Intelligent food management method based on Internet of Things technology

Abstract

The invention relates to the technical field of food management, and discloses an intelligent food management method based on the Internet of Things technology, which comprises the following steps: step 1, obtaining a unique identifier of a food batch and creating a time temperature account book at a warehousing read-write point; 2, temperature, humidity and gas indication values are collected, and accumulated quality guarantee consumption is calculated; 3, determining the remaining shelf life according to the nominal shelf life and the accumulated shelf life consumption, and generating a service mapping matrix; step 4, performing deterministic constraint solution based on the order demand, the inventory quantity and the business mapping matrix to obtain a distribution list; 5, generating an execution token containing a batch unique identifier list and a task cut-off moment according to the distribution list; step 6, updating the quality guarantee state and recording abnormal information; and step 7, after arrival, quality guarantee checking is carried out, a traceable report is generated, and offline updating is carried out on the grading threshold. According to the invention, integrated management and control and whole-process traceable management of distribution execution of food from warehousing to arrival are realized.

Description

Technical Field

[0001] This invention belongs to the field of food management technology, specifically relating to a smart food management method based on Internet of Things (IoT) technology. Background Technology

[0002] As the scale of cold chain logistics and fresh food supply chains continues to expand, the management complexity of food in warehousing, storage, transportation, and distribution is constantly increasing. Existing food management systems typically use batches or categories as basic management units, relying mainly on nominal shelf-life for inventory turnover and order allocation, making it difficult to accurately reflect the environmental changes that food undergoes during actual circulation. Especially in multi-node, multi-route logistics scenarios, food may be in different temperature, humidity, and gas environments at different times, often resulting in a significant discrepancy between its actual usable shelf life and the nominal shelf life.

[0003] While existing technologies utilize the Internet of Things (IoT) to collect food environmental parameters, the data is often collected through independent records or simple monitoring, lacking a systematic connection with food batches, business rules, and logistics decisions. This makes it difficult to directly participate in business processes such as inventory management, order allocation, and execution control. Furthermore, existing solutions typically focus on inventory quantity or transportation timeliness when allocating orders, failing to comprehensively consider the remaining shelf life of food, transit time constraints, and spoilage risks within a unified rule system. This can easily lead to a mismatch between allocation results and actual execution conditions. In addition, existing food management methods primarily rely on post-event recording for handling abnormal environments or execution timeouts during the execution phase, lacking a dynamic update and traceability analysis mechanism based on a unified data ledger, making it difficult to support subsequent adjustments and optimizations of management parameters. Summary of the Invention

[0004] This invention provides a smart food management method based on Internet of Things (IoT) technology, which solves the technical problems in related technologies such as the separation of environmental data and business management rules in the food circulation process, and the difficulty in inventory allocation, execution control and traceability management based on the actual shelf life of food.

[0005] This invention provides a smart food management method based on Internet of Things (IoT) technology, comprising the following steps: Step 1: Obtain the unique identifier of the food batch, bind it at the inbound read / write point and create a time-temperature ledger. The time-temperature ledger records temperature, humidity, gas indicator value, time and nominal shelf life. Step 2: Collect and write temperature, humidity, and gas indicator values ​​according to the sampling time segments. Determine the environmental impact coefficient according to the preset normal range corresponding to the temperature, humidity, and gas indicator values. Combine the sampling time segments to obtain the quality preservation consumption. Accumulate the values ​​to obtain the cumulative quality preservation consumption. Step 3: Obtain the remaining shelf life from the nominal shelf life and the cumulative shelf life consumption. Compare the remaining shelf life with three preset grading thresholds to obtain four levels and generate a business mapping matrix. The business mapping matrix includes picking priority and the maximum allowed transit time. Step 4: Solve deterministic constraints based on order demand, inventory quantity, remaining shelf life, grading results, and business mapping matrix to obtain the allocation list; Step 5: Generate an execution token containing a list of unique batch identifiers and the task deadline based on the allocation list; Step 6: Update the cumulative shelf life consumption and remaining shelf life. When any of the temperature, humidity and gas indicator values ​​exceed the corresponding preset normal range, deduct the amount according to the abnormality penalty coefficient. Record the abnormal information when the execution token expires. Step 7: Upon arrival at the station, verify the remaining shelf life and the necessary time required for unloading and shelving, generate a traceable report based on the time and temperature ledger, and update the preset classification threshold offline.

[0006] The beneficial effects of this invention are as follows: By introducing a data management approach centered on food batches, this invention unifies and processes environmental and temporal information generated at each stage of food warehousing, storage, transportation, and arrival with business management rules. By constructing a time-temperature ledger, information such as temperature, humidity, gas indicator values, and time are continuously recorded and mapped one-to-one with each food batch, allowing the actual shelf-life status of food to participate in inventory management and order allocation processes in a quantifiable and traceable manner. By introducing the remaining shelf life, grading results, and business mapping matrix into the allocation decision process, deterministic constraints are applied under conditions such as order demand, inventory quantity, and logistics time, ensuring that the food allocation process is completed within a unified rule system and avoiding inconsistencies between management decisions and execution conditions. Simultaneously, by setting execution tokens and task deadlines, the allocation results are transformed into verifiable execution constraints, and the food shelf-life status is dynamically updated during execution based on environmental anomalies and time status. Furthermore, this invention generates traceable reports based on full-process data at the arrival stage and updates grading thresholds offline, allowing management parameters to be adjusted based on historical business data without affecting the determinism of online business processing. Attached Figure Description

