An intelligent warehousing logistics system based on Internet of Things

Abstract

The application relates to an intelligent warehousing logistics system based on an Internet of Things, which comprises a sensing layer for collecting data of each sub-warehouse of the intelligent warehousing; a data processing layer for cleaning, standardizing and extracting features of the data collected by the sensing layer and outputting sub-warehouse feature data; a demand portrait layer for generating a three-dimensional demand label of a sub-warehouse based on the sub-warehouse feature data; a logistics system customization model layer comprising a classification model and a recommendation model; the classification model divides the sub-warehouse into a preset sub-warehouse type based on the three-dimensional demand label; the recommendation model outputs a customized logistics system configuration scheme based on the sub-warehouse type and the weight value of the three-dimensional demand label; and an execution layer for deploying the logistics system according to the customized logistics system configuration scheme and monitoring the operation data of the sub-warehouse in real time to dynamically optimize the customized logistics system configuration scheme. The application has the effect of effectively improving the intelligent sub-warehouse logistics efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of warehouse logistics, in particular to an intelligent warehouse logistics system based on Internet of Things. BACKGROUND

[0002] With the rapid development of Internet of Things and artificial intelligence technology, intelligent warehouse logistics systems have become the core infrastructure of modern supply chains. Traditional warehouse systems usually adopt a standardized configuration mode, and realize the efficiency improvement of basic operations through unified automated equipment (such as AGV, vertical shelves) and general management systems. However, in the e-commerce, fast-moving consumer goods, and fresh food industries, in order to meet the nationwide distribution, multi-category storage, and instant demand, a multi-level sub-warehouse architecture of regional general warehouses, city warehouses, and front-end warehouses is generally adopted. In such a large distributed warehouse scenario, due to the differences in responsibility positioning (such as sorting, storage, cold chain, and reverse processing), cargo characteristics (such as fresh food, standard products, and dangerous goods), and regional environment (such as high altitude, low temperature, and coastal areas), the functional requirements of the logistics system are significantly differentiated.

[0003] At this time, the existing traditional intelligent warehouse system still adopts a standardized and unified mode, that is, a set of general equipment configuration (such as AGV, sorting machine, and shelf), management algorithm (such as path planning and inventory allocation), and system module are used to cover all sub-warehouses. It is easy to cause mismatch between equipment and demand, coexistence of functional redundancy and lack, insufficient environmental adaptability, and lack of dynamic optimization mechanism, etc. due to not considering the individualized needs of sub-warehouses, resulting in low logistics efficiency in each intelligent sub-warehouse.

[0004] In view of the above technical problems, there is an urgent need for an intelligent warehouse logistics system based on Internet of Things. SUMMARY

[0005] In order to solve the above problems, the present application provides an intelligent warehouse logistics system based on Internet of Things.

[0006] The present application provides an intelligent warehouse logistics system based on Internet of Things, which adopts the following technical solutions:

[0007] An intelligent warehouse logistics system based on Internet of Things, comprising:

[0008] A perception layer for collecting basic attribute data, responsibility operation data, cargo characteristic data, and environmental parameter data of each sub-warehouse of the intelligent warehouse;

[0009] A data processing layer in communication connection with the perception layer, for cleaning, standardizing, and extracting features of the data collected by the perception layer, and outputting sub-warehouse feature data;

[0010] The demand profiling layer is in communication connection with the data processing layer, and is configured to generate a three-dimensional demand label of a sub-warehouse including a functional demand dimension, a cargo demand dimension and an environmental demand dimension based on sub-warehouse characteristic data, each dimension including at least one label item, and each label item being associated with a weight value;

[0011] The logistics system customization model layer is in communication connection with the demand profiling layer, and includes a classification model and a recommendation model; the classification model is configured to divide the sub-warehouse into a preset sub-warehouse type based on the three-dimensional demand label; the recommendation model is configured to output a customized logistics system configuration scheme based on the sub-warehouse type and the weight value of the three-dimensional demand label; the customized logistics system configuration scheme includes at least one logistics equipment combination, at least one optimization algorithm module, and at least one system function module and an installation and deployment and adaptation modification scheme;

[0012] The execution layer is in communication connection with the logistics system customization model layer, and is configured to deploy a logistics system according to the customized logistics system configuration scheme, and to monitor running data of the sub-warehouse in real time to dynamically optimize the customized logistics system configuration scheme.

[0013] Preferably, the perception layer includes:

[0014] The basic attribute acquisition unit is configured to acquire geographic location information, surrounding traffic data and building parameter information of the sub-warehouse, and to package and generate basic attribute data of the sub-warehouse;

[0015] The job data acquisition unit is configured to acquire daily order quantity, sorting frequency, storage cycle and in-out warehouse peak period data of the sub-warehouse, and to package and generate job data of the sub-warehouse;

[0016] The cargo characteristic acquisition unit includes an RFID reader and a visual recognition device, and is configured to acquire cargo categories, sizes and temperature and humidity requirements, and to package and generate cargo characteristic data of the sub-warehouse;

[0017] The environmental parameter acquisition unit includes a temperature and humidity sensor and a dust sensor, and is configured to acquire temperature, humidity and dust concentration, and to package and generate environmental parameter data of the sub-warehouse.

