A packaging material intelligent selection method and system based on multi-dimensional data analysis

Abstract

The application provides a kind of packaging material intelligent selection method and system based on multidimensional data analysis, it is related to material management field;Through environmental perception module, vibration, deformation and temperature and humidity data are collected in real time to construct feature matrix;Data analysis module fuses historical case library and logistics task length to predict material life, and generates remaining life assessment by associating supply chain inventory;Decision engine module dynamically allocates cost, safety and environmental protection weight, and generates multi-level decision scheme combined with supplier credit score;Execution control module verifies material size tolerance and drives replacement operation, and triggers model calibration through 5% deviation threshold;The system has built-in humidity sensitive material grading standard and deformation safety limit, and starts deformation variable prediction and emergency scheme reconstruction when temperature and humidity mutation exceeds threshold value;Establish a three-level self-optimization mechanism for environmental perception error, material life error and execution error, and realize closed-loop optimization through sensor calibration, supplier credit recalculation and dynamic adjustment of control parameters.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of material management, in particular to a packaging material intelligent selection method and system based on multi-dimensional data analysis. BACKGROUND

[0002] Modern logistics transportation puts higher requirements on the performance of packaging materials; the globalization of commodity circulation intensifies the complexity of the transportation environment; traditional material selection methods rely on manual experience and static parameters; vibration, temperature and humidity changes during transportation lead to dynamic attenuation of material performance; environmental protection regulations upgrade require consideration of material cost and sustainability; intelligent decision-making systems are the key direction for industry upgrading.

[0003] The current mainstream solution uses a single environmental sensor to monitor transportation conditions; a static selection model is established based on material hardness and thickness; some systems incorporate historical transportation data to establish a linear prediction formula; a few solutions integrate supplier databases for inventory matching; a fixed weight algorithm is used to balance cost and safety indicators; and a threshold alarm mechanism triggers manual intervention processes.

[0004] The deficiencies of the prior art are that there is a lack of multi-source data fusion analysis capability; the single dimension of environmental parameter monitoring leads to prediction bias; the static model cannot reflect the dynamic attenuation law of material performance; inventory matching ignores supplier capacity fluctuations and logistics time efficiency; the fixed weight algorithm is difficult to adapt to changes in transportation stage requirements; and the response delay of manual intervention causes material replacement lag risk. SUMMARY

[0005] (I) Technical problems solved

[0006] In view of the deficiencies of the prior art, the present application provides a packaging material intelligent selection method and system based on multi-dimensional data analysis to solve the problems of lack of multi-source data fusion analysis capability; single dimension of environmental parameter monitoring leading to prediction bias; static model unable to reflect dynamic attenuation law of material performance; inventory matching ignoring supplier capacity fluctuations and logistics time efficiency; fixed weight algorithm difficult to adapt to changes in transportation stage requirements; and response delay of manual intervention causing material replacement lag risk.

[0007] (II) Technical solutions

[0008] To achieve the above purpose, the present application is implemented by the following technical solutions: a packaging material intelligent selection method and system based on multi-dimensional data analysis, comprising an environment perception module, a data analysis module, a decision engine module and an execution control module;

[0009] The environment perception module collects vibration frequency spectrum, surface strain and temperature and humidity data in real time through a sensor network deployed on the surface of the packaging box, generates an environment state matrix after feature extraction, and the sensor network includes an automatic calibration unit;

[0010] The data analysis module has a built-in historical transportation case library that stores the performance degradation records of different materials in transportation environments. After receiving the environmental state matrix, it establishes a correlation model between vibration energy distribution and material performance degradation, combines with the inventory turnover rate data in the supply chain database to predict the remaining life of the material, and establishes a data interface with the logistics management system to obtain real-time transportation task duration and path planning data;

[0011] The decision engine module dynamically adjusts the priority of cost, safety and environmental protection targets according to the transportation stage, combines the material life prediction results with supplier capacity data to generate a multi-level decision tree containing automatic execution plans and emergency plans;

[0012] The execution control module pre-stores packaging box size tolerance standards, structural deformation safety limits and registered size database; and converts the selected plan into material replacement instructions to drive the execution mechanism to complete the operation. After that, the error feedback of actual transportation data and predicted values is fed back to the data analysis module to trigger dynamic calibration of model parameters. The model prediction error is divided into three categories: environmental perception error, material performance error and execution control error;

[0013] The supply chain database contains a material moisture-proof characteristic index table and a hygroscopic expansion coefficient safety threshold library, which records the deformation coefficient, protection level and irreversible deformation threshold of each material in different humidity environments, and marks the humidity sensitivity level. At the same time, based on the on-time delivery rate and quality inspection pass rate, a supplier credit scoring system is constructed, and the credit score threshold is set as the value of the normal distribution curve of historical performance data.

