Method and system for real-time monitoring of material inventory based on internet of things perception

Abstract

This invention relates to the field of inventory management technology, and proposes a method and system for real-time monitoring of material inventory based on Internet of Things (IoT) sensing. The method includes: distributing personalized data collection strategies to IoT sensing nodes via a communication network of a central monitoring platform; performing differentiated processing on initial inventory sensing data based on these strategies to generate inventory status records; dynamically adjusting the reporting frequency of status priority tag sequences and sending the adjustment process and records to the central monitoring platform in real time; correlating and analyzing the inventory status records with real-time sensing signals to construct a multi-dimensional dynamic inventory ledger; comparing the real-time inventory quantity in the ledger with a safety stock threshold, and performing task analysis in conjunction with an anomaly triggering mechanism to obtain an adaptive task list. This invention can improve the real-time performance, accuracy, and adaptive efficiency of material inventory monitoring.

Description

Technical Field

[0001] This invention relates to the field of inventory management technology, and in particular to a method and system for real-time monitoring of material inventory based on Internet of Things (IoT) sensing. Background Technology

[0002] In existing technologies, material inventory monitoring methods typically employ fixed-period data collection strategies or manual inventory checks. These methods lack personalized collection strategies that dynamically adjust based on the status of the sensing nodes and network load, making it difficult to achieve differentiated data collection and control across different nodes. This results in uneven node energy consumption, redundant data collection, and wasted communication resources. Furthermore, traditional methods struggle to match the initial data reported by sensing nodes with node classification labels using rules, and lack differentiated data cleaning and verification mechanisms. This leads to a large amount of invalid or noisy data mixed in with the initial inventory sensing data, making it difficult to generate accurate inventory status records and consequently affecting the reliability of subsequent inventory analysis.

[0003] Existing methods for monitoring material inventory lack a dynamic reporting frequency adjustment mechanism based on status priority tags. This makes it difficult to adaptively improve the real-time reporting of high-priority inventory records and reduce the resource consumption of low-priority records when the network load changes. This leads to delays or loss of critical inventory change information during network congestion. Furthermore, it is difficult to perform multi-source correlation analysis between inventory status records and real-time sensing signals from IoT sensing nodes, resulting in a single-dimensional inventory ledger that fails to reflect real-time dynamic changes in materials. In addition, existing methods lack a mechanism to automatically map abnormal records after comparing real-time inventory levels with safety stock thresholds to tasks to be executed. This makes it difficult to generate adaptive task lists for shortages or overstocking anomalies, leading to delayed responses to inventory anomalies and low efficiency in replenishment or allocation. Therefore, there is an urgent need to develop a real-time material inventory monitoring method based on IoT sensing to address the problems of lack of personalized collection strategies, inability to dynamically adjust reporting frequency, difficulty in constructing multi-dimensional ledgers, and imperfect anomaly task mapping, thereby improving the real-time performance, accuracy, and adaptability of inventory monitoring. Summary of the Invention

[0004] This invention provides a method and system for real-time monitoring of material inventory based on Internet of Things (IoT) sensing, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for real-time monitoring of material inventory based on Internet of Things (IoT) sensing, comprising: A1. Based on the communication connection network of the central monitoring platform, personalized data collection strategies are issued to the IoT sensing nodes in the material storage area. A2. Based on the personalized data collection strategy, perform differentiated data processing on the initial inventory sensing data of the IoT sensing nodes in the material storage area to obtain the inventory status record of the material storage area. A3. In the communication connection network, the reporting frequency of the status priority tag sequence in the inventory status record is dynamically adjusted, and the adjustment process and the inventory status record are sent to the central monitoring platform in real time. A4. The inventory status record is correlated and analyzed with the real-time sensing signal of the IoT sensing node to construct a multi-dimensional dynamic inventory ledger of the central monitoring platform. A5. Compare the real-time inventory in the dynamic inventory ledger with the current safety stock threshold of the material storage area in real time, and combine the safety stock anomaly triggering mechanism of the central monitoring platform to perform task parsing on the comparison results to obtain the adaptive task list of the material storage area.

[0006] In a preferred embodiment, the step of issuing personalized data collection strategies to IoT sensing nodes in the material storage area based on the communication connection network of the central monitoring platform includes: Group the regional topology information in the material storage area to obtain a strategy template for the regional topology information; Based on the strategy template and the communication connection network, a strategy identifier is issued to the IoT sensing node to obtain the personalized collection strategy corresponding to the IoT sensing node. The personalized data collection strategy is confirmed, and the confirmation result is returned to the central monitoring platform.

[0007] In a preferred embodiment, the strategy template for grouping the regional topology information in the material storage area to obtain the regional topology information includes: Based on the strategy template selection index in the material storage area, the regional topology information in the material storage area is grouped to obtain the strategy template of the regional topology information. The formula for calculating the strategy template selection index is as follows: in, Select an index for the strategy template. The historical data change rate of the aforementioned material storage area. The maximum historical data change rate in the aforementioned material storage area. The average remaining power of the sensing nodes in the material storage area. This represents the initial charge level of the sensing node. This refers to the average turnover days of materials in the aforementioned material storage area. This refers to the maximum average turnover days in the aforementioned material storage area. The rate of change weighting coefficient, This is the weighting factor for electricity consumption. This is the weighting coefficient for turnover days; The strategy template selection index is matched with the template index range of the material storage area to obtain the strategy template identifier corresponding to the material storage area, and this identifier is used as the strategy identifier. Based on the policy identifier, the IoT sensing nodes within the material storage area group are sent out, resulting in the indirect distribution of the personalized collection policy.

[0008] In a preferred embodiment, the step of performing differentiated data processing on the initial inventory sensing data of the IoT sensing nodes in the material storage area based on the personalized data collection strategy to obtain the inventory status record of the material storage area includes: Personalized data collection strategies are implemented for IoT sensing nodes in the material storage area to report initial inventory sensing data of the IoT sensing nodes. Data rule matching is performed on the node classification labels in the personalized data collection strategy to obtain the differentiated processing rules for the IoT sensing nodes; Based on the differentiated processing rules, the initial inventory sensing data of the IoT sensing node is cleaned to obtain the intermediate inventory data of the IoT sensing node. The intermediate inventory data is compared with the inventory snapshot in the IoT sensing node to obtain the valid change record of the IoT sensing node. The valid change records are compared with the preset material code mapping table to obtain the inventory status record of the material storage area.