[0018] Preferably, the data collected by the perception layer is subjected to cleaning, standardization processing and feature extraction, and the output sub-warehouse characteristic data specifically includes the following steps:

[0019] A1, data cleaning: the original data collected by the perception layer is subjected to data cleaning, and abnormal values and redundant data caused by repeated reporting of devices are removed, and missing data is filled in;

[0020] A2, data standardization: the cleaned data is subjected to normalization processing to obtain standardized data;

[0021] A3, data feature extraction: feature extraction is performed on the standardized data to obtain a feature vector for constructing a demand portrait, the feature vector including static features, dynamic features and environmental features;

[0022] A4, data feature integration: the extracted static features, dynamic features and environmental features are spliced into a vector to form sub-bin feature data representing the overall characteristics of the sub-bin, and output to the demand portrait layer.

[0023] Preferably, the feature extraction on the standardized data to obtain a feature vector for constructing a demand portrait specifically includes the following steps:

[0024] B1, static feature extraction: extracting the structured features of the sub-bin from the basic attribute data, such as the ratio of area to height, the traffic convenience index.

[0025] B2, dynamic feature extraction: extracting time series features from the duty operation data, including but not limited to: order quantity fluctuation coefficient calculated based on the last logistics cycle order data, sorting efficiency calculated based on sorting time.

[0026] B3, environmental feature extraction: extracting statistical features from environmental parameter data, including but not limited to: variance of temperature and humidity data, daily average maximum value of dust concentration, and cumulative duration of ultraviolet intensity exceeding standard in the past week.

[0027] Preferably, the demand portrait layer generates a three-dimensional demand label of the sub-bin including a functional demand dimension, a cargo demand dimension and an environmental demand dimension based on the sub-bin feature data, specifically including the following steps:

[0028] C1, generating a functional demand dimension: generating sorting efficiency label and weight, storage density label and weight, and reverse processing label and weight of the functional demand dimension based on the sub-bin feature data through a pre-set demand portrait model; the demand portrait model is a machine learning model obtained by iterative training of historical data;

[0029] C2, generating a cargo demand dimension: generating cold chain demand label and weight, explosion-proof demand label and weight, and flexible processing label and weight of the cargo demand dimension based on the sub-bin feature data through a pre-set demand portrait model;

[0030] C3, generating an environmental demand dimension: generating low temperature tolerance label and weight, salt spray protection label and weight, and low pressure adaptation label and weight of the environmental demand dimension based on the sub-bin feature data through a pre-set demand portrait model;

[0031] C4, normalization processing: normalizing the weight values of each label item in the functional demand dimension, cargo demand dimension and environmental demand dimension to ensure that the sum of the weight values of each label item in each dimension is 1;

[0032] C5, combining the normalized weight values with the corresponding label items to generate a three-dimensional demand label of the sub-warehouse, and outputting to a logistics system customization model layer.

[0033] Preferably, the classification model is a multi-classification model based on gradient boosting trees, the recommendation model is a collaborative filtering recommendation model, and the preset sub-warehouse types include sorting type, storage type, cold chain type, and dangerous goods type.

[0034] Preferably, the classification model divides the sub-warehouse into the preset sub-warehouse types based on the three-dimensional demand label, and specifically includes the following steps:

[0035] D1, feature vectorization: combining the weight values of each dimension in the three-dimensional demand label into a multi-dimensional numerical feature vector;

[0036] D2, type probability prediction: inputting the multi-dimensional numerical feature vector into a pre-trained classification model to obtain a probability value of the sub-warehouse belonging to each preset sub-warehouse type;

[0037] D3, type determination: selecting the sub-warehouse type with the highest probability value as the final classification result of the sub-warehouse; and when the highest probability value is lower than a preset confidence threshold, marking the sub-warehouse as a pending type and sending it to a management personnel for a manual review process.

[0038] Preferably, the recommendation model outputs a customized logistics system configuration scheme based on the sub-warehouse type and the weight values of the three-dimensional demand label, and specifically includes the following steps:

[0039] E1, candidate scheme preliminary screening: the recommendation model matches and filters to generate a candidate logistics system configuration scheme adapted to the sub-warehouse type based on the sub-warehouse type;

[0040] E2, multi-dimensional matching calculation: the recommendation model calculates the comprehensive matching degree score of each candidate logistics system configuration scheme through a pre-set comprehensive matching degree calculation formula; the comprehensive matching degree calculation formula is specifically:

[0041] ;

[0042] wherein S is the comprehensive matching degree score, 、 、 are respectively the importance coefficients of the function demand dimension, the cargo demand dimension, and the environment demand dimension pre-set for the sub-warehouse type; 、 and represent the weight values of the i-th function demand label item, the j-th cargo demand label item, and the k-th environment demand label item in the three-dimensional demand label of the sub-warehouse generated by the demand portrait layer. , and respectively represent the adaptation score of each candidate logistics system configuration scheme for the i th functional requirement, the j th cargo requirement, and the k th environmental requirement label item; m, n, p respectively represent the total number of label items in the energy requirement dimension, the cargo requirement dimension, and the environmental requirement dimension of the three-dimensional requirement label.

