A method for billet storage acceptance

Abstract

The application provides a billet warehousing acceptance method, which comprises the following steps: obtaining billet material information meeting the warehousing acceptance condition, and forming a list of billets to be accepted; performing aggregation processing according to the list of billets to be accepted, obtaining batch group data, performing multi-dimensional weighted sorting according to the batch group data, and obtaining an acceptance reference record; performing page rendering according to the acceptance reference record and the batch group data, obtaining an interactive acceptance operation interface, using the interactive acceptance operation interface to accept actual measurement values of the billets, storing the actual measurement values of the billets after acceptance, and assigning a globally unique ID to the actual measurement values of the billets; performing multi-dimensional grading early warning judgment on the actual measurement values of the billets according to the globally unique ID and a preset quality standard library, and performing corresponding early warning on the actual measurement values of the billets that do not meet the requirements of the quality standard library. The application solves the problems of data update lag, complicated operation and difficult quality audit in the existing billet warehousing acceptance process.

Description

Technical Field

[0001] This invention relates to the technical field of the steel industry, and in particular to a method for inspecting and accepting steel billets upon warehousing. Background Technology

[0002] In the steel industry, MES (Manufacturing Execution System) is a crucial support for achieving inventory transparency and lean management. One of its core functions is to monitor the entire material flow process in real time and drive data-driven decision-making. As the core raw material for steel rolling production, the accuracy and timeliness of billet warehousing directly impact subsequent production scheduling and inventory turnover efficiency. However, existing MES systems generally rely on manual data entry and static forms for billet warehousing, which is increasingly unable to meet the management needs of modern steel enterprises operating on a high-paced, multi-variety, small-batch production model.

[0003] First, the problem of data update lag is prominent. Acceptance records rely entirely on manual entry. During the process of steel billets being transferred from steelmaking to rolling, information such as their location, quantity, and status cannot be synchronized to the inventory management system in real time, resulting in discrepancies between inventory records and actual inventory. This, in turn, interferes with the scheduling logic of the production scheduling system and may even lead to production lines waiting for materials or inventory backlog.

[0004] Secondly, the acceptance process is cumbersome and lacks automation. Steel billets of the same heat number often arrive in batches and by different trains due to transportation constraints. The existing system lacks the ability to automatically merge and trace batch information. Acceptance personnel need to repeatedly check paper documents and system records, which is not only time-consuming and labor-intensive, but also prone to data errors due to human negligence.

[0005] Furthermore, there is a lack of dynamic filtering and intelligent prompting mechanisms. Traditional MES systems' inventory query modules mostly use fixed-field filtering, failing to automatically filter billets to be accepted based on multiple dimensions such as receipt status, date, furnace number, and supplier. They also cannot prioritize or send alerts. This forces inventory managers to rely on personal experience for judgment, making refined inventory control difficult.

[0006] Furthermore, there are significant shortcomings in the quality audit process. Key parameters such as the surface quality, dimensional deviations, and curvature of steel billets are typically assessed manually or through offline sampling, lacking an automated verification and anomaly warning mechanism linked to the MES system. If quality issues are not detected in a timely manner, substandard steel billets entering the rolling process will lead to finished product defects or even equipment damage, resulting in substantial economic losses. Summary of the Invention

[0007] This application provides a method for accepting steel billets into the warehouse, in order to solve the problems of data update delays, cumbersome operations, and difficulties in quality auditing in the existing steel billet acceptance process.

[0008] The method includes: Obtain information on steel billets that meet the conditions for warehousing and acceptance, and generate a list of billets to be accepted; The batch group data is obtained by aggregating the billet list to be accepted, and then the batch group data is sorted by multi-dimensional weighting to obtain the acceptance benchmark record. The page is rendered based on the acceptance criteria record and the batch group data to obtain an interactive acceptance operation interface. The actual value of the billet is accepted using the interactive acceptance operation interface, and the actual value of the billet after acceptance is completed and a globally unique ID is assigned to the actual value of the billet. Based on the globally unique ID and the preset quality standard library, the measured value of the billet is subject to multi-dimensional hierarchical early warning judgment, and corresponding early warning is issued for the measured value of the billet that does not meet the requirements of the quality standard library.

[0009] In some implementations, the step of obtaining information on steel billet materials that meet the warehousing and acceptance conditions includes: A real-time connection is established with the steel plant's MES database via a data interface, and the billet information is retrieved. The billet information is verified and filtered, and a list is constructed based on the filtered information to form the billet list to be accepted.