[0014] Preferably, the environmental perception module realizes transportation environment data acquisition through a sensor network deployed on the six faces of the packaging box; the sensor network contains three types of detection units: vibration sensors, deformation sensors and temperature and humidity sensors; the vibration sensor collects three-dimensional acceleration data at a sampling rate of 1 kHz, and extracts the energy distribution characteristics of the 0-500Hz frequency band through fast Fourier transform; the deformation sensor monitors the surface micro-strain at a frequency of 100Hz, and uses a spatial interpolation algorithm to construct a deformation gradient field model; the temperature and humidity sensor synchronously detects the environmental parameters of each face, and triggers an abnormal flag when the temperature difference between adjacent faces exceeds 3℃ or the humidity difference exceeds 15%RH; all sensor data is transmitted to the edge node through a low-power wireless network, and time stamp alignment and standardization processing is performed to generate an environmental state matrix containing frequency domain energy spectrum, deformation gradient and temperature and humidity distribution.

[0015] Preferably, the data analysis module has a built-in historical transportation case library to store records of material performance degradation under the coupling effect of vibration, temperature and humidity; after receiving the environmental state matrix, the vibration main frequency energy proportion feature is extracted and matched with the material inherent frequency database for matching degree analysis; when the resonance frequency band energy proportion exceeds the preset threshold, the material buffer efficiency real-time re-estimation process is started; combined with the inventory turnover rate data in the supply chain database, the confidence interval of the material remaining life is calculated; if the predicted life is lower than the transportation task duration obtained from the logistics management system, the inventory real-time verification process is triggered.

[0016] Preferably, the decision engine module dynamically adjusts the target function weight according to the transportation stage; in the transportation preparation stage, the cost weight is set to 0.6, the safety weight is set to 0.3, and the environmental protection weight is set to 0.1; in the middle of the transportation, the safety weight is increased to 0.5; in the customs clearance stage, environmental protection compliance constraints are introduced, and a 1.2 times gain coefficient is applied to the environmental protection target; the module fuses the material life prediction result with the supplier real-time production capacity data to generate a three-level recommendation scheme; when the inventory is less than 120% of the current transportation demand, the priority of the material is automatically reduced, and the supplier database is associated to screen a list of alternative suppliers with sufficient production capacity and logistics time efficiency meeting the shortest delivery cycle.

[0017] Preferably, the execution control module pre-stores the packaging box size tolerance standard and the structural deformation safety limit value; after receiving the material specification parameters issued by the decision engine module, the material size is matched and verified with the three-dimensional model of the packaging box in the registered size database; if it is detected that the tolerance exceeds the preset safety range, the scheme regeneration process based on actual size constraints is triggered; the pneumatic-electromagnetic hybrid actuator is driven to complete the material replacement operation, and the displacement sensor is used to feedback the positioning accuracy in real time; when the actual measured position deviation exceeds 0.5 mm, the PID dynamic correction mechanism is started; after the operation is completed, the actual transportation data is collected, the deviation from the predicted value is calculated and fed back to the data analysis module.

[0018] Preferably, the supply chain database includes a material moisture-proof characteristic index table and a supplier credit scoring system; the moisture-proof characteristic index table records the deformation coefficient and protection level of each material in the humidity range of 30% to 90% RH, and the material with a moisture absorption expansion coefficient ≥0.5 mm / % RH is marked as humidity sensitive; the credit scoring system dynamically calculates based on the on-time delivery rate weight and the quality inspection pass rate weight, wherein the on-time delivery rate weight is 0.6 and the quality inspection pass rate weight is 0.4; the credit score threshold is the μ-2σ value of the normal distribution of historical performance data; when the supplier score is lower than the threshold, the material options of the supplier are automatically shielded; the actual loss rate data fed back by the execution control module is included in the next period scoring calculation with a weight of 0.2.

