Automatic management system and method for gas turbine spare parts

Abstract

The application relates to the technical field of data processing, and discloses an automatic management system and method for gas turbine spare parts in and out of a warehouse, which comprises the following modules: a data storage module for storing spare part information and models; a pre-warning rule management module for configuring a rule threshold; an environment monitoring module for acquiring temperature and humidity data, calculating an expiration loss coefficient according to an over-limit time length and a structure type, and updating an expiration date; a pre-warning engine module for comparing the updated expiration date with the threshold, and generating a pre-warning record; and a stock management module for resetting and deducting a priority according to the expiration date, checking the balance of the number of out-of-warehouse parts, and intercepting abnormal flow, wherein the application applies quantity constraints on a transaction bottom layer to prevent account and reality deviation, establishes an environment physical disturbance and expiration dynamic recalculation mechanism, prevents hidden failure, and realizes multi-dimensional risk pre-warning and high coordination of physical stock.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to an automated management system and method for the inbound and outbound of gas turbine spare parts. Background Technology

[0002] In the long-distance oil and gas pipeline industry, the national pipeline network's gas turbine stations are responsible for the continuous transmission of high-pressure natural gas. The operation and maintenance of gas turbines require a large number of high-value, highly specialized spare parts, including casings, turbine assemblies, compressor blades, and rubber seals. Current warehouse management typically uses enterprise resource planning (ERP) systems or general warehouse management software, supplemented by paper documents and manual data entry to record inbound and outbound transactions. These warehouse data processing systems mostly record inventory quantities in a general ledger manner, making it difficult to simultaneously manage specific batches, production dates, expiration dates, and dynamic safety stock of spare parts. In cases of centralized requisition or abnormal inventory checks, untimely data entry can easily lead to discrepancies between the system's book inventory and the actual inventory. For example… Chinese invention patent application CN113670386A discloses a management method, device, equipment, and storage medium based on temperature and humidity. By comparing the temperature and humidity data collected from the storage area with preset standards, it determines the alarm level and pushes alarm information to the terminal when the duration of exceeding the limit reaches a preset range. However, this solution only treats environmental anomalies as static events for terminal prompts, ignoring the irreversible damage and attenuation mechanism caused by continuous deviation of environmental temperature and humidity to the physical properties of specific material components. It cannot convert the duration of exceeding the environmental temperature and humidity limit into specific shelf-life loss based on the sensitivity of the component material structure, and it is even more difficult to apply the environmental monitoring results in real time to the batch expiration date update and the priority of deduction for outbound shipment in the underlying database.

[0003] When warehouse temperature and humidity exceed the specified range for an extended period, existing systems struggle to calculate expiration dates based on these exceedances, potentially causing discrepancies between recorded expiration dates and the actual lifespan of spare parts. Furthermore, existing early warning functions often rely on fixed thresholds programmed into the system, failing to adjust warning baselines based on historical spare part usage fluctuations and environmentally-induced expiration. They also lack the capability for periodic automatic scanning and parameterized configuration of expiration dates, low inventory, and stagnant inventory. Moreover, during new system deployment or testing, existing technologies lack methods for balancing data security and processing efficiency in data clearing and automatic test data generation. If test data is mixed into the production database, it could increase the risk of accidentally deleting real business data.

[0004] Therefore, how to dynamically recalculate the batch expiration period of gas turbine spare parts, adjust the safety stock according to historical usage, ensure that the inventory change records are consistent with the actual inventory, and achieve coordinated early warning of multiple types of inventory risks has become a technical problem that needs to be solved in the current gas turbine spare parts inbound and outbound management. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: an automated management system for the inbound and outbound of gas turbine spare parts, comprising:

[0006] The data storage module is used to store the basic information model of gas turbine spare parts, the batch-level inventory record model, and the inventory change ledger model.

[0007] The early warning rule management module is used to configure early warning rule models that include rule day thresholds;

[0008] The environmental monitoring module is used to acquire temperature and humidity data of the storage location. When the temperature and humidity data continuously exceed the temperature and humidity threshold range, the duration of continuous exceedance is counted. Based on the duration of continuous exceedance and the structural type of the gas turbine spare parts, the expiration date of the corresponding batch of spare parts in the batch-level inventory record model is updated, and the expiration date change instruction is pushed.