[0009] In a preferred embodiment, the step of associating and parsing the inventory status records with the real-time sensing signals of the IoT sensing nodes to construct a multi-dimensional dynamic inventory ledger for the central monitoring platform includes: The inventory status record and the record entries of the same storage location in the real-time sensing signal are spatiotemporally aligned to obtain the associated data pair between the inventory status record and the IoT sensing node; The real-time sensing signal in the associated data pair is used as a dynamic dimension field, and the inventory quantity in the inventory status record and the material code in the material code mapping table are used as static dimension fields. The dynamic dimension fields and the static dimension fields are integrated into a multi-dimensional dynamic inventory ledger for the storage location.

[0010] In a preferred embodiment, the step of comparing the real-time inventory level in the dynamic inventory ledger with the current safety stock threshold of the material storage area in real time, and combining this with the safety stock anomaly triggering mechanism of the central monitoring platform, performing task parsing on the comparison result to obtain an adaptive task list for the material storage area, includes: The real-time inventory of each storage location in the multi-dimensional dynamic inventory ledger is compared with the upper and lower limits of the safety stock threshold of the storage location to obtain the comparison result of the storage location. Perform abnormal task mapping on the shortage and backlog abnormal records of the storage location to obtain the task items to be executed for the abnormal records in the storage location; Based on the suggested execution order identifier of the storage location, the tasks to be executed are sorted and merged to obtain an adaptive task list for the material storage area.

[0011] In a preferred embodiment, the step of dynamically adjusting the reporting frequency of the status priority tag sequence in the inventory status record within the communication connection network, and sending the adjustment process and the inventory status record to the central monitoring platform in real time, includes: Extract the material code and inventory change from the inventory status record, and generate the status priority label of the inventory status record; Arrange the status priority tags in chronological order to obtain the status priority tag sequence of the inventory status record, and associate the status priority tag sequence with a reporting frequency value; The reporting frequency value is dynamically adjusted based on the real-time load parameters of the communication connection network and the status priority label sequence. When the real-time load parameter is lower than the load threshold of the communication connection network, the reporting frequency of the high-priority sequence in the status priority label sequence is increased; otherwise, the reporting frequency of the low-priority sequence in the status priority label sequence is decreased. The adjusted reporting frequency value is sent to the communication gateway on the central monitoring platform to obtain the reporting data packet of the status priority label sequence; The timestamps, frequency values, adjustment reasons, and reported data packets during the dynamic adjustment process are integrated and sent to the central monitoring platform.

[0012] In a preferred embodiment, the step of performing data rule matching on the node classification labels in the personalized acquisition strategy to obtain the differentiated processing rules for the IoT sensing nodes includes: Data processing operations are performed on the node classification tags of the personalized collection strategy to obtain the rule group identifier of the node classification tags; The verification rules, removal conditions, and smoothing methods corresponding to the rule group identifier are matched with the initial inventory perception data, and then combined and encapsulated to obtain the differentiated processing rules for the IoT perception node. The differentiated processing rules are associated with and stored with the node identifier of the IoT sensing node to obtain the rule call record of the IoT sensing node.

[0013] In a preferred embodiment, the step of mapping the shortage and backlog anomaly records of the storage location to obtain the unexecuted task items for the anomaly records in the storage location includes: Obtain the abnormal record of the storage location from the safety stock abnormality triggering mechanism. The abnormal record includes an abnormality type identifier, target storage location coordinates, and abnormality occurrence timestamp. Based on a preset exception-task mapping rule base, task template matching is performed on the exception type identifier to obtain the task template corresponding to the exception type identifier; Fill the target cargo location coordinates and the timestamp of the anomaly occurrence into the corresponding fields of the task template to obtain the task items to be executed for the anomaly records in the cargo location; The pending tasks of the abnormal records in the storage location are grouped by task type and sorted by priority to obtain a list of pending tasks of the abnormal records in the storage location.

[0014] To address the aforementioned problems, this invention also provides a real-time inventory monitoring system based on Internet of Things (IoT) sensing, used to implement the real-time inventory monitoring method based on IoT sensing as described in claim 1, the system comprising: The personalized data collection strategy module is used to issue personalized data collection strategies to IoT sensing nodes in the material storage area based on the communication connection network of the central monitoring platform. The data processing module is used to perform differentiated data processing on the initial inventory sensing data of the IoT sensing nodes in the material storage area based on the personalized collection strategy, so as to obtain the inventory status record of the material storage area. The frequency adjustment module is used to dynamically adjust the reporting frequency of the status priority tag sequence in the inventory status record in the communication connection network, and send the adjustment process and the inventory status record to the central monitoring platform in real time. The ledger construction module is used to associate and parse the inventory status records with the real-time sensing signals of the IoT sensing nodes in order to construct a multi-dimensional dynamic inventory ledger for the central monitoring platform. The task parsing module is used to compare the real-time inventory quantity in the dynamic inventory ledger with the current safety stock threshold of the material storage area in real time, and combine the safety stock anomaly triggering mechanism of the central monitoring platform to perform task parsing on the comparison result in order to obtain the adaptive task list of the material storage area.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention distributes personalized data collection strategies to IoT sensing nodes in the material storage area based on the communication connection network of the central monitoring platform, thereby achieving differentiated data collection control for different nodes. This avoids the problems of uneven node energy consumption, data collection redundancy, and communication resource waste caused by fixed-period data collection. At the same time, based on the personalized data collection strategy, differentiated data processing is performed on the initial inventory sensing data, including data cleaning, verification, and extraction of valid change records. This effectively removes invalid and noisy data and generates accurate inventory status records, thereby significantly improving the reliability and accuracy of inventory data processing.

