Equipment inventory automatic management system and method based on data analysis

Abstract

The invention relates to the field of automatic inventory management, and provides an equipment inventory automatic management system and method based on data analysis, and the method comprises the steps: determining out-of-stock equipment in a current period in each inspection time period of each inventory period; predicting the data of the out-of-stock equipment based on the historical data of the equipment in the inventory; on the basis of the prediction data of the out-of-stock equipment, a data analysis method is adopted to match replacement equipment for the out-of-stock equipment in the potential inventory, and the successfully matched replacement equipment is added into the inventory; if the unmatched stockout equipment exists, determining attribute distribution of the unmatched stockout equipment based on the prediction data of the unmatched stockout equipment, and searching replacement equipment of the unmatched stockout equipment in the potential inventory based on the attribute distribution of the unmatched stockout equipment, and adding the replacement equipment into the inventory; according to the invention, the efficiency and response speed of inventory management can be improved, and the operation cost and loss caused by stockout are reduced.

Description

Technical Field

[0001] The present invention relates to the field of automated inventory management, and more specifically, to an automated equipment inventory management system and method based on data analysis. Background Art

[0002] In traditional equipment inventory management, manual inspection and empirical judgment are often relied upon to determine which equipment in the inventory is at risk of being out of stock. This method is not only inefficient, but also due to the lack of precise data support, it is often difficult to accurately predict changes in equipment demand and attribute changes, resulting in inflexible inventory management and difficulty in adapting to changing market needs. In addition, when equipment is out of stock, traditional methods often require manual search for alternative equipment in potential inventory, a process that is time-consuming and error-prone, increasing the company's operating costs and risks.

[0003] Existing technologies often lack effective prediction methods when dealing with changes in equipment attributes, especially those that change over time and whose changing patterns are unknown. This makes it difficult to quickly find suitable replacement equipment when equipment is out of stock, affecting the timely replenishment of inventory and the availability of equipment. At the same time, traditional inventory management systems often have problems with low computing efficiency and inaccurate data processing when processing large amounts of data, making it difficult to meet the needs of modern enterprises for high-efficiency and high-precision inventory management.

[0004] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the existing equipment inventory management method lacks an effective prediction and matching mechanism when facing the dynamic changes in equipment attributes, which makes it difficult to quickly find suitable replacement equipment when the equipment is out of stock; in addition, the existing system lacks computing efficiency and accuracy when processing large amounts of equipment data, and it is difficult to meet the high requirements of modern enterprises for inventory management. Summary of the invention

[0005] The invention provides an automatic equipment inventory management method based on data analysis.

[0006] In a first aspect of the present invention, a method for automated equipment inventory management based on data analysis is provided, comprising: in each inspection period of each inventory cycle, determining the out-of-stock equipment in the current cycle; predicting the data of the out-of-stock equipment based on the historical data of the equipment in the inventory; matching substitute equipment for the out-of-stock equipment in potential inventory using a data analysis method based on the predicted data of the out-of-stock equipment, and adding the successfully matched substitute equipment to the inventory; if there is an unmatched out-of-stock equipment, determining the attribute distribution of the unmatched out-of-stock equipment based on the predicted data of the unmatched out-of-stock equipment, and searching for substitute equipment for the unmatched out-of-stock equipment in potential inventory based on the attribute distribution of the unmatched out-of-stock equipment to add to the inventory.

[0007] Furthermore, the data of out-of-stock equipment is predicted based on the historical data of the equipment in the inventory, including: directly predicting the first type of attributes of the out-of-stock equipment; for the second type of attributes of the out-of-stock equipment, using a machine learning algorithm to match similar equipment for the out-of-stock equipment from the inventory equipment, and predicting the second type of attributes of the out-of-stock equipment based on the second type of attribute values ​​of the similar equipment; wherein the first type of attributes are attributes that do not change with time or that change with time but the changing pattern is knowable, and the second type of attributes are attributes that change with time and the changing pattern is unknown.

[0008] Furthermore, the following formula is used to predict the second type of attribute of the out-of-stock device based on the attribute values ​​corresponding to similar devices: ;in, represents the predicted value of the kth second-type attribute for the i-th out-of-stock equipment, represents the value of the mth similar device on the kth second type attribute, and M represents the number of similar devices.

[0009] Furthermore, the out-of-stock propensity score of each device in the current cycle is calculated based on the following formula, and M devices with the closest propensity scores to the out-of-stock devices are selected from the inventory devices as similar devices: ;in, It represents the propensity score of the i-th device being out of stock in the current cycle, reflecting the probability of the device being out of stock under given conditions. Indicates whether the i-th device is out of stock, Indicates that the i-th device is out of stock. represents all attribute values ​​of the ith device in the previous cycle, and P(·) represents the probability.

[0010] Furthermore, a data analysis method is used to match replacement devices for out-of-stock devices in potential inventory, including: performing cluster analysis on each non-classified attribute of the inventory, and keeping the cluster of each classified attribute of the inventory unchanged; for each out-of-stock device, determining the cluster in which each attribute of the out-of-stock device is located based on the predicted data of each attribute of the out-of-stock device; if there is a device in the potential inventory with a consistent distribution of cluster attributes with the out-of-stock device, the match is successful, and a device with a consistent distribution of cluster attributes is randomly selected as a replacement device to match the out-of-stock device.

[0011] Furthermore, the method also includes: if the current time period is not the last time period of the current cycle, searching for the target new equipment in the potential inventory based on the forecast data of the out-of-stock equipment accumulated in the current cycle, and adding the target new equipment to the potential inventory.