[0009] The early warning engine module is used to respond to the expiration date change command, read the rule day threshold in the early warning rule management module, obtain the difference between the updated expiration date and the current system date, and generate an expiration warning record when the difference is less than or equal to the rule day threshold;

[0010] The inventory management module is used to respond to expiration date change instructions, re-lock the deduction priority of the corresponding batch of spare parts in ascending order of the updated expiration date, and verify the arithmetic identity in the outbound deduction database transaction that the changed inventory quantity is equal to the sum of the inventory quantity before the change and the changed quantity. If the arithmetic identity is not true, the database transaction is rolled back.

[0011] Preferably, the environmental monitoring module is configured with a sliding confirmation window. Only when the temperature and humidity exceed the threshold range for multiple consecutive collection cycles is it confirmed to enter the over-limit state, avoiding sensor expiration due to instantaneous noise. For multiple discontinuous temperature and humidity over-limit events in the storage location, the duration of continuous over-limit events for each event is independently counted. After each over-limit event ends, the expiration date is updated based on the remaining effective time of the spare parts at that time. The next over-limit event is calculated based on the expiration date updated in the previous event, so as to achieve the cumulative effect of multiple over-limit damage.

[0012] Preferably, the inventory management module obtains the average daily usage quantity, procurement lead time, service level safety factor, and standard deviation of the average daily usage quantity of gas turbine spare parts over the past 6 months, and calculates the safety stock threshold for gas turbine spare parts. After deducting outbound shipments, the inventory management module calculates the current total inventory quantity of gas turbine spare parts across the entire warehouse. When the current total inventory quantity is less than or equal to the safety stock threshold and there are no pending low inventory warnings, a low inventory trigger signal is pushed to the warning engine module, generating a low inventory warning record. The safety stock threshold is calculated using the formula... The calculation yielded that, As the safety stock threshold, This represents the average daily number of spare parts issued over the past six months. To allow for lead time in procurement, For service level safety factor, This represents the standard deviation of the average daily usage quantity over the past 6 months; the first term in the formula represents the expected usage quantity during the procurement lead time, and the second term represents the fluctuating reserve quantity of usage during the procurement lead time.

[0013] Preferably, the structural types of gas turbine spare parts include casing, turbine, compressor, fasteners, and rubber seals; when the temperature and humidity exceed the limits in the warehouse location corresponding to the rubber seals, the environmental monitoring module starts the cumulative timing of the continuous exceedance duration; the environmental monitoring module reduces the remaining difference between the expiration date of the corresponding batch of spare parts in the batch-level inventory record model and the current system date according to the expiration period depreciation coefficient, obtains the updated expiration date, and synchronously writes the updated expiration date into the batch-level inventory record model in the data storage module.

[0014] Preferably, the early warning rule management module stores a data table containing rule codes, rule names, early warning types, early warning levels, and parameter key-value pairs; the early warning engine module is configured with an asynchronous coroutine-based task scheduler, which reads the early warning rules enabled in the early warning rule management module according to the scheduled scanning cycle; during the stagnant inventory early warning scan, the early warning engine module queries the most recent change timestamp of the corresponding spare parts in the inventory change ledger model, and when the time difference between the current system time and the most recent change timestamp is greater than the stagnant days threshold configured in the data table and there are no pending stagnant warnings, it generates a stagnant inventory early warning record at the prompt level.

[0015] Preferably, when the inventory management module receives an outbound application, it retrieves the total available inventory of the corresponding spare parts under the target storage location from the data storage module. When the total available inventory is greater than or equal to the outbound quantity requested in the outbound application, the inventory management module deducts the inventory quantity of each batch of spare parts in the batch-level inventory record model in ascending order of their expiration dates, until the cumulative deduction equals the outbound quantity requested. When the inventory quantity of a certain batch of spare parts after deduction is equal to 0, the inventory management module marks the batch record in the batch-level inventory record model as canceled.