[0010] In some embodiments, the step of verifying and filtering the billet information includes: The billet information is checked for non-empty required fields, field format, and logical consistency. The billet information that passes the checks is stored in a temporary cache, while the billet information that fails the checks is stored in an exception log table. The verified billet information is filtered in multiple dimensions to form the billet list to be accepted.

[0011] In some implementations, the step of performing multi-dimensional screening on the verified billet information includes: The billet information is sequentially filtered by billet status and then by the comprehensive judgment of the previous process. The billet status filtering includes filtering out information records whose billet status field value is "received". The comprehensive judgment of the previous process includes filtering out information records whose comprehensive judgment result field value is "qualified". The billet information is sequentially filtered by the receiving date, and the filtered billet information is deduplicated and sorted to form the billet list to be accepted.

[0012] In some embodiments, the step of performing polymerization processing based on the billet list to be accepted includes: Extract the fields of furnace number, steel type and specification group from the billet list to be accepted, and concatenate the three fields into a composite primary key; Construct a hash mapping container, wherein the hash mapping container uses the composite primary key as the key and the batch group object as the value to form the batch group data; The batch group data is sequentially subjected to count accumulation verification, timestamp standardization, and comprehensive quality score calculation; the count accumulation verification includes calculating the sum of billet counts in each batch group data and comparing it with the billet count of the corresponding furnace number of the batch group data; the timestamp standardization includes converting the reception time of each batch group data into a unified millisecond-level timestamp.

[0013] In some implementations, the step of performing multi-dimensional weighted sorting based on the batch group data includes: Feature extraction is performed on the batch group data to obtain the number of billet pieces, time freshness, and comprehensive quality score, and the number of billet pieces, time freshness, and comprehensive quality score are normalized. The priority score for each batch group of data is calculated based on the normalized number of billets, the freshness of time, and the comprehensive quality score. All the priority scores are sorted, and the batch group data corresponding to the highest priority score is used as the acceptance benchmark record.

[0014] In some implementations, the step of performing multi-dimensional weighted sorting on the batch group data further includes: When at least two priority scores are the same or the score difference is less than the preset score, they are sorted in the order of larger billet count, more recent warehousing time, and smaller train number. If there is only one record of the batch group, then set it as the acceptance benchmark record; When the billet count is adjusted, the version number of the batch group data corresponding to the furnace number is incremented, and a multi-dimensional weighted sort is performed again.

[0015] In some implementations, the interactive acceptance interface includes: A basic information block, configured to display the billet information; The batch details block is configured to display the number of billets, comprehensive quality score calculation, train number, weight, and warehousing time for each batch group of data. A quality parameter input block is configured to input and display quality parameters. The acceptance conclusion block is configured to display a judgment mark area for acceptance qualified, acceptance unqualified, and pending re-inspection, as well as a corresponding acceptance opinion text box; The operation log block is configured to display the history of changes to the corresponding furnace number in the form of a timeline.

[0016] In some implementations, the step of performing multi-dimensional hierarchical early warning judgment on the measured value of the billet based on the globally unique ID and a preset quality standard library, and issuing corresponding early warnings for the measured value of the billet that does not meet the requirements of the quality standard library, includes: The deviations of the measured values ​​of the billet in length, curvature, diagonal difference, skewness, thickness, width, bulge, and dent from the requirements of the quality standard library are calculated respectively, and graded joint early warning is performed. The graded joint early warning includes a green warning when the deviation is within a first range, a yellow warning when the deviation is between the first and second ranges, and a red warning when the deviation is greater than the second range.

[0017] In some implementations, the hierarchical joint early warning system further includes: When both curvature and bulging show yellow warnings, a comprehensive deformation warning is generated. When the angle difference deviates from the thickness / width, a cross-sectional deformation warning is generated; When a deviation occurs in the slant cutting and the length approaches the deviation limit, a comprehensive early warning for end face quality is generated.

[0018] As described above, this application provides a method for steel billet warehousing and acceptance. The method includes: acquiring information on steel billets that meet the warehousing and acceptance conditions to form a list of billets to be accepted; aggregating the list of billets to be accepted to obtain batch group data; performing multi-dimensional weighted sorting on the batch group data to obtain acceptance benchmark records; rendering a page based on the acceptance benchmark records and the batch group data to obtain an interactive acceptance operation interface; using the interactive acceptance operation interface to accept the measured values ​​of the steel billets, storing the accepted measured values ​​and assigning a globally unique ID to each measured value; performing multi-dimensional graded early warning judgment on the measured values ​​of the steel billets based on the globally unique ID and a preset quality standard library, and issuing corresponding early warnings for measured values ​​of steel billets that do not meet the requirements of the quality standard library. This application solves the problems of delayed data updates, cumbersome operations, and difficult quality audits in the existing steel billet warehousing and acceptance process through the above method. Attached Figure Description