[0007] Figure 1 This is a flowchart of a smart food management method based on Internet of Things (IoT) technology according to the present invention. Detailed Implementation

[0008] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0009] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in one or more embodiments of the present invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0010] like Figure 1 As shown, a smart food management method based on Internet of Things (IoT) technology includes the following steps: Step 1: Obtain the unique identifier of the food batch, bind it at the inbound read / write point and create a time-temperature ledger. The time-temperature ledger records temperature, humidity, gas indicator value, time and nominal shelf life. Step 2: Collect and write temperature, humidity, and gas indicator values ​​according to the sampling time segments. Determine the environmental impact coefficient according to the preset normal range corresponding to the temperature, humidity, and gas indicator values. Combine the sampling time segments to obtain the quality preservation consumption. Accumulate the values ​​to obtain the cumulative quality preservation consumption. Step 3: Obtain the remaining shelf life from the nominal shelf life and the cumulative shelf life consumption. Compare the remaining shelf life with three preset grading thresholds to obtain four levels and generate a business mapping matrix. The business mapping matrix includes picking priority and the maximum allowed transit time. Step 4: Solve deterministic constraints based on order demand, inventory quantity, remaining shelf life, grading results, and business mapping matrix to obtain the allocation list; Step 5: Generate an execution token containing a list of unique batch identifiers and the task deadline based on the allocation list; Step 6: Update the cumulative shelf life consumption and remaining shelf life. When any of the temperature, humidity and gas indicator values ​​exceed the corresponding preset normal range, deduct the amount according to the abnormality penalty coefficient. Record the abnormal information when the execution token expires. Step 7: Upon arrival at the station, verify the remaining shelf life and the necessary time required for unloading and shelving, generate a traceable report based on the time and temperature ledger, and update the preset classification threshold offline.

[0011] In one embodiment of the present invention, a unique identifier for the food batch is obtained, bound to and created at the warehouse read / write point, and a temperature and humidity ledger is created. The temperature and humidity ledger records temperature, humidity, gas indicator values, time, and nominal shelf life, including: Step 11: Obtain the unique identifier of the food batch. The unique identifier of the food batch is a coded information that can uniquely identify a specific food batch and is used to distinguish different food batches in the system. This unique identifier can be obtained through RFID tags, QR codes, or other unique identification carriers. At the warehouse read / write point, the system performs character integrity verification and duplicate verification on the obtained unique identifier of the food batch. Character integrity verification is used to determine whether the unique identifier of the food batch meets the preset coding integrity requirements; duplicate verification is used to determine whether the unique identifier of the food batch already exists in the binding record, specifically by comparing the currently obtained unique identifier of the food batch with the unique identifiers of food batches already existing in the binding record. After the verification passes, the system generates a binding record containing the unique identifier of the food batch and the time, and binds the unique identifier of the food batch to the warehouse read / write point.

[0012] Step 12: After completing the unique identifier binding for the food batch, the system creates a corresponding time-temperature ledger using the unique identifier as an index. This time-temperature ledger is a data record object corresponding one-to-one with a single food batch, used to continuously record environmental data and time information related to food quality during the food circulation process. When creating the time-temperature ledger, the system writes the nominal shelf life into the ledger header information. The nominal shelf life is the theoretical shelf life of the food under normal storage conditions and is the basic parameter for subsequently calculating the remaining shelf life. Simultaneously, the system fixes the record fields of the time-temperature ledger, ensuring that it only contains four types of record fields: temperature, humidity, gas indicator values, and time. Furthermore, the system restricts the time-temperature ledger to allow only append writes, thereby preventing the overwriting or tampering of existing records and ensuring the continuity and traceability of data records. The gas indication value is an indicative numerical value used to characterize the gas state related to the freshness of the environment in which the food batch is located. This gas indication value can be generated by a gas sensing device or gas indicator label installed inside the food packaging or in the food storage environment, reflecting changes in the gas state in the surrounding environment related to changes in food quality. For example, when the gas indicator label inside the food packaging is in a normal state, its corresponding gas indication value can be a value within a first preset value range; when the gas indicator label changes state, its corresponding gas indication value can be a value within a second preset value range.

[0013] Step 13: After food is received and during subsequent storage and transportation, the system writes environmental data to the time-temperature ledger according to a unified time reference. Before each write, the system performs a range consistency check on the temperature, humidity, and gas indication values ​​to be written to the time-temperature ledger based on the time. The range consistency check is used to determine whether the temperature, humidity, and gas indication values ​​fall within their respective preset normal ranges, where the preset normal ranges are reasonable value ranges pre-set for different environmental parameters. If the check passes, the system writes the corresponding temperature, humidity, gas indication values, and time to the time-temperature ledger; if the check fails, the system does not write the corresponding temperature, humidity, and gas indication values ​​for that collection, but instead writes a rejection mark and time to the time-temperature ledger to indicate that there was an abnormal collection at that time point.