[0016] Preferably, the arithmetic logic expression used by the environmental monitoring module to calculate the expiration period reduction factor is as follows: ,in, This is the expiration date reduction factor, which is dimensionless. The lower limit of the expiration date reduction factor is set to 0.10. When the calculated result is less than 0.10, the value is forcibly limited to 0.10. The sensitivity coefficient is determined based on the structural type of the gas turbine spare parts. When the structural type is a rubber seal, Take 0.15, when the structure type is a metal part. Set to 0; The duration of continuous overrun is expressed in hours (h); 720 is the conversion constant corresponding to 720h, also expressed in hours (h); the environmental monitoring module sends the expiration period depreciation factor and the expiration date change instruction to the data storage module for calculating the updated expiration date.

[0017] Preferably, the environmental monitoring module is configured according to the formula. Calculate the updated expiration date ,in, The updated expiration date. The current system date. The original due date. This is the expiration period reduction factor; the calculation logic is as follows: take the remaining valid time between the original expiration date and the current system date, multiply it by the expiration period reduction factor to obtain the reduced remaining valid time, and add it to the current system date to obtain the updated expiration date.

[0018] Preferably, the system settings and test data management module provides a data clearing mechanism based on dual verification of administrator credentials and short-term memory verification codes. When the system settings and test data management module receives a verification code retrieval request from an administrator, it generates a 6-digit random verification code and binds the verification code with the administrator's identity to the memory cache, setting the verification code to have a validity period of 5 minutes. After receiving a reset request containing the login password and verification code, the system settings and test data management module verifies the correctness of the login password, the existence of the verification code, and the consistency of the verification code value in sequence. When all three verifications pass, the system settings and test data management module clears the warning records, environment records, inventory change ledgers, inbound and outbound documents, inventory records, and spare parts files in reverse order of foreign key dependency constraints.

[0019] Preferably, the system settings and test data management module includes an automatic end-to-end test data construction unit, which receives generation instructions containing data scale parameters. Based on the data scale parameters, this unit generates spare parts files with globally unique codes, randomly allocates batch inventory across multiple warehouse locations, calculates corresponding safety stock thresholds, generates inbound and outbound documents with completed and pending statuses, and an inventory change ledger conforming to arithmetic identities. Simultaneously, it generates corresponding environmental monitoring points, environmental time-series records, and tiered early warning data, constructing a closed test dataset. When generating the test dataset, the automatic end-to-end test data construction unit marks all generated data objects with a unique test tag. At the database level, data row tags distinguish test data from real business production data. Test data operations only allow access to data sets with test tags, prohibiting reading and writing of real production business data to prevent test data from mixing into the production database.

[0020] Preferably, the inventory change ledger module writes a logistic record to the inventory change ledger model each time there is an inventory change. This record includes the inventory quantity before the change, the inventory quantity after the change, the changed quantity, the change type, the change timestamp, and the identifier of the associated business document. The change type is taken from inbound, outbound, inventory adjustment, inventory reduction, and scrap. When the change type is inbound or inventory adjustment, the changed quantity is positive; when the change type is outbound, inventory reduction, or scrap, the changed quantity is negative. The inventory change ledger module uses an arithmetic identity to lock the linear relationship between the inventory quantity before the change, the inventory quantity after the change, and the changed quantity. In terms of algebraic relationships, the environmental monitoring module includes environmental monitoring points bound to warehouse locations. These monitoring points acquire real-time temperature and humidity values ​​according to the configured collection intervals, generating environmental monitoring records that include collection timestamps, monitoring point codes, real-time temperature values, real-time humidity values, and exceedance markers. These records are then stored in the environmental record model within the data storage module. When the real-time temperature or humidity value of the same monitoring point continuously exceeds the configured temperature and humidity threshold range within multiple consecutive collection timestamps, the environmental monitoring module calculates the difference between the timestamp of the first exceedance record and the timestamp of the latest exceedance record as the duration of continuous exceedance.

