An intelligent warehousing operation mobilization and data integration method based on an internet of things

Abstract

The application belongs to the technical field of computer application, and discloses a smart warehousing operation mobilization and data integration method based on the Internet of Things, which comprises the following steps: collecting goods marks, warehouse in / out records and environmental parameters through a radio frequency identification device, a two-dimensional code scanning device and a temperature and humidity sensor, performing noise filtering, window aggregation and anomaly detection through an edge computing node to generate data with an anomaly mark; a warehouse management system analyzes the data to generate an operation task, assigns a globally unique task identification code, and synchronously transmits the task identification code to an enterprise resource planning system and an automatic control system; an operation personnel receives a task instruction through a mobile terminal and compares the task instruction with equipment feedback, and generates an anomaly mark when the task instruction is inconsistent with the equipment feedback; a data center completes field mapping and namespace alignment based on a unified semantic model, performs consistency checking and conflict processing on multi-source data, forms a fusion data set, and runs a priority calculation model to generate a task sequence.

Description

Technical Field

[0001] This invention belongs to the field of computer application technology, specifically a method for mobilizing and integrating data in intelligent warehousing operations based on the Internet of Things. Background Technology

[0002] As logistics and manufacturing continue to expand, warehousing not only serves the functions of storing and transferring goods, but also directly relates to the overall efficiency of the enterprise supply chain. However, existing warehousing management methods generally rely on warehouse management systems (WMS), combining barcode scanning and manual data entry to realize inbound and outbound operations. This model can complete inventory recording and query, but it is difficult to support the needs of multi-system collaboration and dynamic task scheduling in complex business scenarios.

[0003] In specific operations, the handling, picking, and storage of goods often require manual operation, while automated equipment also generates execution feedback. However, since most existing systems store manual input and equipment data separately, they lack real-time comparison and consistency verification. When there is a discrepancy between the manual and equipment results, erroneous operations often cannot be detected and rolled back in time, causing potential risks in the execution process.

[0004] Furthermore, with the widespread adoption of IoT devices such as RFID, QR code scanning, and temperature and humidity sensors, warehouses can acquire a large amount of real-time data. However, this data is often directly uploaded to the central system for processing, lacking the ability to preprocess it at the edge. This results in redundant, delayed, or interfering data in the original data not being removed at the source, which in turn affects the accuracy of task generation and the stability of subsequent scheduling.

[0005] In terms of multi-system collaboration, there is a lack of a unified semantic model between warehouse management systems, enterprise resource planning systems, and automated control systems. Differences in task numbers and field standards make it difficult to transfer tasks across systems and result in high comparison costs. This not only increases the management burden but also makes it easy to accumulate errors. Traditional task scheduling mostly relies on experience or fixed rules and lacks comprehensive consideration of multi-dimensional factors such as order timeliness, equipment utilization, personnel status, and warehouse congestion. Furthermore, it fails to use abnormal records as scheduling factors, resulting in insufficient scientific scheduling. Summary of the Invention

[0006] The purpose of this invention is to provide a method for mobilizing and integrating data in intelligent warehousing operations based on the Internet of Things, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for mobilizing and integrating data in intelligent warehousing operations based on the Internet of Things (IoT), the specific steps of which are as follows:

[0008] The system uses radio frequency identification devices, QR code scanning devices, and temperature and humidity sensors to collect cargo identification, warehouse entry and exit records, and environmental parameters. The data is then processed by edge computing nodes to perform noise filtering, sliding window aggregation, and anomaly detection, and outputs formatted data with anomaly markers.

[0009] The warehouse management system generates job tasks based on the formatted data and assigns a globally unique task identifier to each task to ensure consistency between the enterprise resource planning system and the automation control system.

[0010] The operator receives the task instruction corresponding to the task identifier code through the mobile terminal and performs the operation. The mobile terminal simultaneously obtains feedback information from the automated equipment, compares the manual operation with the equipment feedback one by one, and generates an abnormal mark and triggers task rollback when there is a discrepancy.

[0011] The data platform accesses data streams from warehouse management systems, enterprise resource planning systems, automated control systems, and edge nodes through a unified communication interface, and completes field mapping and namespace alignment in a unified semantic model based on the globally unique task identifier.

