is the real-time rainfall.

[0032] Preferably, the first reference coefficient S1 and the second reference coefficient S2 are calculated and obtained by the following formulas respectively:

[0033] S1=CHY×e YSY ;

[0034] S2=HHY×e YSY ;

[0035] In the formula: CHY is the inventory incentive value CHY, HHY is the consumption incentive value HHY, YSY is the transportation difficulty incentive value YSY, and e is the base of the exponential function.

[0036] Preferably, the replenishment alarm coefficient BJX is calculated by the following formula:

[0037]

[0038] Wherein: c1 and c2 are weight values, and the values ​​of c1 and c2 are adjusted and set by the user, and S1 and S2 are the first reference coefficient and the second reference coefficient.

[0039] Preferably, the data analysis module includes a first analysis unit and a second analysis unit;

[0040] The first analysis unit is used to generate a first comparison result, which is as follows;

[0041] When |BJX|<Y, it means that the current medical consumables are in normal replenishment status;

[0042] When |BJX|≥Y, it means that the current medical consumables are in an abnormal replenishment state;

[0043] The second analysis unit is used to generate a second comparison result, which is as follows;

[0044] When |LJX|<R, it means that the current medical consumables are in the first-level abnormal replenishment state;

[0045] When R≤|LJX|<R×105%, it means that the current medical consumables are in the secondary abnormal replenishment state;

[0046] When |LJX|≥R×105%, it means that the current medical consumables are in the third-level abnormal replenishment state;

[0047] The specific calculation formula of the replenishment level value LJX is as follows;

[0048]

[0049] Where: BJX is the replenishment alarm coefficient, and Y is the first threshold.

[0050] Preferably, the alarm signal recorded by the execution module is specifically as follows;

[0051] When medical consumables are in the first-level abnormal replenishment state, the replenishment abnormality is identified to confirm whether it is over-replenishment or under-replenishment. If it is over-replenishment, a yellow alarm message starting with C is sent; if it is under-replenishment, a yellow alarm message starting with Q is sent;

[0052] When medical consumables are in the first-level abnormal replenishment state, the replenishment abnormality is identified to confirm whether it is over-replenishment or under-replenishment. If it is over-replenishment, an orange alarm message starting with C is sent; if it is under-replenishment, a yellow alarm message starting with Q is sent;

[0053] When medical consumables are in the secondary abnormal replenishment state, the replenishment abnormality is identified to confirm whether it is over-replenishment or under-replenishment. If it is over-replenishment, an orange alarm message starting with C is sent; if it is under-replenishment, a yellow alarm message starting with Q is sent;

[0054] When medical consumables are in the third-level abnormal replenishment state, the replenishment abnormality is identified to confirm whether it is over-replenishment or under-replenishment. If it is over-replenishment, a red alarm message starting with C is sent; if it is under-replenishment, a red alarm message starting with Q is sent.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. Through multi-source data processing and intelligent calculation, the system realizes real-time monitoring and analysis of inventory status, can accurately identify abnormal replenishment problems, and respond quickly. While improving the scientificity and accuracy of replenishment decisions, it effectively reduces the risks in inventory management and further optimizes the supply chain management of medical consumables, thereby improving management efficiency and achieving effective risk control.

[0057] 2. By analyzing historical and real-time data simultaneously, the system can better identify potential risks in the demand for medical consumables and supply chain, and adjust strategies in a timely manner. The application of this dual data set enhances the system's sensitivity to abnormal situations, enabling it to respond quickly to emergencies, and improving the flexibility and adaptability of overall supply chain management. By classifying and organizing different data sets, the system achieves refined data management. This not only improves the utilization of data, but also enables the system to maintain efficient operation in a complex environment, thereby providing strong support for the precise replenishment and inventory optimization of medical consumables. Through comprehensive analysis and integration of transportation, inventory and demand data, the system can formulate replenishment plans more accurately, avoiding the problem of insufficient or excessive replenishment due to one-sided or incomplete data, thereby significantly improving the accuracy and efficiency of replenishment decisions.

[0058] 3. The system generates corresponding alarm signals according to different levels of replenishment status. This hierarchical alarm mechanism can more effectively convey the severity of abnormal replenishment, help users quickly identify the urgency of the problem, and take corresponding treatment measures. Through systematic data analysis and detailed alarm mechanisms, this system can provide accurate and timely feedback when replenishment anomalies occur. This improvement greatly improves the response speed and accuracy of replenishment decisions, effectively reduces inventory risks, and improves the overall efficiency of supply chain management. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a system flow chart of the present invention.