[0021] An automated management method for the inbound and outbound operations of gas turbine spare parts, used to operate an automated management system for the inbound and outbound operations of gas turbine spare parts, includes:

[0022] Step S1: Store the basic information model of gas turbine spare parts, the batch-level inventory record model, and the inventory change ledger model through the data storage module;

[0023] Step S2: Configure an early warning rule model that includes a rule day threshold through the early warning rule management module;

[0024] Step S3: Obtain temperature and humidity data of the storage location through the environmental monitoring module. When the temperature and humidity data continuously exceed the temperature and humidity threshold range, calculate the duration of continuous exceedance. Calculate the expiration period depreciation coefficient based on the duration of continuous exceedance and the structural type of the gas turbine spare parts. Update the expiration date of the corresponding batch of spare parts in the batch-level inventory record model and push the expiration date change instruction.

[0025] Step S4: In response to the expiration date change instruction, the early warning engine module reads the rule day threshold in the early warning rule management module, obtains the difference between the updated expiration date and the current system date, and generates an expiration warning record when the difference is less than or equal to the rule day threshold.

[0026] Step S5: In response to the expiration date change instruction, the inventory management module re-locks the deduction priority of the corresponding batch of spare parts in ascending order of the updated expiration date from earliest to latest. In the outbound deduction database transaction, the arithmetic identity is verified that the inventory quantity after the change is equal to the sum of the inventory quantity before the change and the changed quantity. If the arithmetic identity is not true, the database transaction is rolled back.

[0027] The present invention has the following beneficial effects:

[0028] (1) In the automatic management of inbound and outbound of gas turbine spare parts, by verifying the arithmetic relationship between the inventory quantity before the change, the changed quantity and the inventory quantity after the change during the inventory change process, it is possible to promptly detect and intercept inbound and outbound flows with inconsistent quantities, avoid writing erroneous data into the inventory ledger, and even in the case of centralized requisition, concurrent issuance or inventory adjustment, it can reduce the discrepancy between the ledger and the actual inventory caused by input delay and human operation error, so that the inventory quantity recorded by the system is consistent with the actual inventory.

[0029] (2) By linking the temperature and humidity data of the warehouse location with the expiration date of the specific batch of spare parts, the expiration date of the corresponding batch can be adjusted in a timely manner according to the continuous situation of environmental anomalies. The adjusted expiration date can be directly used for expiration warning and outbound sequence management, so that the batches that are more affected by the environment are given priority. This avoids the situation where sensitive spare parts are still issued after they have expired or failed due to management based solely on the original expiration date, thereby improving the safety of gas turbine spare parts storage and issuance management.

[0030] (3) By parameterizing the early warning rules and calculating the safety stock threshold in combination with historical usage, early warnings of expiration, low inventory and stagnant inventory can be carried out in a timely manner according to actual inventory changes. When the early warning conditions need to be adjusted, there is no need to repeatedly modify the system program, which makes it easier for material management personnel to update the rules according to the warehouse management requirements. It is also conducive to the system to regularly check different types of inventory risks, improve the flexibility of early warning management and the efficiency of system maintenance. Attached Figure Description

[0031] Figure 1 This is a flowchart of the dynamic validity period and inventory verification process for spare parts in this invention;

[0032] Figure 2 This is a diagram of the system module interaction structure of the present invention. Detailed Implementation

[0033] The present invention will be further described below with reference to the accompanying drawings and embodiments, but this should not be construed as limiting the present invention.

[0034] An automated inbound and outbound management system for gas turbine spare parts includes:

[0035] The data storage module is used to store the basic information model of gas turbine spare parts, the batch-level inventory record model, and the inventory change ledger model.

[0036] The early warning rule management module is used to configure early warning rule models that include rule day thresholds;

[0037] The environmental monitoring module is used to acquire temperature and humidity data of the storage location. When the temperature and humidity data continuously exceed the temperature and humidity threshold range, the duration of continuous exceedance is counted. Based on the duration of continuous exceedance and the structural type of the gas turbine spare parts, the expiration date of the corresponding batch of spare parts in the batch-level inventory record model is updated, and the expiration date change instruction is pushed.

