Intelligent inventory self-adaptive adjusting system based on materials

Abstract

The invention provides an intelligent inventory self-adaptive adjusting system based on materials. The material-based intelligent inventory adaptive adjustment system comprises a hardware part which comprises a sensor network, an inventory execution unit, a communication module and a data acquisition terminal, the sensor network is used for monitoring the quantity, state and environment information of inventory materials, the inventory execution unit is responsible for dynamically adjusting the inventory according to requirements, and the data acquisition terminal is responsible for acquiring data; the software part comprises inventory management software used for data analysis and decision making, and the software integrates a prediction model, a fault-tolerant algorithm and a data correction module. According to the material-based intelligent inventory adaptive adjustment system, hardware and software parts are creatively integrated, the accuracy and stability of inventory management are remarkably improved, in the aspect of hardware, a sensor network can comprehensively monitor the inventory state, and dynamic adjustment is achieved in combination with an automatic inventory execution unit.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent inventory self-adaptive adjustment systems, and in particular to an intelligent inventory self-adaptive adjustment system based on materials. Background Art

[0002] The system uses IoT technology and big data analysis to monitor changes in inventory material demand in real time, and combines the information collected by sensors for data processing and analysis, thereby dynamically adjusting inventory. Its core structure includes the following parts: sensor network, data processing module, prediction analysis algorithm and inventory execution unit. The sensor network is used to monitor the quantity, status and environmental information of materials in inventory in real time, and upload the data to the cloud. The data processing module uses big data analysis technology to process and analyze this data to predict future material demand. The inventory execution unit automatically adjusts the inventory and replenishment strategy based on the prediction results to achieve adaptive adjustment.

[0003] Although the system can significantly improve the efficiency of inventory management, it still has some flaws. Sensor failure or network delay may lead to inaccurate data, which in turn affects the accuracy of forecasts. The system is highly dependent on big data analysis and forecasting models. If the amount of data is insufficient or the model is improperly designed, it may lead to unreasonable inventory adjustments, or even shortages or surpluses. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a material-based intelligent inventory adaptive adjustment system, which solves the problem that sensor failure or network delay may cause inaccurate data, thereby affecting the accuracy of prediction; data analysis and prediction models are highly dependent. If the data volume is insufficient or the model is improperly designed, it may lead to unreasonable inventory adjustments.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a material-based intelligent inventory adaptive adjustment system, comprising:

[0006] Hardware: including sensor network, inventory execution unit, communication module and data acquisition terminal. The sensor network is used to monitor the quantity, status and environmental information of inventory materials. The inventory execution unit is responsible for dynamically adjusting the inventory according to demand.

[0007] Software: includes inventory management software for data analysis and decision-making, which integrates forecasting models, fault-tolerant algorithms and data correction modules;

[0008] Prediction model: A dynamic prediction model based on machine learning that can predict future material demand based on historical data, real-time sensor data, and external environmental data. The model is calculated using the adaptive prediction formula: D(t) = αH(t) + βS(t) + γE(t) + δC(t)

[0009] Where D(t): predicted material demand at time t, H(t): historical demand data, S(t): real-time sensor data, E(t): external environment data (such as market trends, climate change, etc.), C(t): current inventory status correction data;

[0010] α, β, γ, and δ are weight coefficients, which are adaptively adjusted through machine learning algorithms to optimize the impact of different factors on the prediction results.

[0011] Preferably, the communication module supports multiple communication protocols, including wired communication and wireless communication, and can switch to a backup link when the main communication link fails to ensure the reliability of data transmission. The data acquisition terminal includes a local storage device for collecting and caching data from the sensor network. The terminal has the function of providing historical data in the event of network delays or sensor failures, thereby ensuring the continuity of the data link.

[0012] Preferably, the sensor network includes material sensors and environmental sensors for monitoring the quantity of inventory materials, and the environmental sensors are used to monitor environmental information such as temperature, humidity and pressure that affect the state of the inventory materials.