[0014] Through the above implementation process, this invention establishes a management index centered on the unique identifier of each food batch during the food warehousing stage, and records environmental data related to each food batch in a unified, continuous, and non-overwriteable manner through a time-temperature ledger. On the one hand, this technical solution provides a reliable data foundation for subsequent inventory management, order allocation, and logistics scheduling based on remaining shelf life; on the other hand, by introducing range consistency verification and write rejection marking mechanisms during the accounting stage, abnormally collected data and normally collected data are clearly distinguished in the ledger, preventing abnormal data from directly participating in business calculations, thereby improving the certainty and traceability of data processing in the intelligent food management process.

[0015] In one embodiment of the present invention, temperature, humidity, and gas indicator values ​​are collected and written according to sampling time segments. An environmental impact coefficient is determined based on a preset normal range corresponding to the temperature, humidity, and gas indicator values. The preservation consumption is obtained by combining the sampling time segments, and the cumulative preservation consumption is obtained by summing these values. This includes: Step 21: Based on the unique identifier of the food batch, locate the corresponding temperature and time ledger, and generate the time corresponding to the sampling period according to the preset sampling time segments. Within each sampling time segment, the system collects the temperature, humidity, and gas indicator values ​​of the current environment in which the food batch is located, and forms a sampling record containing temperature, humidity, gas indicator values, and time. The sampling record is written to the temperature and time ledger in an append-only manner. The same sampling record is only allowed to be written once, and it is prohibited to overwrite the temperature, humidity, gas indicator values, and time that have already been written, thereby ensuring the continuity and immutability of the sampling data in the time dimension.

[0016] Step 22: After the sampling record is written into the time-temperature ledger, the system compares the temperature, humidity, and gas indicator values ​​in the sampling record with their corresponding preset normal ranges. The preset normal ranges are reasonable value ranges pre-defined for different environmental parameters, used to characterize the state of food under normal storage or transportation conditions. When the temperature, humidity, and gas indicator values ​​all fall within their respective preset normal ranges, the system determines the environmental impact coefficient corresponding to the sampling record as a first preset value; when any of the temperature, humidity, and gas indicator values ​​exceeds its corresponding preset normal range, the system determines the environmental impact coefficient as a second preset value. Subsequently, the system writes the determined environmental impact coefficient along with the corresponding time into the time-temperature ledger to form a record sequence of environmental impact coefficient changes over time.

[0017] Step 23: After determining the environmental impact coefficient, the system obtains the environmental impact coefficient corresponding to the time period and combines it with the sampling time segment to quantify the shelf-life consumption of the food batch within that sampling time segment. Specifically, the system determines the shelf-life consumption corresponding to that time segment by multiplying the environmental impact coefficient by the sampling time segment. Subsequently, the system accumulates the shelf-life consumption in the time-temperature ledger according to the time sequence to obtain the cumulative shelf-life consumption, and writes the cumulative shelf-life consumption and its corresponding time into the time-temperature ledger. The cumulative shelf-life consumption serves as the basis for subsequent calculations of the remaining shelf life and is continuously updated and stored in the time-temperature ledger.

[0018] Through the above implementation process, this invention organizes temperature, humidity, and gas indicator values ​​collected by the Internet of Things according to a unified sampling time segment. By pre-setting a mapping relationship between normal intervals and environmental impact coefficients, environmental changes are transformed into measurable shelf-life consumption, thereby achieving continuous quantitative management of the shelf-life status of food batches. By recording sampling records, environmental impact coefficients, shelf-life consumption, and cumulative shelf-life consumption in a time-temperature ledger through append-only entries, the consistency and traceability of food batch environmental data and business calculation results in the time dimension are ensured.

[0019] In one embodiment of the present invention, the remaining shelf life is obtained from the nominal shelf life and the cumulative shelf-life consumption. The remaining shelf life is then compared with three preset grading thresholds to obtain four levels, generating a business mapping matrix. The business mapping matrix includes picking priority and the maximum allowed transit time, including: Step 31: Based on the unique identifier of the food batch, locate the corresponding time-temperature ledger and read the latest written nominal shelf life and cumulative shelf-life consumption from the ledger. The cumulative shelf-life consumption is the time consumption amount accumulated based on the environmental impact coefficient and sampling time segments. The system performs a difference calculation between the nominal shelf life as the minuend and the cumulative shelf-life consumption as the subtrahend to obtain the remaining shelf life of the corresponding food batch. The remaining shelf life and the unique identifier of the food batch are then written into the time-temperature ledger for direct use in subsequent business processing steps.

[0020] Step 32: After obtaining the remaining shelf life, the system acquires three pre-set grading thresholds. These three thresholds are used to segment the remaining shelf life, with the first threshold being greater than the second, and the second greater than the third. Based on these thresholds, the system divides the remaining shelf life into four grade ranges and determines which grade the current remaining shelf life falls into, locking that grade as the grading result for the food batch. Subsequently, the system writes the grading result and the unique identifier of the food batch into the time-temperature ledger, making the grading result a management attribute that corresponds one-to-one with each food batch.

[0021] Step 33: After determining the grading results, the system generates a business mapping matrix using the four grades as index keys. The business mapping matrix is ​​a data structure that carries the correspondence between the grading results and business management parameters, where each grade corresponds to a set of business management parameters. Specifically, the business mapping matrix is ​​a two-dimensional data structure with four grades as row indices and column fields including at least picking priority and maximum allowed transit time. The business mapping matrix includes four rows, each corresponding to one grade; it also includes at least two columns, where the first column is the picking priority and the second column is the maximum allowed transit time. The system outputs the generated business mapping matrix along with the grading results as input for subsequent deterministic constraint solving based on order demand, inventory quantity, and logistics conditions.