[0038] The early warning engine module is used to respond to the expiration date change command, read the rule day threshold in the early warning rule management module, obtain the difference between the updated expiration date and the current system date, and generate an expiration warning record when the difference is less than or equal to the rule day threshold;

[0039] The inventory management module is used to respond to expiration date change instructions, re-lock the deduction priority of the corresponding batch of spare parts in ascending order of the updated expiration date, and verify the arithmetic identity in the outbound deduction database transaction that the changed inventory quantity is equal to the sum of the inventory quantity before the change and the changed quantity. If the arithmetic identity is not true, the database transaction is rolled back.

[0040] Preferably, the inventory management module obtains the average daily usage quantity, procurement lead time, service level safety factor, and standard deviation of the average daily usage quantity of gas turbine spare parts over the past 6 months, and calculates the safety stock threshold for gas turbine spare parts; after deducting outbound shipments, the inventory management module calculates the current total inventory quantity of gas turbine spare parts across the entire warehouse. When the current total inventory quantity is less than or equal to the safety stock threshold and there are no pending low inventory warnings, a low inventory trigger signal is pushed to the warning engine module to generate a low inventory warning record.

[0041] Preferably, the structural types of gas turbine spare parts include casing, turbine, compressor, fasteners, and rubber seals; when the temperature and humidity exceed the limits in the warehouse location corresponding to the rubber seals, the environmental monitoring module starts the cumulative timing of the continuous exceedance duration; the environmental monitoring module reduces the remaining difference between the expiration date of the corresponding batch of spare parts in the batch-level inventory record model and the current system date according to the expiration period depreciation coefficient, obtains the updated expiration date, and synchronously writes the updated expiration date into the batch-level inventory record model in the data storage module.

[0042] Preferably, the early warning rule management module stores a data table containing rule codes, rule names, early warning types, early warning levels, and parameter key-value pairs; the early warning engine module is configured with an asynchronous coroutine-based task scheduler, which reads the early warning rules enabled in the early warning rule management module according to the scheduled scanning cycle; during the stagnant inventory early warning scan, the early warning engine module queries the most recent change timestamp of the corresponding spare parts in the inventory change ledger model, and when the time difference between the current system time and the most recent change timestamp is greater than the stagnant days threshold configured in the data table and there are no pending stagnant warnings, it generates a stagnant inventory early warning record at the prompt level.

[0043] Preferably, when the inventory management module receives an outbound application, it retrieves the total available inventory of the corresponding spare parts under the target storage location from the data storage module. When the total available inventory is greater than or equal to the outbound quantity requested in the outbound application, the inventory management module deducts the inventory quantity of each batch of spare parts in the batch-level inventory record model in ascending order of their expiration dates, until the cumulative deduction equals the outbound quantity requested. When the inventory quantity of a certain batch of spare parts after deduction is equal to 0, the inventory management module marks the batch record in the batch-level inventory record model as canceled.

[0044] Preferably, the arithmetic logic expression used by the environmental monitoring module to calculate the expiration period reduction factor is as follows: ,in, This is the expiration date reduction factor. The sensitivity coefficient is determined based on the structural type of the gas turbine spare parts. When the structural type is a rubber seal, Take 0.15, when the structure type is a metal part. Take 0, For the duration of continuous overrun, 720 is the conversion benchmark constant corresponding to 720h; the environmental monitoring module sends the expiration period depreciation coefficient and the expiration date change instruction to the data storage module together to calculate the updated expiration date.

[0045] Preferably, the system settings and test data management module provides a data clearing mechanism based on dual verification of administrator credentials and short-term memory verification codes. When the system settings and test data management module receives a verification code retrieval request from an administrator, it generates a 6-digit random verification code and binds the verification code with the administrator's identity to the memory cache, setting the verification code to have a validity period of 5 minutes. After receiving a reset request containing the login password and verification code, the system settings and test data management module verifies the correctness of the login password, the existence of the verification code, and the consistency of the verification code value in sequence. When all three verifications pass, the system settings and test data management module clears the warning records, environment records, inventory change ledgers, inbound and outbound documents, inventory records, and spare parts files in reverse order of foreign key dependency constraints.