[0012] The data platform performs consistency checks and conflict handling on the mapped multi-source data, with anomaly markers serving as priority factors in conflict resolution to form a fused data view covering inventory, storage locations, equipment, and personnel.

[0013] Based on the fusion data operation priority calculation model, the model comprehensively considers order timeliness, equipment utilization, staff on-duty status, warehouse congestion and anomaly markers, outputs a task priority sequence with weight descriptions, and feeds the results back to the warehouse management system and mobile terminal.

[0014] The task execution results, exception markers, and priority sequences are uniformly stored as an encrypted traceability log. This log serves as both a basis for accountability auditing and a means to update the parameters of the priority calculation model.

[0015] Preferably, in the data collection stage, an RFID device is used to read the electronic tags attached to the goods. The tags store a unique identification code for the goods, which remains unchanged throughout the entire warehousing cycle.

[0016] A QR code scanning device is used to scan the QR codes on the inbound and outbound documents to obtain the inbound and outbound records of the corresponding batch of goods. Temperature and humidity sensors are used to collect the temperature and humidity parameters of the storage environment in real time.

[0017] Before entering the warehouse management system, the collected data is first preprocessed by edge computing nodes deployed at the warehouse site. The preprocessing includes: noise filtering of the raw collected data to remove outliers caused by radio frequency interference or optical recognition errors; and sliding aggregation according to a fixed time window, the window length Δt of which is set by system parameters and ranges from 1 second to 60 seconds, which can be configured according to operational needs.

[0018] Anomaly detection is performed based on thresholds preset by the warehouse management system. These thresholds are determined according to cargo attributes or warehousing standards (such as temperature and humidity control standards). If the temperature or humidity exceeds the allowable range or the cargo code is missing, an anomaly flag is generated and written as a field into the corresponding preprocessed data packet for subsequent system calls.

[0019] Furthermore, edge computing nodes can employ the following processing methods during sliding aggregation and anomaly detection:

[0020] ;

[0021] In the formula, This represents the average value within a window length At;

[0022] The time window length, ranging from 1 second to 60 seconds, is configured by system parameters.

[0023] Indicates the first The raw data collected this time;

[0024] Anomaly detection rules are defined as follows:

[0025] ;

[0026] In the formula, This is an exception flag field; a value of 1 indicates an exception, and a value of 0 indicates normal. and These are the lower and upper thresholds preset by the warehouse management system, set according to the attributes of the goods or warehousing standards (such as temperature and humidity control requirements for perishable goods).

[0027] Preferably, in the task generation stage, the warehouse management system parses the formatted data output by the edge computing node. The parsing content includes the unique identification code of the goods, the inbound or outbound records, and environmental parameters.

[0028] The system generates corresponding job tasks based on the parsing results, such as inbound tasks, outbound tasks, or transfer tasks, and assigns a globally unique task identifier code to each job task.

[0029] The task identifier code adopts a multi-field encoding structure of system number + timestamp + serial number, and its generation rule can be expressed as follows:

[0030] ;

[0031] In the formula, Indicates the task identifier code; This field represents the system identifier, used to distinguish between a warehouse management system (WMS), an enterprise resource planning system (ERP), or a warehouse automation control system (WCS).

[0032] The task generation time field uses a timestamp accurate to the second or millisecond to ensure uniqueness in the time dimension; The serial number field indicates that the serial numbers are sequentially incremented within the same system and the same timestamp, and are used to distinguish multiple concurrently generated tasks.

[0033] symbol" " indicates a field concatenation operation, which involves sequentially connecting the fields to generate the final identifier code;

[0034] After the task identifier is generated, it is written into the task data structure of the warehouse management system and synchronously transmitted to the enterprise resource planning system and the automation control system. In each system, the identifier serves as a unique index for the task to be called and compared, thereby ensuring the consistency of task management across systems and avoiding execution ambiguity caused by task number conflicts or duplications.

[0035] Preferably, during the execution phase, the operator receives the task instruction corresponding to the globally unique task identifier via a mobile terminal and completes the picking, handling, or storage operations.