[0019] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a method for inspecting and accepting steel billets upon warehousing, as described in this application. Figure 2 This is a flowchart illustrating the method for obtaining a list of billets to be inspected in the steel billet warehousing and acceptance process of this application. Figure 3 This is a flowchart illustrating the verification and screening of the billet list to be inspected in a billet receiving and acceptance method according to this application; Figure 4 This is a flowchart illustrating the multi-dimensional screening of the billet list to be inspected in a billet warehousing and acceptance method according to this application; Figure 5 This is a flowchart illustrating the aggregation process based on the billet list to be inspected in a billet warehousing and acceptance method according to this application. Figure 6 This is a flowchart illustrating the multi-dimensional weighted sorting of batch group data in a billet warehousing and acceptance method according to this application. Figure 7 This is a schematic diagram of the interactive acceptance operation interface in the steel billet warehousing acceptance method of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0023] It should be noted that in this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as exemplary or for example in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0024] See Figures 1 to 7As can be seen, this embodiment provides a method for inspecting and accepting steel billets upon warehousing, the method including: S110: Obtain information on steel billet materials that meet the conditions for warehousing and acceptance, and form a list of billets to be accepted.

[0025] Specifically, in this embodiment, the goal of this step is to extract, clean, and screen the billet information that meets the warehousing and acceptance conditions from the steel plant's MES system, forming a structured list of billets to be accepted.

[0026] The system reads the preset field mapping configuration table and maps the original field names to standardized field names that are unified within the system. Standardized fields include at least: receiving area, furnace number, steel grade, specification group (thickness × width × length), number of pieces, weight, receiving time, billet condition, comprehensive judgment result of the previous process, car number, and arrival batch number.

[0027] For missing or inconsistently named fields, fill in or annotate them according to the default value rules to ensure data integrity.

[0028] Furthermore, in some embodiments, the step of obtaining information on steel billet materials that meet the warehousing and acceptance conditions includes: S210 establishes a real-time connection with the steel plant's MES database through a data interface and retrieves the billet information.

[0029] Specifically, in this embodiment, a real-time connection is established with the steel plant's MES database through the standard data interface (WebService) provided by the MES system to obtain the billet information.

[0030] S220, the billet information is verified and filtered, and a list is constructed based on the filtered information to form the billet list to be accepted.

[0031] Specifically, in this embodiment, an incremental fetching strategy is adopted, recording the timestamp T_last of the last successful fetch. Each time, only new or changed records with a release time > T_last are queried to avoid full duplicate transmission, and verification and filtering are performed.

[0032] Furthermore, in some embodiments, the step of verifying and filtering the billet information includes: S310, perform mandatory field non-empty validation, field format validation, and logical consistency validation on the billet information respectively, and store the billet information that passes the validation to a temporary cache and retain the billet information that fails the validation to the exception log table.

[0033] Specifically, in this embodiment, three integrity checks are performed on each record: Required fields must be non-empty: Furnace number, steel type, number of pieces, weight, and billet status cannot be empty; otherwise, the data will be marked as incomplete and written to the exception log.

[0034] Field format validation: The furnace number must conform to an 8-digit code; the steel grade must exist in the system dictionary; the number of pieces and the weight must be positive and within a reasonable range.

[0035] Logical consistency check: The deviation between the weight of a single piece × the number of pieces and the total weight must not exceed ±2%; otherwise, it will be marked as an abnormal weight.

[0036] Records that pass the verification are entered into a temporary data buffer, while records that fail the verification are not included in subsequent filtering but are retained in the exception log table for manual review.

[0037] The steps of verifying and filtering the billet information also include: S320, The steel billet information that has passed the verification is filtered in multiple dimensions to form the billet list to be accepted.

[0038] Specifically, in this embodiment, the billet information that has passed the verification is sequentially filtered by billet status, receiving date, and deduplication and sorting of the filtering results to obtain the billet list to be accepted.

[0039] Furthermore, in some embodiments, the step of performing multi-dimensional screening on the verified billet information includes: S410, the billet information is sequentially filtered for billet status and for the previous process comprehensive judgment qualification; the billet status filtering includes filtering out information records whose billet status field value is received; the previous process comprehensive judgment qualification filtering includes filtering out information records whose previous process comprehensive judgment result field value is qualified. S420, the billet information is sequentially filtered by the receiving date, and the filtered billet information is deduplicated and sorted to form the billet list to be accepted.