[0013] Preferably, the data correction module can automatically detect sensor data anomalies, and perform data correction based on historical data and context information to prevent prediction deviations caused by sensor failures or network delays. The correction formula is:

[0014] D′(t)=D(t)+λ(H′(t)-S(t)

[0015] Where D′(t) is the corrected forecast demand, H′(t) is the corrected historical data, λ is the correction coefficient, and according to the dynamic adjustment of machine learning, the weight parameters α, β, γ, δ in the forecast model are dynamically adjusted according to real-time data through the machine learning algorithm to optimize the adaptability of the forecast model under different time and environment.

[0016] Preferably, the fault-tolerant algorithm is based on a Bayesian network or a Markov decision model, which is used to detect and correct erroneous data input caused by sensor or network anomalies, and optimize decisions through probability calculation. Its fault-tolerant decision formula is:

[0017]

[0018] Where P(x|y) is the corrected probability under abnormal conditions, and P(y|x) is the probability distribution under normal data conditions.

[0019] Preferably, the data correction module can perform forecast correction by analyzing historical periodicity and seasonal factors when the amount of data is insufficient, and the formula is:

[0020]

[0021] Where D(t) is the correction value at time t, D i It is the average distribution result of historical forecast values.

[0022] Preferably, the software part adopts a distributed architecture and processes large-scale data through multiple nodes, thereby improving system processing efficiency and data transmission speed and avoiding performance bottlenecks of centralized systems. The inventory execution unit includes automated equipment such as robots or mechanical arms, which can automatically perform material replenishment or scheduling operations based on software prediction results, thereby improving the level of automation of inventory management.

[0023] Preferably, the sensor has a self-diagnosis function and can send a fault report to the system when it detects its own fault, start redundant sensor replacement work, and prevent data interruption caused by sensor failure. The prediction model improves the prediction accuracy of material demand by integrating external environmental data, such as market trends, weather forecasts and other factors, and avoids deviations caused by relying solely on internal data. The data correction module uses multi-source data fusion technology and combines upstream and downstream supply chain data to cross-validate and correct possible extreme data or erroneous data to improve the fault tolerance of the system. The prediction model in the inventory management software can improve the prediction accuracy through deep learning self-iteration. The model is continuously optimized during long-term operation and can still make reasonable predictions when the input data is incomplete.

[0024] The present invention provides a material-based intelligent inventory adaptive adjustment system, which has the following beneficial effects:

[0025] This material-based intelligent inventory adaptive adjustment system has significantly improved the accuracy and stability of inventory management through innovative integration of hardware and software. In terms of hardware, the sensor network can comprehensively monitor the inventory status and realize dynamic adjustment in combination with the automated inventory execution unit. The software part uses the prediction model and fault-tolerant algorithm optimized by the machine learning algorithm to make accurate predictions based on historical data, real-time sensor data and external environmental data, and solves the problem of prediction deviation caused by data anomalies through adaptive prediction formulas. The system can also ensure data continuity through backup communication links and local caches when the sensor or network fails, effectively avoiding the impact of sensor failure or network delays on the accuracy of the prediction.