[0036] During execution, the mobile terminal obtains real-time feedback status of automated equipment through a wireless communication interface, such as the location information of the shuttle, the action completion signal of the stacker crane, or the operating status of the conveying equipment.

[0037] To ensure the accuracy of task execution, the mobile terminal compares the manual input with the feedback from the automated equipment item by item. The comparison includes the location of the goods, the timestamp, and the quantity of goods. The comparison method can be represented as follows:

[0038] ;

[0039] In the formula, Indicates the comparison difference value; This indicates the first manually entered number. Each comparison item (such as cargo location number, task execution time, and cargo quantity); The first feedback from the automated equipment One comparison item; The total number of comparison items is 3 in this invention; This is a difference function; if the two values ​​are the same, output 0; otherwise, output 1.

[0040] ;

[0041] In the formula, This is an anomaly marker; a value of 1 indicates at least one inconsistency, and a value of 0 indicates all inconsistencies.

[0042] When the exception flag is 1, the mobile terminal writes the flag and the corresponding task identifier code into the exception data record as input conditions for subsequent task cancellation and reassignment, thereby realizing real-time verification and exception recording of the task execution process.

[0043] Preferably, in the integration phase, the data platform receives data streams from the warehouse management system, enterprise resource planning system, automation control system, and edge computing nodes through a unified communication interface;

[0044] To achieve data consistency across systems, the data platform performs processing based on a unified semantic model. The semantic model defines naming rules for task identifiers, storage location identifiers, equipment identifiers, and personnel identifiers. For example, task identifiers use system number + timestamp + serial number, storage location identifiers use warehouse area number + channel number + storage compartment number, equipment identifiers use equipment category number + serial number, and personnel identifiers use position number + personnel code.

[0045] The data platform performs field mapping and namespace alignment based on the semantic model, converting data from different sources into a structured dataset in a unified format. The field mapping relationship can be formalized as follows:

[0046] ;

[0047] In the formula, Indicates a field mapping function; Indicates the first [item] from a certain business system A field, such as the material number in an ERP system or the device tag number in a WCS system; Represents the corresponding first under the unified semantic model A standardized field.

[0048] Preferably, in the verification process, the data platform performs consistency verification and conflict handling on the multi-source data after semantic model mapping;

[0049] Consistency checks include: merging duplicate records when multiple records exist in the same storage location within the same time period; and performing interpolation compensation based on timestamp order to restore the complete time series when some data is delayed due to network latency.

[0050] Specifically, repeated merging can be represented as:

[0051] ;

[0052] In the formula, Indicates time Storage location The merged result; Indicates the first time and location at that time. 1 record; This indicates the number of duplicate records within that time period;

[0053] For compensation of late data, linear interpolation can be used:

[0054] ;

[0055] In the formula, Indicates the time of lateness The compensation value; , These are two adjacent valid timestamps, one before the other. , These are the corresponding valid data values;

[0056] During conflict resolution, anomaly markers serve as a priority factor. When task execution data and device feedback data are inconsistent, the system prioritizes records with anomaly markers for selection. The conflict resolution rules can be expressed as follows:

[0057] ;

[0058] in, Indicates time Storage location The final valid record; This indicates a record marked as abnormal. This indicates a record that is not marked as abnormal.

[0059] After the above processing, the fused dataset output by the data platform includes inventory information, location status, equipment status, and personnel information, providing consistent input for subsequent priority calculations.

[0060] Preferably, in the decision-making process, a task priority calculation model is run based on the fused dataset, and the model uses order delivery time limit, real-time utilization rate of automated equipment, on-duty status of personnel, congestion level of each storage location, and anomaly markers as input parameters;

[0061] Priority calculation can be performed using a weighted summation method, the formula of which is:

[0062] ;

[0063] In the formula, Indicates the task priority value; The number of input parameters is 5 in this invention; Indicates the first The weight values ​​of each parameter range from 0 to 1, and the sum of all weights is 1. Indicates the first The values ​​of each parameter are set by the system according to business logic;

[0064] Specifically:

[0065] when hour, For order delivery time limits, in hours or minutes, with a weight of . ;