[0022] Through the above implementation process, this invention transforms the remaining shelf life of food batches from a continuous time quantity into discrete grading results. Furthermore, it maps these grading results to specific business management parameters via a business mapping matrix, enabling food quality status to directly participate in inventory management, picking and sorting, and logistics scheduling processes in a standardized manner. This technical solution uses a time-temperature ledger to uniformly record and correlate nominal shelf life, cumulative shelf-life consumption, remaining shelf life, and grading results, ensuring the consistency and traceability of various business decision parameters in the intelligent food management process.

[0023] In one embodiment of the present invention, a deterministic constraint solution is performed based on order demand, inventory quantity, remaining shelf life, grading results, and a business mapping matrix to obtain an allocation list, including: Step 41: Obtain the current pending order demands and the corresponding inventory quantity for each food batch, and retrieve the remaining shelf life, grading result, and business mapping matrix for each food batch from the previous steps. Simultaneously, the system obtains the transportation time and receiving buffer time for the routes corresponding to each order demand. Based on the grading result of the food batch, the system indexes the business mapping matrix to obtain the picking priority and maximum allowed transit time corresponding to that grading result, and establishes a correspondence between the inventory quantity, remaining shelf life, picking priority, and maximum allowed transit time and the unique identifier of the food batch, thereby forming a set of batch constraint parameters corresponding to the unique identifier of the food batch.

[0024] Step 42: After forming the batch constraint parameter set, the system adds the transportation time of the route corresponding to each order demand to the receiving buffer time to obtain the necessary time required to complete the order demand. Based on the relationship between the remaining shelf life and the necessary time, the system constructs an arrival feasibility constraint, which limits the food batch to participating in the allocation of the corresponding order demand only if the remaining shelf life of the food batch is not less than the necessary time. Simultaneously, based on the relationship between the route's transportation time and the maximum allowed transit time, the system constructs a maximum transit constraint, which limits the corresponding food batch to participating in the allocation of the route only if the route's transportation time is not greater than the maximum allowed transit time.

[0025] While constructing time-related constraints, the system defines allocation quantity variables to represent the distribution relationship between food batches and order demands. For order demands, the system constructs order satisfaction constraints, which stipulate that for each order demand, the sum of all allocation quantity variables allocated to that order demand should equal the order demand itself. For food batches, the system constructs inventory constraints, which stipulate that for each food batch's unique identifier, the sum of allocation quantity variables allocated to each order demand should not exceed the inventory quantity corresponding to that food batch's unique identifier.

[0026] Step 43: Under the condition that the arrival feasibility constraint, maximum transit constraint, order fulfillment constraint, and inventory constraint are all satisfied, the system performs deterministic constraint solving on the allocation quantity variables based on the deterministic constraint solving model, obtaining the value set of each allocation quantity variable. The deterministic constraint solving model is used to solve the allocation quantity variables under fixed constraints, without involving predictive calculations or stochastic adjustments. Based on the non-zero allocation quantity variables, the system generates allocation list entries one by one. Each allocation list entry corresponds to a unique identifier for a food batch and an order requirement, and includes the corresponding allocation quantity, route transportation time, receiving buffer time, and picking priority. Before outputting the allocation list, the system checks each item in the generated allocation list against the arrival feasibility constraint, maximum transit constraint, order fulfillment constraint, and inventory constraint. The allocation list is output only after the checks are passed.

[0027] Specifically, the construction of the deterministic constraint solution model includes: Step 51: Obtain the allocation quantity variable representing the distribution relationship between food batches and order demands, and simultaneously obtain the remaining shelf life, route transportation time, and receiving buffer time related to the allocation. Furthermore, the system obtains the unit cost coefficient of the batch supply order related to the food batch supply, and the batch loss penalty coefficient characterizing the risk of spoilage due to insufficient shelf life. The system adds the route transportation time and the receiving buffer time to obtain the necessary time required to fulfill the order demand, and uses this necessary time as an important parameter for subsequent assessment of the food batch's in-transit risk.

[0028] Step 52: In constructing the target value, the system calculates the supply cost corresponding to each combination of a unique identifier for each food batch and each order demand, based on the corresponding allocation quantity variable and the unit cost coefficient of the batch supply order. The system then summarizes the supply costs of all combinations to obtain the total supply cost. Simultaneously, based on the relationship between the remaining shelf life and the required time, the system calculates the potential loss risk of a food batch while meeting order demands. This loss risk is obtained by subtracting the required time from the remaining shelf life. The system further combines the loss risk with the batch loss penalty coefficient to obtain the corresponding loss penalty, and then summarizes the loss penalties of all combinations to obtain the overall loss penalty value.

[0029] Step 53: After obtaining the total supply cost and the write-off penalty, the system determines the sum of the total supply cost and the write-off penalty as the target value for evaluating the merits of the allocation scheme. The system uses minimizing this target value as the solution objective, and combines this target value with the previously constructed arrival feasibility constraint, maximum transit constraint, order fulfillment constraint, and inventory constraint to form a deterministic constraint solution model. With the above constraints remaining unchanged, the system performs deterministic constraint solution on the allocation quantity variable to obtain an allocation scheme that comprehensively balances cost and write-off risk.