[0043] E3, optimal scheme generation: selecting the candidate logistics system configuration scheme with the highest comprehensive matching score as the recommended customized logistics system configuration scheme.

[0044] Preferably, the execution layer comprises:

[0045] A scheme deployment unit for loading hardware devices and software modules of the customized logistics system configuration scheme and completing system commissioning and function activation;

[0046] A dynamic monitoring unit for monitoring real-time operation data of the sub-warehouse, the operation data including device operation state, order processing efficiency, energy consumption data, and environmental parameters;

[0047] An optimization triggering unit for triggering the logistics system customization model layer to recalculate and output a new customized logistics system configuration scheme when the deviation of the monitored pre-set key performance indicators from the expected target continuously exceeds the pre-set threshold.

[0048] Preferably, it further comprises a central coordination layer connected to the execution layers of each sub-warehouse for cross-sub-warehouse data synchronization and resource scheduling.

[0049] In summary, the present application includes at least one of the following beneficial technical effects:

[0050] 1. By covering the full-dimensional data of sub-warehouse basic attributes, operations, cargos, and environments through the perception layer, setting three-dimensional requirement labels after data processing, accurately quantifying the individualized requirements of each sub-warehouse in the function, cargo, and environment dimensions, and realizing accurate matching of sub-warehouse logistics requirements, the disadvantages of standardized configuration are overcome; at the same time, through the linkage of classification and recommendation double models, not only can the sub-warehouse type be automatically identified, but also a complete customized scheme covering hardware devices, algorithms, and functional modules can be output, realizing the leap from single-link optimization to system-level customization; and relying on the real-time monitoring and feedback mechanism of the execution layer, the system can continuously optimize the configuration according to the actual operation data, forming a closed loop of perception-decision-execution-optimization, greatly improving the long-term adaptability and stability of the system, and achieving the effect of effectively improving the intelligent sub-warehouse logistics efficiency;

[0051] 2. Adopting the demand portrait model, automatically generating three-dimensional demand labels and weights, completely getting rid of the dependence on artificial experience, ensuring the objectivity, consistency and quantifiability of the demand portrait, laying a solid foundation for accurate customization of logistics solutions; at the same time, through normalization processing, the weight of each dimension is one, eliminating the dimensional difference, so that the label weight of different sub-warehouses is comparable, which helps to accurately and efficiently draw the demand portrait according to the actual situation of the sub-warehouse, and is convenient for efficient customization of logistics solutions for the sub-warehouse;

[0052] 3. Through the type of sub-warehouse, the first screening of a large number of candidate solutions is carried out, the matching range is quickly narrowed, unnecessary waste of computing resources is avoided, and the system response speed and recommendation efficiency are significantly improved; then through the comprehensive matching degree calculation formula, the three core demands of function, goods and environment and their relative importance are comprehensively considered, and the candidate solutions are finely quantitatively evaluated, so that the comprehensive adaptation degree of the final recommended solution is optimized, and the effect of effectively improving the intelligent sub-warehouse logistics efficiency is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 is a system block diagram of an intelligent warehouse logistics system based on the Internet of Things in the embodiments of the present application;

[0054] Figure 2 is a method flow chart of data processing in the embodiments of the present application;

[0055] Figure 3 is a method flow chart of feature extraction in data processing in the embodiments of the present application;

[0056] Figure 4 is a method flow chart of generating three-dimensional demand labels in the embodiments of the present application;

[0057] Figure 5 is a method flow chart of classifying sub-warehouses in the embodiments of the present application;

[0058] Figure 6 is a method flow chart of matching and generating solutions in the embodiments of the present application.

[0059] BRIEF DESCRIPTION OF DRAWINGS: 1, perception layer; 11, basic attribute acquisition unit; 12, operation data acquisition unit; 13, goods feature acquisition unit; 14, environment parameter acquisition unit; 2, data processing layer; 3, demand portrait layer; 4, logistics system customization model layer; 5, execution layer; 51, solution deployment unit; 52, dynamic monitoring unit; 53, optimization triggering unit; 6, central coordination layer. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings Figures 1-6 The present application is further described in detail.

[0061] The embodiment of the application discloses an intelligent warehousing logistics system based on Internet of Things. Figure 1 An intelligent warehousing logistics system based on Internet of Things comprises:

[0062] A perception layer 1 is used for collecting basic attribute data, responsibility operation data, cargo characteristic data and environment parameter data of each sub-warehouse of the intelligent warehousing.

[0063] A data processing layer 2 is in communication connection with the perception layer 1 and is used for cleaning, standardizing and extracting features of the data collected by the perception layer 1 and outputting sub-warehouse characteristic data.