[0066] when hour, Real-time utilization rate of automated equipment, expressed as a percentage, with a weight of . ;

[0067] when hour, The on-duty status of personnel is represented in binary, where 1 indicates on-duty and 0 indicates off-duty, with a weight of 1. ;

[0068] when hour, The degree of warehouse congestion is expressed as a percentage of occupancy, with a weight of . ;

[0069] when hour, This is an anomaly marker, with a value of 1 indicating the presence of an anomaly and 0 indicating the absence of an anomaly. The weight is... ;

[0070] The system calculates the task priority sequence based on the above formula, and writes the priority results and corresponding weight values ​​into the task data record. The final result is converted into task scheduling instructions and fed back to the warehouse management system and mobile terminal to guide task allocation and execution.

[0071] Preferably, in the traceability process, the system writes the task execution results, exception markers, and task priority sequences into the traceability log.

[0072] The traceability log is stored in encrypted form and has anti-tampering function. Specifically, when storing the log, a hash verification value is generated for each record, or the log evidence is written to the blockchain node to ensure immutability.

[0073] The generation of log hash values ​​can be represented as:

[0074] ;

[0075] In the formula, This represents the hash checksum of the log record; This represents a one-way hash function, which can use common algorithms such as SHA-256 and SM3. Indicates the task identifier code; This represents the core data field set of the log, including execution timestamp, operator identifier, equipment feedback status, anomaly marker, and priority weight value; the symbol " "" indicates a field concatenation operation, which concatenates the task identifier code and data fields in sequence and uses the result as input to the hash function;

[0076] Ultimately, the traceability log content includes at least the task identifier code, execution timestamp, operator identifier, equipment feedback status, anomaly marker, and corresponding priority weight value, which are bound to the task identifier code. Through the above binding and hash verification, the integrity and immutability of the log during storage and retrieval are guaranteed, thereby ensuring the accuracy and reliability of the data correspondence during task backtracking.

[0077] The beneficial effects of this invention are as follows:

[0078] 1. This invention achieves synchronous collection of cargo identification, inbound and outbound records, and environmental parameters by simultaneously deploying RFID devices, QR code scanning devices, and temperature and humidity sensors at the warehouse site. Unlike existing technologies that rely on a single identification method and directly upload raw data, this invention performs noise filtering, sliding window aggregation, and threshold anomaly detection on the collected results at edge computing nodes, and generates anomaly markers in the form of fields and writes them into data packets. This design can improve the integrity and accuracy of data at the data source, avoid information deviations caused by identification interference, collection delays, or missing data, and provide high-quality basic data for subsequent task generation and system scheduling, thus improving the problems of poor data quality and anomaly lag in existing warehousing systems.

[0079] 2. This invention constructs a unified semantic model, defines naming rules for task identifiers, location identifiers, equipment identifiers, and personnel identifiers, and completes data alignment between multiple systems based on globally unique task identifier codes. After entering the data platform, data output from different systems is converted into structured data in a unified format through field mapping and namespace correspondence processing. On this basis, the data platform further performs consistency checks and conflict handling, and uses anomaly markers as priority factors in decision-making, generating a fused data view covering inventory, location, equipment, and personnel. This solution solves the problems of inconsistent fields, inconsistent task numbers, and difficulty in data comparison between different systems in existing warehouse management, and improves the coherence and traceability of cross-system data interaction.

[0080] 3. This invention constructs a task priority calculation model based on fused data, incorporating multi-dimensional parameters such as order timeliness, equipment utilization, personnel status, warehouse congestion, and anomaly markers into the calculation. A weighted summation method is used to generate a priority sequence, which is then fed back to the warehouse management system and mobile terminals, ensuring the rationality and dynamic adaptability of task allocation. Simultaneously, the system uniformly writes task execution results, anomaly markers, and priority sequences into a traceability log. The log employs encrypted storage and hash verification mechanisms to ensure data immutability and support accountability. Through these methods, not only is the scientific nature of scheduling decisions improved, but the reliable recording of the execution process is also ensured, solving the problems of single scheduling factors and insufficient log reliability in existing technologies. Attached Figure Description

[0081] Figure 1 This is a flowchart of the IoT-based intelligent warehousing operation mobility and data integration method of the present invention. Detailed Implementation

[0082] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0083] like Figure 1 As shown, this embodiment of the invention provides a method for mobilizing and integrating data in intelligent warehousing operations based on the Internet of Things (IoT). The specific steps of this method are as follows:

[0084] The system uses radio frequency identification devices, QR code scanning devices, and temperature and humidity sensors to collect cargo identification, inbound and outbound records, and environmental parameters. The data undergoes noise filtering, sliding window aggregation, and anomaly detection through edge computing nodes, and outputs formatted data with anomaly markers.

