Early warning and allocation system and method for intelligent inventory of inspection and quarantine reagent consumables

Abstract

The invention relates to the technical field of storage intelligence, and discloses an early warning and allocation system and method for intelligent inventory of inspection and quarantine reagent consumables, and the system comprises a consumption trend extraction module, a demand prediction module, a risk early warning module, an early warning trigger module, a supply feasibility evaluation module and an intelligent allocation module. Performing seasonal decomposition on the historical consumption record to obtain a consumption trend component; periodically superposing the consumption trend component to obtain a demand prediction result; based on the demand prediction result, performing inventory risk research and judgment on the current inventory to obtain an inventory early warning index; when the inventory early warning index exceeds a safety threshold value, inventory early warning information is generated; according to the consumable demand information, carrying out feasibility evaluation on inventory data and logistics constraint conditions of the supply point to obtain a feasible allocation point; generating a deployment scheme according to the safety inventory standard of the feasible deployment point; according to the invention, the perspectiveness and accuracy of early warning and allocation of the intelligent inventory of the inspection and quarantine reagent consumables can be improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent warehousing technology, and in particular to an early warning and allocation system and method for intelligent inventory of inspection and quarantine reagents and consumables. Background Technology

[0002] In the field of inventory management for inspection and quarantine reagents and consumables, existing technologies struggle to accurately decompose historical consumption records at inventory points seasonally and cannot effectively extract consumption trend components that reflect actual consumption patterns. They can only estimate demand through simple historical data aggregation or linear extrapolation, resulting in an inability to capture the cyclical fluctuations in the consumption process of consumables. Consequently, the demand forecast results deviate significantly from the actual demand for consumables. This makes it impossible to predict demand peaks in advance to avoid inventory shortages, or to identify demand troughs to reduce inventory backlogs, seriously affecting the accuracy of inventory management and indirectly hindering the efficient advancement of inspection and quarantine work.

[0003] Existing technologies have significant shortcomings in inventory risk warning and consumable allocation. In risk assessment, they rely solely on comparing current inventory levels with historical consumption data, failing to consider the uncertainties in demand forecasts and the dynamic changes in inventory turnover. This results in one-sided inventory warning indicators that are difficult to accurately identify potential inventory risks and fail to trigger effective warning mechanisms in a timely manner. Furthermore, in the allocation phase, the lack of comprehensive screening of real-time inventory data from supply points and a systematic assessment of logistical constraints makes it impossible to scientifically determine feasible allocation points. Allocation plans are forced to rely on manual experience, leading to low efficiency. When inventory warnings are triggered, consumable allocation cannot be completed quickly and rationally, further exacerbating inventory problems and affecting the continuity of inspection and quarantine work. Summary of the Invention

[0004] This invention provides an early warning and allocation system and method for intelligent inventory of inspection and quarantine reagents and consumables to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides an intelligent inventory early warning and allocation system for inspection and quarantine reagents and consumables. The system comprises a consumption trend extraction module, a demand forecasting module, a risk early warning module, an early warning triggering module, a supply feasibility assessment module, and an intelligent allocation module, wherein:

[0006] The consumption trend extraction module is used to perform seasonal decomposition on the historical consumption records of the inventory point to obtain the consumption trend component of the inventory point.

[0007] The demand forecasting module is used to periodically superimpose the consumption trend components to obtain the demand forecasting result of the inventory point.

[0008] The risk warning module is used to assess the current inventory level of the inventory point based on the demand forecast results, and obtain the inventory warning index of the inventory point.

[0009] The early warning triggering module is used to generate inventory early warning information for the inventory point when the inventory early warning indicator exceeds the safety threshold.

[0010] The supply feasibility assessment module is used to conduct a feasibility assessment of the real-time inventory data and logistics constraints of the supply point based on the consumable demand information of the inventory early warning information, and to obtain the feasible allocation point of the inventory point.

[0011] The intelligent allocation module is used to generate an allocation plan for the inventory point based on the safety stock standard of the feasible allocation point.

[0012] In a preferred embodiment, when the consumption trend extraction module performs seasonal decomposition on the historical consumption records of the inventory point to obtain the consumption trend component of the inventory point, it is specifically used for:

[0013] The historical consumption records of the inventory points are arranged in chronological order to obtain the inventory consumption time sequence of the inventory points.

[0014] The time series sequence of inventory consumption is decomposed to obtain the main periodic patterns of the inventory points;

[0015] Based on the main periodic patterns, the time series of inventory consumption is decomposed into trend features to obtain the consumption trend components of the inventory points.

[0016] In a preferred embodiment, when the demand forecasting module performs periodic superposition of the consumption trend components to obtain the demand forecast result for the inventory point, it is specifically used for:

[0017] The waveform of the fluctuation characteristic parameter of the consumption trend component is reconstructed to obtain the baseline demand fluctuation curve of the inventory point;

[0018] The development trend of the consumption trend component is superimposed and fused with the benchmark demand fluctuation curve to obtain the initial demand forecast sequence of the inventory point;

[0019] The initial demand forecast sequence is smoothed and corrected to obtain the demand forecast result for the inventory point.

[0020] In a preferred embodiment, when the risk warning module performs an inventory risk assessment on the current inventory level of the inventory point based on the demand forecast result to obtain the inventory warning indicator for the inventory point, it is specifically used for:

[0021] Based on the volatility characteristics of the demand forecast results, determine the demand uncertainty range of the demand forecast results;

[0022] Based on the lower limit of the aforementioned demand uncertainty interval, the current inventory level of the inventory point is compared with the difference to obtain the inventory coverage capacity of the inventory point.

[0023] The matching degree between the historical inventory turnover rate of the inventory point and the demand forecast result is evaluated to obtain the dynamic inventory balance status of the inventory point.

[0024] The risk assessment conclusion of the inventory point is obtained by integrating and evaluating the inventory coverage capacity and the dynamic balance status of the inventory.

[0025] The risk assessment conclusions are benchmarked to obtain the inventory early warning index for the inventory point.

[0026] In a preferred embodiment, when the risk warning module performs a difference comparison between the current inventory level of the inventory point and the lower limit value of the demand uncertainty interval to obtain the inventory coverage capacity of the inventory point, it is specifically used for:

[0027] Obtain the minimum daily consumption within the aforementioned demand uncertainty range;

[0028] Based on the minimum daily consumption, the current inventory level is quantitatively analyzed to obtain the resilience cycle of the inventory point.

[0029] The resilience cycle is verified to obtain the inventory coverage capacity of the inventory point.

[0030] In a preferred embodiment, when the risk warning module performs a matching degree assessment between the historical inventory turnover rate of the inventory point and the demand forecast result to obtain the dynamic inventory balance status of the inventory point, it is specifically used for:

[0031] Obtain the historical inventory turnover rate change trajectory;

[0032] By aligning the turnover rate change trajectory with the change characteristics of the demand forecast results in a time series, the dual-track sequence data of the inventory points is obtained.

[0033] Based on the consistency of the dual-track sequence data in terms of the direction and magnitude of change, the dynamic balance state of the inventory point is obtained.

[0034] In a preferred embodiment, when the early warning triggering module performs the function of generating inventory early warning information for the inventory point when the inventory early warning indicator exceeds the safety threshold, it is specifically used for:

[0035] When the inventory warning indicator exceeds the safety threshold, a warning signal is obtained for the inventory point;

[0036] The warning signal is mapped to a level to obtain the warning level of the inventory point;

[0037] Based on the warning level, the inventory data of the inventory point is encapsulated to obtain the inventory warning information of the inventory point.

[0038] In a preferred embodiment, when the supply feasibility assessment module performs a feasibility assessment on the real-time inventory data and logistical constraints of the supply point based on the consumable demand information from the inventory warning information, and obtains a feasible allocation point for the inventory point, it is specifically used for:

[0039] Based on the consumable demand information in the inventory warning information, the real-time inventory data of the supply points is matched and filtered to obtain the supply point set of the inventory points.

[0040] Based on the logistics constraints of the inventory points, a suitability assessment is performed on the supply points to obtain a comprehensive logistics score for each supply point. The formula for calculating the comprehensive logistics score is as follows:

[0041] ;

[0042] In the formula, The overall score for the logistics is given. This is the distance weighting coefficient. The actual transportation distance between the supply point and the inventory point. The preset maximum allowable transportation distance threshold, For time weighting coefficients, To estimate the transportation time, The maximum allowable transit time threshold, This is the cost weighting coefficient. For the cost of a single shipment, The maximum permissible transportation cost within the preset range. For reliability weighting coefficients, The historical on-time delivery rate of the supply points. This is the reliability sensitivity coefficient;

[0043] By performing bidirectional constraint optimization on the supply point set and the logistics comprehensive score, feasible allocation points for the inventory points are obtained.