[0012] In a second aspect of the present invention, there is provided an automated equipment inventory management system based on data analysis, comprising: an out-of-stock equipment determination module, for determining, in each inspection period of each inventory cycle, out-of-stock equipment in the current cycle; a data prediction module, for predicting the data of out-of-stock equipment based on historical data of equipment in the inventory; an alternative equipment matching module, for matching alternative equipment for out-of-stock equipment in potential inventory using a data analysis method based on the predicted data of out-of-stock equipment, and adding successfully matched alternative equipment to the inventory; and an attribute distribution matching module, for determining the attribute distribution of the unmatched out-of-stock equipment based on the predicted data of the unmatched out-of-stock equipment if there is any unmatched out-of-stock equipment, and searching for alternative equipment for the unmatched out-of-stock equipment in potential inventory based on the attribute distribution of the unmatched out-of-stock equipment to add to the inventory.

[0013] Furthermore, the data of out-of-stock equipment is predicted based on the historical data of the equipment in the inventory, including: directly predicting the first type of attributes of the out-of-stock equipment; for the second type of attributes of the out-of-stock equipment, using a machine learning algorithm to match similar equipment for the out-of-stock equipment from the inventory equipment, and predicting the second type of attributes of the out-of-stock equipment based on the second type of attribute values ​​of the similar equipment; wherein the first type of attributes are attributes that do not change with time or that change with time but the changing pattern is knowable, and the second type of attributes are attributes that change with time and the changing pattern is unknown.

[0014] Furthermore, the following formula is used to predict the second type of attribute of the out-of-stock device based on the attribute values ​​corresponding to similar devices: ;in, represents the predicted value of the kth second-type attribute for the i-th out-of-stock equipment, represents the value of the mth similar device on the kth second type attribute, and M represents the number of similar devices.

[0015] Furthermore, the system also includes: a strategy adjustment module, which is used to search for target new equipment in the potential inventory based on the forecast data of out-of-stock equipment accumulated in the current cycle, and add the target new equipment to the potential inventory if the current time period is not the last time period of the current cycle.

[0016] The above-mentioned embodiments of the present invention have at least the following beneficial effects: The present invention adopts an automated equipment inventory management method based on data analysis to improve the accuracy and efficiency of inventory management. By conducting an in-depth analysis of the historical data of the equipment in the inventory, the method can predict the future demand and attribute changes of out-of-stock equipment, so as to make inventory adjustments and optimizations in advance. This method can reduce operational interruptions and cost increases caused by out-of-stock, while also improving the availability and flexibility of equipment. In addition, by matching similar equipment through machine learning algorithms, the second type of attributes, i.e., those attributes that change over time and whose changing rules are unknown, can be predicted more accurately, so as to find more suitable and efficient substitutes for out-of-stock equipment.

[0017] In addition, the method processes non-classified attributes in inventory through cluster analysis while keeping the clustering of classified attributes unchanged, which helps to quickly identify and match equipment with similar attributes. This method can not only achieve timely replenishment of out-of-stock equipment within the current inventory cycle, but also predict and prepare potential new equipment in the early stages of the cycle, thereby ensuring continuous optimization and updating of inventory. Through this automated and intelligent inventory management, enterprises can better respond to market changes, improve customer satisfaction, and ultimately achieve cost savings and profit growth. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0019] Figure 1 A schematic diagram of a process flow of an automated equipment inventory management method based on data analysis provided by an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of the structure of an equipment inventory automation management system based on data analysis provided by an embodiment of the present invention;

[0021] Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0023] Reference below Figure 1 , Figure 1 The following is a flow chart of a method for automated equipment inventory management based on data analysis provided by an embodiment of the present invention. Figure 1 As shown, a method 100 for automated equipment inventory management based on data analysis includes: in each inspection period of each inventory cycle, determining the out-of-stock equipment in the current cycle; predicting the data of the out-of-stock equipment based on the historical data of the equipment in the inventory; matching substitute equipment for the out-of-stock equipment in the potential inventory using a data analysis method based on the predicted data of the out-of-stock equipment, and adding the successfully matched substitute equipment to the inventory; if there is an unmatched out-of-stock equipment, determining the attribute distribution of the unmatched out-of-stock equipment based on the predicted data of the unmatched out-of-stock equipment, and searching for substitute equipment for the unmatched out-of-stock equipment in the potential inventory based on the attribute distribution of the unmatched out-of-stock equipment to add to the inventory.

[0024] It should be noted that, in each inspection period of each inventory cycle, determining the equipment that is out of stock in the current cycle means that the system needs to regularly check the inventory status to identify those equipment that are currently or expected to be out of stock.

[0025] Specifically, out-of-stock equipment can be determined by comparing inventory levels with predetermined safety stock levels. For example, if the current inventory level of a certain equipment is lower than the set safety stock level, the equipment is considered out-of-stock. Safety stock levels can be set based on historical sales data, seasonal changes, supplier delivery times, and other factors.

[0026] Preferably, the system can use real-time monitoring technology, such as barcode scanning or RFID systems, to update inventory data in real time. In addition, an early warning system can be set up to automatically issue an alarm when the inventory level approaches the safety stock level so that timely action can be taken. For out-of-stock situations that are difficult to predict, advanced forecasting algorithms, such as time series analysis or machine learning models, can be used to improve the accuracy of forecasts.

[0027] It should be noted that the data of out-of-stock equipment is predicted based on the historical data of equipment in inventory, which involves using historical sales data, usage frequency, maintenance records and other information to predict future demand and usage of equipment.