[0085] The warehouse management system generates job tasks based on formatted data and assigns globally unique task identifiers to the tasks to ensure consistency in the enterprise resource planning system and the automated control system.

[0086] Operators receive task instructions corresponding to task identification codes and perform operations via mobile terminals. The mobile terminals simultaneously acquire feedback information from automated equipment and compare manual operations with equipment feedback one by one. If there is a discrepancy, an anomaly marker is generated and task rollback is triggered.

[0087] The data platform accesses data streams from warehouse management systems, enterprise resource planning systems, automated control systems, and edge nodes through a unified communication interface, and completes field mapping and namespace alignment in a unified semantic model based on a globally unique task identifier.

[0088] The data platform performs consistency checks and conflict resolution on the mapped multi-source data, with anomaly markers serving as priority factors in conflict resolution to form a fused data view covering inventory, storage locations, equipment, and personnel.

[0089] Based on the integrated data operation priority calculation model, the model comprehensively considers order timeliness, equipment utilization, staff on-duty status, warehouse congestion and anomaly markers, outputs a task priority sequence with weight descriptions, and feeds the results back to the warehouse management system and mobile terminals.

[0090] The task execution results, exception markers, and priority sequences are uniformly stored as an encrypted traceability log. The log serves as both a basis for accountability auditing and a means to update the parameters of the priority calculation model.

[0091] In the data collection process, radio frequency identification (RFID) devices are used to read the electronic tags attached to the goods. The tags store a unique identification code for the goods, which remains unchanged throughout the entire warehousing cycle.

[0092] A QR code scanning device is used to scan the QR codes on the inbound and outbound documents to obtain the inbound and outbound records of the corresponding batch of goods. Temperature and humidity sensors are used to collect the temperature and humidity parameters of the storage environment in real time.

[0093] Before entering the warehouse management system, the collected data is first preprocessed by edge computing nodes deployed on the warehouse site. The preprocessing includes: noise filtering of the raw collected data to remove outliers caused by radio frequency interference or optical recognition errors; and sliding aggregation according to a fixed time window. The window length Δt is set by system parameters, with a value range of 1 second to 60 seconds, which can be configured according to operational needs.

[0094] The system performs anomaly detection based on preset thresholds in the warehouse management system. These thresholds are determined according to the attributes of the goods or warehousing standards (such as temperature and humidity control standards). If the temperature or humidity exceeds the allowable range or the goods code is missing, an anomaly flag is generated and written as a field into the corresponding preprocessed data packet for subsequent system calls.

[0095] Furthermore, edge computing nodes can employ the following processing methods during sliding aggregation and anomaly detection:

[0096] ;

[0097] In the formula, This represents the average value within a window length At;

[0098] The time window length, ranging from 1 second to 60 seconds, is configured by system parameters.

[0099] Indicates the first The raw data collected this time;

[0100] Anomaly detection rules are defined as follows:

[0101] ;

[0102] In the formula, This is an exception flag field; a value of 1 indicates an exception, and a value of 0 indicates normal. and These are the lower and upper thresholds preset by the warehouse management system, set according to the attributes of the goods or warehousing standards (such as temperature and humidity control requirements for perishable goods).

[0103] In the task generation stage, the warehouse management system parses the formatted data output by the edge computing nodes. The parsing content includes the unique identification code of the goods, the inbound or outbound records, and environmental parameters.

[0104] The system generates corresponding job tasks based on the parsing results, such as inbound tasks, outbound tasks, or transfer tasks, and assigns a globally unique task identifier code to each job task.