[0044] In a preferred embodiment, when the intelligent allocation module performs the function of generating an allocation plan for the inventory points based on the safety stock standard of the feasible allocation points, it specifically performs the following functions:

[0045] Based on the safety stock standard, the remaining inventory of the feasible allocation point is determined to obtain the available allocation quantity of the feasible allocation point;

[0046] Based on the consumable demand information and the available quantity, the allocation of the quantity to each feasible allocation point is carried out to obtain the allocation scheme of the inventory point.

[0047] The allocation scheme and logistics constraints are coupled and verified to obtain the impact assessment report of the inventory point;

[0048] The impact assessment report is optimized through strategy synthesis to obtain the allocation plan for the inventory points.

[0049] To address the aforementioned problems, this invention also provides a method for early warning and allocation of intelligent inventory of inspection and quarantine reagents and consumables, the method comprising:

[0050] S1. Perform seasonal decomposition on the historical consumption records of the inventory points to obtain the consumption trend components of the inventory points;

[0051] S2. Periodically superimpose the consumption trend components to obtain the demand forecast result of the inventory point;

[0052] S3. Based on the demand forecast results, assess the current inventory level of the inventory point to determine the inventory risk and obtain the inventory early warning index of the inventory point.

[0053] S4. When the inventory warning indicator exceeds the safety threshold, generate the inventory warning information for the inventory point;

[0054] S5. Based on the consumable demand information in the inventory early warning information, conduct a feasibility assessment of the real-time inventory data and logistics constraints of the supply point to obtain the feasible allocation point of the inventory point.

[0055] S6. Generate an allocation plan for the inventory points based on the safety stock standard of the feasible allocation points.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. This system effectively improves the foresight and accuracy of inventory management through precise consumption trend extraction and demand forecasting. The system utilizes the consumption trend extraction module to seasonally decompose historical consumption records at inventory points, accurately obtaining consumption trend components. These components are then periodically overlaid, reconstructed, and smoothed by the demand forecasting module to generate forecast results that closely match actual demand. Simultaneously, the risk warning module integrates inventory coverage capacity and dynamic inventory balance to generate comprehensive inventory warning indicators. This enables precise identification of potential inventory risks and timely triggering of warnings, ensuring that inventory remains within a reasonable range to meet the needs of inspection and quarantine work, providing a continuous and reliable supply of consumables for the stable operation of inspection and quarantine work.

[0058] 2. This system significantly improves the efficiency and rationality of consumable allocation through scientific supply assessment and intelligent allocation. The supply feasibility assessment module filters real-time inventory at supply points based on consumable demand and conducts adaptability assessments in conjunction with logistics constraints to accurately determine feasible allocation points. The intelligent allocation module determines the available allocation quantity based on safety stock standards, rationally allocates the allocation quantity to each feasible allocation point, and couples logistics constraint verification and optimization to generate the optimal allocation plan. It can quickly complete consumable allocation after inventory warnings are triggered, avoiding the impact of untimely consumable supply on the progress of inspection and quarantine work, while reducing waste of logistics resources and inventory redundancy, further improving the overall efficiency and economic benefits of inventory management for inspection and quarantine reagents and consumables. Attached Figure Description

[0059] Figure 1 This is a system architecture diagram of an intelligent inventory early warning and allocation system for inspection and quarantine reagents and consumables provided in an embodiment of the present invention;

[0060] Figure 2 This is a flowchart illustrating an intelligent inventory early warning and allocation method for inspection and quarantine reagents and consumables provided in an embodiment of the present invention.

[0061] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments belong to some, but not all, embodiments of the present invention. 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.

[0063] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0064] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0065] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0066] In practice, the server-side equipment deployed in a smart inventory early warning and allocation system for inspection and quarantine reagents and consumables may consist of one or more devices. This system can be implemented as a business instance, a virtual machine, or hardware devices. For example, it can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, this system can be understood as software deployed on a cloud node, providing early warning and allocation services for inspection and quarantine reagents and consumables to various user terminals. Alternatively, it can also be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage various user terminals. Alternatively, this intelligent inventory early warning and allocation system for inspection and quarantine reagents and consumables can also be implemented as a server consisting of numerous identical or different types of hardware devices, with one or more hardware devices set up to provide each user terminal with an intelligent inventory early warning and allocation system for inspection and quarantine reagents and consumables.

[0067] In terms of implementation, the intelligent inventory early warning and allocation system for inspection and quarantine reagents and consumables and the user terminal are mutually compatible. That is, if the intelligent inventory early warning and allocation system for inspection and quarantine reagents and consumables is implemented as an application installed on a cloud service platform, then the user terminal is a client that establishes a communication connection with the application; or if the intelligent inventory early warning and allocation system for inspection and quarantine reagents and consumables is implemented as a website, then the user terminal is implemented as a webpage; or if the intelligent inventory early warning and allocation system for inspection and quarantine reagents and consumables is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0068] like Figure 1 The figure shown is a system architecture diagram of an intelligent inventory early warning and allocation system for inspection and quarantine reagents and consumables provided in an embodiment of the present invention.

[0069] The intelligent inventory early warning and allocation system 100 for inspection and quarantine reagents and consumables described in this invention can be set up in a cloud server. In terms of implementation, it can be implemented as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed as a website. Depending on the functions implemented, the intelligent inventory early warning and allocation system 100 for inspection and quarantine reagents and consumables may include a consumption trend extraction module 101, a demand forecasting module 102, a risk early warning module 103, an early warning triggering module 104, a supply feasibility assessment module 105, and an intelligent allocation module 106. The modules described in this invention can also be called units, referring to a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0070] In this embodiment of the invention, in an intelligent inventory early warning and allocation system for inspection and quarantine reagents and consumables, each of the above-mentioned modules can be implemented independently and can be called upon with other modules. Here, "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to the connected modules. In the intelligent inventory early warning and allocation system for inspection and quarantine reagents and consumables provided by this embodiment of the invention, the applicable scope of the system architecture can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the system. In practical applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0071] The following describes, with reference to specific embodiments, the various components and specific workflows of an intelligent inventory early warning and allocation system for inspection and quarantine reagents and consumables:

[0072] The consumption trend extraction module 101 is used to perform seasonal decomposition on the historical consumption records of the inventory point to obtain the consumption trend component of the inventory point.

[0073] When the consumption trend extraction module performs seasonal decomposition on the historical consumption records of the inventory points to obtain the consumption trend components of the inventory points, it is specifically used for:

[0074] The historical consumption records of the inventory points are arranged in chronological order to obtain the inventory consumption time sequence of the inventory points.

[0075] The time series sequence of inventory consumption is decomposed to obtain the main periodic patterns of the inventory points;

[0076] Based on the main periodic patterns, the time series of inventory consumption is decomposed into trend features to obtain the consumption trend components of the inventory points.

[0077] Specifically, all past consumption records of inspection and quarantine reagents and consumables at the inventory points are collected. The records must include the specific date of each consumption, the specific type of consumable, and the specific quantity consumed. Then, based on the date of consumption, these consumption records are arranged one by one in chronological order from earliest to latest. If there are multiple consumption records of the same type of consumable on the same date, the consumption quantities of all consumables of that type on that date are summed up. If there are consumption records of different types of consumables on the same date, the consumption records of different types of consumables are organized independently to ensure that each date corresponds to clear consumption data of each type of consumable. Finally, an inventory consumption time sequence is formed, which is arranged in chronological order and contains consumable consumption information at each time point.

[0078] Furthermore, a full year is defined as the complete analysis period. This complete period is divided into multiple identical smaller time units, such as months. The generated inventory consumption time series is then split according to the divided smaller time units to obtain consumption subsequences corresponding to each smaller time unit. The average consumption quantity of all consumption records in each consumption subsequence is calculated to obtain the average consumption quantity of each smaller time unit. By comparing the average consumption quantity of different smaller time units, the changes in consumption quantity between different smaller time units are observed. If the average consumption quantity of a certain type of consumable is consistently higher than that of other smaller time units in a specific smaller time unit, and this pattern of consumption quantity change repeats in multiple consecutive complete analysis periods, then this recurring pattern of consumption quantity change is identified as the main periodic law of the inventory point.