[0019] Preferably, when the vibration spectrum analysis identifies that the energy value of the material inherent frequency ±10% band increases by more than 50% of the baseline value within 2 seconds, the environment perception module sends a resonance warning signal to the data analysis module; the data analysis module calls the transportation records of the same frequency band characteristics in the historical case library, and calculates the damage rate of the corresponding material; if the damage rate exceeds the safety threshold, a scheme re-evaluation request is initiated to the decision engine module; the decision engine module generates a priority list of alternative materials and associates with the supplier database to screen alternative suppliers that meet the delivery deadline.

[0020] Preferably, the humidity-sensitive material processing strategy implementation process of the data analysis module is: calling the material list with a hygroscopic expansion coefficient ≥0.5mm / %RH from the supply chain database, and starting a special prediction model when the humidity change rate >15%RH / h is found in the environment perception data; the prediction model uses LSTM neural network, the input layer includes the current humidity value, the change gradient and the material hygroscopic curve characteristics, and the output layer predicts the deformation amount in the next 2 hours; when the predicted deformation exceeds 80% of the structural deformation safety limit, a three-level response mechanism is triggered: the first level sends a downgrade use suggestion to the decision engine, the second level starts the automatic desiccant injection system, and the third level calls the moisture-proof material replacement scheme; at the same time, the abnormal data packet is marked as a high-value training sample and is preferentially used for model iteration update.

[0021] Preferably, the fast approval process of the execution control module is: when the decision engine generates a recommended list of alternative materials, the system automatically calls the customs HS code database to verify material compliance, and adds an electronic fence mark to the scheme involving restricted substances; through the enterprise WeChat API, an approval request is pushed to the responsible engineer, attached with a material parameter comparison table and a risk analysis report; the engineer uses a digital certificate to perform electronic signature confirmation, and the system automatically records the signature timestamp and device fingerprint; after approval, a production work order is immediately issued to the MES system, and the WMS inventory state is updated synchronously; the entire process must be completed within 15 minutes, and if it is not approved within the time limit, it is automatically upgraded to the superior supervisor and the standby emergency plan is started.

[0022] Preferably, the model parameter dynamic calibration process operation includes: when receiving a calibration request from the execution control module, extracting the original sensor data of the corresponding time window from the data lake; performing joint analysis of time and frequency domain on the vibration signal, and extracting instantaneous frequency characteristics using the Wigner-Ville distribution; residual analysis is performed on the actual loss data and the predicted value of the material, and the main source of error is determined to be the dimension; for humidity-sensitive materials, the Bayesian optimization algorithm is preferentially used to update the humidity coupling coefficient in the attenuation model, and the Jacobian matrix is calculated each time to determine the parameter adjustment direction; after calibration, a version number identified model file is generated, which is published to the decision engine and production database after digital signature verification.

[0023] Preferably, the data analysis module classifies the model prediction error periodically; at the end of the quarter, a global health assessment is started, and the model prediction error is classified by source: when the environmental perception error exceeds 5%, the sensor network is analyzed for channel quality and node distribution optimization; when the material performance error exceeds 8%, the supplier-provided material sample is resampled for destructive testing, and the basic parameter database is updated; when the execution control error exceeds 2mm, the drive mechanism transmission components are subjected to laser interferometer precision detection, and the ball screw with wear exceeding the tolerance band is replaced; all optimization operations form a closed loop control, and after each adjustment, a verification test through three complete transportation cycles is required to ensure that the system stability improvement meets the preset KPI indicators.

[0024] Preferably, the execution control module monitors the initial buffer efficiency parameters in real time after the installation of new materials is completed; when the deviation between the measured value and the model prediction value exceeds 5%, a calibration request is sent to the data analysis module; the request includes the environmental data timestamp and the material batch code; the data analysis module extracts the environmental feature data for the corresponding period, and preferentially updates the performance degradation model of humidity-sensitive materials; the updated model parameters are verified by digital signature and synchronized to the decision engine module.