[0016] 2. This invention dynamically adjusts the reporting frequency of the status priority tag sequence of inventory status records in the communication connection network. This adaptively improves the real-time reporting capability of high-priority inventory records and reduces the resource consumption of low-priority records based on network load changes, effectively avoiding delays or loss of critical inventory change information during network congestion. Simultaneously, by performing multi-source correlation analysis between inventory status records and real-time sensing signals from IoT sensing nodes, a multi-dimensional dynamic inventory ledger containing both dynamic and static dimension fields is constructed. This solves the problem of traditional inventory ledgers having a single dimension and failing to reflect real-time dynamic changes in materials. Furthermore, by comparing real-time inventory levels with safety stock thresholds and automatically mapping shortage or backlog anomalies as pending tasks to generate an adaptive task list, rapid response and automated task scheduling for inventory anomalies are achieved, significantly improving the efficiency and intelligence of replenishment, allocation, and inventory early warning. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a method for real-time monitoring of material inventory based on Internet of Things (IoT) sensing, according to an embodiment of the present invention. Figure 2 A functional block diagram of a real-time monitoring system for material inventory based on Internet of Things (IoT) sensing, provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for real-time monitoring of material inventory based on Internet of Things (IoT) sensing. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for real-time monitoring of material inventory based on IoT sensing can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a real-time inventory monitoring method based on Internet of Things (IoT) sensing according to an embodiment of the present invention. In this embodiment, the real-time inventory monitoring method based on IoT sensing includes: A1. Based on the communication connection network of the central monitoring platform, personalized data collection strategies are issued to the IoT sensing nodes in the material storage area. In this embodiment of the invention, the step of issuing personalized data collection strategies to IoT sensing nodes in the material storage area based on the communication connection network of the central monitoring platform includes: Group the regional topology information in the material storage area to obtain a strategy template for the regional topology information; Based on the strategy template and the communication connection network, a strategy identifier is issued to the IoT sensing node to obtain the personalized collection strategy corresponding to the IoT sensing node. The personalized data collection strategy is confirmed, and the confirmation result is returned to the central monitoring platform.

[0021] The strategy template for grouping the regional topology information in the material storage area to obtain the regional topology information includes: Based on the strategy template selection index in the material storage area, the regional topology information in the material storage area is grouped to obtain the strategy template of the regional topology information. The formula for calculating the strategy template selection index is as follows: in, Select an index for the strategy template. The historical data change rate of the aforementioned material storage area. The maximum historical data change rate in the aforementioned material storage area. The average remaining power of the sensing nodes in the material storage area. This represents the initial charge level of the sensing node. This refers to the average turnover days of materials in the aforementioned material storage area. This refers to the maximum average turnover days in the aforementioned material storage area. These are the weighting coefficients for the rate of change, the electricity consumption weighting coefficient, and the turnover days weighting coefficient, respectively. The strategy template selection index is matched with the template index range of the material storage area to obtain the strategy template identifier corresponding to the material storage area, and this identifier is used as the strategy identifier. Based on the policy identifier, the IoT sensing nodes within the material storage area group are sent out, resulting in the indirect distribution of the personalized collection policy.

[0022] The central monitoring platform acquires the regional topology information of the material storage area. This information includes the deployment location of each IoT sensing node and the shelf area group to which each node belongs. The central monitoring platform categorizes and organizes this regional topology information according to shelf area groups, obtaining a set of regional topology information for each shelf area group. Based on this set of regional topology information for each shelf area group, the central monitoring platform matches a strategy template for each group from a pre-set collection strategy template library. This strategy template specifies the sampling interval, reporting trigger condition type, and sensor start / stop rules for the nodes within that group, thus obtaining the strategy template for that area's topology information.

[0023] The central monitoring platform converts the parameters in the matched policy template into a policy identifier, which is an index value pointing to a locally stored policy configuration table. The central monitoring platform sends this policy identifier to each IoT sensing node in the shelf area group via the communication network. After receiving the policy identifier, each IoT sensing node looks up the corresponding personalized collection policy from its own locally stored policy configuration table, thus obtaining the personalized collection policy for that node.

[0024] Based on the found personalized data collection strategy, the IoT sensing node applies the sampling interval, reporting trigger conditions, and sensor operating modes from the strategy to its own data collection and control logic, thus confirming the effectiveness of the personalized data collection strategy. The IoT sensing node encapsulates the confirmation result into a confirmation response message and returns it to the central monitoring platform along the original communication link. Upon receiving the confirmation response, the central monitoring platform records that the strategy was successfully issued by the node.

[0025] The central monitoring platform calculates the strategy template selection index for each shelf area group. The index is calculated as follows: the central monitoring platform obtains the historical data change rate of the area, the average remaining power of all sensing nodes in the area, and the average turnover days of the stored materials in the area. It then compares the historical data change rate with the maximum historical data change rate of all areas, compares the average remaining power with the initial power of the sensing nodes, and compares the average turnover days with the maximum average turnover days of all areas. Finally, it performs a weighted summation of the above three comparison results according to a preset weight allocation to obtain a dimensionless comprehensive value as the strategy template selection index.

[0026] The central monitoring platform matches the calculated strategy template selection index with multiple preset template index intervals one by one, with each template index interval corresponding to a strategy template identifier. When the strategy template selection index falls into a certain template index interval, the central monitoring platform extracts the strategy template identifier corresponding to that interval and uses it as the strategy identifier for that shelf area group.

[0027] The central monitoring platform distributes policy identifiers to each IoT sensing node in the shelf area group via the communication network. After receiving the policy identifier, each IoT sensing node selects the corresponding personalized collection policy from the locally stored policy configuration table and applies it, thereby completing the indirect distribution of personalized collection policies without the need for the central monitoring platform to directly distribute complete policy parameters.

[0028] The strategy template selection index represents the comprehensive score used when matching strategy templates for grouping shelf areas within the material storage area.

[0029] Historical data change rate refers to the frequency with which the inventory sensing data of the material storage area changes within a unit of time. This value is obtained by the central monitoring platform based on the number of changes in the data reported by each Internet of Things sensing node.

[0030] The maximum historical data change rate refers to the maximum value of the historical data change rate among all shelf area groups within the material storage area, and is used to normalize the change rates of other areas.

[0031] The average remaining power of the sensing nodes refers to the arithmetic mean of the current remaining power of all IoT sensing nodes in the shelf area group, reflecting the overall energy consumption status of the nodes in the area.

[0032] The initial battery level of a sensing node refers to the full battery level of an IoT sensing node when it is first deployed, serving as a benchmark reference value for the proportion of power consumption.

[0033] The average turnover days of materials refers to the average number of days between the entry and exit of materials stored in a particular shelf area group. It is calculated by the central monitoring platform based on historical entry and exit records.