[0079] Furthermore, in the generated inventory consumption time series, based on the determined main periodicity, all consumption data that conform to this pattern are marked. If the main periodicity is characterized by a high consumption quantity in a specific small time unit, then all consumption data in the time series corresponding to that small time unit are marked. Then, these marked periodic consumption data are separated from the inventory consumption time series, and the remaining non-periodic consumption data in the time series are retained. These non-periodic consumption data are divided into groups according to multiple adjacent dates, and the average consumption quantity of all days in each group is calculated. This average value is used as the consumption data of the middle date in the group. The same processing is performed on all non-periodic consumption data groups in turn, and finally a trend line that can reflect the long-term change direction of consumable consumption is formed. The set of consumption data corresponding to this trend line is the consumption trend component of the inventory point.

[0080] In general, the step of organizing the historical consumption records of inventory points in chronological order to obtain the inventory consumption time series of inventory points requires first collecting the consumption records of past inspection and quarantine reagents and consumables at the inventory points, then sorting and arranging them in chronological order from early to late based on the date. If the same type of consumables is consumed multiple times on the same date, the quantity is accumulated. Consumption records of different types of consumables are sorted independently, and finally an inventory consumption time series containing consumable consumption data at each time node is formed with time as the logical order.

[0081] In summary, the step of decomposing the inventory consumption time series to obtain the main periodic patterns of inventory points requires first determining the complete analysis period, then dividing the inventory consumption time series into corresponding consumption subsequences according to the smaller time units within the period, calculating the average consumption quantity of each subsequence, comparing the average consumption quantity of different smaller time units and observing the changes. If the average consumption quantity of a certain type of consumable in a specific smaller time unit is consistently higher than that in other units, and this change pattern repeats in multiple complete periods, then this pattern is identified as the main periodic pattern.

[0082] In summary, the step of decomposing the inventory consumption time series based on the main periodic patterns to obtain the consumption trend components of inventory points requires first marking the consumption data that conforms to the main periodic patterns in the inventory consumption time series, separating these periodic data and retaining the non-periodic data, grouping the non-periodic data according to multiple adjacent time nodes, calculating the average consumption quantity of each group and using it as the data of the middle time node within the group, processing all groups in sequence, and finally forming a trend line that reflects the long-term change direction of consumable consumption. The set of consumption data corresponding to this trend line is the consumption trend component.

[0083] The demand forecasting module 102 is used to periodically superimpose the consumption trend components to obtain the demand forecasting result of the inventory point.

[0084] In this embodiment of the invention, when the demand forecasting module performs periodic superposition of the consumption trend components to obtain the demand forecast result of the inventory point, it is specifically used for:

[0085] The waveform of the fluctuation characteristic parameter of the consumption trend component is reconstructed to obtain the baseline demand fluctuation curve of the inventory point;

[0086] The development trend of the consumption trend component is superimposed and fused with the benchmark demand fluctuation curve to obtain the initial demand forecast sequence of the inventory point;

[0087] The initial demand forecast sequence is smoothed and corrected to obtain the demand forecast result for the inventory point.

[0088] Specifically, the fluctuation characteristic parameters of the consumption trend component are first extracted. This involves observing the fluctuation of the consumption trend component in different time periods, determining the difference between the highest and lowest values ​​of each fluctuation to clarify the fluctuation amplitude, observing the time interval between two adjacent fluctuation peaks to clarify the fluctuation frequency, and recording the rhythm of the value change from rising to falling during the fluctuation process to clarify the fluctuation pattern. Then, with time as the horizontal axis, the fluctuation curve segments corresponding to the time period are reconstructed segment by segment according to the extracted fluctuation amplitude, frequency, and pattern. These curve segments are connected sequentially in chronological order to ensure that the fluctuation characteristics of each segment completely match the extracted parameters, ultimately forming a benchmark demand fluctuation curve that can reflect the regular fluctuation pattern of consumable demand.

[0089] Furthermore, we first analyze the direction of change of the consumption trend component over a longer time span to determine its development trend. Specifically, we compare the overall values ​​of the consumption trend component in the earlier and recent periods. If the value in the recent period is consistently higher than that in the earlier period and shows a gradual upward trend, the development trend is determined to be continuous upward. If the values ​​in the earlier and recent periods are basically consistent and there is no significant increase or decrease, the development trend is determined to be stable. If the value in the recent period is consistently lower than that in the earlier period and shows a gradual downward trend, the development trend is determined to be gradual downward. Then, using time as a unified reference dimension, at each time node, the value of the consumption trend component development trend corresponding to that node is merged with the value of the benchmark demand fluctuation curve at that node. That is, the final value at each time node is the superposition result of the trend value and the fluctuation curve value. Subsequently, the superimposed values ​​of all nodes are arranged in chronological order to form the initial demand forecast sequence.

[0090] Furthermore, the numerical changes of each adjacent time node in the initial demand forecast sequence are examined segment by segment to identify abnormal time nodes where the values ​​suddenly rise or fall sharply. For each abnormal node, the values ​​corresponding to the preceding and following time nodes are selected, and the average value of these two values ​​is calculated. This average value is then used to replace the original value of the abnormal node, thus correcting the individual abnormal node. The corrected initial demand forecast sequence is then iterated again. If any nodes are found where the numerical changes exceed the normal demand fluctuation range, the same method is used to select adjacent node values, calculate the average, and replace them, until the numerical changes of all time nodes in the sequence remain smooth and conform to the actual change logic of consumable demand. Finally, a demand forecast result that can accurately predict the future demand for consumables at the inventory point is obtained.

[0091] In summary, the step of reconstructing the waveform of the fluctuation characteristic parameters of the consumption trend component to obtain the baseline demand fluctuation curve of the inventory point requires first observing the fluctuation of the consumption trend component in different time periods, extracting fluctuation characteristic parameters such as fluctuation amplitude, fluctuation frequency, and fluctuation pattern, and then using time as the horizontal axis to reconstruct the fluctuation curve segments of the corresponding time periods according to the extracted parameters. These segments are then connected sequentially in chronological order, ensuring that the fluctuation characteristics are completely matched, and finally forming a baseline demand fluctuation curve that reflects the regular fluctuation pattern of consumable demand.

[0092] In summary, the step of overlaying and fusing the development trend of the consumption trend component with the benchmark demand fluctuation curve to obtain the initial demand forecast sequence for the inventory point requires first comparing the overall values ​​of the consumption trend component in the early and recent periods to determine the development trend. Then, using time as a unified reference dimension, the trend value of the node is overlaid with the value of the benchmark demand fluctuation curve at each time node. The overlaid values ​​of all nodes are arranged in chronological order to finally form the initial demand forecast sequence.

[0093] In summary, the step of smoothing and correcting the initial demand forecast sequence to obtain the demand forecast result for the inventory point requires first checking the numerical changes of adjacent time nodes in the initial sequence segment by segment to identify abnormal nodes with significant increases or decreases. For each abnormal node, the average value of the adjacent nodes before and after it is selected and the abnormal value is replaced. Then, the corrected sequence is traversed again, and the above operation is repeated until the numerical changes of all nodes are smooth and conform to the actual change logic of consumable demand. Finally, the demand forecast result that can accurately predict the future demand for consumables at the inventory point is obtained.

[0094] The risk warning module 103 is used to assess the current inventory level of the inventory point based on the demand forecast results and obtain the inventory warning index of the inventory point.

[0095] In this embodiment of the invention, when the risk warning module performs an inventory risk assessment on the current inventory level of the inventory point based on the demand forecast result to obtain the inventory warning indicator for the inventory point, it is specifically used for:

[0096] Based on the volatility characteristics of the demand forecast results, determine the demand uncertainty range of the demand forecast results;

[0097] Based on the lower limit of the aforementioned demand uncertainty interval, the current inventory level of the inventory point is compared with the difference to obtain the inventory coverage capacity of the inventory point.

[0098] The matching degree between the historical inventory turnover rate of the inventory point and the demand forecast result is evaluated to obtain the dynamic inventory balance status of the inventory point.

[0099] The risk assessment conclusion of the inventory point is obtained by integrating and evaluating the inventory coverage capacity and the dynamic balance status of the inventory.

[0100] The risk assessment conclusions are benchmarked to obtain the inventory early warning index for the inventory point.

[0101] When the risk warning module performs a difference comparison between the current inventory level of the inventory point and the lower limit value of the demand uncertainty interval to obtain the inventory coverage capacity of the inventory point, it is specifically used for:

[0102] Obtain the minimum daily consumption within the aforementioned demand uncertainty range;

[0103] Based on the minimum daily consumption, the current inventory level is quantitatively analyzed to obtain the resilience cycle of the inventory point.

[0104] The resilience cycle is verified to obtain the inventory coverage capacity of the inventory point.