[0040] Specifically, in this embodiment, the billet condition is screened (two layers of screening): First layer: billet status filtering: only retain records where the billet status field value is "received" (other statuses such as "awaiting transfer" or "returned" are filtered out).

[0041] Second layer: Pre-process comprehensive judgment pass screening: Only records with a pre-process comprehensive judgment result field value of "pass" are retained. Records awaiting re-inspection are placed in a temporary queue and marked with a yellow warning; records that are unqualified or accepted with concessions are written into the abnormal billet list and marked with a red warning.

[0042] Filter by receiving date: Based on the specified receiving date (single day or date range) selected by the acceptance personnel on the interface, the date portion of the receiving time in the billet record is matched with it, and only records that meet the conditions are retained.

[0043] Deduplication and sorting of filter results: Deduplication: Using the composite deduplication key of furnace number + steel grade + specification group + train number, the record with the latest receiving time is retained, and the earlier duplicate record is marked as suspected duplicate and written to the exception log.

[0044] Sorting: Arrange in ascending order by receiving time, and within the same time period, arrange in ascending order by furnace number to form an orderly list of billets to be accepted.

[0045] Generate a list of blanks to be accepted: The final list is stored in memory as structured data. Each record includes: furnace number, steel type, specification group, number of pieces, weight, receiving time, car number, batch number, and receiving area.

[0046] The method further includes: S120: Aggregate the billet list to be accepted to obtain batch group data, and perform multi-dimensional weighted sorting on the batch group data to obtain acceptance benchmark records.

[0047] Specifically, in this embodiment, billet data scattered across multiple train runs and time points under the same heat number are automatically aggregated into batch groups, and the batch with the highest overall priority is selected as the acceptance benchmark record for that heat number.

[0048] Furthermore, in some embodiments, the step of performing polymerization processing based on the billet list to be accepted includes: S510, extract the fields of furnace number, steel type and specification group from the billet list to be accepted, and concatenate the three fields into a composite primary key.

[0049] Specifically, in this embodiment, records are read one by one from the billet list to be accepted, and the three fields of furnace number, steel grade, specification group are extracted and concatenated into a composite primary key Key=(furnace number, steel grade, specification group).

[0050] The step of performing polymerization processing based on the billet list to be accepted further includes: S520, construct a hash mapping container, wherein the hash mapping container uses the composite primary key as the key and the batch group object as the value to form the batch group data.

[0051] Specifically, in this embodiment, a hash map container (HashMap) is initialized in memory, using the composite primary key as the key and batch group objects as the values. The batch group object contains: the composite primary key, an ordered list of all batches under this furnace number, a reference to the base record, and a version number (initially 1). The time complexity is O(n).

[0052] The step of performing polymerization processing based on the billet list to be accepted further includes: S530, the batch group data is sequentially subjected to count accumulation verification, timestamp standardization, and comprehensive quality score calculation; the count accumulation verification includes calculating the sum of billet counts in each batch group data and comparing it with the billet count of the corresponding furnace number of the batch group data; the timestamp standardization includes converting the reception time of each batch group data into a unified millisecond-level timestamp.

[0053] Specifically, in this embodiment: Batch group data integrity verification and preprocessing: Count verification: Calculate the sum of the counts of all batches within the batch group and compare it with the production feed count of the furnace number in the MES system. If the deviation exceeds ±3%, mark the count as abnormal.

[0054] Timestamp standardization: Convert the reception time of each batch into a millisecond-level Unix timestamp.

[0055] Overall Quality Score Calculation: Calculate the overall quality score Q for each batch using the formula Q = w1 × Chemical Composition Pass Rate + w2 × Surface Quality Rating Normalized Value + w3 × Low Magnification Structure Rating Normalized Value (w1 + w2 + w3 = 1). If quality data is missing, take the average of the Q values ​​of other batches within the group and mark the batch as missing.

[0056] Furthermore, in some embodiments, the step of performing multi-dimensional weighted sorting based on the batch group data includes: S610, feature extraction is performed based on the batch group data to obtain the number of billet pieces, time freshness, and comprehensive quality score, and the number of billet pieces, time freshness, and comprehensive quality score are normalized. S620, calculate the priority score for each batch of data based on the normalized number of billets, the freshness of time, and the comprehensive quality score; S630, sort all the priority scores and use the batch group data corresponding to the highest priority score as the acceptance benchmark record.