[0034] The maximum average turnover days refers to the maximum average turnover days of materials in all shelving area groups within the material storage area, and is used to normalize the turnover days of other areas.

[0035] The rate of change weighting coefficient is a pre-set coefficient value used to adjust the proportion of influence of the historical data rate of change in the strategy template selection index.

[0036] The power weighting coefficient is a pre-set value used to adjust the proportion of the average remaining power in the strategy template selection index.

[0037] The turnover days weighting coefficient is a pre-set coefficient value used to adjust the proportion of influence of the average turnover days of materials in the strategy template selection index.

[0038] The central monitoring platform divides the historical data change rate by the maximum historical data change rate to obtain a normalized value of the change rate for that region. The larger this value is, the more drastic the data change in that region.

[0039] The central monitoring platform divides the average remaining power by the initial power to obtain the remaining power ratio. Then, it subtracts this ratio from the value to obtain the power consumption ratio. The larger the ratio, the more severe the power consumption of the nodes in that area.

[0040] The central monitoring platform divides the average turnover days of materials by the maximum average turnover days to obtain a normalized value of turnover days. The larger this value is, the slower the material flow in that area.

[0041] The central monitoring platform multiplies the normalized value of the rate of change by its weight coefficient, the power consumption ratio by its weight coefficient, and the normalized value of the turnover days by its weight coefficient. Then, it adds the three products together to obtain the final value of the strategy template selection index.

[0042] The beneficial effects are as follows: This invention achieves personalized data collection strategy distribution for different shelf areas by grouping regional topology information and matching it with strategy templates. It can dynamically adjust strategy templates based on the historical data change rate of the area, the remaining power of nodes, and the material turnover days, effectively reducing communication overhead and node energy consumption. Through the indirect distribution mechanism of strategy identifiers, this invention reduces the amount of data transmission between the central monitoring platform and the sensing nodes, improving the efficiency and reliability of strategy distribution. By completing strategy confirmation and returning results, this invention ensures that each sensing node accurately applies the personalized data collection strategy, avoiding data collection errors caused by inconsistent strategies.

[0043] A2. Based on the personalized data collection strategy, perform differentiated data processing on the initial inventory sensing data of the IoT sensing nodes in the material storage area to obtain the inventory status record of the material storage area. In this embodiment of the invention, the step of performing differentiated data processing on the initial inventory sensing data of the IoT sensing nodes in the material storage area based on the personalized collection strategy to obtain the inventory status record of the material storage area includes: Personalized data collection strategies are implemented for IoT sensing nodes in the material storage area to report initial inventory sensing data of the IoT sensing nodes. Data rule matching is performed on the node classification labels in the personalized data collection strategy to obtain the differentiated processing rules for the IoT sensing nodes; Based on the differentiated processing rules, the initial inventory sensing data of the IoT sensing node is cleaned to obtain the intermediate inventory data of the IoT sensing node. The intermediate inventory data is compared with the inventory snapshot in the IoT sensing node to obtain the valid change record of the IoT sensing node. The valid change records are compared with the preset material code mapping table to obtain the inventory status record of the material storage area.

[0044] The step of associating and parsing the inventory status records with the real-time sensing signals of the IoT sensing nodes to construct a multi-dimensional dynamic inventory ledger for the central monitoring platform includes: The inventory status record and the record entries of the same storage location in the real-time sensing signal are spatiotemporally aligned to obtain the associated data pair between the inventory status record and the IoT sensing node; The real-time sensing signal in the associated data pair is used as a dynamic dimension field, and the inventory quantity in the inventory status record and the material code in the material code mapping table are used as static dimension fields. The dynamic dimension fields and the static dimension fields are integrated into a multi-dimensional dynamic inventory ledger for the storage location.

[0045] The central monitoring platform sends a reporting instruction to each IoT sensing node in the material storage area. The instruction requires the node to send the currently collected inventory sensing data back to the central monitoring platform according to the sampling interval and triggering conditions specified in the personalized collection strategy.

[0046] After receiving the reporting instruction, each IoT sensing node obtains the current sensing value from its own sensor or RFID reader, packages these values ​​together with the node's own identifier and collection timestamp into an initial inventory sensing data, and then sends it to the central monitoring platform through the communication connection network.

[0047] After receiving the initial inventory sensing data from each IoT sensing node, the central monitoring platform extracts the node classification label corresponding to each node from the stored personalized collection strategy. The node classification label marks whether the node is a high-precision node, a low-power node, or an event-driven node.

[0048] Based on the node classification label, the central monitoring platform searches the preset data processing rule library for the verification rules, outlier removal conditions, and data smoothing methods that match the label, and combines these rules into a differentiated processing rule for that node.

[0049] The central monitoring platform retrieves the raw values ​​from the initial inventory perception data, performs validity checks on each value according to the verification rules in the differentiated processing rules, removes outliers that do not conform to the format or exceed the reasonable range, and then filters the continuous values ​​according to the smoothing processing method to obtain the cleaned intermediate inventory data.

[0050] The central monitoring platform reads the inventory snapshot saved by the IoT sensing node in the previous round from the local storage. The inventory snapshot records the inventory quantity and status of the corresponding storage location of the node at the previous moment.

[0051] The central monitoring platform compares the current inventory quantity in the intermediate inventory data with the historical inventory quantity in the inventory snapshot item by item, identifies the location identifiers and changes in inventory quantity that differ between the two, and extracts these differences as valid change records.

[0052] The central monitoring platform will match the material identifiers in the effective change records with the material codes in the preset material code mapping table. The material code mapping table records the material name, specifications and code information corresponding to each material identifier.

[0053] The central monitoring platform combines the successfully matched material code, the location coordinates in the valid change record, the current inventory quantity, and the change timestamp to generate a complete inventory status record that reflects the latest inventory status of the location.

[0054] The central monitoring platform continuously receives real-time sensing signals from IoT sensing nodes. These signals include dynamic information such as the weight value currently collected by the node, location signal strength, or ambient temperature and humidity.

[0055] The central monitoring platform aligns the previously generated inventory status records with the real-time sensing signals according to the same storage location coordinates. That is, it finds the inventory status record entries and real-time sensing signal entries belonging to the same storage location, pairs them, and performs time-series calibration based on timestamps to obtain associated data pairs.