[0064] A demand portrait layer 3 is in communication connection with the data processing layer 2 and is used for generating a three-dimensional demand label of a sub-warehouse including a functional demand dimension, a cargo demand dimension and an environment demand dimension based on the sub-warehouse characteristic data, each dimension including at least one label item, and each label item being associated with a weight value.

[0065] A logistics system customization model layer 4 is in communication connection with the demand portrait layer 3 and comprises a classification model and a recommendation model; the classification model divides the sub-warehouse into a preset sub-warehouse type based on the three-dimensional demand label; the recommendation model outputs a customized logistics system configuration scheme based on the sub-warehouse type and the weight value of the three-dimensional demand label; the customized logistics system configuration scheme includes at least one logistics equipment combination, at least one optimization algorithm module and at least one system function module and installation deployment and adaptation modification scheme; for example, the logistics equipment combination includes AGV, a sorting machine and a stereoscopic cargo shelf, the optimization algorithm module includes a path optimization algorithm and an inventory allocation algorithm, and the system function module includes a temperature control traceability module and an abnormality early warning module.

[0066] An execution layer 5 is in communication connection with the logistics system customization model layer 4 and is used for deploying a logistics system according to the customized logistics system configuration scheme and monitoring running data of the sub-warehouse in real time to dynamically optimize the customized logistics system configuration scheme. The perception layer 1 covers the full-dimension data of the basic attributes, operation, cargo and environment of the sub-warehouse, sets the three-dimensional demand label after data processing, accurately quantifies the individualized demand of each sub-warehouse in the functional, cargo and environment dimensions, realizes accurate matching of the logistics demand of the sub-warehouse, and breaks the disadvantages of standardized configuration; meanwhile, through linkage of the classification and recommendation double models, not only can the sub-warehouse type be automatically identified, but also a complete customization scheme covering hardware equipment, algorithms and function modules can be output, realizing a leap from single-link optimization to system-level customization; and relying on the real-time monitoring and feedback mechanism of the execution layer 5, the system can continuously optimize the configuration according to the actual running data, forms a closed loop of perception, decision, execution and optimization, greatly improves the long-term adaptability and stability of the system, and achieves the effect of effectively improving the intelligent sub-warehouse logistics efficiency.

[0067] Refer to Figure 1Further comprising a central coordination layer 6 connected to the execution layer 5 of each sub-warehouse, for cross-sub-warehouse data synchronization and resource scheduling. By setting the central coordination layer 6, the data and resource barriers of each sub-warehouse are broken down, which helps to realize the unified scheduling and optimal allocation of resources such as inventory, manpower and transportation capacity, greatly improving the whole-chain efficiency and emergency response capability of the entire warehouse network. At the same time, through centralized coordination and management, the failure or fluctuation of a single sub-warehouse can be compensated by the global deployment of resources, enhancing the stability of the overall network, and also facilitating the rapid access and integrated management of new sub-warehouses.

[0068] Referring to Figure 1 , the perception layer 1 comprises:

[0069] A basic attribute acquisition unit 11 is configured to acquire geographic location information, surrounding traffic data and building parameter information of the sub-warehouse, and package the basic attribute data of the sub-warehouse;

[0070] A job data acquisition unit 12 is configured to acquire daily order quantity, sorting frequency, storage period and in-out warehouse peak period data of the sub-warehouse, and package the job data of the sub-warehouse;

[0071] A cargo feature acquisition unit 13 comprising an RFID reader and a visual recognition device is configured to acquire cargo category, size and temperature and humidity requirements, and package the cargo feature data of the sub-warehouse;

[0072] An environmental parameter acquisition unit 14 comprising a temperature and humidity sensor and a dust sensor is configured to acquire temperature, humidity and dust concentration, and package the environmental parameter data of the sub-warehouse. Through the setting of each unit, the basic attribute data, job data, cargo feature data and environmental parameter data of the sub-warehouse are accurately acquired; the inherent conditions of the sub-warehouse are first anchored, focusing on geographic location, traffic and building parameters, to provide core basic data for subsequent equipment layout and transportation connection, avoiding blind configuration that deviates from the inherent attributes of the sub-warehouse; then the dynamic data such as daily orders and sorting frequency are accurately captured to clearly reflect the sub-warehouse operation load and peak value law, providing operation basis for subsequent algorithm scheduling (such as peak period equipment expansion) and efficiency optimization; at the same time, RFID (efficient cargo recognition) and visual recognition (accurate size / cargo category) are combined to comprehensively acquire cargo category, size, temperature and humidity requirements, directly matching cargo adaptation requirements (such as temperature and humidity corresponding to cold chain equipment), reducing the adaptation deviation between cargo and the system; finally, multiple environmental sensors are integrated to realize real-time monitoring of key parameters such as temperature, humidity and dust, providing direct data support for special cargo storage and environmental adaptability equipment configuration, effectively ensuring cargo safety and stable operation environment.