[0028] Specifically, the forecasting model can include machine learning algorithms such as linear regression, decision tree, neural network, etc. Parameter settings should be adjusted according to the characteristics of historical data. For example, if the data shows that equipment demand has obvious seasonality, then the forecasting model should be able to capture this seasonal change.

[0029] More specifically, the forecasting model can include a feature selection step to determine which historical data features are most important for predicting demand for out-of-stock equipment. In addition, techniques such as cross-validation can be used to optimize model parameters to ensure that the model has good generalization capabilities.

[0030] It should be noted that based on the forecast data of out-of-stock equipment, data analysis methods are used to match alternative equipment for the out-of-stock equipment in potential inventory, which involves searching for equipment with similar functions to the out-of-stock equipment in existing inventory or supplier catalogs.

[0031] Specifically, the matching of the replacement devices can be achieved by attribute comparison, similarity calculation, etc. For example, cosine similarity, Euclidean distance, etc. can be used to evaluate the similarity between devices.

[0032] Preferably, the matching process can include a user feedback link to allow operators to review and adjust the recommended alternative equipment. In addition, factors such as equipment availability, cost-effectiveness ratio and supplier reputation can also be considered to ensure that the selected alternative equipment is not only technically feasible but also economically optimal.

[0033] In some embodiments, data of out-of-stock equipment is predicted based on historical data of equipment in inventory, including: directly predicting first-type attributes of out-of-stock equipment; for second-type attributes of out-of-stock equipment, using a machine learning algorithm to match similar equipment to the out-of-stock equipment from inventory equipment, and predicting the second-type attributes of the out-of-stock equipment based on the second-type attribute values ​​of similar equipment; wherein the first-type attributes are attributes that do not change with time or that change with time but the changing pattern is knowable, and the second-type attributes are attributes that change with time and the changing pattern is unknown.

[0034] It should be noted that forecasting the data of out-of-stock equipment based on the historical data of equipment in inventory involves using historical sales data, frequency of use, maintenance records and other information to predict future demand and usage of equipment.

[0035] Specifically, the prediction model can include machine learning algorithms such as linear regression, decision trees, and neural networks. Parameter settings should be adjusted according to the characteristics of historical data. For example, if the data shows that equipment demand has obvious seasonality, then the prediction model should be able to capture this seasonal change. The first type of attributes may include attributes that do not change over time, such as equipment specifications and models, while the second type of attributes may include attributes that change over time, such as the frequency of use and number of maintenance of the equipment.

[0036] Preferably, the prediction model can include a feature selection step to determine which historical data features are most important for predicting the demand for out-of-stock equipment. In addition, techniques such as cross-validation can be used to optimize model parameters to ensure that the model has good generalization ability.

[0037] It should be noted that, for the second type of attributes of the out-of-stock device, a machine learning algorithm is used to match similar devices to the out-of-stock device from the inventory devices, and the second type of attributes of the out-of-stock device is predicted based on the second type of attribute values ​​of the similar devices.

[0038] Specifically, similar devices can be matched by attribute comparison, similarity calculation, etc. For example, cosine similarity, Euclidean distance, etc. can be used to evaluate the similarity between devices. The selection of similar devices can be based on multiple attributes, such as frequency of use, number of repairs, service life, etc.

[0039] More specifically, the matching process can include a user feedback link that allows operators to review and adjust the recommended similar equipment. In addition, factors such as equipment availability, cost-effectiveness ratio, and supplier reputation can also be considered to ensure that the selected similar equipment is not only technically feasible but also economically optimal.

[0040] It should be noted that the following formula is used to predict the second type of attribute of the out-of-stock device based on the attribute values ​​corresponding to the similar devices, which involves the application of a mathematical model to quantify the attribute relationship between the similar devices and the out-of-stock device.

[0041] Specifically, the parameters in the formula may include the number of similar devices, the value of each similar device on the second type attribute, etc. For example, a weighted average method may be used to calculate the predicted value, wherein the weight may be determined based on the similarity between the similar device and the out-of-stock device.

[0042] Preferably, the prediction formula can be further refined to include a time decay factor to reflect the effect of changes in attribute values ​​over time. In addition, multiple prediction models can be used for comparison to select the best prediction method.

[0043] In some embodiments, the following formula is used to predict the second type of attribute of the out-of-stock device based on the attribute values ​​corresponding to similar devices: ;in, represents the predicted value of the kth second-type attribute for the i-th out-of-stock equipment, represents the value of the mth similar device on the kth second type attribute, and M represents the number of similar devices.

[0044] It should be noted that using the following formula to predict the second type of attribute of out-of-stock equipment based on the attribute values ​​corresponding to similar equipment means that we use a mathematical model to quantify and predict attributes that change over time and whose changing patterns are unknown.

[0045] Specifically, the parameters in the formula may include the number of similar devices, the value of each similar device on the second type of attribute, etc. For example, a weighted average method may be used to calculate the predicted value, where the weight may be determined based on the similarity between the similar devices and the out-of-stock device. The similarity here may be calculated in a variety of ways, such as cosine similarity, Euclidean distance, etc.

[0046] Preferably, the prediction formula can be further refined to include a time decay factor to reflect the effect of changes in attribute values ​​over time. In addition, multiple prediction models can be used for comparison to select the best prediction method.

[0047] It should be noted that the represents the predicted value of the kth second-type attribute for the i-th out-of-stock equipment, and represents the value of the kth second-type attribute of the mth similar device, and M represents the number of similar devices. This formula is a weighted average calculation method, in which the attribute value of each similar device contributes to the predicted value.