[0056] The central monitoring platform extracts dynamic values ​​from the real-time sensing signals of each associated data pair and uses these dynamic values ​​as dynamic dimension fields to describe the real-time change status of the materials in that location.

[0057] The central monitoring platform extracts the inventory quantity from the inventory status record and the material code from the material code mapping table from each associated data pair, and uses this fixed or slow-changing information as a static dimension field.

[0058] The central monitoring platform merges dynamic and static dimension fields together and organizes them into a multi-dimensional entry that includes the location coordinates, material code, current inventory quantity, real-time sensing signal value, and timestamp, according to a preset ledger format. This entry is a record in the multi-dimensional inventory dynamic ledger for that location.

[0059] The central monitoring platform aggregates multi-dimensional entries from all storage locations to form a multi-dimensional dynamic inventory ledger covering the entire material storage area. Data for each storage location in the ledger can be queried and updated independently.

[0060] The beneficial effects are as follows: This invention obtains differentiated processing rules by matching data rules to the node classification tags in the personalized data collection strategy. Different data cleaning methods can be used for different node types, effectively filtering out invalid and noisy data and generating accurate intermediate inventory data. By comparing with inventory snapshots to extract valid change records and combining them with a material code mapping table to obtain inventory status records, duplicate processing of all data is avoided, reducing data redundancy. Furthermore, the inventory status records are spatiotemporally aligned with real-time sensing signals to construct a multi-dimensional dynamic inventory ledger. This ledger simultaneously includes static inventory information and dynamic sensing information, providing a rich data foundation for subsequent inventory monitoring and anomaly detection, and improving the completeness and real-time performance of inventory status description.

[0061] A3. In the communication connection network, the reporting frequency of the status priority tag sequence in the inventory status record is dynamically adjusted, and the adjustment process and the inventory status record are sent to the central monitoring platform in real time. In this embodiment of the invention, the material code and inventory change are extracted from the inventory status record to generate a status priority label for the inventory status record; Arrange the status priority tags in chronological order to obtain the status priority tag sequence of the inventory status record, and associate the status priority tag sequence with a reporting frequency value; The reporting frequency value is dynamically adjusted based on the real-time load parameters of the communication connection network and the status priority label sequence. When the real-time load parameter is lower than the load threshold of the communication connection network, the reporting frequency of the high-priority sequence in the status priority label sequence is increased; otherwise, the reporting frequency of the low-priority sequence in the status priority label sequence is decreased. The adjusted reporting frequency value is sent to the communication gateway on the central monitoring platform to obtain the reporting data packet of the status priority label sequence; The timestamps, frequency values, adjustment reasons, and reported data packets during the dynamic adjustment process are integrated and sent to the central monitoring platform.

[0062] The central monitoring platform reads each record from the generated inventory status records and extracts the material code field and the inventory change field from the record. The material code is used to uniquely identify a material, and the inventory change field indicates the increase or decrease in the inventory quantity of the material.

[0063] The central monitoring platform queries the preset priority configuration table based on the extracted material codes. The priority configuration table predefines high priority, medium priority, or low priority levels for each material according to the consumption rate and replenishment lead time. The central monitoring platform uses this level as the status priority label for the inventory status record.

[0064] The central monitoring platform arranges multiple inventory status records corresponding to the same material code or the same storage location in chronological order according to their timestamps, and then puts the arranged status priority tags into a sequence to form the status priority tag sequence corresponding to that record.

[0065] The central monitoring platform associates a reporting frequency value with each status priority tag sequence. The reporting frequency value indicates how often the inventory status record in the sequence is sent. Initially, this frequency value is set to a default value based on the priority level of the sequence.

[0066] The central monitoring platform monitors the real-time load parameters of the communication network in real time. The real-time load parameters reflect the current level of data transmission in the network. The central monitoring platform obtains this parameter value by counting the number of data packets sent and received per unit time.

[0067] The central monitoring platform simultaneously acquires the current reporting frequency value and priority level of each status priority label sequence, and prepares to dynamically adjust these frequency values.

[0068] The central monitoring platform compares real-time load parameters with a pre-set load threshold for the communication connection network. The load threshold is a critical value used to determine whether the network is congested.

[0069] When the central monitoring platform determines that the real-time load parameters are below the load threshold, it indicates that the current network is in an idle state. The central monitoring platform selects the sequences with high priority from all status priority label sequences and increases the reporting frequency of these high priority sequences by one level, that is, increases the number of transmissions per unit time.

[0070] When the central monitoring platform determines that the real-time load parameters are not lower than the load threshold, it indicates that the current network is in a busy or congested state. The central monitoring platform selects the sequences with low priority from all status priority label sequences and reduces the reporting frequency of these low priority sequences by one level, that is, reduces the number of transmissions per unit time, in order to free up network resources for high priority data.

[0071] The central monitoring platform sends the adjusted reporting frequency value to the communication gateway on the central monitoring platform through the communication connection network. The communication gateway is responsible for managing the data reception and forwarding of all IoT sensing nodes.

[0072] The communication gateway retrieves inventory status records sequentially from each sequence according to the new reporting frequency value corresponding to each status priority label sequence, and encapsulates these records into reporting data packets according to the communication protocol format. Each reporting data packet contains multiple inventory status records and their corresponding sequence identifiers.

[0073] The central monitoring platform records the timestamps, reporting frequency values ​​before and after the adjustment, and the reasons that triggered the adjustment during this dynamic adjustment process. The reasons for the adjustment may be that the network load is below or above the threshold.

[0074] The central monitoring platform integrates these adjustment process records with the encapsulated reporting data packets, that is, it appends the adjustment records as additional information to the header or footer of the reporting data packets to form a complete data packet.

[0075] The central monitoring platform sends the integrated data packets to its own receiving port via the communication network, so that the central monitoring platform can perform subsequent inventory ledger updates and anomaly detection and handling.

[0076] The beneficial effects are as follows: This invention generates status priority tags by extracting material codes and inventory changes from inventory status records, which can automatically distinguish different levels of inventory records based on the importance and consumption rate of the materials. By arranging the status priority tag sequence in chronological order and associating it with reporting frequency values, orderly management of inventory change events is achieved. The reporting frequency value is dynamically adjusted based on the real-time load parameters of the communication network. When the network is idle, the reporting frequency of high-priority sequences is increased to ensure timely delivery of critical data; when the network is busy, the reporting frequency of low-priority sequences is decreased to avoid network congestion, thus effectively balancing data real-time performance and network resource consumption. The timestamps, frequency values, and reasons for adjustments are integrated with the reported data packets and sent, providing a complete operation log for subsequent network optimization and fault tracing, improving the adaptability and operational stability of the inventory monitoring system.