[0026] The system further enhances its fault tolerance and self-optimization performance through multi-source data fusion and deep learning technology. The data correction module can not only make adjustments based on historical periodicity and seasonal factors when data is insufficient, but also combine upstream and downstream supply chain data to enhance the system's ability to correct extreme or erroneous data. The fault-tolerant algorithm is based on Bayesian networks or Markov decision models, which can detect and correct erroneous decisions caused by abnormal data input, ensuring the rationality and efficiency of inventory management. The overall technical solution improves the automation and intelligence level of inventory management, and overcomes the problem of over-reliance on data and networks in traditional systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] like Figure 1 As shown, an embodiment of the present invention provides a material-based intelligent inventory adaptive adjustment system, including a hardware part: a sensor network, an inventory execution unit, a communication module and a data acquisition terminal. The sensor network is used to monitor the quantity, status and environmental information of inventory materials. The inventory execution unit is responsible for dynamically adjusting the inventory according to demand. The communication module supports multiple communication protocols, including wired communication and wireless communication, and can switch to a backup link when the main communication link fails to ensure the reliability of data transmission. The data acquisition terminal includes a local storage device for collecting and caching data from the sensor network. The terminal has the function of providing historical data in the event of network delay or sensor failure to ensure the continuity of the data chain. The sensor network includes a material sensor and an environmental sensor for monitoring the quantity of inventory materials. The environmental sensor is used to monitor environmental information such as temperature, humidity and pressure that affect the status of inventory materials.

[0030] Software part: including inventory management software for data analysis and decision-making, which integrates prediction models, fault-tolerant algorithms and data correction modules. The software part adopts a distributed architecture and processes large-scale data through multiple nodes, thereby improving system processing efficiency and data transmission speed, avoiding performance bottlenecks of centralized systems. The inventory execution unit includes automated equipment such as robots or mechanical arms, which can automatically perform material replenishment or scheduling operations according to the software prediction results, thereby improving the automation level of inventory management. The sensor has a self-diagnosis function and can send a fault report to the system when it detects its own fault, start redundant sensor replacement work, and prevent data interruption due to sensor failure. The prediction model improves the prediction accuracy of material demand by integrating external environmental data, such as market trends, weather forecasts and other factors, and avoids deviations caused by relying solely on internal data. The data correction module uses multi-source data fusion technology, combined with upstream and downstream supply chain data, to cross-validate and correct possible extreme data or erroneous data, thereby improving the fault tolerance of the system. The prediction model in the inventory management software can improve the prediction accuracy through deep learning self-iteration. The model is continuously optimized in long-term operation, and can still make reasonable predictions when the input data is incomplete.

[0031] Prediction model: A dynamic prediction model based on machine learning that can predict future material demand based on historical data, real-time sensor data, and external environmental data. The model is calculated using the adaptive prediction formula: D(t) = αH(t) + βS(t) + γE(t) + δC(t)

[0032] Among them, D(t): material demand forecast value at time t, H(t): historical demand data, S(t): real-time sensor data, E(t): external environment data (such as market trends, climate change, etc.), C(t): current inventory status correction data.

[0033] α, β, γ, δ are weight coefficients, which are adaptively adjusted through machine learning algorithms to optimize the impact of different factors on the prediction results. The data correction module can automatically detect sensor data anomalies and perform data correction based on historical data and context information to prevent prediction deviations caused by sensor failures or network delays. The correction formula is:

[0034] D′(t)=D(t)+λ(H′(t)-S(t)

[0035] Where D′(t) is the revised forecast demand, H′(t) is the revised historical data, and λ is the correction coefficient. Based on the dynamic adjustment of machine learning, the weight parameters α, β, γ, and δ in the forecast model are dynamically adjusted according to real-time data through machine learning algorithms to optimize the adaptive fault-tolerant algorithm of the forecast model at different times and environments. It is based on Bayesian networks or Markov decision models, which are used to detect and correct erroneous data inputs caused by sensor or network anomalies, and optimize decisions through probability calculations. Its fault-tolerant decision formula is:

[0036]

[0037] Where P(x|y) is the probability after correction under abnormal conditions, and P(y|x) is the probability distribution under normal data conditions. The data correction module can make prediction corrections by analyzing historical periodicity and seasonal factors when the amount of data is insufficient. The formula is:

[0038]

[0039] Where D(t) is the correction value at time t, D i It is the average distribution result of historical forecast values.