[0025] Preferably, the packaging box registration size database includes full-size measurement of each batch of packaging boxes using a three-dimensional laser scanner, collecting the spatial coordinates of not less than 2000 feature points; using the ICP algorithm to register the point cloud data with the design drawings, calculating the statistical distribution characteristics of each size parameter; setting dynamic tolerance bands for length, width, and height, respectively, the tolerance band width is ±(0.1%×nominal value+0.5mm); when the size standard deviation of the same batch of packaging boxes exceeds 50% of the tolerance band, a quality abnormality report is automatically generated and the supplier's deduction process is triggered; the database is fragmented and indexed every month to ensure that the query response time is less than 50ms.

[0026] (Three) beneficial effects

[0027] The present application provides a packaging material intelligent selection method and system based on multi-dimensional data analysis. It has the following beneficial effects:

[0028] 1、The present application significantly improves the accuracy and adaptability of packaging material selection through multi-source data fusion and dynamic decision mechanism; the environmental perception module captures vibration spectrum, surface strain and temperature and humidity gradient data in real time, establishes a material performance attenuation prediction model combined with the historical transportation case library, effectively identifies resonance risk and deformation critical state, and ensures transportation safety; the decision engine module dynamically adjusts the cost, safety and environmental protection target weight based on the transportation stage priority, generates multi-level recommendation scheme and associates with real-time supply chain data to realize resource utilization optimization; the execution control module controls the material replacement error within 5% through positioning and closed-loop feedback mechanism, and continuously improves the system prediction accuracy combined with model parameter dynamic calibration; the multi-dimensional data collaboration mechanism forms a closed-loop optimization of environmental monitoring, material life prediction and supply chain scheduling.

[0029] 2、The intelligent collaborative network constructed by the present application greatly enhances the response efficiency and risk resistance of the supply chain; the supply chain database integrates the material moisture-proof characteristic index table and the moisture absorption and expansion safety critical value library, combined with the humidity sensitive material dynamic calibration mechanism, quickly generates a replacement scheme when the temperature and humidity suddenly change, and executes it through a three-step approval process; the supplier credit scoring system dynamically selects high-quality suppliers based on the normal distribution threshold, combined with the actual loss data closed-loop feedback mechanism; the self-optimization mechanism triggers sensor calibration, supplier data review and control parameter optimization respectively through classification attribution model error, so that the monthly iteration efficiency of the system is improved by 50%; the introduction of the registered size database and the tolerance compatibility screening logic completely eliminates the size matching error of the packaging box and the material. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0031] The embodiment of the present application provides a packaging material intelligent selection method and system based on multi-dimensional data analysis, and the specific implementation process is that when the system starts, the environmental perception module collects transportation environment data in real time through the sensor network deployed on the six surfaces of the packaging box; the vibration sensor captures three-dimensional acceleration data at a sampling rate of 1kHz, and extracts the energy distribution characteristics of the 0-500Hz frequency band through fast Fourier transform; the deformation sensor monitors the surface micro-strain at a frequency of 100Hz, and constructs a deformation gradient field model using a spatial interpolation algorithm; the temperature and humidity sensor synchronously detects the environmental parameters of each surface, and triggers an abnormal mark when the temperature difference between adjacent surfaces exceeds 3℃ or the humidity difference exceeds 15%RH.

[0032] All sensor data is transmitted to the edge node through a low-power wireless network, and the time stamp alignment and standardization process generates an environmental state matrix containing frequency energy spectrum, deformation gradient, and temperature and humidity distribution; After the data analysis module receives the environmental state matrix, it extracts the vibration main frequency energy proportion feature and matches it with the material inherent frequency database for matching degree analysis. When the resonance frequency band energy proportion exceeds the preset threshold, the material buffer efficiency real-time re-estimation process is started; Combined with the inventory turnover rate data in the supply chain database, the confidence interval of the material remaining life is calculated, and if the predicted life is lower than the transportation task duration obtained from the logistics management system, the inventory real-time checking process is triggered.

[0033] Among them, the decision engine module dynamically adjusts the target function weight according to the transportation stage. In the transportation preparation stage, the cost weight is set to 0.6, the safety weight is set to 0.3, and the environmental protection weight is set to 0.1. In the middle of transportation, the safety weight is increased to 0.5, and in the customs clearance stage, the environmental protection compliance constraint is introduced to apply a 1.2 times gain coefficient to the environmental protection target; The module fuses the material life prediction result and the supplier real-time capacity data to generate a three-level recommendation scheme. When the inventory is less than 120% of the current transportation demand, the priority of the material is automatically reduced, and the supplier database is associated to screen a list of alternative suppliers with sufficient capacity and logistics time that meets the shortest delivery cycle.