[0046] Preferably, the system settings and test data management module includes an automatic end-to-end test data construction unit, which receives generation instructions containing data scale parameters. The automatic end-to-end test data construction unit generates spare parts files with globally unique codes based on the data scale parameters, randomly allocates batch inventory in multiple warehouse locations, calculates the corresponding safety stock threshold, generates inbound and outbound documents containing completed and pending statuses, and an inventory change ledger that conforms to the arithmetic identity. Simultaneously, it generates supporting environmental monitoring points, environmental time-series records, and hierarchical early warning data to construct a closed test dataset.

[0047] Preferably, the inventory change ledger module writes a logistic record to the inventory change ledger model each time there is an inventory change. This record includes the inventory quantity before the change, the inventory quantity after the change, the changed quantity, the change type, the change timestamp, and the identifier of the associated business document. The change type is taken from inbound, outbound, inventory adjustment, inventory reduction, and scrap. When the change type is inbound or inventory adjustment, the changed quantity is positive; when the change type is outbound, inventory reduction, or scrap, the changed quantity is negative. The inventory change ledger module uses an arithmetic identity to lock the linear relationship between the inventory quantity before the change, the inventory quantity after the change, and the changed quantity. In terms of algebraic relationships, the environmental monitoring module includes environmental monitoring points bound to warehouse locations. These monitoring points acquire real-time temperature and humidity values ​​according to the configured collection intervals, generating environmental monitoring records that include collection timestamps, monitoring point codes, real-time temperature values, real-time humidity values, and exceedance markers. These records are then stored in the environmental record model within the data storage module. When the real-time temperature or humidity value of the same monitoring point continuously exceeds the configured temperature and humidity threshold range within multiple consecutive collection timestamps, the environmental monitoring module calculates the difference between the timestamp of the first exceedance record and the timestamp of the latest exceedance record as the duration of continuous exceedance.

[0048] An automated management method for the inbound and outbound operations of gas turbine spare parts, used to operate an automated management system for the inbound and outbound operations of gas turbine spare parts, includes:

[0049] Step S1: Store the basic information model of gas turbine spare parts, the batch-level inventory record model, and the inventory change ledger model through the data storage module;

[0050] Step S2: Configure an early warning rule model that includes a rule day threshold through the early warning rule management module;

[0051] Step S3: Obtain temperature and humidity data of the storage location through the environmental monitoring module. When the temperature and humidity data continuously exceed the temperature and humidity threshold range, calculate the duration of continuous exceedance. Calculate the expiration period depreciation coefficient based on the duration of continuous exceedance and the structural type of the gas turbine spare parts. Update the expiration date of the corresponding batch of spare parts in the batch-level inventory record model and push the expiration date change instruction.

[0052] Step S4: In response to the expiration date change instruction, the early warning engine module reads the rule day threshold in the early warning rule management module, obtains the difference between the updated expiration date and the current system date, and generates an expiration warning record when the difference is less than or equal to the rule day threshold.

[0053] Step S5: In response to the expiration date change instruction, the inventory management module re-locks the deduction priority of the corresponding batch of spare parts in ascending order of the updated expiration date from earliest to latest. In the outbound deduction database transaction, the arithmetic identity is verified that the inventory quantity after the change is equal to the sum of the inventory quantity before the change and the changed quantity. If the arithmetic identity is not true, the database transaction is rolled back.