[0105] The task identifier code adopts a multi-field encoding structure of system number + timestamp + serial number, and its generation rule can be expressed as follows:

[0106] ;

[0107] In the formula, Indicates the task identifier code; This field represents the system identifier, used to distinguish between a warehouse management system (WMS), an enterprise resource planning system (ERP), or a warehouse automation control system (WCS).

[0108] The task generation time field uses a timestamp accurate to the second or millisecond to ensure uniqueness in the time dimension; The serial number field indicates that the serial numbers are sequentially incremented within the same system and the same timestamp, and are used to distinguish multiple concurrently generated tasks.

[0109] symbol" " indicates a field concatenation operation, which involves sequentially connecting the fields to generate the final identifier code;

[0110] After the task identifier is generated, it is written into the task data structure of the warehouse management system and synchronously transmitted to the enterprise resource planning system and the automation control system. In each system, the identifier serves as a unique index for the task to be called and compared, thereby ensuring the consistency of task management across systems and avoiding execution ambiguity caused by task number conflicts or duplications.

[0111] During the execution phase, operators receive task instructions corresponding to a globally unique task identifier via mobile terminals and complete picking, handling, or storage operations.

[0112] During execution, the mobile terminal obtains the real-time feedback status of the automated equipment through the wireless communication interface, such as the location information of the shuttle, the action completion signal of the stacker crane, or the operating status of the conveying equipment.

[0113] To ensure the accuracy of task execution, the mobile terminal compares the manual input with the feedback from the automated equipment item by item. The comparison includes the location of the goods, the timestamp, and the quantity of goods. The comparison method can be represented as follows:

[0114] ;

[0115] In the formula, Indicates the comparison difference value; This indicates the first manually entered number. Each comparison item (such as cargo location number, task execution time, and cargo quantity); The first feedback from the automated equipment One comparison item; The total number of comparison items is 3 in this invention; This is a difference function; if the two values ​​are the same, output 0; otherwise, output 1.

[0116] ;

[0117] In the formula, This is an anomaly marker; a value of 1 indicates at least one inconsistency, and a value of 0 indicates all inconsistencies.

[0118] When the exception flag is 1, the mobile terminal writes the flag and the corresponding task identifier code into the exception data record as input conditions for subsequent task cancellation and reassignment, thereby realizing real-time verification and exception recording of the task execution process.

[0119] In the integration phase, the data platform receives data streams from the warehouse management system, enterprise resource planning system, automation control system, and edge computing nodes through a unified communication interface.

[0120] To achieve data consistency across systems, the data platform performs processing based on a unified semantic model. The semantic model defines the naming rules for task identifiers, storage location identifiers, equipment identifiers, and personnel identifiers. For example, task identifiers use system number + timestamp + serial number, storage location identifiers use warehouse area number + channel number + storage compartment number, equipment identifiers use equipment category number + serial number, and personnel identifiers use position number + personnel code.

[0121] The data platform performs field mapping and namespace alignment based on the semantic model, converting data from different sources into a structured dataset in a unified format. The field mapping relationship can be formalized as follows:

[0122] ;

[0123] In the formula, Indicates a field mapping function; Indicates the first [item] from a certain business system A field, such as the material number in an ERP system or the device tag number in a WCS system; Represents the corresponding first under the unified semantic model A standardized field.

[0124] In the verification process, the data platform performs consistency verification and conflict handling on the multi-source data after semantic model mapping.

[0125] Consistency checks include: merging duplicate records when multiple records exist in the same storage location within the same time period; and performing interpolation compensation based on timestamp order to restore the complete time series when some data is delayed due to network latency.

[0126] Specifically, repeated merging can be represented as:

[0127] ;

[0128] In the formula, Indicates time Storage location The merged result; Indicates the first time and location at that time. 1 record; This indicates the number of duplicate records within that time period;

[0129] For compensation of late data, linear interpolation can be used:

[0130] ;

[0131] In the formula, Indicates the time of lateness The compensation value; , These are two adjacent valid timestamps, one before the other. , These are the corresponding valid data values;

[0132] During conflict resolution, anomaly markers serve as a priority factor. When task execution data and device feedback data are inconsistent, the system prioritizes records with anomaly markers for selection. The conflict resolution rules can be expressed as follows:

[0133] ;

[0134] in, Indicates time Storage location The final valid record; This indicates a record marked as abnormal. This indicates a record that is not marked as abnormal.