[0048] Specifically, the contribution of each similar device to the predicted value can be weighted by its similarity. The similarity can be calculated based on multiple factors, such as the frequency of use of the device, maintenance records, historical performance, etc. The weight setting can be adjusted according to the actual situation to ensure the accuracy of the prediction results.

[0049] More specifically, an ensemble learning method in machine learning, such as random forest or gradient boosting tree, can be used to determine the weight of each similar device on the predicted value. In addition, the use environment and use conditions of the device can also be considered to further improve the accuracy of the prediction.

[0050] In some embodiments, the out-of-stock propensity score of each device in the current period is calculated based on the following formula, and M devices with the closest propensity scores to the out-of-stock devices are selected from the inventory devices as similar devices: ;in, It represents the propensity score of the i-th device being out of stock in the current cycle, reflecting the probability of the device being out of stock under given conditions. Indicates whether the i-th device is out of stock, Indicates that the i-th device is out of stock. represents all attribute values ​​of the ith device in the previous cycle, and P(·) represents the probability.

[0051] It should be noted that the propensity score of each device being out of stock in the current cycle is calculated based on the following formula, and M devices with the closest propensity score to the out-of-stock device are selected from the inventory devices as similar devices, which involves a quantitative assessment of the probability of the device being out of stock. This method helps to identify devices with similar usage patterns and out-of-stock risks, thereby finding the best substitute for the out-of-stock device.

[0052] Specifically, the formula represents the propensity score of out-of-stock of the i-th equipment in the current period, represents all attribute values ​​of the ith device in the previous period, and P(·) represents the probability. The propensity score can be calculated by logistic regression, decision tree, or other classification algorithms, which can predict the probability of out-of-stock of a device based on its historical data.

[0053] Preferably, the calculation of the propensity score may include multiple factors, such as the frequency of use of the equipment, maintenance records, inventory levels, etc. In addition, the use environment and use conditions of the equipment may also be considered to further improve the accuracy of the prediction.

[0054] It should be noted that the calculation formula of the propensity score can be designed to reflect the risk of equipment out-of-stock. The higher the score, the greater the possibility that the equipment is out of stock in the current cycle.

[0055] Specifically, the propensity score can be calculated through the following steps: first, collect historical data of the equipment, including sales records, usage frequency, maintenance records, etc.; then, use an appropriate machine learning algorithm to train the model to predict the out-of-stock risk of the equipment; finally, calculate the propensity score of each device based on the output of the model.

[0056] More specifically, cross-validation can be used to optimize model parameters and ensure that the model has good generalization capabilities. In addition, real-time data, such as the latest sales trends and market changes, can be taken into account to dynamically adjust the calculation of the propensity score.

[0057] It should be noted that the M devices with the closest propensity scores to the out-of-stock device are selected as similar devices, which helps to find devices that are similar to the target device in terms of usage patterns and out-of-stock risks.

[0058] Specifically, the selection of similar devices can be achieved by calculating the propensity score differences between devices. For example, the Euclidean distance or Manhattan distance can be used to measure the score differences between devices, and the M devices with the smallest score differences can be selected.

[0059] Preferably, the selection process of similar equipment can include a user review process, allowing management to review and adjust the recommended similar equipment. In addition, factors such as equipment availability, cost-effectiveness ratio, and supplier reputation can also be considered to ensure that the selected similar equipment is not only technically feasible but also economically optimal.

[0060] In some embodiments, a data analysis method is used to match replacement devices for out-of-stock devices in potential inventory, including: performing cluster analysis on each non-classified attribute of the inventory, and keeping the cluster of each classified attribute of the inventory unchanged; for each out-of-stock device, determining the cluster in which each attribute of the out-of-stock device is located based on the predicted data of each attribute of the out-of-stock device; if there is a device in the potential inventory with a consistent distribution of cluster attributes with the out-of-stock device, the match is successful, and a device with a consistent distribution of cluster attributes is randomly selected as a replacement device to match the out-of-stock device.

[0061] Specifically, data analysis methods can include cluster analysis, classification analysis, or association rule learning. Cluster analysis can help identify devices with similar attributes, while classification analysis can help predict whether a device is suitable as a replacement. The parameter settings of these methods need to be adjusted according to the specific attributes of the device and historical data.

[0062] Preferably, the data analysis method can combine real-time data and predictive models to improve the accuracy of matching. For example, real-time inventory data and predicted equipment demand can be used to dynamically adjust the parameters of clustering and classification.

[0063] It should be noted that cluster analysis is performed for each non-classified attribute of the inventory, and clustering for each categorical attribute of the inventory remains unchanged, which means that when processing inventory data, it is necessary to distinguish different types of attributes and use appropriate methods for analysis. Non-classified attributes may include the frequency of use of equipment, the number of repairs, etc., while categorical attributes may include the type and brand of equipment, etc.

[0064] Specifically, cluster analysis can be performed by calculating the attribute similarity of the devices. For example, a K-means algorithm can be used to cluster non-classification attributes, while classification attributes can keep their original classification unchanged.

[0065] More specifically, more complex clustering methods such as hierarchical clustering or density clustering can be used to process high-dimensional data. In addition, the use environment and conditions of the equipment can also be considered to further improve the accuracy of cluster analysis.

[0066] It should be noted that for each out-of-stock device, the cluster to which each attribute of the out-of-stock device belongs is determined according to the prediction data of each attribute of the out-of-stock device, which involves matching the attributes of the out-of-stock device with the attributes of the device in the potential inventory.

[0067] Specifically, matching can be performed by calculating the attribute similarity between the out-of-stock device and each device in the potential inventory. For example, a metric such as cosine similarity or Euclidean distance can be used to evaluate the similarity between devices.