[0034] The execution control module pre-stores the packaging box size tolerance standard and structure deformation safety limit value. After receiving the material specification parameters issued by the decision engine module, the material size is matched and verified with the three-dimensional model of the packaging box in the registered size database. If it is detected that the tolerance exceeds the preset safety range, the scheme regeneration process based on the actual size constraint is triggered; The pneumatic-electromagnetic hybrid actuator drives the material replacement operation, and the displacement sensor feedbacks the positioning accuracy in real time. When the measured position deviation exceeds 0.5mm, the PID dynamic correction mechanism is started; After the operation is completed, the actual transportation data is collected to calculate the deviation from the predicted value, and feedback to the data analysis module to trigger the dynamic calibration of the model parameters.

[0035] In the credit scoring system in the supply chain database, based on the on-time delivery rate weight 0.6 and the quality inspection pass rate weight 0.4, dynamic calculation is carried out, when the supplier score is lower than the μ-2σ value of the normal distribution of historical performance data, the material options are automatically shielded; the actual loss rate data fed back by the execution control module is included in the next cycle score calculation with a weight of 0.2, forming a closed loop feedback mechanism; when the vibration spectrum analysis identifies that the energy value of the inherent frequency ±10% frequency band of the material increases by more than 50% of the baseline value within 2 seconds, the environmental perception module sends a resonance warning signal to the data analysis module, the data analysis module calls the transportation records of the same frequency band characteristics in the historical case library, and calculates the damage rate of the corresponding material, if the damage rate exceeds the safety threshold, a scheme re-evaluation request is initiated to the decision engine module, the decision engine generates a priority list of alternative materials, and associates the supplier database to screen alternative suppliers that meet the delivery time limit; when the humidity-sensitive material processing strategy is implemented, the material list with a hygroscopic swelling coefficient ≥0.5mm / %RH is called from the supply chain database, and when the humidity change rate ≥15%RH / h is found in the environmental perception data, the special LSTM neural network prediction model is started, the input layer includes the current humidity value, the change gradient and the material hygroscopic curve characteristics, and the output layer predicts the deformation within the next 2 hours, and when the predicted deformation exceeds 80% of the structural deformation safety limit, a three-level response mechanism is triggered.

[0036] In the fast approval process, the system automatically calls the customs HS code database to verify the material compliance, adds an electronic fence mark to the scheme involving restricted substances, pushes the approval request to the responsible engineer through the enterprise WeChat API, and attaches the material parameter comparison table and risk analysis report, the engineer uses the digital certificate to perform electronic signature, the system automatically records the timestamp and device fingerprint of the signing, and after the approval is passed, immediately issues a production work order to the MES system, synchronously updates the WMS inventory state, the entire process needs to be completed within 15 minutes, if it is not approved within the time limit, it is automatically upgraded to the superior supervisor and the standby emergency scheme is started; at the end of the quarter, global health assessment is started, the model prediction error is classified by source, when the environmental perception error exceeds 5%, the channel quality analysis is performed on the sensor network, and when the node distribution optimization material performance error exceeds 8%, the material sample provided by the supplier is resampled for destructive testing, and the basic parameter database is updated; when the execution control error exceeds 2mm, the transmission parts of the driving mechanism are detected by laser interferometer, and the ball screws with wear exceeding the tolerance band are replaced, all optimization operations form a closed loop control, and after each adjustment, it needs to pass through three complete transportation cycles of verification test to ensure that the system stability improvement meets the preset KPI index.