[0060] In the figure: 1. Data acquisition module; 11. First acquisition unit; 12. Second acquisition unit; 13. Third acquisition unit; 2. Data integration module; 21. Data preprocessing unit; 22. Data sorting unit; 3. Data calculation module; 31. First calculation unit; 32. Second calculation unit; 33. Third calculation unit; 4. Data analysis module; 41. First analysis unit; 42. Second analysis unit; 5. Execution module; 6. Feedback module. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Example 1: Please refer to Figure 1 , a terminal replenishment monitoring system for medical consumables, comprising a data acquisition module 1, a data integration module 2, a data calculation module 3, a data analysis module 4, an execution module 5 and a feedback module 6;

[0063] The data collection module 1 is used to collect the real-time data of the terminal and the quarterly data of the same period of previous years;

[0064] The data integration module 2 is used to process the multi-source data collected by the data collection module 1 and reorganize them into a first data set, a second data set and a third data set;

[0065] The data calculation module 3 is used to integrate and calculate the first data set, the second data set and the third data set, so as to generate a replenishment alarm coefficient BJX;

[0066] The data analysis module 4 is used to compare the replenishment alarm coefficient BJX with the preset first threshold value Y to generate a first comparison result, and determine whether abnormal replenishment of current medical consumables occurs according to the first comparison result;

[0067] If the judgment result is that abnormal replenishment occurs, the replenishment alarm coefficient BJX is integrated and calculated with the preset first threshold value Y to generate a replenishment level value LJX, and the replenishment level value LJX is compared with the preset second threshold value R to generate a second comparison result, and the abnormal replenishment level of the current medical consumables is judged according to the second comparison result;

[0068] The execution module 5 is used for executing sending a corresponding alarm signal according to the second comparison result;

[0069] The feedback module 6 is used to feed back the multi-source data to the operation page.

[0070] In this embodiment: Data collection module 1 is responsible for real-time monitoring of the use of medical consumables at the terminal and collecting data from the same quarter of previous years. Through comprehensive and timely data collection, this module can ensure that the system has the most accurate information on the consumption of medical consumables, providing a solid data foundation for subsequent analysis and decision-making. This combination of real-time and historical data not only improves the integrity of the data, but also lays the foundation for subsequent replenishment analysis, ensuring the scientificity and accuracy of replenishment decisions.

[0071] The data integration module 2 processes the multi-source data obtained by the data acquisition module 1 and organizes the data into the first data set, the second data set and the third data set. The main task of this module is to standardize the original data and classify and organize it according to different dimensions to ensure the availability and consistency of the data. This multi-level data organization method can better support subsequent calculations and analysis, ensuring that the system can still operate efficiently and accurately when facing complex data.

[0072] The data calculation module 3 is responsible for integrating and calculating the first, second and third data sets after sorting, and generating the replenishment alarm coefficient BJX. Through the comprehensive calculation of different data sets, the module can intelligently identify potential replenishment needs. This calculation process not only improves the accuracy of replenishment decisions, but also can timely warn of possible inventory shortages or excesses based on the comprehensive analysis results of multi-source data, providing a scientific basis for replenishment management.

[0073] The data analysis module 4 conducts an in-depth analysis of the replenishment alarm coefficient BJX, compares it with the preset first threshold value Y, generates a first comparison result, and determines whether there is an abnormal replenishment of the current medical consumables based on the comparison result. If abnormal replenishment is detected, the module will further calculate the replenishment level value LJX and compare it with the preset second threshold value R to determine the severity of the abnormal replenishment. Through this multi-level analysis, the module can more accurately identify and quantify abnormal replenishment situations, avoiding potential risks in inventory management.

[0074] The execution module 5 executes the corresponding alarm signal transmission according to the second comparison result provided by the data analysis module 4. The function of this module is to convert the analysis results into actual operations to ensure that the management personnel can obtain the early warning information in time and take corresponding actions. The automatic processing capability of this module reduces the time cost of human intervention and improves the response speed of the system, thereby effectively preventing supply chain problems caused by abnormal inventory.

[0075] Feedback module 6 feeds back multiple data in the system to the operation page, allowing managers to intuitively view and understand the current inventory status and analysis results. The design of this module makes information transmission more transparent, ensuring that managers can grasp key data in a timely manner and make reasonable replenishment decisions. Through this real-time data feedback mechanism, the module greatly improves the user experience of the system and makes operation more convenient and intuitive.