[0068] Preferably, the matching process can include a user feedback link to allow operators to review and adjust the recommended alternative equipment. In addition, factors such as equipment availability, cost-effectiveness ratio and supplier reputation can also be considered.

[0069] In some embodiments, the method further includes: if the current period is not the last period of the current cycle, searching for the target new equipment in the potential inventory based on the forecast data of out-of-stock equipment accumulated in the current cycle, and adding the target new equipment to the potential inventory.

[0070] It should be noted that if the current period is not the last period of the current cycle, the target new equipment will be found in the potential inventory based on the forecast data of out-of-stock equipment accumulated in the current cycle, and the target new equipment will be added to the potential inventory. This sentence means that the system not only pays attention to the current out-of-stock situation, but also has a forward-looking nature, and can predict future demand and prepare in advance.

[0071] Specifically, the system can use a forecasting model to predict the type and quantity of equipment that may be needed in the future by analyzing the attributes and demand patterns of all out-of-stock equipment in the current cycle. These forecast data can guide procurement decisions to ensure timely replenishment of inventory.

[0072] Preferably, the system can combine market trend analysis and historical sales data to optimize the forecasting model. In addition, the supplier's delivery time and cost can also be taken into account to determine the best purchase time and quantity.

[0073] It should be noted that the process of finding target new equipment involves the evaluation and screening of potential inventory. This requires a comprehensive evaluation mechanism to determine which equipment is most likely to become out-of-stock equipment in the future.

[0074] Specifically, the potential risk of equipment out-of-stock can be assessed by analyzing data such as equipment usage frequency, maintenance records, and customer feedback. In addition, the life cycle and replacement cycle of the equipment can also be considered to predict future demand.

[0075] More specifically, machine learning algorithms, such as random forests or support vector machines, can be used to evaluate and rank potential new equipment. These algorithms can predict the probability of equipment demand based on historical data and current trends.

[0076] It should be noted that adding target new equipment to potential inventory is a dynamic process that needs to be constantly adjusted according to new data and market changes. The system can set up a dynamic update mechanism to update the equipment list in potential inventory regularly or in real time according to new sales data and market information.

[0077] Preferably, the system can provide a user interface that allows managers to manually adjust the list of equipment in the potential inventory to take into account special circumstances or urgent needs that may not have been predicted by the algorithm. In addition, an early warning system can be set up to automatically remind managers to make inventory adjustments when a high risk of out-of-stock is predicted.

[0078] It should be understood that, in the present invention, predicting the data of out-of-stock equipment based on the historical data of equipment in inventory is a key link in inventory management automation. Those skilled in the art will understand that predicting the data of out-of-stock equipment can be achieved in a variety of ways, and these ways are all based on in-depth analysis of historical data, aiming to improve the accuracy and efficiency of inventory management and reduce operating costs and losses caused by out-of-stock.

[0079] For the first type of attributes of out-of-stock equipment (i.e., attributes that do not change over time or change over time but with known patterns, such as equipment specifications, models, initial installation parameters, etc.), technicians in this field can use a variety of conventional methods to make predictions. For example, directly refer to the product manual or technical documentation of the equipment to obtain fixed attribute information, which remains basically unchanged throughout the life cycle of the equipment and can be used as basic data for prediction. In addition, for some attributes that change slowly over time but with known patterns, such as the rated service life of the equipment, calculations and predictions can be made based on the time used and the established attenuation laws. In actual operation, if the specifications and models of the equipment are consistent with the records in the inventory management system, the information can be directly called for subsequent analysis; for attributes such as service life, the remaining service life can be calculated based on the length of time the equipment is used and the preset aging model, thereby predicting its availability in future cycles.

[0080] For the second type of attributes of out-of-stock equipment (i.e., attributes that change over time and whose rules are unknown, such as the actual frequency of use of equipment, the number of failures, performance degradation, etc.), the present invention provides a method for matching similar equipment from inventory equipment based on a machine learning algorithm, and then predicting the second type of attributes of out-of-stock equipment. However, those skilled in the art should know that in addition to this method, other technical means can also be used to achieve the same purpose. For example, the historical frequency of use data of the equipment can be modeled based on a time series analysis method, such as using an autoregressive moving average model (ARMA) or an autoregressive integrated moving average model (ARIMA), etc., and the future frequency of use can be predicted by analyzing the trend, seasonality, and periodicity characteristics in the historical data. At the same time, for the number of failures, a probability distribution model based on the historical failure interval time, such as a Weibull distribution model, can be constructed to estimate the probability and time of the next failure, thereby predicting the failure risk of out-of-stock equipment in the future cycle.

[0081] When matching similar devices based on machine learning algorithms, the present invention adopts a method of calculating the difference in propensity scores between devices (such as Euclidean distance or Manhattan distance) to select M devices with the closest propensity scores to the out-of-stock device as similar devices. Those skilled in the art can adopt other effective similarity measurement methods according to actual conditions. For example, a distance measurement method based on information entropy can be used to measure similarity by calculating the difference between the information entropy of device attributes; or a density-based clustering method can be used to regard devices with similar density distribution as similar devices. In addition, when selecting similar devices, in addition to considering the attributes of the equipment such as frequency of use, number of repairs, and service life, factors such as the operating environment and workload of the equipment can also be included to more comprehensively evaluate the similarity between devices.