[0057] Specifically, in this embodiment: Multidimensional feature extraction and Min-Max normalization: For each batch i within the batch group, extract three features: Number of blanks Ci: The number of blanks in this batch.

[0058] Time freshness Ti: Ti=1 / (1+Δti / τ), where Δti=t_now-ti, and τ is the time decay constant (default 24 hours in milliseconds).

[0059] Overall quality score Qi: This is the Q value calculated in the previous step.

[0060] Perform Min-Max normalization within the batch group: C_norm_i=(Ci-C_min) / (C_max-C_min); T_norm_i=(Ti-T_min) / (T_max-T_min); Q_norm_i=(Qi-Q_min) / (Q_max-Q_min); Boundary condition: If there is only one batch in the batch group, all normalized values ​​are set to 1.0 and directly used as the baseline record.

[0061] Overall priority score calculation: Calculate the priority score for each batch: Score_i = α × C_norm_i + β × T_norm_i + γ × Q_norm_i.

[0062] Default weights: α=0.5 (number of branches), β=0.3 (timeliness), γ=0.2 (quality), satisfying α+β+γ=1.

[0063] Weights can be configured in the management backend to suit the needs of different production lines or steel grades.

[0064] Baseline record selection and attribute inheritance: Sort the score values ​​of all batches within the batch group in descending order, and select the batch with the highest score as the acceptance benchmark record.

[0065] Attribute inheritance: baseline number of items, baseline warehousing time, baseline quality score; simultaneously calculate the total number of items (the sum of the number of items in all batches), first arrival time, and last arrival time.

[0066] Some specific cases are as follows: Merge conflict detection and resolution: Score conflict: If the difference between multiple batches of score values ​​is less than 0.001, the batches will be compared and resolved in the following order of priority: larger number of items → more recent entry time → smaller train number.

[0067] Cross-specification group conflict: Different specification groups of the same furnace number are merged independently and displayed in separate lines on the acceptance page.

[0068] Orphan batch processing: If a furnace number has only one record, it is directly used as the baseline record.

[0069] Incremental recalculation mechanism driven by anomalies: When changes occur such as changes in the number of pieces, returns, deletions, or additions, the system captures the event, increments the batch group version number only for the affected furnace number, and re-executes steps S510 to S530, while other furnace numbers remain unchanged.

[0070] The computational complexity is reduced from O(n) to O(k), where k is the number of affected furnace numbers, significantly reducing system overhead.

[0071] The method further includes: S130, based on the acceptance benchmark record and the batch group data, the page is rendered to obtain an interactive acceptance operation interface. The actual value of the billet is accepted using the interactive acceptance operation interface, and the actual value of the billet after acceptance is completed and a globally unique ID is assigned to the actual value of the billet.

[0072] Specifically, in this embodiment, the dynamically merged baseline records and batch group data are rendered into an interactive acceptance operation interface 70.

[0073] Basic Information Block 71: Displays furnace number, steel type, specification group, total number of pieces received, baseline number of pieces, baseline warehousing time, first / last arrival time, receiving area, and train number (read-only).

[0074] Batch Details Block 72: A collapsible table that displays only the baseline batch by default. Non-baseline batches are collapsed and hidden. It can be expanded to view the number of pieces, weight, warehousing time, train number, and overall quality score for each batch.

[0075] Quality Parameter Input Block 73: Core Interaction Area, containing input fields for 8 quality parameters.

[0076] Acceptance Conclusion Block 74: Contains three buttons: Acceptance Passed, Acceptance Failed, and Pending Re-inspection, as well as an acceptance comment text box.

[0077] Operation Log Block 75: Displays the history of changes to this furnace number in a timeline format.

[0078] Each quality parameter includes: parameter name and standard code, standard value / nominal value (read-only), allowable deviation range (read-only), measured value input box, judgment result identifier (qualified / within deviation / out of tolerance), number of measurements and mean (supports adding new measurement points and automatically calculating the mean), and remarks input box.

[0079] Acceptance submission and data persistence: After the acceptance personnel have entered all the measured values, they click the acceptance conclusion button.

[0080] The system performs a mandatory field check; if any field is missing, it will be highlighted.

[0081] Encapsulate all data on the page into an acceptance record object in JSON format.

[0082] Call the distributed unique ID allocation algorithm to assign a globally unique ID to the record.

[0083] The acceptance record table in the Oracle database is written, and the baseline record attributes are synchronized to the MES inventory ledger to achieve real-time updates.

[0084] The front end displays a message indicating successful acceptance submission and automatically redirects to the next furnace number awaiting acceptance.