[0076] Through multi-source data processing and intelligent calculation, this system realizes real-time monitoring and analysis of inventory status, can accurately identify abnormal replenishment problems, and respond quickly. While improving the scientificity and accuracy of replenishment decisions, it effectively reduces the risks in inventory management, further optimizes the supply chain management of medical consumables, thereby improving management efficiency and achieving effective risk control.

[0077] Example 2: Please refer to Figure 1 , the data acquisition module 1 includes a first acquisition unit 11, a second acquisition unit 12 and a third acquisition unit 13;

[0078] The first collection unit 11 and the second collection unit 12 are respectively used to collect the previous quarterly information and real-time information of medical consumables, including inventory, fluctuation value, purchase cycle, usage, medical activity frequency and disease transmission frequency;

[0079] The third collecting unit 13 is used to collect the transportation information of the medical consumables including the transportation time, transportation distance, traffic congestion, wind force value and rainfall.

[0080] In this embodiment: the first acquisition unit 11 and the second acquisition unit 12 respectively collect the previous quarterly information and real-time information of medical consumables, including inventory, fluctuation value, procurement cycle, usage, frequency of medical activities and frequency of disease transmission. By combining historical data with real-time data, the system can more accurately predict future demand changes and ensure the scientific nature of replenishment decisions. By collecting multi-dimensional data information such as inventory, usage, frequency of medical activities, etc., the system can fully grasp the actual use of medical consumables. Such data coverage can help the system identify potential abnormal replenishment problems and provide effective early warnings to ensure the rationality and stability of inventory.

[0081] The third collection unit 13 is specifically used to collect information related to the transportation of medical consumables, including transportation time, transportation distance, traffic congestion, wind force value and rainfall. The collection of these data enables the system to consider the impact of external transportation conditions on the supply of consumables, so as to more accurately plan the replenishment time and quantity and reduce the supply chain risks caused by transportation delays.

[0082] Through the collaborative work of the above-mentioned multiple collection units, the system can not only monitor the inventory status in real time, but also predict potential risks in transportation in advance and optimize supply chain management. This comprehensive data collection method greatly improves the decision-making accuracy of the system and effectively reduces the uncertainty and risk in inventory management. Through comprehensive data collection of various links such as the use, storage, procurement, and transportation of medical consumables, the system can adjust and optimize replenishment plans in real time, avoiding inventory shortages or surpluses caused by insufficient or delayed information, and further improving the overall efficiency of supply chain management.

[0083] Example 3: Please refer to Figure 1 , the data integration module 2 includes a data preprocessing unit 21 and a data sorting unit 22;

[0084] The data preprocessing unit 21 is used to preprocess the multi-source data collected by the data collection module 1;

[0085] The data sorting unit 22 is used to dimensionally transform the multi-source data after preprocessing, sort the dimensionally transformed data, and reclassify all data according to the previous quarterly data and real-time data, so as to generate the first data set, the second data set and the third data set.

[0086] The first data set includes the inventory volume WCH of the same quarter in the past, the fluctuation value WSB of the same quarter in the past, the purchase cycle WZQ of the same quarter in the past, the usage volume WSY of the same quarter in the past, the medical activity frequency WPL of the same quarter in the past, and the disease transmission rate WYD of the same quarter in the past;

[0087] The second data set includes real-time inventory quantity SCH, real-time fluctuation value SSB, real-time purchase cycle SZQ, real-time usage SSY, real-time medical activity frequency SPL, and real-time disease transmission rate SYD;

[0088] The third data set includes the transportation time WSJ of the same quarter in the past, the transportation distance WLC of the same quarter in the past, the traffic congestion WJT of the same quarter in the past, the wind force value WFL of the same quarter in the past, the rainfall WJY of the same quarter in the past, the real-time transportation time SSJ, the real-time transportation distance SLC, the real-time traffic congestion SJT, the real-time wind force value SFL and the real-time rainfall SJY.

[0089] In this embodiment, the data preprocessing unit 21 preprocesses the collected multi-source data, which can effectively remove noise, fill missing values ​​and perform data standardization, thereby improving the quality and consistency of the data. This process ensures the accuracy of subsequent analysis and reduces calculation errors and decision-making errors caused by data quality issues.

[0090] The data sorting unit 22 performs dimensionless processing on the preprocessed data, so that data of different dimensions can be analyzed and compared on the same scale. This dimensionless process eliminates the dimension differences between different data dimensions, making the data more scientific and reasonable during calculation and analysis, and ensuring the accuracy of the calculation results. The data sorting unit 22 reclassifies the dimensionless data and normalizes the data according to the previous quarterly data and real-time data, thereby generating the first data set, the second data set, and the third data set. This classification and sorting process not only enhances the structured degree of the data, but also provides clear input for subsequent calculations and analysis, enabling the system to process complex data more efficiently.