[0082] As for the second type of attribute prediction of out-of-stock equipment based on the attribute values ​​of similar equipment, those skilled in the art can understand that in practical applications, the formula can be improved and optimized according to specific needs. For example, a weight factor can be introduced to assign different weights according to the similarity between similar equipment and out-of-stock equipment in key attributes, so that the prediction results are more in line with the actual situation. In addition, in addition to the weighted average method, statistics such as the median and mode can also be used to represent the attribute values ​​of similar equipment to predict the attributes of out-of-stock equipment. At the same time, during the prediction process, the prediction results can be corrected and adjusted in combination with expert experience or industry standards to improve the accuracy of the prediction.

[0083] In actual inventory management scenarios, different companies have different equipment types, usage environments, and management requirements. Therefore, technicians in this field can make adaptive adjustments to the forecasting method according to specific circumstances. For example, for some companies that have high requirements for the real-time performance of equipment, the frequency of real-time data collection and analysis can be increased, and the forecasting model can be updated in a timely manner; for companies with a wide variety of equipment, a hierarchical forecasting method can be adopted, first grouping according to equipment category or function, and then developing personalized forecasting strategies for each group of equipment. In addition, with the development of technology and the accumulation of new data, technicians in this field can continuously optimize the forecasting model and introduce new algorithms and technologies, such as recurrent neural networks (RNN) or long short-term memory networks (LSTM) in deep learning algorithms, to better process time series data and improve the ability to predict out-of-stock equipment data.

[0084] The above-mentioned embodiments of the present invention have the following beneficial effects: the equipment inventory automation management method based on data analysis described in the present invention can systematically handle the problem of equipment shortage. The method can automatically determine the out-of-stock equipment within the inspection period of each inventory cycle, and predict the property changes of these equipment through historical data analysis, so as to prepare replacement equipment in advance. This method can reduce production interruptions caused by equipment shortages, and at the same time improve the scientificity of inventory management and the accuracy of prediction through data analysis.

[0085] Furthermore, the method matches similar devices through machine learning algorithms and predicts the properties of out-of-stock devices based on the properties of similar devices, which can more accurately find suitable substitutes for out-of-stock devices. In addition, the method also includes finding devices in potential inventory that have the same property distribution as the out-of-stock devices, which can improve the matching efficiency of substitute devices and the utilization rate of inventory. Through these measures, enterprises can manage inventory more effectively, reduce inventory backlogs, reduce costs, and improve the response speed to market changes.

[0086] like Figure 2 As shown, some embodiments of an automated equipment inventory management system 200 based on data analysis, the system 200 includes: an out-of-stock equipment determination module 201, used to determine the out-of-stock equipment in the current cycle in each inspection period of each inventory cycle; a data prediction module 202, used to predict the data of the out-of-stock equipment based on the historical data of the equipment in the inventory; a substitute equipment matching module 203, used to match substitute equipment for the out-of-stock equipment in the potential inventory using a data analysis method based on the predicted data of the out-of-stock equipment, and add the successfully matched substitute equipment to the inventory; an attribute distribution matching module 204, used to determine the attribute distribution of the unmatched out-of-stock equipment based on the predicted data of the unmatched out-of-stock equipment if there is an unmatched out-of-stock equipment, and search for substitute equipment for the unmatched out-of-stock equipment in the potential inventory based on the attribute distribution of the unmatched out-of-stock equipment to add to the inventory.

[0087] It is understandable that the modules and references in the equipment inventory automation management system 200 based on data analysis are Figure 1 Therefore, the operations, features and beneficial effects described above for the equipment inventory automation management method based on data analysis are also applicable to the equipment inventory automation management system 200 based on data analysis and the modules contained therein, and will not be repeated here.

[0088] In some embodiments, data of out-of-stock equipment is predicted based on historical data of equipment in inventory, including: directly predicting first-type attributes of out-of-stock equipment; for second-type attributes of out-of-stock equipment, using a machine learning algorithm to match similar equipment to the out-of-stock equipment from inventory equipment, and predicting the second-type attributes of the out-of-stock equipment based on the second-type attribute values ​​of similar equipment; wherein the first-type attributes are attributes that do not change with time or that change with time but the changing pattern is knowable, and the second-type attributes are attributes that change with time and the changing pattern is unknown.

[0089] It should be noted that the data of out-of-stock equipment is predicted based on the historical data of the equipment in inventory, including directly predicting the first type of attributes of the out-of-stock equipment; for the second type of attributes of the out-of-stock equipment, a machine learning algorithm is used to match similar equipment to the out-of-stock equipment from the inventory equipment, and the second type of attributes of the out-of-stock equipment are predicted based on the second type of attribute values ​​of the similar equipment.

[0090] Specifically, the first type of attributes may include attributes such as the specifications, models, and service life of the equipment that do not change over time or whose changing patterns are known. The second type of attributes may include attributes such as usage frequency and failure rate that change over time and whose changing patterns are unknown. Direct prediction can use methods such as time series analysis and linear regression, while indirect prediction requires first finding similar devices through clustering, classification, and other methods, and then making predictions based on the attributes of these devices.

[0091] Preferably, the prediction model can combine multiple machine learning algorithms, such as support vector machines, random forests, neural networks, etc., to improve the accuracy and robustness of the prediction. In addition, ensemble learning methods, such as model fusion or stacking, can also be used to synthesize the prediction results of different models.

[0092] It should be noted that the following formula is used to predict the second type of attribute of the out-of-stock device based on the attribute values ​​corresponding to the similar devices. This formula is a mathematical expression used to quantify the attribute relationship between the similar devices and the out-of-stock device and make predictions based on it.