[0037] Embodiment two:

[0038] The present embodiment is based on the basis of embodiment one: optimize the process of humidity sensitive material and strengthen the error traceability mechanism; The specific implementation is that the environmental perception module adds a high-precision dew point sensor to monitor the risk of condensation on the surface of the packaging box with a resolution of 0.1℃, and when the temperature difference between the detected dew point temperature and the environment is less than 2℃, the moisture-proof preprocessing instruction is triggered; The LSTM neural network of the data analysis module is upgraded to a spatio-temporal attention model input layer, and the surface deformation gradient field data output layer is added to predict the time window expansion to 4 hours; The humidity sensitive material determination standard is tightened, which means that the determination standard for humidity sensitive materials becomes more stringent, specifically, the list of materials with a moisture absorption expansion coefficient ≥0.8mm / %RH is dynamically updated every hour.

[0039] When the predicted deformation exceeds 60% of the safety limit, the response mechanism is triggered, and the fourth level of automatic desiccant is added and the fifth level of cold chain logistics switching scheme is added; The end of the mechanical arm of the execution control module is equipped with a micro hot air gun, which performs 30 second preheating treatment on the contact surface of the packaging box before installing the moisture-proof material, and eliminates the surface condensate film; The calibration process introduces the adversarial sample generation technology to simulate extreme humidity mutation scenarios, and trains the model robustness; The error attribution system adds a supplier batch defect detection function that automatically freezes inventory and traces back to the production batch number when the same batch of materials has an abnormally high loss rate; The rapid approval process integrates blockchain storage technology, and all electronic signatures and approval records are stored after being hashed and encrypted; Compared with embodiment one, the present embodiment improves the accuracy of moisture-proof decision making by 18% by refining the humidity control strategy and enhancing data credibility, reduces the abnormal loss rate of materials by 27%, and improves the compliance audit efficiency of the approval process by 45%.

[0040] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A packaging material intelligent selection system based on multi-dimensional data analysis, characterized by: The system comprises an environment perception module, a data analysis module, a decision engine module and an execution control module. The environment perception module collects vibration frequency spectrum, surface strain and temperature and humidity data in real time through a sensor network deployed on the surface of the packaging box, generates an environment state matrix after feature extraction, and the sensor network includes an automatic calibration unit, wherein the surface strain data is used to construct a deformation gradient field model through a spatial interpolation algorithm to quantify the strain distribution characteristics of the packaging box surface. The data analysis module has a built-in historical transportation case library to store performance degradation records of different materials in transportation environments, establishes a correlation model between vibration energy distribution and material performance degradation after receiving the environment state matrix, predicts the remaining life of the material in combination with the inventory turnover rate data in the supply chain database, and establishes a data interface with the logistics management system to obtain real-time transportation task duration and path planning data. The decision engine module dynamically adjusts the priority of cost, safety and environmental protection targets according to the transportation stage, combines the material life prediction results with supplier capacity data to generate a multi-level decision tree containing automatic execution plans and emergency plans. The execution control module pre-stores packaging box size tolerance standards, structural deformation safety limits and registered size databases, converts the selected plan into material replacement instructions, drives the execution mechanism to complete the operation, collects the error feedback of actual transportation data and predicted values to the data analysis module to trigger dynamic calibration of model parameters, and the model prediction error is divided into environment perception error, material performance error and execution control error. The supply chain database comprises a material moisture-proof characteristic index table and a hygroscopic expansion coefficient safety critical value library, records the deformation coefficient, protection level and irreversible deformation threshold of each material in different humidity environments, and marks the humidity sensitivity level; at the same time, based on the on-time delivery rate and the quality inspection pass rate, a dynamic calculation is carried out to construct a supplier credit scoring system, and the credit score threshold is set as the value of the normal distribution curve of historical performance data.

2. The intelligent packaging material selection system based on multi-dimensional data analysis according to claim 1, characterized in that: When the vibration frequency spectrum data analysis identifies that the energy value of the frequency band near the material's natural frequency increases by more than the baseline threshold within a preset time window, the environment perception module sends a resonance risk warning signal to the data analysis module; the data analysis module responds to the warning, retrieves the transportation records of the same frequency band energy characteristics in the historical transportation case library, and calculates the damage rate data of the corresponding material, if the damage rate exceeds the safety threshold, it initiates a re-evaluation request for the current recommended plan to the decision engine module, and generates a priority list of alternative materials.