[0091] The reasonable design of data preprocessing and sorting process effectively reduces the computational complexity in data analysis. By preprocessing, dimensionless and classifying data, the system can quickly extract the required information in subsequent calculations, optimize the calculation process, shorten the decision response time, and scientifically classify different data sets to enable the system to perform data analysis and calculation more accurately. When processing historical data and real-time data, the system can make more reasonable analysis and predictions based on different time dimensions and data sources, thereby improving the scientific nature of replenishment decisions.

[0092] The first and second data sets cover the past quarterly and real-time data of medical consumables, including inventory, fluctuation value, procurement cycle, usage, frequency of medical activities, and disease transmission rate. This comprehensive integration of multi-dimensional data enables the system to consider both historical trends and current conditions, so as to more accurately forecast demand and manage inventory, and improve the scientific nature of replenishment decisions. The third data set integrates past and real-time transportation data such as transportation time, transportation distance, traffic congestion, wind force, and rainfall, so that the system can more comprehensively evaluate the impact of transportation conditions on the supply of consumables. This data integration can help the system fully consider external transportation factors when replenishing, thereby optimizing replenishment time and quantity and reducing supply chain risks caused by changes in external conditions.

[0093] By analyzing historical and real-time data simultaneously, the system can better identify potential risks in the demand for medical consumables and supply chain, and adjust strategies in a timely manner. The application of this dual data set enhances the system's sensitivity to abnormal situations, enabling it to respond quickly to emergencies, and improving the flexibility and adaptability of overall supply chain management. By classifying and organizing different data sets, the system achieves refined data management. This not only improves the utilization of data, but also enables the system to maintain efficient operation in complex environments, thereby providing strong support for accurate replenishment and inventory optimization of medical consumables. Through comprehensive analysis and integration of transportation, inventory and demand data, the system can formulate replenishment plans more accurately, avoiding the problem of insufficient or excessive replenishment due to one-sided or incomplete data, thereby significantly improving the accuracy and efficiency of replenishment decisions.

[0094] Example 4: Please refer to Figure 1 , the data calculation module 3 includes a first calculation unit 31, a second calculation unit 32 and a third calculation unit 33;

[0095] The first calculation unit 31 is used for integrating and calculating the first data set, the second data set and the third data set, so as to generate the inventory incentive value CHY, the consumption incentive value HHY and the transportation difficulty incentive value YSY;

[0096] The second calculation unit 32 is used for integrating the inventory incentive value CHY, the consumption incentive value HHY and the transportation difficulty incentive value YSY into a first reference coefficient S1 and a second reference coefficient S2;

[0097] The third calculation unit 33 is used to integrate the first reference coefficient S1 and the second reference coefficient S2 to calculate the replenishment alarm coefficient BJX.

[0098] The inventory incentive value CHY, consumption incentive value HHY and transportation difficulty incentive value YSY are calculated and obtained by the following formulas respectively;

[0099]

[0100]

[0101] Wherein: a1, a2, b1 and b2 are weight values, ln is a logarithmic function, and the values ​​of a1, a2, b1 and b2 are adjusted and set by the user;

[0102] WCH is the inventory volume in the same quarter of the previous period, WSB is the fluctuation value in the same quarter of the previous period, WZQ is the purchase cycle in the same quarter of the previous period, WSY is the usage volume in the same quarter of the previous period, WPL is the frequency of medical activities in the same quarter of the previous period, WYD is the disease transmission rate in the same quarter of the previous period, WSJ is the transportation time in the same quarter of the previous period, WLC is the transportation distance in the same quarter of the previous period, WJT is the traffic congestion in the same quarter of the previous period, WFL is the wind force value in the same quarter of the previous period, and WJY is the rainfall in the same quarter of the previous period;

[0103] SCH is the real-time inventory, SSB is the real-time fluctuation value, SZQ is the real-time procurement cycle, SSY is the real-time usage, SPL is the real-time frequency of medical activities, SYD is the real-time disease transmission rate, SSJ is the real-time transportation time, SLC is the real-time transportation distance, SJT is the real-time traffic congestion, SFL is the real-time wind value, and SJY is the real-time rainfall.