[0093] Specifically, the parameters in the formula need to be set according to the actual data. For example, if the weighted average method is used, the weight can be determined based on the similarity between similar devices and out-of-stock devices. The similarity can be calculated using methods such as cosine similarity and Euclidean distance.

[0094] More specifically, an adaptive weight adjustment mechanism can be used to dynamically adjust the weights according to the accuracy of historical predictions to improve the adaptability and accuracy of the prediction model. In addition, regularization terms can be introduced to avoid overfitting and improve the generalization ability of the model.

[0095] It should be noted that this approach also includes continuous optimization and adjustment of the forecasting model to adapt to changing data and environmental conditions. Through continuous learning and optimization, the system can improve its ability to predict the attributes of out-of-stock equipment, thereby managing inventory more effectively.

[0096] Specifically, the model can be continuously updated through online learning or incremental learning methods to reflect the latest data trends. In addition, model evaluation and parameter adjustment can be performed regularly to ensure the accuracy of the prediction results.

[0097] Preferably, the system can provide a feedback mechanism to allow users to evaluate and provide feedback on the prediction results, which can be used to further train and optimize the model. In addition, expert knowledge, such as the experience and judgment of maintenance personnel, can also be introduced to assist the model's prediction and decision-making process.

[0098] In some embodiments, the following formula is used to predict the second type of attribute of the out-of-stock device based on the attribute values ​​corresponding to similar devices: ;in, represents the predicted value of the kth second-type attribute for the i-th out-of-stock equipment, represents the value of the mth similar device on the kth second type attribute, and M represents the number of similar devices.

[0099] It should be noted that the following formula is used to predict the second type of attribute of the out-of-stock device based on the attribute values ​​corresponding to similar devices. This involves a specific mathematical model for predicting the attributes of the out-of-stock device based on the attributes of known similar devices. This method can improve the accuracy of the prediction because it uses the actual usage data of the devices already in stock.

[0100] Specifically, the formula represents the predicted value of the i-th out-of-stock equipment on the k-th second type attribute, and represents the value of the mth similar device on the kth second type attribute, and M represents the number of similar devices. This formula is a weighted average calculation method, in which the attribute value of each similar device contributes to the predicted value.

[0101] Preferably, the prediction formula can be further refined to include a time decay factor to reflect the effect of changes in attribute values ​​over time. In addition, multiple prediction models can be used for comparison to select the best prediction method.

[0102] It should be noted that the denominator in the formula, that is, the expression under the sum symbol, represents the weighted sum of the kth attribute values ​​of all similar devices. This weight can be determined based on the similarity between the similar devices and the out-of-stock devices. The higher the similarity, the greater the weight.

[0103] Specifically, the similarity can be calculated using a variety of methods, such as cosine similarity, Euclidean distance, etc. The weight can also be adjusted based on other factors such as the frequency of use, maintenance records, and historical performance of the equipment.

[0104] More specifically, an ensemble learning method in machine learning, such as random forest or gradient boosting tree, can be used to determine the weight of each similar device on the predicted value. In addition, the use environment and use conditions of the device can also be considered to further improve the accuracy of the prediction.

[0105] Specifically, the system can set up a dynamic update mechanism to update the forecast model regularly or in real time based on new sales data and market information. The system can provide a user interface that allows managers to manually adjust the parameters of the forecast model to take into account special circumstances or urgent needs that may not be predicted by the algorithm. In addition, an early warning system can be set up to automatically remind managers to make inventory adjustments when a high risk of out-of-stock is predicted.

[0106] In some embodiments, the system further includes: a policy adjustment module for searching for target new equipment in potential inventory based on forecast data of out-of-stock equipment accumulated in the current cycle and adding the target new equipment to the potential inventory if the current period is not the last period of the current cycle.

[0107] It should be noted that the system also includes a strategy adjustment module, which is used to search for target new equipment in the potential inventory based on the forecast data of out-of-stock equipment accumulated in the current cycle if the current time period is not the last time period of the current cycle, and add the target new equipment to the potential inventory.

[0108] Specifically, the strategy adjustment module can be a software component that uses historical data and real-time data to predict future equipment demand. This module can analyze the usage and out-of-stock records of all equipment in the current cycle to predict which equipment may become out-of-stock equipment in the future.

[0109] Preferably, the strategy adjustment module can integrate advanced data analysis tools and forecasting algorithms, such as time series analysis, machine learning models, etc., to improve the accuracy of forecasts. In addition, market trends, seasonal changes and other external factors can also be considered to optimize forecast results.

[0110] It should be noted that the working principle of the strategy adjustment module is to identify potential new equipment needs based on forecast data. This involves in-depth analysis of equipment usage patterns and predictions of future market changes.

[0111] Specifically, the module can set a series of thresholds and parameters, such as prediction confidence, device importance level, etc., to determine which devices should be considered as target new devices. These parameters can be set based on historical data and business rules.

[0112] More specifically, the strategy adjustment module can use an adaptive learning algorithm to continuously adjust the forecast model based on past forecast results and actual inventory conditions. In addition, a user intervention mechanism can be set up to allow managers to manually adjust forecast results based on business needs.

[0113] Specifically, the system can set up a dynamic update mechanism to update the list of equipment in potential inventory based on new sales data and market information regularly or in real time. The system can provide a user interface that allows managers to manually adjust the list of equipment in potential inventory to take into account special circumstances or urgent needs that may not be predicted by the algorithm. In addition, an early warning system can be set up to automatically remind managers to make inventory adjustments when a high risk of out-of-stock is predicted.