[0054] Example 1: After the spare parts warehouse at the compressor station of the national pipeline natural gas long-distance transmission line enters the high-load supply phase during winter, the automatic management system for the inbound and outbound of gas turbine spare parts receives a batch of newly delivered gas turbine compressor interstage rubber seals. The data storage module writes the basic attributes of this spare part into the basic information model of gas turbine spare parts, where the spare part has a unique code. The record is SP-GT-2026-001, the spare part name is recorded as compressor interstage rubber seal, the structure type is set as rubber seal, the source is set as imported OEM, and the nominal shelf life is... Set for 24 months, upper limit of storage temperature and storage temperature limit The storage temperature is set at 25℃ and 10℃ respectively, with the upper limit of humidity. and lower limit of storage humidity The inventory management module sets the percentages to 65% and 30% respectively, retrieves the historical requisition data for this spare part over the past six months, and obtains the average daily requisition quantity. The average number of items issued per day is 2.5, and the standard deviation of the average daily number of items issued is [not specified]. 0.8 units, procurement lead time The service level security factor is read for 16 days. It is 1.65, according to the formula. Calculate the safety stock threshold The total was 45.28 items, of which, The safety stock threshold, This represents the average daily amount received. To allow for lead time in procurement, For service level safety factor, The standard deviation of the average daily requisition quantity is given. The product of the average daily requisition quantity and the procurement lead time corresponds to the expected requisition quantity within the procurement lead time. The product of the service level safety factor, the standard deviation of the average daily requisition quantity, and the square root of the procurement lead time corresponds to the requisition fluctuation reserve within the procurement lead time. The two parts are added together to form the safety stock threshold for this spare part.

[0055] The warehouse manager enters the receiving order number IN-20260805-01, batch number... LOT-20260801, Production Date The quantity received as of August 1, 2026. 50 items, target storage location For LOC-A-01-02, the inventory management module calculates the original expiration date based on the production date and the nominal shelf life. The data for this batch is set for July 21, 2028, and is written to the batch-level inventory record model, the Inventory model. During the writing process, the inventory change ledger module reads the inventory quantity before the change. The number is 0, so the quantity changed this time is 0. Recorded as 50 units, the changed inventory quantity is obtained. There are 50 items, and the arithmetic identities are verified. After the verification is passed, the inventory movement ledger module writes the inbound transaction records into the inventory movement ledger model, the InventoryMovement model, so that the batch-level inventory records are associated with the corresponding inventory movement records.

[0056] When this batch of rubber sealing rings is stored at location LOC-A-01-02 until August 1, 2027, the current system date is... On August 1, 2027, the environmental monitoring point MON-A-01 associated with this storage location continuously reported excessively high temperatures due to a malfunction in its temperature control equipment. The environmental monitoring data was collected every 5 minutes. The real-time temperature rose to 32℃, exceeding the storage temperature limit of 25℃. The environmental monitoring module then initiated an over-limit timer, and the duration of the continuous over-limit was recorded. For 240 hours, based on the rubber seal structure type recorded in the gas turbine spare parts basic information model, the environmental monitoring module reads the corresponding structural sensitivity coefficient. It is 0.15, and is calculated using the formula. Calculate the shelf-life reduction factor It is 0.95, of which, This is the expiration date reduction factor. The structural sensitivity coefficient is determined based on the structural type of gas turbine spare parts. For the duration of continuous over-limit, 720 is the conversion reference constant corresponding to 720 hours. Within the temperature range of 25℃ to 35℃, the thermo-oxidative aging curve of polymer materials is approximated by local engineering. This formula is used to express the reduction relationship between the duration of continuous over-limit and the remaining shelf life. In order to avoid the shelf life reduction coefficient from being negative or too small due to the excessive duration of continuous over-limit, the calculation module sets the lower limit of the shelf life reduction coefficient to 0.10; when the calculation result is lower than 0.10, it is taken as 0.10.

[0057] The environmental monitoring module will include the expiration date reduction factor. The expiration date change instruction is sent to the data storage module along with the original expiration date, which then reads the original expiration date from the batch-level inventory record model. and current system date According to the formula Calculate the updated expiration date ,in, The updated expiration date. The current system date. The original expiration date is set to 355 days. After deducting the expiration period, the updated expiration date is July 3, 2028. The data storage module writes the updated expiration date into the batch-level inventory record model. After writing, the environmental monitoring module pushes the expiration date change instruction to the early warning engine module and the inventory management module.

[0058] In response to the expiration date change command, the early warning engine module reads the enabled SHELF_LIFE early warning rule from the early warning rule management module through an asynchronous coroutine-based task scheduler to obtain the rule day threshold corresponding to the severe early warning. The typical warning period is 7 days, which corresponds to the rule-based threshold number of days. The period is 15 days. When the current system date advances to June 20, 2028, the early warning engine module calculates the difference between the updated expiration date and the current system date. The difference is 13 days, and this value satisfies... The warning engine module retrieves the corresponding expiration warning record for the batch from the Warning table. If no record of the same type with a pending status exists, a general-level expiration warning record is generated and pushed to the front-end workbench. At the same time, the inventory management module responds to the expiration date change instruction, retrieves all batch records of the spare part in the entire warehouse, and re-locks the deduction priority in ascending order from earliest to latest according to the updated expiration date, adjusting batch LOT-20260801 to the first deduction order.