[0104] The first reference coefficient S1 and the second reference coefficient S2 are calculated and obtained by the following formulas respectively;

[0105] S1=CHY×e YSY ;

[0106] S2=HHY×e YSY ;

[0107] In the formula: CHY is the inventory incentive value CHY, HHY is the consumption incentive value HHY, YSY is the transportation difficulty incentive value YSY, and e is the base of the exponential function.

[0108] The replenishment alarm coefficient BJX is calculated by the following formula:

[0109]

[0110] Wherein: c1 and c2 are weight values, and the values ​​of c1 and c2 are adjusted and set by the user, and S1 and S2 are the first reference coefficient and the second reference coefficient.

[0111] In this embodiment: the first calculation unit 31 generates the inventory incentive value CHY, the consumption incentive value HHY and the transportation difficulty incentive value YSY by integrating and calculating the first data set, the second data set and the third data set. This integrated analysis of multi-dimensional data can not only fully reflect the changes in key factors such as inventory, consumption and transportation, but also provide a more scientific basis for subsequent replenishment decisions through in-depth mining of these factors.

[0112] The second calculation unit 32 further integrates and calculates the inventory incentive value CHY, the consumption incentive value HHY and the transportation difficulty incentive value YSY to generate a first reference coefficient S1 and a second reference coefficient S2. By accurately calculating these reference coefficients, the system can more accurately evaluate the current inventory status and replenishment needs, thereby avoiding decision-making errors caused by a single factor and achieving more refined inventory management.

[0113] The third calculation unit 33 integrates the first reference coefficient S1 and the second reference coefficient S2 to calculate the replenishment alarm coefficient BJX. This process realizes the automation of replenishment warning. Through the replenishment alarm coefficient BJX, the system can quickly identify abnormal situations and trigger warning signals, thereby improving the response speed to inventory risks and effectively reducing the potential interruption risks in the supply chain.

[0114] By integrating and calculating multi-dimensional incentive values ​​and reference coefficients, the system can provide more comprehensive and accurate support for replenishment decisions. This calculation process takes into account multiple factors such as inventory, consumption, and transportation, making the decision-making process more comprehensive and reasonable, thereby optimizing the execution effect of the replenishment plan. Through the setting of data calculation module 3, the system realizes intelligent management of the entire process from data collection to early warning triggering. This not only improves the efficiency of supply chain management, but also significantly enhances the system's adaptive ability in complex environments, ensuring that the supply of medical consumables can respond to changes in demand in a timely manner and reduce the risk of insufficient or excessive inventory.

[0115] The inventory incentive value CHY formula comprehensively considers the historical inventory, procurement cycle and its corresponding fluctuation value, and processes the relative changes between data through logarithmic functions, so that the system can more accurately capture the subtle fluctuations of inventory changes. This refined inventory management method can effectively avoid the problem of insufficient or excessive inventory and ensure the scientific and timely nature of replenishment decisions.

[0116] The consumption incentive value HHY formula can dynamically evaluate the consumption trend of consumables by analyzing the usage, frequency of medical activities and disease transmission rate in real-time and historical data. This real-time evaluation method helps the system to adjust the replenishment strategy in time according to actual usage, avoid supply chain risks caused by changes in demand, and thus improve the efficiency of the use of medical consumables.

[0117] The transportation difficulty incentive value YSY formula achieves a comprehensive assessment of the complexity of the transportation environment by weighting multiple factors such as transportation time, distance, traffic congestion, wind and rainfall. This comprehensive analysis can help the system identify possible transportation obstacles in advance, and then optimize the logistics route and time arrangement to ensure that medical consumables can arrive at the destination on time and reduce logistics risks.

[0118] By introducing user-adjustable weight values, the system provides great flexibility, allowing users to adjust the influence of various factors in the formula according to specific needs and actual conditions. This flexibility ensures that the system can adapt to changes in demand in different environments and scenarios, thereby better supporting personalized supply chain management strategies.

[0119] By combining the inventory incentive value CHY with the transportation difficulty incentive value YSY to calculate the first reference coefficient S1, and combining the consumption incentive value HHY with the transportation difficulty incentive value YSY to calculate the second reference coefficient S2, this system fully considers factors such as inventory status, consumption dynamics, and logistics difficulty in replenishment decisions. This multi-dimensional comprehensive consideration method can ensure that the system comprehensively evaluates the impact of different links, thereby more accurately reflecting actual demand.

[0120] The exponential function is introduced into the calculation of the reference coefficient, so that the impact of transportation difficulty on replenishment decision-making is nonlinearly strengthened. This processing method can more sensitively reflect changes in transportation conditions, ensure that replenishment decisions can still be made in a timely and accurate manner in a complex logistics environment, and further improve the system's responsiveness and decision-making accuracy.