[0077] A4. The inventory status record is correlated and analyzed with the real-time sensing signal of the IoT sensing node to construct a multi-dimensional dynamic inventory ledger of the central monitoring platform. In this embodiment of the invention, the inventory status record and the record entries of the same storage location in the real-time sensing signal are spatiotemporally aligned to obtain the associated data pair between the inventory status record and the IoT sensing node; The real-time sensing signal in the associated data pair is used as a dynamic dimension field, and the inventory quantity in the inventory status record and the material code in the material code mapping table are used as static dimension fields. The dynamic dimension fields and the static dimension fields are integrated into a multi-dimensional dynamic inventory ledger for the storage location.

[0078] The central monitoring platform retrieves previously generated inventory status records from the storage. Each inventory status record contains the coordinates of the storage location, the material code, the current inventory quantity, and the most recent change timestamp.

[0079] The central monitoring platform simultaneously receives real-time sensing signals continuously sent by various IoT sensing nodes from the communication connection network. Each real-time sensing signal includes node identifier, signal acquisition timestamp, and dynamic information such as weight value, location signal strength, or ambient temperature and humidity.

[0080] The central monitoring platform compares the location coordinates in the inventory status records with the preset location coordinates corresponding to the node identifiers in the real-time sensing signals to identify inventory status record entries and real-time sensing signal entries with the same location coordinates.

[0081] The central monitoring platform aligns the inventory status records and real-time sensing signal entries belonging to the same storage location according to their timestamps, ensuring that the timestamps are within the allowable error range. Then, the two entries are paired and combined into a related data pair.

[0082] The central monitoring platform generates a data pair for each storage location, which includes both the inventory status record and the corresponding real-time sensing signal for that storage location.

[0083] The central monitoring platform extracts dynamic values ​​from the real-time sensing signals of each associated data pair, such as weight values ​​or signal strength values, and uses these dynamic values ​​as dynamic dimension fields to describe the real-time changes in the status of the materials in that location.

[0084] The central monitoring platform extracts the current inventory quantity and material code from the inventory status record of each associated data pair. At the same time, it looks up the corresponding material name and specification information from the material code mapping table based on the material code, and uses this relatively fixed information as static dimension fields.

[0085] The central monitoring platform merges dynamic and static dimension fields together, that is, it simultaneously writes dynamic sensing values ​​and static inventory information into the records of the same storage location, forming a multi-dimensional entry for that storage location.

[0086] The central monitoring platform organizes all the multi-dimensional entries of the storage locations according to the location coordinates or time sequence to form a complete multi-dimensional dynamic inventory ledger. Each storage location entry in this ledger can be accessed and updated independently.

[0087] This multi-dimensional dynamic inventory ledger is stored in the local storage of the central monitoring platform for use in subsequent inventory comparison and anomaly detection steps.

[0088] The beneficial effects are as follows: This invention achieves precise matching between static inventory information and dynamic sensing information by spatiotemporally aligning inventory status records with records of the same storage location in real-time sensing signals to obtain associated data pairs. By using real-time sensing signals as dynamic dimension fields and inventory quantity and material codes as static dimension fields, the ledger simultaneously reflects changes in inventory quantity and real-time physical status. Integrating dynamic and static dimension fields into a multi-dimensional dynamic inventory ledger solves the problem of single-dimensional traditional inventory ledgers, providing a more comprehensive data view for inventory monitoring and improving the accuracy and timeliness of inventory status judgment.

[0089] A5. Compare the real-time inventory in the dynamic inventory ledger with the current safety stock threshold of the material storage area in real time, and combine the safety stock anomaly triggering mechanism of the central monitoring platform to perform task parsing on the comparison results to obtain the adaptive task list of the material storage area.

[0090] In this embodiment of the invention, the real-time inventory of each storage location in the multi-dimensional dynamic inventory ledger is compared with the upper and lower limits of the safety stock threshold of the storage location to obtain the comparison result of the storage location. Perform abnormal task mapping on the shortage and backlog abnormal records of the storage location to obtain the task items to be executed for the abnormal records in the storage location; Based on the suggested execution order identifier of the storage location, the tasks to be executed are sorted and merged to obtain an adaptive task list for the material storage area.

[0091] The central monitoring platform reads the real-time inventory of each storage location from the established multi-dimensional dynamic inventory ledger. The real-time inventory indicates the actual quantity of materials currently stored in that storage location.

[0092] The central monitoring platform also reads the safety stock threshold corresponding to each storage location from the safety stock configuration library. The safety stock threshold includes an upper limit and a lower limit. The upper limit represents the maximum amount of inventory that the storage location is allowed to store, and the lower limit represents the minimum amount of inventory that the storage location must maintain.

[0093] The central monitoring platform compares the real-time inventory of each storage location with the upper and lower limits of the safety stock for that location, and determines whether the real-time inventory is greater than the upper limit, less than the lower limit, or between the upper and lower limits. Based on the determination result, it generates the comparison result for that storage location.

[0094] When the real-time inventory is less than the lower limit in the comparison results, the central monitoring platform generates a shortage anomaly record. This record includes the anomaly type identifier as shortage, the coordinates of the target storage location, and the current real-time inventory and shortage quantity.

[0095] When the real-time inventory level in the comparison results exceeds the upper limit, the central monitoring platform generates a backlog anomaly record. This record includes the anomaly type identifier as backlog, the coordinates of the target storage location, and the current real-time inventory level and the excess quantity.

[0096] When the real-time inventory level in the comparison results is between the upper and lower limits, the central monitoring platform does not generate any abnormal records and continues to process the next storage location.

[0097] The central monitoring platform collects all generated shortage and backlog anomaly records. Each anomaly record includes an anomaly type identifier, target cargo location coordinates, and an anomaly occurrence timestamp.

[0098] For each abnormal record, the central monitoring platform searches for the corresponding task template in the preset abnormal task mapping rule library based on its abnormality type identifier. The task template specifies the task type, execution priority, and expected execution time to be executed for that type of abnormality.