[0085] The method further includes: S140, based on the globally unique ID and the preset quality standard library, perform multi-dimensional hierarchical early warning judgment on the measured value of the billet, and issue corresponding early warnings for the measured value of the billet that does not meet the requirements of the quality standard library.

[0086] Specifically, in this embodiment, a quality standard library is pre-built, using (steel type, thickness range, width range) as the index key to store the allowable deviation rules of quality parameters.

[0087] Calculation of allowable deviation range: Length: Based on the nominal length L, find the upper and lower deviations from the table, and calculate L_min = L - ΔL_lower and L_max = L + ΔL_upper.

[0088] Curvature: Based on the standard allowable value B_max (mm / m) and the actual length l_actual, calculate B_allow = B_max × l_actual / 1000, and the judgment threshold is [0, B_allow].

[0089] Diagonal difference: The allowable value D_max is obtained by looking up the table based on the aspect ratio, and the threshold is [0, D_max].

[0090] Skew cut: Based on the standard-specified skew-to-thickness ratio S_max%, calculate S_allow=H×S_max%, and the threshold is [0,S_allow].

[0091] Thickness: Based on the nominal thickness H and accuracy grade, find the upper and lower deviations from the table, and calculate H_min = H + ΔH_lower, H_max = H + ΔH_upper.

[0092] Width: Similarly, calculate W_min = W + ΔW_down and W_max = W + ΔW_up.

[0093] Drum belly: According to the standard (e.g., ≤H×2%), calculate G_allow=H×2%, and the judgment threshold is [0,G_allow].

[0094] Indentation: According to the standard (e.g., ≤2mm), the threshold for judgment is [0,X_max].

[0095] Real-time assessment and three-level early warning mechanism: Qualified (green): The measured value is within the allowable range and the distance from the boundary is greater than the warning margin δ (default δ = 10% of the allowable range).

[0096] Within Deviation / Critical Limit (Yellow): The measured value is within the allowable range but the distance from the boundary is ≤δ, and a yellow warning is generated to indicate that the parameter is close to the deviation limit.

[0097] Out of tolerance (red): The measured value exceeds the allowable range, generating a red out of tolerance warning, automatically changing the judgment mark to out of tolerance, and recommending that the acceptance conclusion be changed to acceptance failure (acceptance personnel can manually change it back but must fill in an objection statement).

[0098] When a parameter exceeds the tolerance, a red banner is displayed at the top of the page listing all parameters that exceed the tolerance, and a system message notification is sent to the quality control manager. At the same time, the tolerance event is written into the quality anomaly tracking table (marked as pending).

[0099] Quality parameter correlation verification: Joint determination of curvature and bulging: When both of them show a yellow warning, a comprehensive deformation warning is generated, and it is recommended to increase the measurement section.

[0100] Joint judgment of diagonal difference and thickness / width: When the diagonal difference exceeds the tolerance and the thickness or width is deviated, a cross-sectional deformation warning is generated, and a full cross-section re-inspection is recommended.

[0101] Joint judgment of skew and length: When the skew exceeds the tolerance and the length is close to the deviation limit, a comprehensive warning of end face quality is generated, and it is recommended to trace the cutting process.

[0102] Furthermore, in some embodiments, a distributed unique ID allocation algorithm based on business semantic awareness is used: Each acceptance record is assigned a globally unique 64-bit ID to ensure uniqueness, traceability, and high concurrency stability.

[0103] ID structure definition and business semantic embedding calculation: 64-bit ID structure: sign bit (1 bit, fixed 0) + business identifier bit (4 or 8 bits, dynamic) + timestamp (41 bits - dynamically adjusted bits) + node ID (10 bits) + serial number (12 bits - dynamically adjusted bits).

[0104] Dynamic allocation of business identifier bits: Query the warehousing frequency f of this furnace number in the past 24 hours. If f > 50 (high frequency), allocate 8 business identifier bits; otherwise, allocate 4 bits. The value of the business identifier bit is obtained by taking the hash value of the furnace number string using the FNV-1a hash algorithm, and then taking the lower n bits.

[0105] Load-aware node routing and dynamic node ID allocation: When each service node starts up, it writes heartbeat records and load metrics (number of active threads T, number of pending billet batches B) to the Oracle node registry.

[0106] Load score LoadScore = α_load × T + β_load × B (default α = 0.6, β = 0.4), the lower the value, the more idle the node.

[0107] The load balancer forwards the ID generation request to the live node with the lowest current LoadScore. The ID of this node (0~1023, 10 bits) is embedded as the workerid.