[0073] Referring to Figure 2 , the data processing layer 2 performs cleaning, standardization processing and feature extraction on the data collected by the perception layer 1, and outputs the sub-warehouse feature data, which comprises the following steps:

[0074] A1. Data cleaning: Clean the raw data collected by the perception layer 1, remove outliers and redundant data caused by repeated reporting by the device, and fill in the missing data.

[0075] A2. Data Standardization: Normalize the cleaned data to obtain standardized data;

[0076] A3. Data Feature Extraction: Extracting features from standardized data to obtain feature vectors for constructing demand profiles. The feature vectors include static features, dynamic features, and environmental features.

[0077] A4. Data Feature Integration: The extracted static, dynamic, and environmental features are concatenated into vectors to form sub-warehouse feature data that represents the comprehensive characteristics of the sub-warehouse, which is then output to the demand profiling layer 3. Through a systematic data cleaning and standardization process, noise is effectively removed and missing data is repaired, transforming multi-source heterogeneous raw data into high-quality, computable standard data, providing reliable input for subsequent intelligent models. Static, dynamic, and environmental features are intelligently extracted from the standardized data to deeply explore the sub-warehouse's operational patterns, business models, and environmental constraints hidden beneath the raw data, providing highly abstract and information-rich feature vectors for accurate demand profiling. Finally, feature vectors from different dimensions are concatenated and integrated to form standardized feature data that comprehensively describes the sub-warehouse, providing a structured and unified input for demand profiling layer 3, greatly improving the accuracy of demand profiling.

[0078] Reference Figure 3 The above-mentioned feature extraction of standardized data to obtain the feature vector used to construct the demand profile specifically includes the following steps:

[0079] B1. Static Feature Extraction: Extract structured features of sub-warehouses from basic attribute data, such as the ratio of area to floor height and the traffic convenience index.

[0080] B2. Dynamic Feature Extraction: Extract time-series features from job operation data, including but not limited to: order volume fluctuation coefficient calculated based on order data from the previous logistics cycle, and sorting efficiency calculated based on sorting time.

[0081] B3. Environmental Feature Extraction: Statistical features are extracted from environmental parameter data, including but not limited to: the variance of temperature and humidity data, the daily average maximum value of dust concentration, and the cumulative duration of ultraviolet radiation exceeding the standard in the past week. Through the above steps, the most core feature indicators (such as structural ratio, fluctuation coefficient, and environmental variance) are extracted from attribute, operational, and environmental data respectively, directly corresponding to the three major demand dimensions of function, goods, and environment, providing the most representative input for generating high-precision demand labels. Among them, static feature extraction anchors the inherent adaptability of sub-warehouses, dynamic feature extraction captures the operational rhythm pattern, and environmental feature extraction quantifies the environmental risk dimension. The simultaneous extraction of static (space), dynamic (efficiency), and environmental (risk) features fully covers the physical constraints, operational efficiency, and external challenges of sub-warehouses, avoiding the profile bias caused by single-dimensional features, and further improving the accuracy of demand profiles.

[0082] Reference Figure 4 The above-mentioned requirement profiling layer 3 generates a three-dimensional requirement label for the sub-warehouse based on the sub-warehouse feature data, which includes functional requirement dimensions, cargo requirement dimensions, and environmental requirement dimensions. Specifically, this includes the following steps:

[0083] C1. Generating Functional Requirement Dimensions: Based on sub-warehouse feature data, sorting efficiency labels and weights, storage density labels and weights, and reverse processing labels and weights are generated for the functional requirement dimensions through a pre-set requirement profile model; the requirement profile model is a machine learning model obtained through iterative training of historical data.

[0084] C2. Generate cargo demand dimensions: Based on the sub-warehouse feature data, generate cold chain demand labels and weights, explosion-proof demand labels and weights, and flexible processing labels and weights for cargo demand dimensions through a pre-set demand profile model;

[0085] C3. Generation of environmental requirements dimension: Based on the sub-compartment feature data, the low temperature tolerance label and weight, salt spray protection label and weight, and low pressure adaptation label and weight are generated through a pre-set requirement profile model.

[0086] C4. Normalization: Normalize the weight values ​​of each label item in the functional requirements dimension, the goods requirements dimension, and the environmental requirements dimension to ensure that the sum of the weight values ​​of each label item in each dimension is 1.

[0087] C5. Generate 3D Demand Labels: Combine the normalized weight values ​​with the corresponding label items to generate the 3D demand labels for the sub-warehouse, and output them to the logistics system's customized model layer 4. Through the above steps, the demand profiling model automatically generates 3D demand labels and weights, completely eliminating reliance on manual experience and ensuring the objectivity, consistency, and quantifiability of the demand profile, laying a solid foundation for precise customization of logistics solutions. Simultaneously, normalization ensures that the weights of each dimension are summed to one, eliminating dimensional differences and making the label weights of different sub-warehouses comparable. This helps to accurately and efficiently draw demand profiles based on the actual situation of each sub-warehouse, facilitating efficient customization of logistics solutions for each sub-warehouse.