[0114] Reference below Figure 3 , which shows a schematic diagram of a structure 300 of an electronic device suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0115] like Figure 3As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 to a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0116] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0117] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0118] The above description is only an explanation of some preferred embodiments of the present invention and the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by the specific combination of the above technical features.

Claims

1. A method for automated equipment inventory management based on data analysis, characterized in that: The following steps are involved: During each inspection period of each inventory cycle, identify the equipment that is out of stock during the current cycle; Forecasting of out-of-stock equipment based on historical data of equipment in inventory; Based on the forecast data of out-of-stock equipment, data analysis methods are used to match substitute equipment for out-of-stock equipment in potential inventory, and the successfully matched substitute equipment is added to the inventory; If there is an unmatched out-of-stock device, the attribute distribution of the unmatched out-of-stock device is determined based on the prediction data of the unmatched out-of-stock device, and a replacement device for the unmatched out-of-stock device is searched in the potential inventory based on the attribute distribution of the unmatched out-of-stock device to add to the inventory.

2. The method for automated equipment inventory management based on data analysis according to claim 1, characterized in that: Forecasting of out-of-stock equipment data based on historical data of equipment in inventory, including: For the first type of attributes of out-of-stock equipment, forecasts are made directly; For the second type of attribute of the out-of-stock device, a machine learning algorithm is used to match similar devices to the out-of-stock device from the inventory devices, and the second type of attribute of the out-of-stock device is predicted based on the second type of attribute values ​​of the similar devices; The first type of attribute is an attribute that does not change with time or changes with time but the change pattern is known, and the second type of attribute is an attribute that changes with time but the change pattern is unknown.

3. The equipment inventory automation management method based on data analysis according to claim 2 is characterized in that: The following formula is used to predict the second type of attribute of out-of-stock equipment based on the corresponding attribute values ​​of similar equipment: ； in, represents the predicted value of the kth second-type attribute for the i-th out-of-stock equipment, represents the value of the mth similar device on the kth second type attribute, and M represents the number of similar devices.

4. The method for automated equipment inventory management based on data analysis according to claim 2, characterized in that: The out-of-stock propensity score of each device in the current cycle is calculated based on the following formula, and M devices with the closest propensity scores to the out-of-stock devices are selected from the inventory devices as similar devices: )； in, It represents the propensity score of the i-th device being out of stock in the current cycle, reflecting the probability of the device being out of stock under given conditions. Indicates whether the i-th device is out of stock, Indicates that the i-th device is out of stock. represents all attribute values ​​of the ith device in the previous cycle, and P(·) represents the probability.

5. The method for automated equipment inventory management based on data analysis according to claim 1, characterized in that: Use data analysis methods to match out-of-stock equipment with replacement equipment in potential inventory, including: Cluster analysis is performed for each non-categorical attribute of the inventory, and the clustering remains unchanged for each categorical attribute of the inventory; For each out-of-stock device, determine the cluster to which each attribute of the out-of-stock device belongs according to the prediction data of each attribute of the out-of-stock device; If there is a device in the potential inventory with the same clustering attribute distribution as the out-of-stock device, the match is successful, and a device with the same clustering attribute distribution is randomly selected as the replacement device for the out-of-stock device.

6. The method for automated equipment inventory management based on data analysis according to claim 1, further comprising: If the current time period is not the last time period of the current cycle, the target new equipment is searched in the potential inventory based on the forecast data of the out-of-stock equipment accumulated in the current cycle, and the target new equipment is added to the potential inventory.

7. An automated equipment inventory management system based on data analysis, the system implementing the method according to claim 1, characterized in that: Includes the following modules: An out-of-stock equipment determination module is used to determine the out-of-stock equipment in the current cycle in each inspection period of each inventory cycle; A data forecasting module is used to forecast the data of out-of-stock equipment based on the historical data of equipment in inventory; The substitute equipment matching module is used to match substitute equipment for out-of-stock equipment in potential inventory based on the forecast data of out-of-stock equipment by using data analysis methods, and add the successfully matched substitute equipment to the inventory; The attribute distribution matching module is used to determine the attribute distribution of the unmatched out-of-stock equipment based on the prediction data of the unmatched out-of-stock equipment if there is an unmatched out-of-stock equipment, and to search for a replacement device for the unmatched out-of-stock equipment in the potential inventory based on the attribute distribution of the unmatched out-of-stock equipment to add to the inventory.

8. The equipment inventory automation management system based on data analysis according to claim 7 is characterized in that: Forecasting of out-of-stock equipment data based on historical data of equipment in inventory, including: For the first type of attributes of out-of-stock equipment, forecasts are made directly; For the second type of attribute of the out-of-stock device, a machine learning algorithm is used to match similar devices to the out-of-stock device from the inventory devices, and the second type of attribute of the out-of-stock device is predicted based on the second type of attribute values ​​of the similar devices; The first type of attribute is an attribute that does not change with time or changes with time but the change pattern is known, and the second type of attribute is an attribute that changes with time but the change pattern is unknown.

9. The equipment inventory automation management system based on data analysis according to claim 8, characterized in that: The following formula is used to predict the second type of attribute of out-of-stock equipment based on the corresponding attribute values ​​of similar equipment: ； in, represents the predicted value of the kth second-type attribute for the i-th out-of-stock equipment, represents the value of the mth similar device on the kth second type attribute, and M represents the number of similar devices.

10. The equipment inventory automation management system based on data analysis according to claim 7, the system further comprising: The strategy adjustment module is used to search for target new equipment in the potential inventory based on the forecast data of out-of-stock equipment accumulated in the current cycle if the current period is not the last period of the current cycle, and add the target new equipment to the potential inventory.

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