[0121] The calculation formula of the replenishment alarm coefficient BJX introduces a user-adjustable weight value, which gives the system the ability to adapt to different scenarios. Users can adjust the weight of each factor in the final decision according to actual needs, thereby achieving a more personalized replenishment strategy. This flexibility greatly enhances the practicality and adaptability of the system, and can better cope with the complex and changing needs of medical consumables management.

[0122] By combining the first reference coefficient S1 and the second reference coefficient S2 and calculating the replenishment alarm coefficient BJX, the system can more accurately assess the current replenishment risk. This coefficient can effectively identify high-risk replenishment situations, help users take countermeasures in advance, avoid inventory shortages or excesses caused by improper replenishment decisions, and thus optimize overall supply chain management.

[0123] Through the above integrated calculations, this system avoids the subjectivity of traditional experience-based decision-making and uses a data-driven approach to improve the scientificity and accuracy of replenishment decisions. This improvement not only improves the system's ability to adapt to actual conditions, but also effectively reduces uncertainty and risks in inventory management.

[0124] Example 5: Please refer to Figure 1 , the data analysis module 4 includes a first analysis unit 41 and a second analysis unit 42;

[0125] The first analysis unit 41 is used to generate a first comparison result, specifically as follows;

[0126] When |BJX| < Y, it represents that the current medical consumable is in a normal replenishment state;

[0127] When |BJX| ≥ Y, it represents that the current medical consumable is in an abnormal replenishment state;

[0128] The second analysis unit 42 is used to generate a second comparison result, specifically as follows;

[0129] When |LJX| < R, it represents that the current medical consumable is in a first-level abnormal replenishment state;

[0130] When R ≤ |LJX| < R × 105%, it represents that the current medical consumable is in a second-level abnormal replenishment state;

[0131] When |LJX| ≥ R × 105%, it represents that the current medical consumable is in a third-level abnormal replenishment state;

[0132] The specific calculation formula of the replenishment magnitude value LJX is as follows;

[0133]

[0134] In the formula: BJX is the replenishment alarm coefficient, and Y is the first threshold.

[0135] The alarm signals recorded in the execution module 5 are specifically as follows;

[0136] When the medical consumable is in a first-level abnormal replenishment state, identify whether the replenishment anomaly is overstock replenishment or shortage replenishment. If it is overstock replenishment, send a yellow alarm message starting with C. If it is shortage replenishment, send a yellow alarm message starting with Q;

[0137] When the medical consumable is in a first-level abnormal replenishment state, identify whether the replenishment anomaly is overstock replenishment or shortage replenishment. If it is overstock replenishment, send an orange alarm message starting with C. If it is shortage replenishment, send a yellow alarm message starting with Q;

[0138] When the medical consumable is in a second-level abnormal replenishment state, identify whether the replenishment anomaly is overstock replenishment or shortage replenishment. If it is overstock replenishment, send an orange alarm message starting with C. If it is shortage replenishment, send a yellow alarm message starting with Q;

[0139] When medical consumables are in the third-level abnormal replenishment state, the replenishment abnormality is identified to confirm whether it is over-replenishment or under-replenishment. If it is over-replenishment, a red alarm message starting with C is sent; if it is under-replenishment, a red alarm message starting with Q is sent.

[0140] In this embodiment: the design of the data analysis module 4 provides an accurate abnormal state identification mechanism through the first analysis unit 41 and the second analysis unit 42. Specifically, when the absolute value |BJX| of the replenishment alarm coefficient BJXBJX is less than the preset first threshold Y, the system determines that it is a normal replenishment state; when |BJX| is greater than or equal to Y, it is identified as an abnormal replenishment state. This method ensures that the system can promptly and accurately identify and respond to possible replenishment anomalies, thereby improving the real-time and reliability of replenishment monitoring.

[0141] The second analysis unit 42 further subdivides the abnormal replenishment status into level one, level two and level three abnormal status, based on the specific calculation result of the replenishment level value LJX. Such refined abnormal level division can provide more targeted alarms and countermeasures, level one abnormal status: by comparing with the preset second threshold R, the system can identify and distinguish between over-replenishment and under-replenishment, and generate corresponding alarm information accordingly, level two and level three abnormal status: the system further subdivides the abnormal degree, provides a more sophisticated abnormal identification and alarm mechanism, and helps users understand the severity of replenishment problems more accurately.