[0108] Node ID allocation and reclamation: At startup, the smallest unoccupied ID is allocated; heartbeat updates are performed every 10 seconds; if there is no heartbeat for 30 consecutive seconds, the node is considered to have crashed, and the ID is released for standby nodes to take over after 60 seconds.

[0109] Clock rewind self-healing mechanism: Each node maintains a recent timestamp-sequence number sliding window of length 10.

[0110] When clock rollback is detected (t_now≤t_last), enter prediction mode: perform linear regression on the sliding window data to predict the next available sequence number seq_pred, and write 1 in the conflict flag bit of the ID to indicate it as a compensation ID.

[0111] If the predicted sequence number is already in use, the microsecond-level spin wait will last up to 1ms. If the sequence number is still not caught up, an alarm log will be generated.

[0112] ID assembly and conflict verification: In normal mode, 64-bit IDs are concatenated in bit order.

[0113] Before writing to the database, a memory Bloom filter (expected 10 million elements, false positive rate 0.001%) is used to quickly determine whether the ID already exists.

[0114] If the Bloom filter determines that the file does not exist, it is written directly.

[0115] If a conflict is suspected, the Oracle primary key index is queried for confirmation. If a conflict does occur, a sequence number step-size jump retry is triggered: the sequence number is increased by the step size Δ = [current node concurrency number × 1.5], reassembled and verified until no conflict exists.

[0116] After generation, the mapping relationship between ID, furnace number, and batch number is written to the Redis cache (TTL=7 days).

[0117] ID generation performance monitoring and adaptive tuning: Generate latency monitoring: Calculate the P99 latency in the last minute. If it exceeds 5ms, generate a performance alarm.

[0118] Conflict rate monitoring: If the actual conflict rate exceeds 0.01%, the initial step size of the serial number will be automatically increased from 1 to 3.

[0119] Node load balancing monitoring: Monitor the standard deviation of LoadScore for each node. If it exceeds the average by 30%, automatically adjust the routing weight and direct more requests to low-load nodes.

[0120] This embodiment has the following advantages: Through automatic filtering and dynamic merging mechanisms, billet warehousing information can be synchronized to the inventory system in real time without manual input, solving the problem of data update lag in traditional MES and providing accurate and timely inventory status for production scheduling.

[0121] The dynamic sorting algorithm automatically identifies the largest batch of billets under the same heat number, reducing repeated acceptance operations; the improved random number allocator algorithm ensures data uniqueness, avoids duplicate entry, and improves overall acceptance efficiency by more than 30%.

[0122] The system has a built-in quality parameter verification and early warning mechanism, which automatically determines whether the billet size and geometric tolerances are qualified, reducing errors in manual measurement and judgment, and improving the accuracy and consistency of quality audit.

[0123] By dynamically merging and uniquely marking data from the same batch and furnace number, inventory data presents a clear structured view, allowing managers to monitor the warehousing status of billets from each furnace number and batch in real time, providing data support for lean production decisions such as hot charging and production scheduling.

[0124] It supports data change and deletion operations, and combined with the automatic generation function of standard formats, it meets the dual needs of enterprises for dynamic management and compliance of inventory data.

[0125] For ease of explanation, the above description has been provided in conjunction with specific embodiments. However, the discussion in some embodiments is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. Various modifications and variations can be obtained based on the above teachings. The selection and description of the above embodiments are for the purpose of better explaining the contents of this disclosure, thereby enabling those skilled in the art to better utilize the embodiments.

Claims

1. A method for inspecting and accepting steel billets upon warehousing, characterized in that, The method includes: Obtain information on steel billets that meet the conditions for warehousing and acceptance, and generate a list of billets to be accepted; The batch group data is obtained by aggregating the billet list to be accepted, and then the batch group data is sorted by multi-dimensional weighting to obtain the acceptance benchmark record. The page is rendered based on the acceptance criteria record and the batch group data to obtain an interactive acceptance operation interface. The actual value of the billet is accepted using the interactive acceptance operation interface, and the actual value of the billet after acceptance is completed and a globally unique ID is assigned to the actual value of the billet. Based on the globally unique ID and the preset quality standard library, the measured value of the billet is subject to multi-dimensional hierarchical early warning judgment, and corresponding early warning is issued for the measured value of the billet that does not meet the requirements of the quality standard library.

2. The method for inspecting and accepting steel billets upon warehousing according to claim 1, characterized in that, The steps for obtaining information on steel billet materials that meet the warehousing and acceptance conditions include: A real-time connection is established with the steel plant's MES database via a data interface, and the billet information is retrieved. The billet information is verified and filtered, and a list is constructed based on the filtered information to form the billet list to be accepted.