[0088] Furthermore, the classification model of the logistics system's customized model layer 4 is a multi-classification model based on gradient boosting trees, and the recommendation model is a collaborative filtering recommendation model. The preset sub-warehouse types include sorting, storage, cold chain, and hazardous materials. The gradient boosting tree model can efficiently and accurately process the multi-dimensional features (static / dynamic / environmental) of sub-warehouses, precisely classifying them into four core types: sorting, storage, cold chain, and hazardous materials. The classification logic aligns with actual operational scenarios, effectively narrowing the subsequent recommendation range and avoiding interference from irrelevant configurations. The collaborative filtering model can draw on the adaptation experience of similar sub-warehouses, deeply exploring the implicit correlation between historical solutions and sub-warehouse needs. Combining the three-dimensional demand label weights of sub-warehouses (such as the "low-temperature tolerance weight" for cold chain warehouses), it outputs solutions that conform to both type commonalities (such as cold chain requiring low-temperature equipment) and individual differences (such as cold chain warehouses with high sorting requirements being equipped with temperature-controlled sorting machines), resulting in better adaptability. By setting up a two-tier architecture, efficiency and accuracy are balanced. The process of classifying first and then recommending reduces the computational redundancy of the recommendation model (no need to match non-target type devices), improves the efficiency of solution generation, and avoids the generalization bias of a single model by focusing on classification and refining recommendations, ensuring the accuracy of configuration. It intelligently matches the most suitable combination of devices, algorithms and functional modules for various sub-warehouses, realizing true personalized customization.

[0089] Among them, reference Figure 5 The above classification model, based on the three-dimensional demand labels, divides sub-warehouses into preset sub-warehouse types, specifically including the following steps:

[0090] D1. Feature vectorization: Combine the weight values ​​of each dimension in the three-dimensional demand label into a multi-dimensional numerical feature vector;

[0091] D2. Type Probability Prediction: Input the multidimensional numerical feature vector into the pre-trained classification model to obtain the probability value of the sub-warehouse belonging to each preset sub-warehouse type;

[0092] D3. Type Determination: The sub-warehouse type with the highest probability value is selected as the final classification result for that sub-warehouse. If the highest probability value is lower than a preset confidence threshold, the sub-warehouse is marked as a pending type and sent to management for manual review. The weight values ​​of the three-dimensional requirement labels are combined into a multi-dimensional numerical vector, transforming qualitative requirements into quantitative data. The classification model then outputs the probability values ​​of each sub-warehouse belonging to its respective type, rather than directly classifying them. This clearly reflects the type matching degree, providing valuable decision-making reference information for uncertain scenarios and enhancing the system's practicality and flexibility in handling complex real-world scenarios. Simultaneously, the introduction of a confidence threshold-triggered manual review mechanism provides reliable error prevention and correction for low-confidence predictions, forming a collaborative decision-making model with machine judgment as the primary method and human intervention as a backup, greatly improving the overall reliability of the classification results.

[0093] Reference Figure 6 The above recommendation model, based on the sub-warehouse type and the weight values ​​of the three-dimensional demand labels, outputs a customized logistics system configuration scheme, specifically including the following steps:

[0094] E1. Initial screening of candidate solutions: The recommendation model generates candidate logistics system configuration solutions that are suitable for the sub-warehouse type based on the sub-warehouse type matching and screening.

[0095] E2. Multi-dimensional matching calculation: The recommendation model calculates the comprehensive matching score of each candidate logistics system configuration scheme using a pre-set comprehensive matching score calculation formula; the comprehensive matching score calculation formula is as follows:

[0096] ;

[0097] Where S is the overall matching score, , , These are the importance coefficients for the functional requirements, cargo requirements, and environmental requirements dimensions pre-set for the corresponding sub-warehouse types. , and These represent the weight values ​​of the i-th functional requirement label, the j-th goods requirement label, and the k-th environmental requirement label in the sub-warehouse 3D requirement labels generated by the requirement profile layer, respectively. , and These represent the adaptability scores of each candidate logistics system configuration scheme for the i-th functional requirement, j-th cargo requirement, and k-th environmental requirement tag item, respectively; m, n, and p represent the total number of tag items in the three-dimensional requirement tags for the energy requirement dimension, cargo requirement dimension, and environmental requirement dimension, respectively.

[0098] E3. Optimal Solution Generation: The candidate logistics system configuration with the highest comprehensive matching score is selected as the recommended customized logistics system configuration. Through the above steps, a massive pool of candidate solutions is initially screened based on sub-warehouse type, quickly narrowing the matching range, avoiding unnecessary waste of computational resources, and significantly improving system response speed and recommendation efficiency. Then, using the comprehensive matching score calculation formula, the three core requirements of function, goods, and environment, along with their relative importance, are comprehensively considered to conduct a refined quantitative evaluation of the candidate solutions, scientifically ensuring that the final recommended solution has the optimal comprehensive suitability, effectively improving the logistics efficiency of intelligent sub-warehouses.

[0099] Reference Figure 1 Execution layer 5 includes:

[0100] The solution deployment unit 51 is used to load the hardware and software modules of the customized logistics system configuration solution, and to complete the system integration and function activation.