[0030] Through the above implementation process, this invention unifies the constraints of remaining shelf life, grading results, and business mapping matrix with order demand, inventory quantity, route transportation time, and receiving buffer time to form an allocation list. Under the conditions that arrival feasibility constraints, maximum in-transit constraints, order fulfillment constraints, and inventory constraints are met, the unit cost coefficient of batch supply orders and batch damage penalty coefficient are introduced to construct target values ​​and perform deterministic constraint solving. This ensures that the allocation scheme maintains a consistent decision-making approach between supply costs and damage risks, thereby improving the rationality and consistency of allocation decisions in the process of intelligent food management.

[0031] In one embodiment of the present invention, generating an execution token containing a list of batch unique identifiers and a task deadline based on an allocation list includes: Step 61: Obtain the allocation list generated in the previous steps, and use the allocation list as the sole basis for generating the execution token. The system extracts the unique identifier of the food batch corresponding to each item in the allocation list, and performs deduplication on the extraction results to obtain a batch unique identifier list. The batch unique identifier list is used to represent the range of all food batches involved in the current execution task, thereby avoiding interference with food batches not in the allocation list during subsequent execution control.

[0032] Step 62: After determining the batch unique identifier list, the system reads the remaining shelf life, route transportation time, and receiving buffer time corresponding to each item in the allocation list. The system adds the route transportation time and the receiving buffer time to obtain the necessary time required to complete the item, and further calculates the difference between this necessary time and the corresponding remaining shelf life to obtain the remaining margin time corresponding to the allocation list item. The system compares the remaining margin time of all items in the allocation list and uses the minimum value as a unified time constraint benchmark to determine the task deadline corresponding to this execution task. By determining the task deadline with the minimum remaining margin time, the system ensures that the execution process is limited to the most urgent food batches.

[0033] Step 63: After determining the task deadline, the system generates an execution token and writes the batch unique identifier list and the task deadline into the execution token. Simultaneously, the system generates a verification digest for consistency checking based on the allocation list and writes this verification digest into the execution token as well. The generated execution token is distributed to the warehouse terminal and the vehicle terminal. Upon receiving the execution token, the terminal generates a corresponding verification digest based on the locally obtained allocation list and compares it with the verification digest in the execution token. It also checks whether the current time is not later than the task deadline. When the verification digests match and the time verification is successful, the terminal enters the execution state; when the verification digests do not match or the time verification is unsuccessful, the terminal records the exception information for subsequent management and traceability.

[0034] Through the above implementation process, this invention transforms the allocation list into an execution token with time constraints and consistency verification capabilities, enabling unified control over the allocation results of food batches during warehouse operations and transportation. By determining the task deadline based on the minimum remaining margin time in the allocation list, the execution process is prevented from exceeding the acceptable time range for the food batch; and by verifying the summary and time verification mechanisms, the consistency between the execution process and the allocation list is guaranteed.

[0035] In one embodiment of the present invention, the cumulative shelf-life consumption and remaining shelf life are updated. When any of the temperature, humidity, and gas indicator values ​​exceeds the corresponding preset normal range, an abnormality penalty coefficient is deducted. When the execution token expires, abnormal information is recorded, including: Step 71: Based on the unique identifier of the food batch, locate the corresponding temperature and time ledger, and read the latest written cumulative shelf-life consumption, remaining shelf life, temperature, humidity, gas indicator value, and time from the temperature and time ledger. Simultaneously, the system reads the execution token corresponding to the food batch and extracts the task deadline from the execution token. The system compares the current time with the task deadline to determine the current task status. If the current time is not later than the task deadline, the task status is determined to be in an active state; if the current time is later than the task deadline, the task status is determined to be in an inactive state.

[0036] Step 72: After determining the task status, the system compares the temperature, humidity, and gas indicator values ​​recorded in the time-temperature ledger with their corresponding preset normal ranges. When all three values ​​fall within their respective preset normal ranges, the system maintains the cumulative shelf-life consumption unchanged. When any one of these parameters exceeds its corresponding preset normal range, the system reads a pre-set anomaly penalty coefficient and combines the current shelf-life consumption with the penalty coefficient to calculate an anomaly deduction. The system adds this anomaly deduction to the original cumulative shelf-life consumption to obtain an updated cumulative shelf-life consumption, thus reflecting the additional consumption impact of abnormal environmental conditions on food shelf-life.

[0037] Step 73: After updating the cumulative shelf-life consumption, the system reads the nominal shelf-life from the temperature-controlled ledger and calculates the difference between the nominal shelf-life and the updated cumulative shelf-life consumption to obtain the updated remaining shelf-life. The system writes both the updated cumulative shelf-life consumption and the updated remaining shelf-life into the temperature-controlled ledger to keep the shelf-life status of the food batch up-to-date. Simultaneously, when the task status is determined to be invalid, the system generates exception information corresponding to that food batch and writes the exception information into the temperature-controlled ledger to identify the abnormal situation where the execution token was triggered outside its validity period.