[0099] The central monitoring platform fills the target cargo location coordinates and the timestamp of the anomaly occurrence in the anomaly record into the corresponding fields of the task template, generating a complete task item to be executed. This task item includes the task type, target cargo location coordinates, execution priority, and expected execution time.

[0100] The central monitoring platform collects all the pending task items converted from the abnormal records, forming a set of pending task items for the abnormal records in that storage location.

[0101] The central monitoring platform reads the suggested execution order identifier for each storage location from the inventory dynamic ledger. This identifier is a sorting criterion value that is pre-set based on the urgency of the materials and the order of their departure.

[0102] The central monitoring platform sorts all pending tasks according to the suggested execution order identifier, that is, tasks with smaller identifier values ​​are listed first and executed first, while tasks with larger identifier values ​​are listed last.

[0103] The central monitoring platform merges task items with the same target location coordinates from the sorted pending task items. That is, multiple task items for the same location are merged into one comprehensive task item, which contains all the operations that need to be performed for that location.

[0104] The central monitoring platform organizes all sorted and merged task items into an adaptive task list for the material storage area. This list lists the tasks to be performed and their order, which guides replenishment, allocation, or inventory operations.

[0105] The beneficial effects are as follows: This invention accurately identifies two abnormal states—shortage and overstock—by comparing the real-time inventory level of each storage location in a multi-dimensional dynamic inventory ledger with the upper and lower limits of the safety stock threshold, avoiding delays and errors from manual inventory counting. By mapping shortage and overstock abnormality records to abnormal tasks, the abnormality type is automatically converted into corresponding tasks to be executed, achieving an automated closed loop from abnormality detection to task generation. Based on the suggested execution order identifier of the storage location, the tasks to be executed are sorted and merged, and the generated adaptive task list can rationally arrange task execution according to the urgency of materials and the order of operations, improving the response speed and operational efficiency of inventory abnormality handling, and reducing production stoppages caused by inventory shortages or capital tied up due to overstock.

[0106] like Figure 2 The diagram shown is a functional block diagram of a real-time monitoring system for material inventory based on Internet of Things (IoT) sensing, provided in an embodiment of the present invention.

[0107] The IoT-based real-time inventory monitoring system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the IoT-based real-time inventory monitoring system 100 may include a personalized data acquisition strategy module 101, a data processing module 102, a frequency adjustment module 103, a ledger construction module 104, and a task parsing module 105. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0108] In this embodiment, the functions of each module / unit are as follows: The personalized data acquisition strategy module 101 is used to issue personalized data acquisition strategies to IoT sensing nodes in the material storage area based on the communication connection network of the central monitoring platform. The data processing module 102 is used to perform differentiated data processing on the initial inventory sensing data of the Internet of Things sensing nodes in the material storage area based on the personalized collection strategy, so as to obtain the inventory status record of the material storage area. The frequency adjustment module 103 is used to dynamically adjust the reporting frequency of the status priority tag sequence in the inventory status record in the communication connection network, and send the adjustment process and the inventory status record to the central monitoring platform in real time. The ledger construction module 104 is used to associate and parse the inventory status record with the real-time sensing signal of the Internet of Things sensing node in order to construct a multi-dimensional dynamic inventory ledger of the central monitoring platform. The task parsing module 105 is used to compare the real-time inventory quantity in the dynamic inventory ledger with the current safety stock threshold of the material storage area in real time, and, in conjunction with the safety stock anomaly triggering mechanism of the central monitoring platform, to perform task parsing on the comparison result to obtain an adaptive task list for the material storage area. In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0109] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0110] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0111] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0112] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for real-time monitoring of material inventory based on Internet of Things (IoT) sensing, characterized in that, The method includes: A1. Based on the communication connection network of the central monitoring platform, personalized data collection strategies are issued to the IoT sensing nodes in the material storage area. A2. Based on the personalized data collection strategy, perform differentiated data processing on the initial inventory sensing data of the IoT sensing nodes in the material storage area to obtain the inventory status record of the material storage area. A3. In the communication connection network, the reporting frequency of the status priority tag sequence in the inventory status record is dynamically adjusted, and the adjustment process and the inventory status record are sent to the central monitoring platform in real time. A4. The inventory status record is correlated and analyzed with the real-time sensing signal of the IoT sensing node to construct a multi-dimensional dynamic inventory ledger of the central monitoring platform. A5. Compare the real-time inventory in the dynamic inventory ledger with the current safety stock threshold of the material storage area in real time, and combine the safety stock anomaly triggering mechanism of the central monitoring platform to perform task parsing on the comparison results to obtain the adaptive task list of the material storage area.

2. The method for real-time monitoring of material inventory based on Internet of Things sensing as described in claim 1, characterized in that, The process of issuing personalized data collection strategies to IoT sensing nodes in the material storage area based on the communication connection network of the central monitoring platform includes: Group the regional topology information in the material storage area to obtain a strategy template for the regional topology information; Based on the strategy template and the communication connection network, a strategy identifier is issued to the IoT sensing node to obtain the personalized collection strategy corresponding to the IoT sensing node. The personalized data collection strategy is confirmed, and the confirmation result is returned to the central monitoring platform.

3. The method for real-time monitoring of material inventory based on Internet of Things sensing as described in claim 2, characterized in that, The strategy template for grouping the regional topology information in the material storage area to obtain the regional topology information includes: Based on the strategy template selection index in the material storage area, the regional topology information in the material storage area is grouped to obtain the strategy template of the regional topology information. The formula for calculating the strategy template selection index is as follows: ； in, Select an index for the strategy template. The historical data change rate of the aforementioned material storage area. The maximum historical data change rate in the aforementioned material storage area. The average remaining power of the sensing nodes in the material storage area. This represents the initial charge level of the sensing node. This refers to the average turnover days of materials in the aforementioned material storage area. This refers to the maximum average turnover days in the aforementioned material storage area. These are the weighting coefficients for the rate of change, the electricity consumption weighting coefficient, and the turnover days weighting coefficient, respectively. The strategy template selection index is matched with the template index range of the material storage area to obtain the strategy template identifier corresponding to the material storage area, and this identifier is used as the strategy identifier. Based on the policy identifier, the IoT sensing nodes within the material storage area group are sent out, resulting in the indirect distribution of the personalized collection policy.