[0142] The replenishment level value LJX dynamically reflects the degree of replenishment anomaly by comparing the replenishment alarm coefficient BJX with the first threshold value Y. This dynamic calculation method makes the identification of replenishment anomalies more flexible and timely, and helps the system to respond quickly to changes in replenishment demand.

[0143] Execution module 5 generates corresponding alarm signals according to different levels of replenishment status. This hierarchical alarm mechanism can more effectively convey the severity of abnormal replenishment, help users quickly identify the urgency of the problem, and take corresponding treatment measures. Through systematic data analysis and detailed alarm mechanism, this system can provide accurate and timely feedback when replenishment anomalies occur. This improvement greatly improves the response speed and accuracy of replenishment decisions, effectively reduces inventory risks, and improves the overall efficiency of supply chain management.

[0144] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0145] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A terminal replenishment monitoring system for medical consumables, characterized by: It includes a data collection module (1), a data integration module (2), a data calculation module (3), a data analysis module (4), an execution module (5) and a feedback module (6); The data collection module (1) is used to collect the real-time data of the terminal and the quarterly data of the same period of previous years; The data integration module (2) is used to process the multi-source data collected by the data collection module (1) and reorganize them into a first data set, a second data set and a third data set; The data calculation module (3) is used to integrate and calculate the first data set, the second data set and the third data set, so as to generate a replenishment alarm coefficient BJX; The data analysis module (4) is used to compare the replenishment alarm coefficient BJX with a preset first threshold value Y to generate a first comparison result, and determine whether abnormal replenishment of current medical consumables occurs based on the first comparison result; If the judgment result is that abnormal replenishment occurs, the replenishment alarm coefficient BJX is integrated and calculated with the preset first threshold value Y to generate a replenishment level value LJX, and the replenishment level value LJX is compared with the preset second threshold value R to generate a second comparison result, and the abnormal replenishment level of the current medical consumables is judged according to the second comparison result; The execution module (5) is used for executing sending of a corresponding alarm signal according to the second comparison result; The feedback module (6) is used to feed back the multi-source data to the operation page.

2. A terminal replenishment monitoring system for medical consumables according to claim 1, characterized in that: The data acquisition module (1) comprises a first acquisition unit (11), a second acquisition unit (12) and a third acquisition unit (13); The first collection unit (11) and the second collection unit (12) are respectively used to collect the past quarterly information and real-time information of medical consumables, including inventory, fluctuation value, purchase cycle, usage, medical activity frequency and disease transmission frequency; The third collection unit (13) is used to collect the transportation information of medical consumables including transportation time, transportation distance, traffic congestion, wind force value and rainfall.

3. A terminal replenishment monitoring system for medical consumables according to claim 2, characterized in that: The data integration module (2) includes a data preprocessing unit (21) and a data sorting unit (22); The data preprocessing unit (21) is used to preprocess the multi-source data collected by the data collection module (1); The data sorting unit (22) is used to non-dimensionalize the multi-source data after pre-processing, sort the non-dimensionalized data, and reclassify all the data according to the previous quarterly data and real-time data, so as to generate a first data set, a second data set and a third data set.

4. A terminal replenishment monitoring system for medical consumables according to claim 3, characterized in that: The first data set includes the inventory volume WCH of the same quarter in the past, the fluctuation value WSB of the same quarter in the past, the purchase cycle WZQ of the same quarter in the past, the usage volume WSY of the same quarter in the past, the medical activity frequency WPL of the same quarter in the past, and the disease transmission rate WYD of the same quarter in the past; The second data set includes real-time inventory quantity SCH, real-time fluctuation value SSB, real-time purchase cycle SZQ, real-time usage SSY, real-time medical activity frequency SPL, and real-time disease transmission rate SYD; The third data set includes the transportation time WSJ of the same quarter in the past, the transportation distance WLC of the same quarter in the past, the traffic congestion WJT of the same quarter in the past, the wind force value WFL of the same quarter in the past, the rainfall WJY of the same quarter in the past, the real-time transportation time SSJ, the real-time transportation distance SLC, the real-time traffic congestion SJT, the real-time wind force value SFL and the real-time rainfall SJY.

5. A terminal replenishment monitoring system for medical consumables according to claim 4, characterized in that: The data calculation module (3) comprises a first calculation unit (31), a second calculation unit (32) and a third calculation unit (33); The first calculation unit (31) is used for integrating and calculating the first data set, the second data set and the third data set, so as to generate the inventory incentive value CHY, the consumption incentive value HHY and the transportation difficulty incentive value YSY; The second calculation unit (32) is used for integrating the inventory incentive value CHY, the consumption incentive value HHY and the transportation difficulty incentive value YSY into a first reference coefficient S1 and a second reference coefficient S2; The third calculation unit (33) is used to integrate the first reference coefficient S1 and the second reference coefficient S2 to calculate the replenishment alarm coefficient BJX.