[0040] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A material-based intelligent inventory adaptive adjustment system, characterized in that: include: Hardware: including sensor network, inventory execution unit, communication module and data acquisition terminal. The sensor network is used to monitor the quantity, status and environmental information of inventory materials. The inventory execution unit is responsible for dynamically adjusting the inventory according to demand. Software: includes inventory management software for data analysis and decision-making, which integrates forecasting models, fault-tolerant algorithms and data correction modules; Prediction model: A dynamic prediction model based on machine learning that can predict future material demand based on historical data, real-time sensor data, and external environmental data. The model is calculated using the adaptive prediction formula: D(t) = αH(t) + βS(t) + γE(t) + δC(t) Where D(t): predicted material demand at time t, H(t): historical demand data, S(t): real-time sensor data, E(t): external environment data (such as market trends, climate change, etc.), C(t): current inventory status correction data; αβ,γ,δ are weight coefficients, which are adaptively adjusted through machine learning algorithms to optimize the impact of different factors on the prediction results.

2. The intelligent inventory adaptive adjustment system based on materials according to claim 1 is characterized by: The communication module supports multiple communication protocols, including wired communication and wireless communication, and can switch to a backup link when the main communication link fails to ensure the reliability of data transmission. The data acquisition terminal includes a local storage device for collecting and caching data from the sensor network.

3. The intelligent inventory adaptive adjustment system based on materials according to claim 1 is characterized by: The sensor network includes material sensors and environmental sensors for monitoring the quantity of inventory materials. The environmental sensors are used to monitor environmental information such as temperature, humidity, and pressure that affect the status of inventory materials.

4. The intelligent inventory self-adaptive adjustment system based on materials according to claim 1 is characterized by: The data correction module can automatically detect sensor data anomalies and perform data correction based on historical data and context information to prevent prediction deviations caused by sensor failures or network delays. The correction formula is: D′(t)=D(t)+λ(H′(t)-S(t)) Where D′(t) is the corrected forecast demand, H′(t) is the corrected historical data, λ is the correction coefficient, and according to the dynamic adjustment of machine learning, the weight parameters α, β, γ, δ in the forecast model are dynamically adjusted according to real-time data through the machine learning algorithm to optimize the adaptability of the forecast model under different time and environment.

5. The intelligent inventory self-adaptive adjustment system based on materials according to claim 1 is characterized in that: The fault-tolerant algorithm is based on a Bayesian network or Markov decision model, which is used to detect and correct erroneous data input caused by sensor or network anomalies, and optimize decisions through probability calculation. Its fault-tolerant decision formula is: Where P(x|y) is the corrected probability under abnormal conditions, and P(y|x) is the probability distribution under normal data conditions.

6. The intelligent inventory self-adaptive adjustment system based on materials according to claim 1 is characterized by: The data correction module can make prediction corrections by analyzing historical periodicity and seasonal factors when the amount of data is insufficient. The formula is: Where D(t) is the correction value at time t, D i It is the average distribution result of historical forecast values.

7. The intelligent inventory self-adaptive adjustment system based on materials according to claim 1 is characterized by: The software part adopts a distributed architecture and processes large-scale data through multiple nodes. The inventory execution unit includes automated equipment such as robots or mechanical arms, which can automatically perform material replenishment or scheduling operations based on the software prediction results.

8. The intelligent inventory self-adaptive adjustment system based on materials according to claim 1 is characterized by: The sensor has a self-diagnosis function and can send a fault report to the system when it detects its own fault, start redundant sensor replacement work, and prevent data interruption caused by sensor failure. The prediction model improves the prediction accuracy of material demand by integrating external environmental data, such as market trends, weather forecasts and other factors, and avoids deviations caused by relying solely on internal data. The data correction module uses multi-source data fusion technology, combined with upstream and downstream supply chain data, to cross-validate and correct possible extreme data or erroneous data, thereby improving the system's fault tolerance. The prediction model in the inventory management software can improve prediction accuracy through deep learning self-iteration. The model is continuously optimized during long-term operation and can still make reasonable predictions when the input data is incomplete.

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