[0105] When the risk warning module performs a matching degree assessment between the historical inventory turnover rate of the inventory point and the demand forecast results to obtain the dynamic inventory balance status of the inventory point, it is specifically used for:

[0106] Obtain the historical inventory turnover rate change trajectory;

[0107] By aligning the turnover rate change trajectory with the change characteristics of the demand forecast results in a time series, the dual-track sequence data of the inventory points is obtained.

[0108] Based on the consistency of the dual-track sequence data in terms of the direction and magnitude of change, the dynamic balance state of the inventory point is obtained.

[0109] Specifically, we first analyze the volatility characteristics of the demand forecast results. This involves observing the numerical changes of the demand forecast results in different time periods throughout the entire forecast period, comparing the differences in the forecast values ​​in different time periods, recording the highest and lowest values ​​during each fluctuation, identifying the range of the largest numerical deviation in all fluctuations, and then using this range as a basis to determine the numerical interval that can cover all possible fluctuations. The upper limit of the interval is the highest value of the fluctuation within the forecast period, and the lower limit of the interval is the lowest value of the fluctuation within the forecast period. This ultimately forms the demand uncertainty interval of the demand forecast results.

[0110] Furthermore, we first obtain the lower limit of the demand uncertainty interval, which represents the lowest possible demand level within the forecast period. Then, we calculate the current actual inventory level at the inventory point and the relationship between the current inventory level and the unit time demand corresponding to the lower limit of the demand uncertainty interval. Specifically, we divide the current inventory level by the unit time demand corresponding to the lower limit of the demand uncertainty interval to obtain the length of time that the current inventory level can sustain at the minimum demand level. At the same time, we verify whether this length of time can meet the needs of continuous inspection and quarantine work under normal circumstances. If it can, it indicates that the inventory coverage capacity is strong; if it cannot, it indicates that the inventory coverage capacity is weak. Finally, we determine the inventory coverage capacity of the inventory point.

[0111] Furthermore, the historical inventory turnover rate of each inventory point is first calculated. Specifically, the cumulative outbound volume and average inventory volume of each inventory point over a period of time are collected. The historical inventory turnover rate is obtained by dividing the cumulative outbound volume by the average inventory volume. Then, the trend of the historical inventory turnover rate in different periods is analyzed. At the same time, the trend of the demand forecast results in the corresponding periods is analyzed, and the direction of the two changes is compared to see if they are consistent. For example, if the demand forecast results show an upward trend and the historical inventory turnover rate also increases with the increase in demand, it indicates that the two are highly matched. If the demand forecast increases but the historical inventory turnover rate decreases, it indicates that the matching degree is low. Based on this matching degree, the dynamic balance state of the inventory at each inventory point is determined.

[0112] Furthermore, the evaluation results of inventory coverage capacity and inventory dynamic balance status are first clarified separately. For example, inventory coverage capacity is divided into two categories: "strong" and "weak," and inventory dynamic balance status is divided into two categories: "excellent" and "poor." Then, the two evaluation results are combined for analysis. If the inventory coverage capacity is strong and the inventory dynamic balance status is excellent, the inventory risk is judged to be low, and no emergency adjustment is required. If the inventory coverage capacity is strong but the inventory dynamic balance status is poor, the risk of inventory backlog is judged, and the turnover rate needs to be optimized. If the inventory coverage capacity is weak but the inventory dynamic balance status is excellent, the risk of short-term supply disruption is judged, and inventory needs to be replenished in a timely manner. If the inventory coverage capacity is weak and the inventory dynamic balance status is poor, the risk of serious inventory is judged, and immediate adjustment is required. The risk assessment conclusion of the inventory point is formed by combining these analyses.

[0113] Furthermore, a unified risk benchmark standard is first established. This standard clearly defines the specific characteristics corresponding to different risk levels. For example, "low risk" corresponds to a situation where there are no obvious risk points in the risk assessment conclusion and the inventory can stably support the work; "medium risk" corresponds to a situation where there is a single risk point and attention and adjustment are required; and "high risk" corresponds to a situation where there are multiple risk points and emergency handling is required. Then, the risk assessment conclusions of the inventory points obtained earlier are compared with this benchmark standard to determine the risk level to which the risk assessment conclusion belongs. Subsequently, the risk level is transformed into a standardized expression or symbol to ensure that the symbol can clearly and accurately reflect the actual degree of inventory risk, and finally obtain the inventory early warning indicator for the inventory points.

[0114] Specifically, first, define the established demand uncertainty range, which includes the lowest possible demand level within the forecast period. Then, evenly distribute the lower limit of the demand uncertainty range to each day according to the duration of the forecast period, and calculate the minimum consumption quantity corresponding to each day. This minimum consumption quantity corresponding to each day is the minimum daily consumption amount within the demand uncertainty range. The entire process must ensure that the calculated minimum daily consumption amount accurately reflects the lowest possible consumption level each day within the forecast period.

[0115] Furthermore, first, calculate the current actual inventory level at the inventory point. This inventory level must cover all inspection and quarantine reagents and consumables in stock. When calculating, consumables that have been issued but not yet handed over, as well as damaged or expired consumables that cannot be used, must be excluded to ensure that the statistical result is the actual usable current inventory level. Then, divide the current inventory level by the minimum daily consumption rate to obtain the length of time that the current inventory level can be maintained at the minimum daily consumption rate. This length of time is the resilience period of the inventory point. During the calculation process, it is necessary to ensure that the statistical dimensions of the current inventory level and the minimum daily consumption rate are consistent and that both are calculated using the same consumable measurement standard.

[0116] Furthermore, data on the inventory support time required for continuous inspection and quarantine work in the past was collected first. This determined the minimum inventory support time required to avoid work interruptions under normal circumstances. The resilience period was then compared with this minimum inventory support time. If the resilience period was greater than or equal to the minimum inventory support time, it indicated that the current inventory, at the minimum consumption rate, could meet the needs of continuous routine work, indicating strong inventory coverage. If the resilience period was less than the minimum inventory support time, it indicated that the current inventory, even at the minimum consumption rate, could not support continuous routine work, posing a risk of work interruption, indicating weak inventory coverage. Through this support verification process, the inventory coverage capacity of the inventory points was ultimately determined.

[0117] Specifically, the cumulative outbound volume of inventory points over a period of time is collected. The cumulative outbound volume requires the total amount of all inspection and quarantine reagents and consumables transferred from inventory points and actually used for inspection and quarantine work during that period. At the same time, the average inventory volume during that period is calculated. The average inventory volume requires first recording the actual inventory volume at the end of each day during that period, then calculating the total inventory volume for all days and dividing it by the total number of days in that period. The cumulative outbound volume is then divided by the average inventory volume to obtain the inventory turnover rate for that period. The inventory turnover rates for each period are arranged sequentially from morning to night to form a curve that can intuitively reflect the changes in the historical inventory turnover rate over time. This curve is the historical inventory turnover rate change trajectory.

[0118] Furthermore, a unified time division standard is first determined. This standard must be completely consistent with the time dimension of the demand forecast results. For example, the entire forecast period is divided into two periods of the same length. The specific turnover rate data corresponding to each divided period is extracted from the turnover rate change trajectory. At the same time, the change characteristics within each corresponding period are analyzed from the demand forecast results. The change characteristics are the rise, fall, or stable state of the demand forecast value within that period. The turnover rate data of each period is matched one-to-one with the change characteristics of the demand forecast results for that period. These corresponding data pairs are arranged sequentially from morning to night to form a sequence that contains both turnover rate change data and demand forecast change characteristics. This sequence is the dual-track sequence data of the inventory points.

[0119] Furthermore, the relationship between the direction of turnover rate change and the direction of demand forecast change in each time period of the dual-track sequence data is analyzed one by one. If the turnover rate shows an upward trend and the demand forecast also shows an upward trend in a certain time period, or the turnover rate shows a downward trend and the demand forecast also shows a downward trend, or both are in a stable state, then it is determined that the two change directions are consistent in that time period. If the direction of turnover rate change is opposite to the direction of demand forecast change, such as the turnover rate increasing while the demand forecast decreases, then it is determined that the change directions are inconsistent. At the same time, the matching between the magnitude of turnover rate change and the magnitude of demand forecast change in each time period is analyzed. If the change in turnover rate is similar to the change in demand forecast, meaning that the change in one does not significantly exceed the change in the other, then the change is considered consistent. The data from the dual-track sequence is analyzed for time periods where both the direction and magnitude of change are consistent. If such time periods constitute a high percentage, it indicates that the turnover rate changes well follow the demand forecast, and the inventory can adapt to demand fluctuations; the inventory dynamic balance is considered excellent. If such time periods constitute a low percentage, it indicates that the turnover rate changes are out of sync with the demand forecast, and the inventory cannot match demand changes; the inventory dynamic balance is considered poor. Finally, the inventory dynamic balance status at each inventory point is determined.