[0038] Through the above implementation process, this invention combines environmental monitoring results with the time constraint of the execution token during the execution phase to dynamically update the shelf-life status of food batches. When environmental parameters exceed the preset normal range, the cumulative shelf-life consumption is deducted and corrected through an anomaly penalty coefficient, ensuring that the remaining shelf life reflects the actual consumption under abnormal environmental conditions. When the execution token expires, the abnormal execution behavior is identified through an anomaly information recording mechanism. This technical solution realizes the coordinated processing of execution control and quality status updates in intelligent food management methods, ensuring the consistency and traceability of food batch status throughout the entire process management.

[0039] In one embodiment of the present invention, upon arrival at the station, the remaining shelf life and the necessary time required for unloading and shelving are checked, and a traceable report is generated based on the time-temperature ledger. The preset grading thresholds are then updated offline, including: Step 81: Locate the corresponding temperature ledger based on the unique identifier of the food batch and read the latest updated remaining shelf life from the ledger. Simultaneously, the system obtains the necessary time required for the food batch to be unloaded and shelved. The system compares the updated remaining shelf life with the necessary time required from unloading to shelving and records the corresponding verification mark. When the updated remaining shelf life is less than the necessary time required from unloading to shelving, the system generates an anomaly information corresponding to that food batch, indicating a risk of insufficient time during the warehouse circulation stage after arrival.

[0040] Step 82: After completing the arrival verification, the system extracts historical data related to the food batch based on the time-temperature ledger. This historical data includes nominal shelf life, temperature, humidity, gas indicator value, time, cumulative shelf-life consumption, remaining shelf life, updated cumulative shelf-life consumption, and updated remaining shelf life. The system associates this historical data with verification markers and anomaly information to generate a traceability report for the corresponding food batch. Subsequently, the system summarizes the traceability reports according to the unique identifier of each food batch, forming an offline sample set for offline analysis. The system filters out samples from the offline sample set that have failed verification or contain anomaly information, obtaining a valid sample set for parameter updates.

[0041] Step 83: After obtaining the valid sample set, the system sorts the updated remaining shelf life of each food batch in the valid sample set offline. Based on preset quantiles, it selects three threshold positions from the sorting results, corresponding to the updated remaining shelf life, as candidate thresholds. The system writes these candidate thresholds into three preset grading thresholds and verifies the relationship between them to confirm that the first preset grading threshold is greater than the second preset grading threshold, and the second preset grading threshold is greater than the third preset grading threshold. When the verification is successful, the system solidifies the three preset grading thresholds into usable versions and writes them into the threshold configuration storage for subsequent grading processes.

[0042] Through the above implementation process, this invention introduces a verification mechanism for remaining shelf life and the necessary length of warehouse circulation during the food arrival stage, enabling feasibility assessment of food batches before they enter the warehouse. Furthermore, it generates traceable reports covering the entire process based on temperature and time ledgers, providing a complete data foundation for management and analysis. By statistically processing valid samples offline and updating preset grading thresholds, grading parameters can be adjusted based on historical business data without affecting the determinism of online allocation and execution.

[0043] The embodiments of the present invention have been described above, but the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of the present embodiments, all of which are within the protection scope of the present embodiments.