[0135] After the above processing, the fused dataset output by the data platform includes inventory information, location status, equipment status, and personnel information, providing consistent input for subsequent priority calculations.

[0136] In the decision-making process, a task priority calculation model is run based on the fused dataset. The model takes order delivery time limit, real-time utilization rate of automated equipment, staff on-duty status, congestion level of each storage location, and anomaly markers as input parameters.

[0137] Priority calculation can be performed using a weighted summation method, the formula of which is:

[0138] ;

[0139] In the formula, Indicates the task priority value; The number of input parameters is 5 in this invention; Indicates the first The weight values ​​of each parameter range from 0 to 1, and the sum of all weights is 1. Indicates the first The values ​​of each parameter are set by the system according to business logic;

[0140] Specifically:

[0141] when hour, For order delivery time limits, in hours or minutes, with a weight of . ;

[0142] when hour, Real-time utilization rate of automated equipment, expressed as a percentage, with a weight of . ;

[0143] when hour, The on-duty status of personnel is represented in binary, where 1 indicates on-duty and 0 indicates off-duty, with a weight of 1. ;

[0144] when hour, The degree of warehouse congestion is expressed as a percentage of occupancy, with a weight of . ;

[0145] when hour, This is an anomaly marker, with a value of 1 indicating the presence of an anomaly and 0 indicating the absence of an anomaly. The weight is... ;

[0146] The system calculates the task priority sequence based on the above formula, and writes the priority results and corresponding weight values ​​into the task data record. The final result is converted into task scheduling instructions and fed back to the warehouse management system and mobile terminal to guide task allocation and execution.

[0147] In the traceability process, the system writes the task execution results, exception markers, and task priority sequences into the traceability log.

[0148] The traceability log is stored in encrypted form and has anti-tampering function. Specifically, when storing the log, a hash verification value is generated for each record, or the log evidence is written to the blockchain node to ensure immutability.

[0149] The generation of log hash values ​​can be represented as:

[0150] ;

[0151] In the formula, This represents the hash checksum of the log record; This represents a one-way hash function, which can use common algorithms such as SHA-256 and SM3. Indicates the task identifier code; This represents the core data field set of the log, including execution timestamp, operator identifier, equipment feedback status, anomaly marker, and priority weight value; the symbol " "" indicates a field concatenation operation, which concatenates the task identifier code and data fields in sequence and uses the result as input to the hash function;

[0152] Ultimately, the traceability log content includes at least the task identifier code, execution timestamp, operator identifier, equipment feedback status, anomaly marker, and corresponding priority weight value, which are bound to the task identifier code. Through the above binding and hash verification, the integrity and immutability of the log during storage and retrieval are guaranteed, thereby ensuring the accuracy and reliability of the data correspondence during task backtracking.

[0153] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0154] Although embodiments of the invention 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 to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for mobilizing and integrating data in intelligent warehousing operations based on the Internet of Things, characterized in that: The specific steps of this IoT-based intelligent warehousing operation mobility and data integration method are as follows: The system uses radio frequency identification devices, QR code scanning devices, and temperature and humidity sensors to collect cargo identification, warehouse entry and exit records, and environmental parameters. The data is then processed by edge computing nodes to perform noise filtering, sliding window aggregation, and anomaly detection, and outputs formatted data with anomaly markers. The warehouse management system generates job tasks based on the formatted data and assigns a globally unique task identifier to each task. The operator receives the task instruction corresponding to the task identifier code through the mobile terminal and performs the operation. The mobile terminal simultaneously obtains feedback information from the automated equipment, compares the manual operation with the equipment feedback one by one, and generates an abnormal mark and triggers task rollback when there is a discrepancy. The data platform accesses data streams from warehouse management systems, enterprise resource planning systems, automated control systems, and edge nodes through a unified communication interface, and completes field mapping and namespace alignment in a unified semantic model based on the globally unique task identifier. The data platform performs consistency checks and conflict resolution on the mapped multi-source data, with anomaly markers serving as priority factors in conflict resolution. Based on the fusion data operation priority calculation model generated by the data platform, the model comprehensively considers order timeliness, equipment utilization, staff on-duty status, warehouse congestion and anomaly markers, outputs a task priority sequence with weight descriptions, and feeds the results back to the warehouse management system and mobile terminal. The task execution results, exception markers, and priority sequences are uniformly stored as an encrypted traceability log. This log serves as both a basis for accountability auditing and a means to update the parameters of the priority calculation model.