[0120] In general, to determine the demand uncertainty range of the demand forecast based on the volatility characteristics of the demand forecast results, it is necessary to first observe the numerical changes of the demand forecast results in different time periods within the forecast period, sort out the range of the largest numerical deviation among all fluctuations, and use this range as the basis to determine the numerical range covering all possible fluctuations, thus forming the demand uncertainty range of the demand forecast results.

[0121] In summary, to determine the inventory coverage capacity of a storage point by comparing the current inventory level with the lower limit of the demand uncertainty range, it is necessary to first obtain the lower limit of the demand uncertainty range and determine the corresponding minimum daily consumption. Then, the actual usable current inventory level is calculated. The resilience period is obtained through quantitative analysis of the current inventory level and the minimum daily consumption. The resilience period is then compared and verified with the shortest inventory support time required for routine inspection and quarantine work. Finally, the inventory coverage capacity of the storage point is determined.

[0122] In summary, the step of assessing the matching degree between historical inventory turnover rates and demand forecasts to obtain the dynamic inventory balance of an inventory point requires first calculating the inventory turnover rates for each historical period and forming a turnover rate change trajectory in chronological order. Then, the turnover rate change trajectory is aligned with the time sequence of demand forecast changes using a unified time standard to obtain dual-track sequence data. The consistency of the direction and magnitude of change in the dual-track sequence data in each time period is analyzed, and the dynamic inventory balance is determined based on the proportion of consistent time periods, ultimately yielding the dynamic inventory balance of the inventory point.

[0123] In summary, to arrive at a risk assessment conclusion for an inventory point by integrating the evaluation of inventory coverage capacity and inventory dynamic balance, it is necessary to first clarify the specific evaluation results of inventory coverage capacity and inventory dynamic balance, then combine the two for comprehensive analysis, determine the specific type and degree of risk of the inventory based on different combinations, and finally form a risk assessment conclusion for the inventory point.

[0124] In general, the step of benchmarking risk assessment conclusions to obtain inventory early warning indicators for inventory points requires first establishing a unified risk benchmark standard, comparing the obtained risk assessment conclusions with the benchmark standard to determine the corresponding risk level, and then converting the risk level into a standardized expression or symbol that can clearly reflect the degree of risk, ultimately obtaining the inventory early warning indicators for inventory points.

[0125] In summary, to obtain the minimum daily consumption for the demand uncertainty interval, it is necessary to first define the determined demand uncertainty interval, which includes the minimum total demand that may occur within the forecast period. Then, the lower limit of the demand uncertainty interval is evenly distributed to each day according to the length of the forecast period. The minimum consumption quantity corresponding to each day is calculated through this distribution method, and finally the minimum daily consumption for the demand uncertainty interval is obtained, ensuring that this consumption quantity can accurately reflect the minimum possible consumption level each day within the forecast period.

[0126] In summary, the step of quantitatively analyzing the current inventory level based on the minimum daily consumption to obtain the resilience cycle of the inventory point requires first calculating the actual usable inventory level at the inventory point. During the calculation process, consumables that have been issued but not yet handed over, are damaged, or expired and cannot be used must be excluded to ensure the accuracy of the inventory data. Then, the current inventory level obtained from the calculation is divided by the minimum daily consumption to calculate the length of time that the current inventory level can be maintained at the minimum daily consumption rate. This length of time is the resilience cycle of the inventory point. When calculating, it is necessary to ensure that the measurement standard of the current inventory level and the minimum daily consumption is consistent.

[0127] In summary, the step of verifying the resilience cycle to determine the inventory coverage capacity of a storage point requires first collecting data on the inventory support time needed for continuous inspection and quarantine work in the past. This determines the minimum inventory support time required to avoid interruptions during routine operations. Then, the calculated resilience cycle is compared with this minimum inventory support time. If the resilience cycle is greater than or equal to the minimum inventory support time, it indicates that the current inventory can meet the needs of continuous routine operations, and the inventory coverage capacity is considered strong. If the resilience cycle is less than the minimum inventory support time, it indicates that the current inventory cannot support continuous routine operations, and the inventory coverage capacity is considered weak. This process ultimately yields the inventory coverage capacity of the storage point.

[0128] In general, to obtain the historical inventory turnover rate change trajectory, it is necessary to first collect the cumulative outbound volume and average inventory volume of the inventory points over a period of time. The inventory turnover rate for each time period is calculated by dividing the cumulative outbound volume by the average inventory volume. Then, the inventory turnover rates for each time period are arranged in order from morning to night to form a curve that can intuitively reflect the changes in the inventory turnover rate over time. This curve is the historical inventory turnover rate change trajectory.

[0129] In summary, to obtain dual-track sequence data of inventory points by aligning the turnover rate change trajectory with the change characteristics of demand forecast results in a time series, it is necessary to first determine a unified time division standard, which must be completely consistent with the time dimension of the demand forecast results. The turnover rate data corresponding to each divided time period is extracted from the turnover rate change trajectory. At the same time, the change characteristics of each corresponding time period in the demand forecast results are analyzed. The turnover rate data of each time period is matched one-to-one with the change characteristics of the demand forecast results for that time period. These corresponding data pairs are arranged in chronological order, and finally, dual-track sequence data containing turnover rate change data and demand forecast change characteristics are formed.

[0130] In summary, to determine the dynamic inventory balance of a storage point based on the consistency of the direction and magnitude of change in the dual-track sequence data, it is necessary to analyze whether the direction of turnover rate change in each time period of the dual-track sequence data is consistent with the direction of demand forecast change, and at the same time, analyze whether the magnitudes of change are similar. Statistically, the time periods in which both the direction and magnitude of change are consistent should be counted. If such time periods account for a high proportion, it indicates that the turnover rate change adapts to the demand forecast change, and the dynamic inventory balance is considered excellent. If such time periods account for a low proportion, it indicates that the turnover rate change is out of sync with the demand forecast, and the dynamic inventory balance is considered poor. Finally, the dynamic inventory balance of the storage point is obtained.

[0131] The early warning triggering module 104 is used to generate inventory early warning information for the inventory point when the inventory early warning indicator exceeds the safety threshold.

[0132] In this embodiment of the invention, when the early warning triggering module performs the function of generating inventory early warning information for the inventory point when the inventory early warning indicator exceeds the safety threshold, it is specifically used for:

[0133] When the inventory warning indicator exceeds the safety threshold, a warning signal is obtained for the inventory point;

[0134] The warning signal is mapped to a level to obtain the warning level of the inventory point;

[0135] Based on the warning level, the inventory data of the inventory point is encapsulated to obtain the inventory warning information of the inventory point.

[0136] Specifically, based on the range of early warning indicators from the stable operation of past inspection and quarantine reagent and consumable inventory at the inventory point, and combined with the basic demand scale of inspection and quarantine work in the future, a fixed safety threshold standard is set. This standard clearly distinguishes between a safe and risky inventory state, and remains consistent after being set, without arbitrary changes. Then, the previously obtained inventory early warning indicators are directly compared with this safety threshold. If the value of the inventory early warning indicator is higher than the safety threshold, it indicates that the inventory currently faces a risk exceeding the safe range. At this time, a signal is immediately generated to clearly indicate the existence of the risk; this signal is the early warning signal for the inventory point. The generation process relies solely on the clear comparison result between the indicator and the threshold, without introducing any other additional judgment conditions.

[0137] Furthermore, a pre-defined early warning level system is established. This system clearly defines different early warning levels, each corresponding to a specific degree of risk severity. Clear criteria are set for each level, specifically the range by which inventory warning indicators exceed the safety threshold. Different ranges correspond to different levels, and the range division is based on the exceedance of indicators corresponding to different risk levels in historical inventory risk cases, ensuring reasonable classification. Next, the actual extent to which the inventory warning indicators associated with the generated early warning signals exceed the safety threshold is analyzed. Based on this extent, the corresponding level is searched in the pre-defined early warning level system. The early warning signal and the found level are directly correlated and matched. The matching process strictly follows the correspondence in the pre-defined system without additional adjustments, ultimately yielding the early warning level for the inventory point.