Claims

1. A food intelligent management method based on Internet of Things technology, characterized in that, The method comprises the following steps: Step 1, obtaining the unique identifier of the food batch, binding and creating the time-temperature account book at the warehouse reading and writing point, and recording the temperature, humidity, gas indicating value, time and nominal shelf life in the time-temperature account book; Step 2, collecting and writing the temperature, humidity and gas indicating value according to the sampling time segment, determining the environmental influence coefficient according to the preset normal interval corresponding to the temperature, humidity and gas indicating value, and obtaining the shelf life consumption combined with the sampling time segment to obtain the cumulative shelf life consumption; Step 3, obtaining the remaining shelf life from the nominal shelf life and the cumulative shelf life consumption, comparing the remaining shelf life with the three preset grading thresholds to obtain four grades, generating a business mapping matrix, and the business mapping matrix includes the picking priority and the maximum allowed transit time; Step 4, determining the assignment list according to the order demand, inventory quantity, remaining shelf life, grading result and business mapping matrix; Step 5, generating an execution token including the batch unique identifier list and task deadline according to the assignment list; Step 6, updating the cumulative shelf life consumption and the remaining shelf life, and deducting according to the abnormal penalty coefficient when any of the temperature, humidity and gas indicating value exceeds the corresponding preset normal interval, and recording abnormal information when the execution token is invalid; Step 7, checking the remaining shelf life with the necessary time required for unloading to the shelf after arrival, and generating a traceable report based on the time-temperature account book, and updating the preset grading threshold offline. 2.The food intelligent management method based on the Internet of Things technology according to claim 1, characterized in that, Obtaining the unique identifier of the food batch, binding and creating the time-temperature account book at the warehouse reading and writing point, and recording the temperature, humidity, gas indicating value, time and nominal shelf life in the time-temperature account book, comprising: Step 11, obtaining the unique identifier of the food batch, performing character integrity check and repeatability check on the unique identifier of the food batch at the warehouse reading and writing point, the repeatability check comprising comparing the unique identifier of the food batch with the unique identifier of the food batch already existing in the binding record; after the check is passed, a binding record containing the unique identifier and the time is generated and the binding with the warehouse reading and writing point is completed; Step 12, creating a time-temperature account book with the unique identifier of the food batch as the index, writing the nominal shelf life into the account head information of the time-temperature account book, and fixing the record fields of the time-temperature account book as temperature, humidity, gas indicating value, time, and limiting the time-temperature account book to only allow additional writing; Step 13, performing range consistency check on the temperature, humidity and gas indicating value to be written into the time-temperature account book according to time, the range consistency check comprising determining whether the temperature, humidity and gas indicating value fall into the corresponding preset normal interval respectively; the temperature, humidity, gas indicating value and time that pass the check are written into the time-temperature account book, and the write-in rejection mark and time are written into the time-temperature account book without writing the corresponding temperature, humidity and gas indicating value when the check fails. 3.The food intelligent management method based on the Internet of Things technology according to claim 1, characterized in that, Collecting and writing the temperature, humidity and gas indicating value according to the sampling time segment, determining the environmental influence coefficient according to the preset normal interval corresponding to the temperature, humidity and gas indicating value, and obtaining the shelf life consumption combined with the sampling time segment to obtain the cumulative shelf life consumption, comprising: Step 21, based on the unique identification of the food batch, locate the corresponding time-temperature account book, generate time according to the sampling time segment, and form a sampling record containing temperature, humidity, gas indicator value, and time. The sampling record is appended to the time-temperature account book. The same sampling record is only allowed to be written once and is prohibited from being overwritten with temperature, humidity, gas indicator value, and time. Step 22, compare the temperature, humidity, and gas indicator value in the sampling record with the corresponding preset normal interval respectively. When the temperature, humidity, and gas indicator value all fall within their respective corresponding preset normal interval, the environmental influence coefficient is determined to be a first preset value. When any of the temperature, humidity, and gas indicator value exceeds its corresponding preset normal interval, the environmental influence coefficient is determined to be a second preset value. The environmental influence coefficient and the time are written into the time-temperature account book. Step 23, obtain the environmental influence coefficient corresponding to the time, and determine the quality preservation consumption as the product of the environmental influence coefficient and the sampling time segment. The cumulative quality preservation consumption is obtained by accumulating each quality preservation consumption in the time-temperature account book in chronological order, and the cumulative quality preservation consumption and the time are written into the time-temperature account book. 4.The food intelligent management method based on the Internet of Things technology according to claim 1, characterized in that, The remaining shelf life is obtained from the nominal shelf life and the cumulative quality preservation consumption. Four levels are obtained by comparing the remaining shelf life with three preset grading thresholds. A business mapping matrix is generated, which includes picking priority and maximum allowed transit time, including: Step 31, based on the unique identification of the food batch, locate the corresponding time-temperature account book, read the latest written nominal shelf life and cumulative quality preservation consumption in the time-temperature account book, calculate the difference between the two to obtain the remaining shelf life, and write the remaining shelf life and the unique identification of the food batch into the time-temperature account book; Step 32, obtain three preset grading thresholds, divide the value range of the remaining shelf life into four level corresponding interval ranges, lock the level corresponding interval range corresponding to the remaining shelf life as the grading result, and write the grading result and the unique identification of the food batch into the time-temperature account book; Step 33, use the four levels as the index key of the business mapping matrix, write the picking priority and the maximum allowed transit time corresponding to the four levels to generate the business mapping matrix, and output the grading result and the business mapping matrix. 5.The food intelligent management method based on the Internet of Things technology according to claim 1, characterized in that, Determine the assignment list by solving the deterministic constraints according to the order demand, inventory quantity, remaining shelf life, grading result, and business mapping matrix, including: Step 41, obtain the order demand, inventory quantity, remaining shelf life, grading result, business mapping matrix, transportation time of the route, and delivery buffer time, index the business mapping matrix based on the grading result to obtain the picking priority and the maximum allowed transit time, and form a batch constraint parameter set corresponding to the unique identification of the food batch; Step 42, add the order demand and the transportation time and delivery buffer time of the route to obtain the necessary time, construct a station feasibility constraint that the remaining shelf life is not less than the necessary time, and construct a maximum transit constraint that the transportation time of the route is not greater than the maximum allowed transit time. At the same time, define the allocation quantity variable and construct the order satisfaction constraint and the inventory constraint. Step 43, under the conditions that the station feasibility constraint, the maximum en route constraint, the order satisfaction constraint and the inventory constraint are established, the assignment quantity variable is determined by a deterministic constraint solving model to obtain a value set of each assignment quantity variable; a distribution list item is generated one by one according to the assignment quantity variable whose value is not zero, each distribution list item corresponds to a unique identification of a food batch and an order demand, and writes the assignment quantity, the transportation time of the route, the receiving buffer time and the picking priority; the distribution list is checked item by item to output the distribution list after the station feasibility constraint, the maximum en route constraint, the order satisfaction constraint and the inventory constraint are established. 