3. A packaging material intelligent selection system based on multi-dimensional data analysis according to claim 2, characterized in that: The data analysis module matches the material remaining life prediction curve with the transportation task duration obtained from the logistics management system, and when the predicted life is lower than the task duration, it triggers the real-time inventory verification process of the supply chain database; If the inventory check result shows that the inventory is less than 120% of the current transportation demand, the decision engine module automatically reduces the priority level of the material in the recommended plan, and associates the supply chain database to screen a list of alternative suppliers with sufficient capacity and logistics time that meet the shortest delivery cycle, the generation of the alternative supplier list needs to match the material types in the alternative material priority list.

4. The intelligent packaging material selection system based on multidimensional data analysis according to claim 1, characterized in that: The recommended plan output by the decision engine module also includes material specification parameters and installation coordinate data on the packaging box, and the execution control module receives the data and performs matching verification on the material specification parameters and the registered size data of the current packaging box. If the material size and the installation position of the packaging box are detected to exceed the preset safe range, a parameter conflict alarm is returned to the decision engine module, triggering a recommended scheme regeneration process based on actual size constraints, and preferentially screening material options with a tolerance compatibility higher than 95%.

5. The intelligent packaging material selection system based on multidimensional data analysis according to claim 1, characterized in that: The execution control module monitors the initial buffer performance parameters of the new material in real time after installation is completed, and sends a calibration request containing the environment data timestamp and material batch code to the data analysis module when the deviation between the measured buffer efficiency and the model predicted value exceeds 5%; the data analysis module extracts the environmental feature data of the corresponding time window according to the request, preferentially updates the performance degradation model of humidity-sensitive materials, and synchronizes the updated model version to the decision engine module.

6. The intelligent packaging material selection system based on multidimensional data analysis according to claim 1, characterized in that: The supplier credit score in the supply chain database real-time influences the scheme generation rules of the decision engine module, and when the supplier credit score is lower than the threshold set by the historical performance data normal distribution curve, the decision engine module automatically shields the material options supplied by the supplier; at the same time, the material actual loss rate data recorded by the execution control module is returned to the supply chain database, dynamically adjusting the weight proportion of on-site performance data in the supplier quality evaluation index, forming a closed-loop feedback mechanism between credit score and actual performance.

7. The intelligent packaging material selection system based on multidimensional data analysis according to claim 1, characterized in that: When the environmental perception module detects that the temperature and humidity change rate exceeds the safe critical value of the material moisture absorption expansion coefficient, the data analysis module immediately starts deformation variable prediction calculation, and evaluates the risk level in combination with the packaging box structure deformation safety limit value; if the predicted deformation variable exceeds 80% of the safety limit value, the decision engine module terminates the current scheme execution and sends an emergency reinforcement instruction to the execution control module, and based on the moisture-proof characteristic index table of the material in the supply chain database, generates a replacement material recommendation list and starts a fast approval process, which includes automatic compliance verification, electronic signature confirmation by the responsible person, and automatic issuance of execution instructions.

8. The intelligent packaging material selection system based on multidimensional data analysis according to claim 1, characterized in that: The data analysis module periodically classifies and attributes the model prediction error, triggers the sensor calibration unit of the environmental perception module to perform precision verification when the environmental perception error exceeds the preset tolerance limit for three consecutive times, starts the supplier historical data review process and recalculates the credit score when the material performance error accumulates, and dynamically adjusts the control parameter optimization rules of the execution mechanism when the execution control error persists until the measured operation precision reaches the preset standard.

9. The method of claim 1, wherein the method comprises: First, the environmental perception module uses the sensor network on the surface of the packaging box to collect vibration frequency spectrum, surface strain, and temperature and humidity data in real time to generate an environmental state matrix; the data analysis module combines the historical transportation case library and the supply chain database to predict the remaining life of the material, and interfaces with the logistics management system to obtain transportation task information; The decision engine module dynamically adjusts the target priority according to the transportation stage, and generates a multi-level decision tree by combining material life prediction and supplier capacity data; The execution control module executes the material replacement operation according to a selected scheme, collects error feedback of actual transportation data and predicted values to the data analysis module, triggers dynamic calibration of model parameters, and the supply chain database contains a material moisture-proof characteristic index table and a hygroscopic expansion coefficient safety critical value library, records the deformation coefficient, protection level and irreversible deformation threshold of each material in different humidity environments, and constructs a supplier credit scoring system based on the on-time delivery rate and the quality inspection pass rate.