2. The method for mobile intelligent warehousing operations and data integration based on the Internet of Things according to claim 1, characterized in that: The method involves using a radio frequency identification device to collect the unique identification code of the goods, using a QR code scanning device to collect the entry and exit records, and using a temperature and humidity sensor to collect the temperature and humidity parameters of the storage environment. Before entering the warehouse management system, the collected data is preprocessed by edge computing nodes. The preprocessing includes noise filtering of the raw data, sliding aggregation according to a fixed time window, and anomaly detection based on a preset threshold. The detected anomalies are written into the preprocessed data packet in the form of a tag field.

3. The method for mobile intelligent warehousing operations and data integration based on the Internet of Things according to claim 2, characterized in that: The warehouse management system parses the formatted data preprocessed by the edge computing nodes, generates corresponding job tasks based on the parsing results, and assigns a globally unique task identifier code to each job task. The task identification code is written into the task data structure by the warehouse management system and synchronously transmitted to the enterprise resource planning system and the automation control system. The identification code serves as a unique index for the task in each system for calling and comparing task data.

4. The method for mobile intelligent warehousing operations and data integration based on the Internet of Things according to claim 3, characterized in that: The operator receives and performs the task instructions corresponding to the globally unique task identifier code through a mobile terminal. During the operation, the mobile terminal obtains the real-time feedback status of the automated equipment through a data interface. The mobile terminal compares the results of manual operation with the feedback status of automated equipment item by item. When there is an inconsistency in the comparison results, an anomaly mark is generated and written into the anomaly data record along with the corresponding task identifier code.

5. The method for mobile and data integration of intelligent warehousing operations based on the Internet of Things according to claim 4, characterized in that: The data platform receives data streams from the warehouse management system, enterprise resource planning system, automation control system and edge computing nodes through a unified communication interface, and performs field mapping processing in the unified semantic model based on the globally unique task identifier. The unified semantic model specifies the naming rules for task identifiers, storage location identifiers, equipment identifiers, and personnel identifiers. The data platform performs field mapping and namespace alignment according to these naming rules.

6. The method for mobile and data integration of intelligent warehousing operations based on the Internet of Things according to claim 5, characterized in that: The data platform performs consistency checks and conflict handling on multi-source data mapped by a unified semantic model. The consistency check includes merging duplicate data for the same storage location and performing time-series compensation processing on late data. During conflict resolution, anomaly markers are used as priority factors. When task data and device feedback data are inconsistent, the data platform prioritizes data selection and correction based on the anomaly markers.

7. The method for mobile intelligent warehousing operations and data integration based on the Internet of Things according to claim 6, characterized in that: The model for calculating the priority of tasks based on the integrated data generated by the data platform takes the order delivery time limit, the real-time utilization rate of automated equipment, the on-duty status of personnel, the congestion level of each storage location, and the anomaly marker as input parameters. The model assigns corresponding weight values ​​to each input parameter and generates a task priority sequence based on the weighted calculation results; The task priority sequence and its corresponding weight value are stored together in the data record and transmitted to the warehouse management system and mobile terminal as task scheduling instructions.

8. The method for mobile intelligent warehousing operations and data integration based on the Internet of Things according to claim 7, characterized in that: The task execution results, exception markers and corresponding task priority sequences are uniformly written into the traceability log, which is stored in encrypted form and has anti-tampering function. The traceability log records the following information: task identifier code, execution timestamp, operator identifier, equipment feedback status, anomaly marker, and priority weight value corresponding to the task. The log is bound to the task identifier code.

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