6.The food intelligent management method based on the Internet of Things technology according to claim 5, characterized in that, The order satisfaction constraint is that, for each order demand, the sum of the assignment quantity variables assigned to the order demand is equal to the order demand; The inventory constraint is that, for each unique identification of a food batch, the sum of the assignment quantity variables of the food batch assigned to each order demand is not greater than the inventory quantity corresponding to the unique identification of the food batch. 7.The food intelligent management method based on the Internet of Things technology according to claim 5, characterized in that, The deterministic constraint solving model comprises: Step 51, the assignment quantity variable, the remaining shelf life, the transportation time of the route, the receiving buffer time, the unit cost coefficient of the batch supply order and the batch loss penalty coefficient are obtained, and the sum of the transportation time of the route and the receiving buffer time is determined as the necessary time; Step 52, for each combination of the unique identification of a food batch and each order demand, the product of the assignment quantity variable and the unit cost coefficient of the batch supply order is determined as the supply cost, and the total supply cost is obtained by summation; the loss risk quantity is obtained by subtracting the necessary time from the remaining shelf life, and the product of the loss risk quantity and the batch loss penalty coefficient is determined as the loss penalty, and the loss penalty is obtained by summation; Step 53, the sum of the total supply cost and the loss penalty is determined as the target value, and the minimization of the target value is taken as the solving target, and the target value and the station feasibility constraint, the maximum en route constraint, the order satisfaction constraint and the inventory constraint together constitute the deterministic constraint solving model. 8.The food intelligent management method based on the Internet of Things technology according to claim 1, characterized in that, The execution token containing the batch unique identification list and the task deadline is generated according to the distribution list, comprising: Step 61, the distribution list is obtained, the unique identification of a food batch is extracted from the distribution list, and the batch unique identification list is obtained by performing a de-duplication process, and the distribution list is determined as the only basis for generating the execution token; Step 62, the corresponding remaining shelf life, transportation time of the route and receiving buffer time of the distribution list item are read, the sum of the transportation time of the route and the receiving buffer time is determined as the necessary time, the item remaining margin time is obtained by subtracting the necessary time from the remaining shelf life, and the task deadline is determined based on the minimum value of the item remaining margin time in the distribution list; Step 63, the execution token is generated and written into the batch unique identification list and the task deadline, the check summary is generated based on the distribution list and written into the execution token, the execution token is issued to the in-warehouse terminal and the vehicle-mounted terminal, the terminal obtains the distribution list to generate the check summary and compares it with the check summary in the execution token, at the same time, the current time is checked to be no later than the task deadline, the check is consistent and the time check is established, and the execution state is entered, and the abnormal information is recorded when the check is inconsistent. 9.The food intelligent management method based on the Internet of Things technology according to claim 1, characterized in that, The cumulative quality consumption and the remaining shelf life are updated, and when any of the temperature, humidity, and gas indication values exceeds the corresponding preset normal interval, the abnormal penalty coefficient is deducted, and when the token is invalidated, the abnormal information is recorded, including: Step 71, based on the unique identification of the food batch, the corresponding time-temperature account book is located, the latest cumulative quality consumption, remaining shelf life, temperature, humidity, gas indication value and time written in the time-temperature account book are read, the execution token is read and the task deadline is extracted, and the task status is obtained by comparing the time with the task deadline. The task status includes: non-failure state and failure state; Step 72, respectively compare the temperature, humidity, and gas indication value with the preset normal interval of the temperature, humidity, and gas indication value, when the temperature, humidity, and gas indication value all fall within the corresponding preset normal interval, the cumulative quality consumption remains unchanged, when any of the temperature, humidity, and gas indication value exceeds the corresponding preset normal interval, the abnormal penalty coefficient is read, the product of the quality consumption and the abnormal penalty coefficient is determined as the abnormal deduction amount, and the abnormal deduction amount is added to the cumulative quality consumption to obtain the updated cumulative quality consumption; Step 73, read the nominal shelf life, calculate the difference between the nominal shelf life and the updated cumulative quality consumption to obtain the updated remaining shelf life, and write the updated cumulative quality consumption and the updated remaining shelf life into the time-temperature account book; when the task status is the failure state, generate the abnormal information and write it into the time-temperature account book. 10.The food intelligent management method based on the Internet of Things technology according to claim 1, characterized in that, After arriving at the station, the remaining shelf life is checked with the necessary time required for unloading to the shelf, and the traceable report is generated based on the time-temperature account book, and the preset classification threshold is updated offline, including: Step 81, based on the unique identification of the food batch, the corresponding time-temperature account book is located, the latest updated remaining shelf life written in the time-temperature account book is read, the necessary time required for unloading to the shelf is obtained, the updated remaining shelf life is compared with the necessary time required for unloading to the shelf, and the check mark is recorded, when the updated remaining shelf life is less than the necessary time required for unloading to the shelf, the abnormal information is generated; Step 82, based on the time-temperature account book, the nominal shelf life, temperature, humidity, gas indication value, time, cumulative quality consumption, remaining shelf life, updated cumulative quality consumption, and updated remaining shelf life are extracted, and the traceable report is generated combined with the check mark and the abnormal information, the traceable report is summarized according to the unique identification of the food batch to form an offline sample set, and the samples containing the check mark that fails or the abnormal information are excluded to obtain an effective sample set; Step 83, sort the updated remaining shelf life in the effective sample set, and select the updated remaining shelf life of the three threshold positions as candidate thresholds according to the preset quantile points and write them into the three preset classification thresholds. The first preset classification threshold is greater than the second preset classification threshold, and the second preset classification threshold is greater than the third preset classification threshold. When the check is true, it is solidified as a usable version and written into the threshold configuration storage.

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