[0138] Furthermore, the scope of inventory data to be packaged is first determined based on different warning levels. Different warning levels correspond to different data scopes; higher risk levels require more comprehensive data. For example, lower risk levels require current inventory levels, short-term demand forecasts, and explanations of risk sources, while higher risk levels also require supplementary data on historical inventory fluctuations, recent consumption rates, and information on external influencing factors. The determination of the data scope is based on the information needs of staff conducting response work at different risk levels. Then, the determined inventory data is organized according to a standardized format, categorized and arranged, with each data item clearly labeled with its name and source to ensure accuracy and easy identification. After organization, a complete data package with no missing information is formed. This data package represents the inventory warning information for the inventory points, ensuring that the information fully supports subsequent risk response work.

[0139] In summary, the step of obtaining an early warning signal for an inventory point when the inventory warning indicator exceeds the safety threshold requires first setting a fixed safety threshold based on the range of warning indicators during the stable operation of the inventory of inspection and quarantine reagents and consumables in the past, combined with the basic demand scale of future inspection and quarantine work. Then, the previously obtained inventory warning indicator is directly compared with this safety threshold. If the inventory warning indicator is higher than the safety threshold, it indicates that there is a risk that the inventory exceeds the safety range. At this time, a signal is directly generated to indicate the existence of the risk, which is the early warning signal for the inventory point.

[0140] In summary, the step of mapping warning signals to obtain the warning level of an inventory point requires first establishing a pre-set warning level system. This system clearly defines warning levels with different levels of risk severity and matches each level with a corresponding judgment criterion, namely the specific range by which the inventory warning indicator exceeds the safety threshold. Then, the actual range by which the inventory warning indicator associated with the warning signal exceeds the safety threshold is analyzed. Based on this range, the corresponding warning level is found in the pre-set system, and the warning signal is directly associated and matched with this level to finally obtain the warning level of the inventory point.

[0141] In summary, the step of encapsulating inventory data at inventory points according to the warning level to obtain inventory warning information requires first determining the corresponding inventory data encapsulation range according to different warning levels, then organizing the inventory data within the corresponding range according to a unified standard format, clearly labeling each data item with its name and source to ensure that the data is accurate and easy to identify, and finally integrating the organized data into a complete data package, which is the inventory warning information for the inventory point.

[0142] The supply feasibility assessment module 105 is used to conduct a feasibility assessment of the real-time inventory data and logistics constraints of the supply point based on the consumable demand information of the inventory early warning information, and to obtain the feasible allocation point of the inventory point.

[0143] In this embodiment of the invention, when the supply feasibility assessment module performs a feasibility assessment on the real-time inventory data and logistics constraints of the supply point based on the consumable demand information of the inventory warning information to obtain a feasible allocation point for the inventory point, it is specifically used for:

[0144] Based on the consumable demand information in the inventory warning information, the real-time inventory data of the supply points is matched and filtered to obtain the supply point set of the inventory points.

[0145] Based on the logistics constraints of the inventory points, a suitability assessment is performed on the supply points to obtain a comprehensive logistics score for each supply point. The formula for calculating the comprehensive logistics score is as follows:

[0146] ;

[0147] In the formula, The overall score for the logistics is given. This is the distance weighting coefficient. The actual transportation distance between the supply point and the inventory point. The preset maximum allowable transportation distance threshold, For time weighting coefficients, To estimate the transportation time, The maximum allowable transit time threshold, This is the cost weighting coefficient. For the cost of a single shipment, The maximum permissible transportation cost within the preset range. For reliability weighting coefficients, The historical on-time delivery rate of the supply points. This is the reliability sensitivity coefficient;

[0148] By performing bidirectional constraint optimization on the supply point set and the logistics comprehensive score, feasible allocation points for the inventory points are obtained.

[0149] Specifically, the process begins by extracting complete consumable demand information from inventory warning information. This information must clearly specify the type of consumable to be replenished, the total quantity required, and the delivery time to the inventory point, ensuring that the acquired demand information is complete and clearly stated. Next, real-time inventory data from all potential supply points is collected. During this process, a real-time connection is established with the inventory management systems of each supply point to directly obtain the current actual inventory of the corresponding type of consumable at each point. Quantities of consumables that have been reserved or are in the outbound process are excluded to ensure the authenticity and accuracy of the real-time inventory data. Finally, the real-time inventory of the corresponding type of consumable at each supply point is compared with the total quantity required in the consumable demand information. Supply points with real-time inventory greater than or equal to the required quantity are selected and organized into a unified supply point set for the inventory points.

[0150] Furthermore, the logistics constraints for inventory points are first clarified, specifically including four dimensions: transportation distance, transportation time, transportation cost, and delivery on-time rate. Transportation distance refers to the actual road mileage between the supply point and the inventory point; transportation time refers to the estimated time required from shipment from the supply point to receipt at the inventory point; transportation cost refers to the total cost of a single shipment of the corresponding type of consumables; and delivery on-time rate refers to the proportion of deliveries completed on time by the supply point in the past six months out of the total number of deliveries. For each supply point in the supply point cluster, its performance across the four constraint dimensions is evaluated: shorter transportation distance, shorter transportation time, lower transportation cost, and higher delivery on-time rate all indicate higher compliance. The compliance evaluation results of the four dimensions are integrated with equal weight to form a comprehensive score reflecting the logistics suitability of each supply point; this score is the overall logistics score for the supply point.

[0151] Furthermore, based on case data of successful consumable allocation from inventory points over the past year, the overall logistics score of the supply points in these cases is statistically analyzed. The average score of all cases is taken as the passing standard for the overall logistics score, and this standard, once determined, serves as the fixed basis for this evaluation. Subsequently, for each supply point in the supply point cluster, its overall logistics score is compared with the passing standard, and supply points with scores greater than or equal to the passing standard are selected. At the same time, the real-time inventory data of these selected supply points is re-verified to confirm that their current inventory levels can still meet the total amount required in the consumable demand information, avoiding situations where supply cannot be provided due to inventory changes. Supply points that simultaneously meet the overall logistics score standard and have sufficient real-time inventory are ultimately identified as eligible targets, and these targets together constitute the feasible allocation points for inventory points.

[0152] Specifically, The comprehensive logistics score is used to quantify whether the supply point is suitable for providing the distribution services of inspection and quarantine reagents and consumables to the inventory point. The score directly reflects the supply point's ability to meet the logistics needs of the inventory point. A high score means that the supply point is more suitable for the distribution needs in terms of transportation distance, time, cost and on-time delivery rate. The distance weighting coefficient is determined by analyzing the contribution of transportation distance to the overall effect of each successful logistics dispatch case through statistical analysis. The average contribution ratio of multiple cases is used to reflect the degree of influence of transportation distance on logistics adaptability. This refers to the actual transportation distance between the supply point and the inventory point. It is calculated by using an electronic map tool, inputting the specific geographical locations of both points, and selecting a conventional logistics transportation route. It represents the actual road transportation mileage, and temporary closures or impassable routes must be excluded during the calculation to ensure accuracy. This refers to the preset maximum allowable transportation distance threshold, which is determined based on the minimum requirements for the delivery time of consumables in inspection and quarantine work, combined with the maximum transportation range that commonly used logistics transportation methods in the region can cover under the time requirement. A buffer zone for regional road traffic is reserved during the calculation. The time weight coefficient is determined by analyzing the dispatch success rates corresponding to different transportation times in past dispatch cases, and determining the degree of influence of transportation time on dispatch success rate based on the quantitative results of the influence, so as to ensure that the weight of transportation time in the comprehensive score matches its actual impact. This refers to the estimated transportation time, calculated based on the actual transportation distance and the average speed of commonly used logistics transportation vehicles at the supply point on the corresponding route. It also includes the time required for necessary steps such as loading and unloading of goods and document verification. It is the estimated time from the time the goods are shipped from the supply point to the time the goods are received at the inventory point. This refers to the maximum allowable transportation time threshold, which is determined based on the warning level in the inventory warning information. The higher the warning level, the shorter the threshold, in order to filter out supply points with faster transportation speeds; the lower the warning level, the more relaxed the threshold can be. When setting it, the transportation time data of successful allocation under different warning levels in the past should be taken into account. The cost weighting coefficient is determined by analyzing the total cost composition of past dispatch cases, statistically analyzing the proportion of single transportation cost in the total cost of each dispatch, and taking the average of multiple cases to ensure that the weight of transportation cost in the comprehensive score conforms to the actual cost structure. This refers to the cost of a single shipment, obtained directly from the supply point or its cooperating logistics service provider. It covers all costs for transporting the required quantity of inspection and quarantine reagents and consumables from the supply point to the storage point, including transportation fees, loading and unloading fees, and necessary insurance or protective measures costs. After obtaining the cost, the cost details need to be verified to eliminate omissions or duplicate calculations. This refers to the maximum allowable transportation cost threshold within a preset range, determined based on the annual or quarterly consumable allocation cost budget for inventory management. First, calculate the upper limit of transportation costs that can be allocated to a single allocation in the budget, and then adjust this upper limit in combination with the current required consumable types to ensure that the transportation costs of the selected supply points are within the budget controllable range. The reliability weighting coefficient is determined by statistically analyzing the impact of on-time delivery rate on subsequent dispatching results in past dispatching cases, based on the difference between the two ratios, and is used to highlight the importance of on-time delivery rate to logistics adaptability. This refers to the historical on-time delivery rate of the supply point. It is calculated by collecting all records of the supply point's delivery of inspection and quarantine reagents and consumables to various inventory points over a period of time, counting the number of records that were completed on time as agreed in the delivery agreement, and dividing the number of records by the total number of delivery records. In the statistics, cases of delays caused by force majeure such as extreme weather and road repairs need to be excluded to reflect the supply point's regular delivery capacity. The reliability sensitivity coefficient is determined based on the standard requirements of the inspection and quarantine industry for the timeliness of consumable delivery, combined with past risk cases of delays or interruptions in inspection and quarantine work caused by untimely delivery. It is analyzed to determine the sensitivity required so that the comprehensive logistics score can clearly distinguish supply points with different on-time rate levels.