3. The method for inspecting and accepting steel billets upon warehousing according to claim 2, characterized in that, The steps for verifying and filtering the steel billet information include: The billet information is checked for non-empty required fields, field format, and logical consistency. The billet information that passes the checks is stored in a temporary cache, while the billet information that fails the checks is stored in an exception log table. The verified billet information is filtered in multiple dimensions to form the billet list to be accepted.

4. The method for inspecting and accepting steel billets upon warehousing according to claim 3, characterized in that, The step of performing multi-dimensional screening on the verified steel billet information includes: The billet information is sequentially filtered by billet status and then by the comprehensive judgment of the previous process. The billet status filtering includes filtering out information records whose billet status field value is "received". The comprehensive judgment of the previous process includes filtering out information records whose comprehensive judgment result field value is "qualified". The billet information is sequentially filtered by the receiving date, and the filtered billet information is deduplicated and sorted to form the billet list to be accepted.

5. The method for inspecting and accepting steel billets upon warehousing according to claim 1, characterized in that, The step of performing polymerization processing based on the billet list to be accepted includes: Extract the fields of furnace number, steel type and specification group from the billet list to be accepted, and concatenate the three fields into a composite primary key; Construct a hash mapping container, wherein the hash mapping container uses the composite primary key as the key and the batch group object as the value to form the batch group data; The batch group data is sequentially subjected to count accumulation verification, timestamp standardization, and comprehensive quality score calculation; the count accumulation verification includes calculating the sum of billet counts in each batch group data and comparing it with the billet count of the corresponding furnace number of the batch group data; the timestamp standardization includes converting the reception time of each batch group data into a unified millisecond-level timestamp.

6. The method for inspecting and accepting steel billets upon warehousing according to claim 1, characterized in that, The step of performing multi-dimensional weighted sorting based on the batch group data includes: Feature extraction is performed on the batch group data to obtain the number of billet pieces, time freshness, and comprehensive quality score, and the number of billet pieces, time freshness, and comprehensive quality score are normalized. The priority score for each batch group of data is calculated based on the normalized number of billets, the freshness of time, and the comprehensive quality score. All the priority scores are sorted, and the batch group data corresponding to the highest priority score is used as the acceptance benchmark record.

7. The method for inspecting and accepting steel billets upon warehousing according to claim 6, characterized in that, The steps for performing multi-dimensional weighted sorting of the batch group data also include: When at least two priority scores are the same or the score difference is less than the preset score, they are sorted in the order of larger billet count, more recent warehousing time, and smaller train number. If there is only one record of the batch group, then set it as the acceptance benchmark record; When the billet count is adjusted, the version number of the batch group data corresponding to the furnace number is incremented, and a multi-dimensional weighted sort is performed again.

8. The method for inspecting and accepting steel billets upon warehousing according to claim 1, characterized in that, The interactive acceptance operation interface includes: A basic information block, configured to display the billet information; The batch details block is configured to display the number of billets, comprehensive quality score calculation, train number, weight, and warehousing time for each batch group of data. A quality parameter input block is configured to input and display quality parameters. The acceptance conclusion block is configured to display a judgment mark area for acceptance qualified, acceptance unqualified, and pending re-inspection, as well as a corresponding acceptance opinion text box; The operation log block is configured to display the history of changes to the corresponding furnace number in the form of a timeline.

9. The method for inspecting and accepting steel billets upon warehousing according to claim 1, characterized in that, The step of performing multi-dimensional hierarchical early warning judgment on the measured value of the billet based on the globally unique ID and the preset quality standard library, and issuing corresponding early warnings for the measured value of the billet that does not meet the requirements of the quality standard library, includes: The deviations of the measured values ​​of the billet in length, curvature, diagonal difference, skewness, thickness, width, bulge, and dent from the requirements of the quality standard library are calculated respectively, and graded joint early warning is performed. The graded joint early warning includes a green warning when the deviation is within a first range, a yellow warning when the deviation is between the first and second ranges, and a red warning when the deviation is greater than the second range.

10. The method for inspecting and accepting steel billets upon warehousing according to claim 9, characterized in that, The tiered joint early warning system also includes: When both curvature and bulging show yellow warnings, a comprehensive deformation warning is generated. When the angle difference deviates from the thickness / width, a cross-sectional deformation warning is generated; When a deviation occurs in the slant cutting and the length approaches the deviation limit, a comprehensive early warning for end face quality is generated.

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