[0101] The dynamic monitoring unit 52 is used to monitor the operation data of the sub-warehouse in real time. The operation data includes equipment operating status, order processing efficiency, energy consumption data and environmental parameters.

[0102] The optimization trigger unit 53 is used to trigger the logistics system customization model layer 4 to recalculate and output a new customized logistics system configuration scheme when the deviation between the monitored pre-set key performance indicators and the expected targets continuously exceeds a preset threshold. The scheme deployment unit 51 achieves standardized operation of the entire process of hardware loading, software configuration, and system integration, ensuring that the customized scheme can be deployed and implemented efficiently and accurately in the physical sub-warehouse, eliminating errors in the implementation stage. Through the collaborative work of dynamic monitoring and the optimization trigger unit 53, a complete closed loop from data perception to decision optimization is constructed, enabling the system to continuously self-adjust and optimize based on the actual operating status and the deviation of key performance indicators as the basis for optimization triggering, which helps to improve the system's adaptability, universality, and service life.

[0103] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on these embodiments, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still combine, add, delete, or otherwise adjust the features of the various embodiments of the present invention according to the circumstances without conflict or creative effort, thereby obtaining different technical solutions that do not fundamentally depart from the concept of the present invention. These technical solutions also fall within the scope of protection of the present invention.

Claims

1. An intelligent warehousing and logistics system based on the Internet of Things, characterized in that, include: The perception layer (1) is used to collect basic attribute data, duty operation data, cargo characteristic data and environmental parameter data of each sub-warehouse of the smart warehouse; The data processing layer (2) is communicatively connected to the perception layer (1) and is used to clean, standardize and extract features from the data collected by the perception layer (1) and output sub-warehouse feature data. The demand profile layer (3) is connected to the data processing layer (2) and is used to generate a three-dimensional demand label for the sub-warehouse based on the sub-warehouse feature data, which includes functional demand dimension, cargo demand dimension and environmental demand dimension. Each dimension includes at least one label item, and each label item is associated with a weight value. The logistics system customization model layer (4) is connected to the demand profiling layer (3) and includes a classification model and a recommendation model; The classification model divides sub-warehouses into preset sub-warehouse types based on the three-dimensional demand labels; the recommendation model outputs a customized logistics system configuration scheme based on the weight values ​​of the sub-warehouse types and the three-dimensional demand labels; the customized logistics system configuration scheme includes at least one combination of logistics equipment, at least one optimization algorithm module, and at least one system function module and installation, deployment and adaptation scheme. The execution layer (5) is connected to the logistics system customization model layer (4) and is used to deploy the logistics system according to the customized logistics system configuration scheme and monitor the operation data of the sub-warehouse in real time to dynamically optimize the customized logistics system configuration scheme. The requirement profiling layer (3) generates three-dimensional requirement labels for sub-warehouses based on sub-warehouse feature data, including functional requirement dimensions, cargo requirement dimensions, and environmental requirement dimensions. Specifically, this includes the following steps: C1. Generating Functional Requirement Dimensions: Based on sub-warehouse feature data, sorting efficiency labels and weights, storage density labels and weights, and reverse processing labels and weights are generated for the functional requirement dimensions through a pre-set requirement profile model; the requirement profile model is a machine learning model obtained through iterative training of historical data. C2. Generate cargo demand dimensions: Based on the sub-warehouse feature data, generate cold chain demand labels and weights, explosion-proof demand labels and weights, and flexible processing labels and weights for cargo demand dimensions through a pre-set demand profile model; C3. Generation of environmental requirements dimension: Based on the sub-compartment feature data, the low temperature tolerance label and weight, salt spray protection label and weight, and low pressure adaptation label and weight are generated through a pre-set requirement profile model. C4. Normalization: Normalize the weight values ​​of each label item in the functional requirements dimension, the goods requirements dimension, and the environmental requirements dimension to ensure that the sum of the weight values ​​of each label item in each dimension is 1. C5. Combine and generate three-dimensional demand labels: Combine the normalized weight values ​​with the corresponding label items to generate the three-dimensional demand labels of the sub-warehouse and output them to the logistics system customized model layer (4). The classification model is a multi-classification model based on gradient boosting trees, the recommendation model is a collaborative filtering recommendation model, and the preset sub-warehouse types include sorting type, storage type, cold chain type, and hazardous material type.

2. The intelligent warehousing and logistics system based on the Internet of Things according to claim 1, characterized in that, The sensing layer (1) includes: The basic attribute collection unit (11) is used to collect the geographical location information, surrounding traffic data and building parameter information of the sub-warehouse, and package them to generate the basic attribute data of the sub-warehouse. The operation data acquisition unit (12) is used to collect the daily average order volume, sorting frequency, storage cycle and peak inbound and outbound time data of the sub-warehouse, and package them to generate the responsibility operation data of the sub-warehouse; The cargo feature collection unit (13) includes an RFID reader and a visual recognition device, used to collect cargo category, size and temperature and humidity requirements, and package and generate cargo feature data for the sub-warehouse; The environmental parameter acquisition unit (14) includes a temperature and humidity sensor and a dust sensor, which are used to collect temperature, humidity and dust concentration and package them to generate environmental parameter data of the sub-compartment.