6. A terminal replenishment monitoring system for medical consumables according to claim 5, characterized in that: The inventory incentive value CHY, consumption incentive value HHY and transportation difficulty incentive value YSY are calculated and obtained by the following formulas respectively; Wherein: a1, a2, b1 and b2 are weight values, ln is a logarithmic function, and the values ​​of a1, a2, b1 and b2 are adjusted and set by the user; WCH is the inventory volume in the same quarter of the previous period, WSB is the fluctuation value in the same quarter of the previous period, WZQ is the purchase cycle in the same quarter of the previous period, WSY is the usage volume in the same quarter of the previous period, WPL is the frequency of medical activities in the same quarter of the previous period, WYD is the disease transmission rate in the same quarter of the previous period, WSJ is the transportation time in the same quarter of the previous period, WLC is the transportation distance in the same quarter of the previous period, WJT is the traffic congestion in the same quarter of the previous period, WFL is the wind force value in the same quarter of the previous period, and WJY is the rainfall in the same quarter of the previous period; SCH is the real-time inventory, SSB is the real-time fluctuation value, SZQ is the real-time procurement cycle, SSY is the real-time usage, SPL is the real-time frequency of medical activities, SYD is the real-time disease transmission rate, SSJ is the real-time transportation time, SLC is the real-time transportation distance, SJT is the real-time traffic congestion, SFL is the real-time wind value, and SJY is the real-time rainfall.

7. A terminal replenishment monitoring system for medical consumables according to claim 6, characterized in that: The first reference coefficient S1 and the second reference coefficient S2 are calculated and obtained by the following formulas respectively; S1=C HY×e YSY ; S2=H HY×e YSY ; In the formula: CHY is the inventory incentive value CHY, HHY is the consumption incentive value HHY, YSY is the transportation difficulty incentive value YSY, and e is the base of the exponential function.

8. A terminal replenishment monitoring system for medical consumables according to claim 7, characterized in that: The replenishment alarm coefficient BJX is calculated by the following formula: Wherein: c1 and c2 are weight values, and the values ​​of c1 and c2 are adjusted and set by the user, and S1 and S2 are the first reference coefficient and the second reference coefficient.

9. A terminal replenishment monitoring system for medical consumables according to claim 8, characterized in that: The data analysis module (4) comprises a first analysis unit (41) and a second analysis unit (42); The first analysis unit (41) is used to generate a first comparison result, specifically as follows; When |BJX|<Y, it means that the current medical consumables are in normal replenishment status; When |BJX|≥Y, it means that the current medical consumables are in an abnormal replenishment state; The second analysis unit (42) is used to generate a second comparison result, specifically as follows; When |LJX|<R, it means that the current medical consumables are in the first-level abnormal replenishment state; When R≤|LJX|<R×105%, it means that the current medical consumables are in the secondary abnormal replenishment state; When |LJXI≥R×105%, it means that the current medical consumables are in the third-level abnormal replenishment state; The specific calculation formula of the replenishment level value LJX is as follows; Where: BJX is the replenishment alarm coefficient, and Y is the first threshold.

10. A terminal replenishment monitoring system for medical consumables according to claim 9, characterized in that: The alarm signal recorded by the execution module (5) is specifically as follows; When medical consumables are in the first-level abnormal replenishment state, the replenishment abnormality is identified to confirm whether it is over-replenishment or under-replenishment. If it is over-replenishment, a yellow alarm message starting with C is sent; if it is under-replenishment, a yellow alarm message starting with Q is sent; When medical consumables are in the first-level abnormal replenishment state, the replenishment abnormality is identified to confirm whether it is over-replenishment or under-replenishment. If it is over-replenishment, an orange alarm message starting with C is sent; if it is under-replenishment, a yellow alarm message starting with Q is sent; When medical consumables are in the secondary abnormal replenishment state, the replenishment abnormality is identified to confirm whether it is over-replenishment or under-replenishment. If it is over-replenishment, an orange alarm message starting with C is sent; if it is under-replenishment, a yellow alarm message starting with Q is sent; When medical consumables are in the third-level abnormal replenishment state, the replenishment abnormality is identified to confirm whether it is over-replenishment or under-replenishment. If it is over-replenishment, a red alarm message starting with C is sent; if it is under-replenishment, a red alarm message starting with Q is sent.

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