4. The method for real-time monitoring of material inventory based on Internet of Things sensing as described in claim 1, characterized in that, Based on the personalized data collection strategy, the initial inventory sensing data of the IoT sensing nodes in the material storage area is processed differentially to obtain the inventory status record of the material storage area, including: Personalized data collection strategies are implemented for IoT sensing nodes in the material storage area to report initial inventory sensing data of the IoT sensing nodes. Data rule matching is performed on the node classification labels in the personalized data collection strategy to obtain the differentiated processing rules for the IoT sensing nodes; Based on the differentiated processing rules, the initial inventory sensing data of the IoT sensing node is cleaned to obtain the intermediate inventory data of the IoT sensing node. The intermediate inventory data is compared with the inventory snapshot in the IoT sensing node to obtain the valid change record of the IoT sensing node. The valid change records are compared with the preset material code mapping table to obtain the inventory status record of the material storage area.

5. The method for real-time monitoring of material inventory based on Internet of Things sensing as described in claim 4, characterized in that, The step of associating and parsing the inventory status records with the real-time sensing signals of the IoT sensing nodes to construct a multi-dimensional dynamic inventory ledger for the central monitoring platform includes: The inventory status record and the record entries of the same storage location in the real-time sensing signal are spatiotemporally aligned to obtain the associated data pair between the inventory status record and the IoT sensing node; The real-time sensing signal in the associated data pair is used as a dynamic dimension field, and the inventory quantity in the inventory status record and the material code in the material code mapping table are used as static dimension fields. The dynamic dimension fields and the static dimension fields are integrated into a multi-dimensional dynamic inventory ledger for the storage location.

6. The method for real-time monitoring of material inventory based on Internet of Things sensing as described in claim 1, characterized in that, The process involves comparing the real-time inventory levels in the dynamic inventory ledger with the current safety stock threshold of the material storage area in real time, and combining this with the safety stock anomaly triggering mechanism of the central monitoring platform to perform task parsing on the comparison results, thereby obtaining an adaptive task list for the material storage area, including: The real-time inventory of each storage location in the multi-dimensional dynamic inventory ledger is compared with the upper and lower limits of the safety stock threshold of the storage location to obtain the comparison result of the storage location. Perform abnormal task mapping on the shortage and backlog abnormal records of the storage location to obtain the task items to be executed for the abnormal records in the storage location; Based on the suggested execution order identifier of the storage location, the tasks to be executed are sorted and merged to obtain an adaptive task list for the material storage area.

7. The method for real-time monitoring of material inventory based on Internet of Things sensing as described in claim 1, characterized in that, In the communication connection network, the reporting frequency of the status priority tag sequence in the inventory status record is dynamically adjusted, and the adjustment process and the inventory status record are sent to the central monitoring platform in real time, including: Extract the material code and inventory change from the inventory status record, and generate the status priority label of the inventory status record; Arrange the status priority tags in chronological order to obtain the status priority tag sequence of the inventory status record, and associate the status priority tag sequence with a reporting frequency value; The reporting frequency value is dynamically adjusted based on the real-time load parameters of the communication connection network and the status priority label sequence. When the real-time load parameter is lower than the load threshold of the communication connection network, the reporting frequency of the high-priority sequence in the status priority label sequence is increased; otherwise, the reporting frequency of the low-priority sequence in the status priority label sequence is decreased. The adjusted reporting frequency value is sent to the communication gateway on the central monitoring platform to obtain the reporting data packet of the status priority label sequence; The timestamps, frequency values, adjustment reasons, and reported data packets during the dynamic adjustment process are integrated and sent to the central monitoring platform.

8. The method for real-time monitoring of material inventory based on Internet of Things sensing as described in claim 4, characterized in that, The step of performing data rule matching on the node classification labels in the personalized data collection strategy to obtain the differentiated processing rules for the IoT sensing nodes includes: Data processing operations are performed on the node classification tags of the personalized collection strategy to obtain the rule group identifier of the node classification tags; The verification rules, removal conditions, and smoothing methods corresponding to the rule group identifier are matched with the initial inventory perception data, and then combined and encapsulated to obtain the differentiated processing rules for the IoT perception node. The differentiated processing rules are associated with and stored with the node identifier of the IoT sensing node to obtain the rule call record of the IoT sensing node.

9. The method for real-time monitoring of material inventory based on Internet of Things sensing as described in claim 6, characterized in that, The process of mapping shortage and backlog anomaly records for the storage location to anomaly records yields pending task items for the anomaly records in the storage location, including: Obtain the abnormal record of the storage location from the safety stock abnormality triggering mechanism. The abnormal record includes an abnormality type identifier, target storage location coordinates, and abnormality occurrence timestamp. Based on a preset exception-task mapping rule base, task template matching is performed on the exception type identifier to obtain the task template corresponding to the exception type identifier; Fill the target cargo location coordinates and the timestamp of the anomaly occurrence into the corresponding fields of the task template to obtain the task items to be executed for the anomaly records in the cargo location; The pending tasks of the abnormal records in the storage location are grouped by task type and sorted by priority to obtain a list of pending tasks of the abnormal records in the storage location.

10. A real-time monitoring system for material inventory based on Internet of Things (IoT) sensing, characterized in that, The system is used to implement the real-time monitoring method for material inventory based on Internet of Things (IoT) sensing as described in claim 1, the system comprising: The personalized data collection strategy module is used to issue personalized data collection strategies to IoT sensing nodes in the material storage area based on the communication connection network of the central monitoring platform. The data processing module is used to perform differentiated data processing on the initial inventory sensing data of the IoT sensing nodes in the material storage area based on the personalized collection strategy, so as to obtain the inventory status record of the material storage area. The frequency adjustment module is used to dynamically adjust the reporting frequency of the status priority tag sequence in the inventory status record in the communication connection network, and send the adjustment process and the inventory status record to the central monitoring platform in real time. The ledger construction module is used to associate and parse the inventory status records with the real-time sensing signals of the IoT sensing nodes in order to construct a multi-dimensional dynamic inventory ledger for the central monitoring platform. The task parsing module is used to compare the real-time inventory quantity in the dynamic inventory ledger with the current safety stock threshold of the material storage area in real time, and combine the safety stock anomaly triggering mechanism of the central monitoring platform to perform task parsing on the comparison result in order to obtain the adaptive task list of the material storage area.

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