3. The intelligent warehousing and logistics system based on the Internet of Things according to claim 1, characterized in that, The process of cleaning, standardizing, and extracting features from the data collected by the perception layer (1) to output sub-warehouse feature data specifically includes the following steps: A1. Data cleaning: Clean the raw data collected by the perception layer (1), remove outliers and redundant data caused by repeated reporting by the device, and fill in the missing data. A2. Data Standardization: Normalize the cleaned data to obtain standardized data; A3. Data Feature Extraction: Extracting features from standardized data to obtain feature vectors for constructing demand profiles. The feature vectors include static features, dynamic features, and environmental features. A4. Data feature integration: The extracted static features, dynamic features and environmental features are vectorized and concatenated to form sub-warehouse feature data that represents the full range of characteristics of the sub-warehouse, and output to the demand profile layer (3).

4. The intelligent warehousing and logistics system based on the Internet of Things according to claim 3, characterized in that, The process of extracting features from standardized data to obtain feature vectors for constructing demand profiles specifically includes the following steps: B1. Static Feature Extraction: Extract structured features of sub-warehouses from basic attribute data, such as the ratio of area to floor height and the traffic convenience index; B2. Dynamic Feature Extraction: Extract time-series features from job operation data, including but not limited to: order volume fluctuation coefficient calculated based on order data from the previous logistics cycle, and sorting efficiency calculated based on sorting time. B3. Environmental Feature Extraction: Extract statistical features from environmental parameter data, including but not limited to: the variance of temperature and humidity data, the daily average maximum value of dust concentration, and the cumulative duration of ultraviolet radiation exceeding the standard in the past week.

5. The intelligent warehousing and logistics system based on the Internet of Things according to claim 1, characterized in that, The classification model divides sub-warehouses into preset sub-warehouse types based on the three-dimensional demand labels, specifically including the following steps: D1. Feature vectorization: Combine the weight values ​​of each dimension in the three-dimensional demand label into a multi-dimensional numerical feature vector; D2. Type Probability Prediction: Input the multidimensional numerical feature vector into the pre-trained classification model to obtain the probability value of the sub-warehouse belonging to each preset sub-warehouse type; D3. Type determination: Select the sub-warehouse type with the highest probability value as the final classification result of the sub-warehouse; and when the highest probability value is lower than the preset confidence threshold, the sub-warehouse is marked as a pending type and sent to the management personnel for manual review.

6. The intelligent warehousing and logistics system based on the Internet of Things according to claim 1, characterized in that, The recommendation model, based on the sub-warehouse type and the weight values ​​of the three-dimensional demand tags, outputs a customized logistics system configuration scheme, specifically including the following steps: E1. Initial screening of candidate solutions: The recommendation model generates candidate logistics system configuration solutions that are suitable for the sub-warehouse type based on the sub-warehouse type matching and screening. E2. Multi-dimensional matching calculation: The recommendation model calculates the comprehensive matching score of each candidate logistics system configuration scheme using a pre-set comprehensive matching score calculation formula; the comprehensive matching score calculation formula is as follows: ； Where S is the overall matching score, , , These are the importance coefficients for the functional requirements, cargo requirements, and environmental requirements dimensions pre-set for the corresponding sub-warehouse types. , and These represent the weight values ​​of the i-th functional requirement label, the j-th goods requirement label, and the k-th environmental requirement label in the sub-warehouse 3D requirement labels generated by the requirement profile layer, respectively. , and These represent the adaptability scores of each candidate logistics system configuration scheme for the i-th functional requirement, j-th cargo requirement, and k-th environmental requirement tag item, respectively; m, n, and p represent the total number of tag items in the three-dimensional requirement tags for the energy requirement dimension, cargo requirement dimension, and environmental requirement dimension, respectively. E3. Optimal Solution Generation: Select the candidate logistics system configuration scheme with the highest comprehensive matching score as the recommended customized logistics system configuration scheme.

7. The intelligent warehousing and logistics system based on the Internet of Things according to claim 1, characterized in that, The execution layer (5) includes: The solution deployment unit (51) is used to load the hardware and software modules of the customized logistics system configuration solution and to complete the system integration and function activation. The dynamic monitoring unit (52) is used to monitor the operation data of the sub-warehouse in real time. The operation data includes equipment operating status, order processing efficiency, energy consumption data and environmental parameters. The optimization trigger unit (53) is used to trigger the logistics system customization model layer (4) to recalculate and output a new customized logistics system configuration scheme when the deviation between the monitored pre-set key performance indicators and the expected target continues to exceed the preset threshold.

8. The intelligent warehousing and logistics system based on the Internet of Things according to claim 1, characterized in that: It also includes a central coordination layer (6), which connects to the execution layer (5) of each sub-warehouse and is used for cross-sub-warehouse data synchronization and resource scheduling.

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