[0153] In summary, the step of matching and filtering the real-time inventory data of supply points based on the consumable demand information in the inventory warning information to obtain the supply point set of inventory points requires first extracting complete consumable demand information from the inventory warning information, clarifying the specific type of consumables to be replenished, the total amount required to be replenished, and the delivery time requirements, then collecting the real-time inventory data of all potential supply points, comparing the real-time inventory of the corresponding type of consumables at each supply point with the total demand, filtering out supply points whose inventory meets the demand, and organizing them uniformly to finally form the supply point set of inventory points.

[0154] In summary, the step of assessing the suitability of supply points based on the logistics constraints of inventory points to obtain a comprehensive logistics score for the supply points requires first clarifying that the logistics constraints include four dimensions: transportation distance, transportation time, transportation cost, and delivery on-time rate. Then, for each supply point in the supply point cluster, its compliance with each dimension is assessed separately. The compliance assessment results of the four dimensions are integrated with equal weight to form a score that comprehensively reflects the logistics suitability of the supply points. This score is the comprehensive logistics score of the supply points.

[0155] In summary, the step of optimizing the supply point set and logistics comprehensive score through two-way constraints to obtain feasible allocation points for inventory points requires first taking the average value of the logistics comprehensive score of successful inventory point allocation cases over the past year as the qualification standard. Then, the scores of each supply point in the supply point set are compared with the qualification standard to screen out the supply points that meet the score. At the same time, it is checked whether the real-time inventory of these supply points can still meet the demand to exclude the situation where supply cannot be provided due to inventory changes. Finally, the supply points that meet both the score and sufficient inventory are determined as feasible allocation points for inventory points.

[0156] The intelligent allocation module 106 is used to generate an allocation plan for the inventory point based on the safety stock standard of the feasible allocation point.

[0157] In this embodiment of the invention, when the intelligent allocation module executes the function of generating an allocation plan for the inventory point based on the safety stock standard of the feasible allocation point, it is specifically used for:

[0158] Based on the safety stock standard, the remaining inventory of the feasible allocation point is determined to obtain the available allocation quantity of the feasible allocation point;

[0159] Based on the consumable demand information and the available quantity, the allocation of the quantity to each feasible allocation point is carried out to obtain the allocation scheme of the inventory point.

[0160] The allocation scheme and logistics constraints are coupled and verified to obtain the impact assessment report of the inventory point;

[0161] The impact assessment report is optimized through strategy synthesis to obtain the allocation plan for the inventory points.

[0162] Specifically, first, the safety stock standard for feasible allocation points is defined. This standard is determined based on the average consumption rate of inspection and quarantine reagents and consumables at each feasible allocation point in the past, the regular replenishment cycle, and the emergency supply needs of inspection and quarantine work for such consumables. This clarifies the minimum inventory level that each feasible allocation point must maintain to maintain its normal supply and cope with unforeseen needs. Next, the current actual inventory level of each feasible allocation point is calculated. During the calculation, quantities of pre-ordered but not yet received, damaged, or expired consumables must be excluded to ensure data accuracy. The current actual inventory level of each feasible allocation point is subtracted from its corresponding safety stock standard inventory level. The difference is the quantity that the feasible allocation point can allocate externally. If the current actual inventory level is lower than the safety stock standard inventory level, the available allocation quantity for that feasible allocation point is zero. Finally, the available allocation quantity for each feasible allocation point is obtained.

[0163] Furthermore, firstly, extract the types of consumables needed to be replenished at each inventory point and the total required quantity from the inventory warning information to clarify the total consumable demand that must be met. Next, summarize the available quantities from all feasible allocation points to confirm that the total available quantity can cover the total demand of each inventory point. Based on the proportion of each feasible allocation point's available quantity to the total available quantity of all feasible allocation points, calculate the allocation quantity that each feasible allocation point should handle. During the calculation, ensure that the quantity allocated to each feasible allocation point does not exceed its own available quantity, and that the sum of the allocated quantities from all feasible allocation points equals the total demand of the inventory point. Record the allocation quantity and consumable type corresponding to each feasible allocation point to form an inventory point allocation plan.

[0164] Further, the specific details of the logistics constraints are first clarified, including the maximum permissible transportation distance between supply points and inventory points, the longest permissible transportation time, the highest permissible transportation cost, and the minimum on-time delivery rate requirement. For the allocation volume of each feasible allocation point in the allocation plan, the corresponding logistics conditions are checked one by one to ensure they meet the constraints: The actual transportation distance between the feasible allocation point and the inventory point is confirmed using an electronic map to ensure it is within the maximum permissible range; the estimated transportation time is calculated based on the transportation distance and the average speed of commonly used transportation vehicles to determine if it is within the longest permissible transportation time; the transportation cost corresponding to the allocated volume is confirmed with the logistics service provider to check if it does not exceed the maximum permissible transportation cost; and the past on-time delivery rate records of the feasible allocation points are reviewed to ensure they meet the minimum on-time delivery rate requirement. The verification results and the potential impact of non-compliance with the constraints are compiled to form an impact assessment report for the inventory points.

[0165] Furthermore, we first analyze the issues in the impact assessment report that do not meet the logistics constraints, and then formulate adjustment strategies for each issue: If the transportation cost of a feasible allocation point exceeds the limit, the allocation quantity at that point can be appropriately reduced, and the reduced portion can be allocated to other feasible allocation points with lower transportation costs and remaining allocation capacity; if the estimated transportation time of a feasible allocation point exceeds the deadline, we can prioritize increasing the allocation quantity at feasible allocation points with shorter transportation times. After adjustments, we re-verify whether the allocation quantities of all feasible allocation points meet the logistics constraints, while ensuring that the adjusted total allocation quantity still meets the total demand of the inventory points. Once all constraints are met, we integrate the final determined allocation quantities, transportation routes, estimated delivery times, and responsible logistics service providers for each feasible allocation point to form a complete and executable inventory point allocation plan.

[0166] In general, the step of determining the margin of inventory at feasible allocation points based on safety stock standards to obtain the available allocation quantity at each feasible allocation point requires first clarifying the safety stock standards for each feasible allocation point, then calculating the current actual inventory at each feasible allocation point, subtracting the corresponding safety stock standard inventory from the current actual inventory, and if the difference is positive, it is the available allocation quantity; if the difference is negative, the available allocation quantity is zero. Finally, the available allocation quantity for each feasible allocation point is obtained.

[0167] In summary, the step of allocating the allocation quantity of each feasible allocation point based on consumable demand information and available allocation quantity to obtain the allocation plan for the inventory point requires first extracting the types of consumables required for the inventory point and the total demand from the inventory warning information, summarizing the available allocation quantity of all feasible allocation points and confirming that it can cover the total demand, allocating the allocation quantity according to the proportion of the available allocation quantity of each feasible allocation point to the total available allocation quantity, recording the allocation quantity of each point in correspondence with the consumable type, and finally forming the allocation plan for the inventory point.

[0168] In general, the step of coupling and verifying the allocation plan with logistics constraints to obtain the impact assessment report of the inventory points requires first sorting out the logistics constraints, checking one by one whether the logistics conditions corresponding to the allocation of each feasible allocation point meet the constraints, recording the problems that do not meet the constraints and their possible impacts, and finally compiling the impact assessment report of the inventory points.

[0169] In general, the step of optimizing the impact assessment report to obtain the inventory point allocation plan requires first analyzing the issues in the assessment report that do not meet the logistics constraints, formulating adjustment strategies, and then re-verifying the constraint compliance and whether the total allocation volume meets the demand. Once all conditions are met, the final allocation quantity, transportation route, estimated delivery time, and responsible logistics service provider of each feasible allocation point are integrated to finally form the inventory point allocation plan.

[0170] Reference Figure 2 The diagram shown is a flowchart illustrating an intelligent inventory early warning and allocation method for inspection and quarantine reagents and consumables according to an embodiment of the present invention. In this embodiment, the intelligent inventory early warning and allocation method for inspection and quarantine reagents and consumables includes:

[0171] S1. Perform seasonal decomposition on the historical consumption records of the inventory points to obtain the consumption trend components of the inventory points;

[0172] S2. Periodically superimpose the consumption trend components to obtain the demand forecast result of the inventory point;

[0173] S3. Based on the demand forecast results, assess the current inventory level of the inventory point to determine the inventory risk and obtain the inventory early warning index of the inventory point.

[0174] S4. When the inventory warning indicator exceeds the safety threshold, generate the inventory warning information for the inventory point;

[0175] S5. Based on the consumable demand information in the inventory early warning information, conduct a feasibility assessment of the real-time inventory data and logistics constraints of the supply point to obtain the feasible allocation point of the inventory point.

[0176] S6. Generate an allocation plan for the inventory points based on the safety stock standard of the feasible allocation points.

[0177] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0178] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An early warning and deployment system for inspecting and quarantining reagent consumable intelligent inventory, characterized in that, The system comprises a consumption trend extraction module, a demand prediction module, a risk early warning module, an early warning triggering module, a supply feasibility evaluation module and an intelligent allocation module, wherein: The consumption trend extraction module is configured to perform seasonal decomposition on historical consumption records of the inventory point to obtain a consumption trend component of the inventory point. The demand prediction module is configured to perform periodic superposition on the consumption trend component to obtain a demand prediction result of the inventory point. The risk early warning module is configured to perform inventory risk research and judgment on a current inventory quantity of the inventory point based on the demand prediction result to obtain an inventory early warning index of the inventory point. The early warning triggering module is configured to generate inventory early warning information of the inventory point when the inventory early warning index exceeds a safety threshold. The supply feasibility evaluation module is configured to perform feasibility evaluation on real-time inventory data and logistics constraint conditions of a supply point according to consumable demand information of the inventory early warning information to obtain a feasible allocation point of the inventory point. The intelligent allocation module is configured to generate an allocation scheme of the inventory point according to a safety inventory standard of the feasible allocation point.

2. The early warning and deployment system for inspection and quarantine reagent consumable intelligent inventory of claim 1, wherein, When performing seasonal decomposition on historical consumption records of the inventory point to obtain a consumption trend component of the inventory point, the consumption trend extraction module is specifically configured to: arrange the historical consumption records of the inventory point in chronological order to obtain an inventory consumption time sequence of the inventory point; perform time series decomposition on the inventory consumption time sequence to obtain a main periodicity rule of the inventory point; and perform trend feature decomposition on the inventory consumption time sequence based on the main periodicity rule to obtain the consumption trend component of the inventory point.

3. The early warning and deployment system for inspection and quarantine reagent consumable intelligent inventory of claim 1, wherein, When performing periodic superposition on the consumption trend component to obtain a demand prediction result of the inventory point, the demand prediction module is specifically configured to: perform waveform reconstruction on fluctuation feature parameters of the consumption trend component to obtain a benchmark demand fluctuation curve of the inventory point; superimpose and fuse a development trend of the consumption trend component with the benchmark demand fluctuation curve to obtain an initial demand prediction sequence of the inventory point; and perform smoothing correction on the initial demand prediction sequence to obtain the demand prediction result of the inventory point.

4. The early warning and deployment system for inspection and quarantine reagent consumable intelligent inventory of claim 1, wherein, When performing inventory risk research and judgment on a current inventory quantity of the inventory point based on the demand prediction result to obtain an inventory early warning index of the inventory point, the risk early warning module is specifically configured to: determine a demand uncertainty interval of the demand prediction result according to a fluctuation feature of the demand prediction result; perform difference comparison on the current inventory quantity of the inventory point based on a lower limit value of the demand uncertainty interval to obtain an inventory coverage capacity of the inventory point; perform matching degree evaluation on a historical inventory turnover rate of the inventory point and the demand prediction result to obtain an inventory dynamic balance state of the inventory point; perform fusion evaluation on the inventory coverage capacity and the inventory dynamic balance state to obtain a risk evaluation conclusion of the inventory point; and perform benchmarking processing on the risk evaluation conclusion to obtain the inventory early warning index of the inventory point.

5. The early warning and deployment system for inspection and quarantine reagent consumable intelligent inventory of claim 4, wherein, The risk early warning module is specifically used for: obtaining a single-day minimum consumption of the demand uncertainty interval; quantitatively analyzing the current inventory quantity based on the single-day minimum consumption, to obtain a resilience period of the inventory point; supporting verification of the resilience period, to obtain an inventory coverage capability of the inventory point.

6. The early warning and deployment system for inspection and quarantine reagent consumable intelligent inventory of claim 4, wherein, The risk early warning module is specifically used for: obtaining a turnover rate change trajectory of the historical inventory turnover rate; aligning the turnover rate change trajectory with a change feature of the demand prediction result in time sequence, to obtain double-track sequence data of the inventory point; obtaining an inventory dynamic balance state of the inventory point according to a consistency degree of the double-track sequence data in change direction and change amplitude.

7. The early warning and deployment system for inspection and quarantine reagent consumable intelligent inventory of claim 1, wherein, The early warning triggering module is specifically used for: obtaining an early warning signal of the inventory point when the inventory early warning index exceeds the safety threshold; performing grade mapping on the early warning signal, to obtain an early warning grade of the inventory point; performing data encapsulation on inventory data of the inventory point according to the early warning grade, to obtain inventory early warning information of the inventory point.

8. The early warning and deployment system for inspection and quarantine reagent consumable intelligent inventory of claim 1, wherein, The supply feasibility evaluation module is specifically used for: performing demand matching screening on real-time inventory data of a supply point according to consumable demand information of the inventory early warning information, to obtain a supply point set of the inventory point; performing adaptability evaluation on the supply point based on the inventory point logistics constraint condition, to obtain a logistics comprehensive score of the supply point, wherein a calculation formula of the logistics comprehensive score is as follows: ； wherein, is the comprehensive score of the stream, is the distance weight coefficient, is the actual transportation distance between the supply point and the inventory point, is the preset maximum allowed transportation distance threshold value, is the time weight coefficient, is the predicted transportation time, is the allowed maximum transportation time threshold value, is the cost weight coefficient, is the single transportation cost, is the maximum allowed transportation cost within the preset range, is the reliability weight coefficient, is the historical delivery punctuality rate of the supply point, is the reliability sensitivity coefficient; performing bidirectional constraint optimization on the supply point set and the logistics comprehensive score, to obtain a feasible allocation point of the inventory point.

9. The early warning and deployment system for inspection and quarantine reagent consumable intelligent inventory of claim 1, wherein, The intelligent allocation module is specifically used for: performing margin determination on inventory quantities of the feasible allocation points according to the safety inventory standard, to obtain allocable quantities of the feasible allocation points; performing allocation quantity distribution on each feasible allocation point according to the consumable demand information and the allocable quantities, to obtain an allocation quantity distribution scheme of the inventory point; performing coupling verification on the allocation quantity distribution scheme and the logistics constraint condition, to obtain an impact evaluation report of the inventory point; performing strategy synthesis optimization on the impact evaluation report, to obtain an allocation scheme of the inventory point.

10. A method for early warning and allocation of intelligent inventory of inspection and quarantine reagent consumables, characterized in that, The method comprises: S1, performing seasonal decomposition on historical consumption records of an inventory point, to obtain a consumption trend component of the inventory point; S2, periodically superimpose the consumption trend component to obtain the demand prediction result of the inventory point; S3, based on the demand prediction result, carry out inventory risk research and judgment on the current inventory quantity of the inventory point to obtain the inventory early warning index of the inventory point; S4, when the inventory early warning index exceeds the safety threshold, generate the inventory early warning information of the inventory point; S5, according to the consumable demand information of the inventory early warning information, carry out feasibility evaluation on the real-time inventory data and logistics constraint conditions of the supply point to obtain the feasible allocation point of the inventory point; S6, according to the safety inventory standard of the feasible allocation point, generate the allocation scheme of the inventory point.

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