[0059] The asynchronous coroutine-based task scheduler also reads the enabled stagnant inventory warning rules according to the configured scanning cycle. When performing stagnant inventory warning scans, the warning engine module queries the inventory change ledger model based on the spare part's unique code to obtain the most recent change timestamp of the spare part, and calculates the time difference between the current system time and the most recent change timestamp. When the time difference is greater than the stagnant days threshold configured in the warning rule, the warning engine module searches the Warning table. If there are no pending stagnant inventory warning records, a warning-level stagnant inventory warning record is generated.

[0060] When a compressor unit undergoes an emergency maintenance, maintenance personnel submit an outbound request form (OUT-20280621-02) via the front end, requesting the requisition of 15 compressor interstage rubber seals. The inventory management module receives the request form, retrieves the available inventory of spare part SP-GT-2026-001 under the target storage location LOC-A-01-02, and finds that the available inventory is 50 pieces. After confirming that the available inventory exceeds the requested outbound quantity, the inventory management module, according to the re-locked deduction priority, selects the batch LOT-20260801 with the earliest due date for deduction. In the outbound deduction database transaction, the inventory management module reads the inventory quantity before the change. The quantity will be changed to 50 pieces. Recorded as -15 units, the changed inventory quantity is calculated. There are 35 items, and the arithmetic identities are verified under database lock protection. After the verification is successful, the inventory management module submits the database transaction, and the inventory change ledger module writes a transaction record with the change type of "outbound" to the inventory change ledger model; if the verification fails, the inventory management module rolls back the database transaction and does not write the corresponding abnormal transaction record.

[0061] When a single outbound shipment requires deductions across multiple batches, the inventory management module executes sub-deductions sequentially within the same atomic database transaction, based on the expiration dates of spare parts for each batch from earliest to latest. For each batch involved in the deduction, the inventory management module reads the inventory quantity before the change under transaction lock protection, determines the changed quantity based on the sub-deduction quantity undertaken by that batch, calculates the changed inventory quantity, and verifies the corresponding arithmetic identities. When the inventory quantity after deduction for a certain batch is equal to 0, the inventory management module marks that batch record in the batch-level inventory record model as canceled. Only when the arithmetic identities of all batches pass verification, and the cumulative deduction quantity of all batches equals the requested outbound quantity, does the inventory management module commit the database transaction. If any batch fails verification or the inventory quantity is insufficient, the entire database transaction is rolled back.

[0062] After the outbound deduction is completed, the inventory management module calculates the current total inventory quantity of spare part SP-GT-2026-001 across the entire warehouse. 35 items were obtained and compared with the safety stock threshold. The comparison shows that the current total inventory is less than the safety stock threshold of 45.28 units. The inventory management module retrieves the corresponding low inventory warning record for the spare part from the Warning table. If no low inventory warning record is available, a low inventory trigger signal is pushed to the warning engine module. The warning engine module receives the signal, generates a low inventory warning record with a warning level of "warning", writes it to the Warning table, and pushes it to the front-end workbench. This allows material management personnel to initiate the procurement and replenishment process based on this record. The above process sequentially associates the temperature and humidity data collected at the storage location, the batch expiration date, the deduction priority, the inventory change log, and the safety stock threshold, so that changes in expiration dates caused by environmental exceedances can participate in expiration warnings, outbound deductions, and low inventory warnings.

[0063] Example 2: This example verifies the operation of the automated inbound and outbound management system for gas turbine spare parts under conditions of warehousing environmental interference and high-concurrency inbound and outbound operations. A simulated warehousing test platform was built, with 10 warehouse locations and corresponding environmental monitoring points numbered MON-A-01 to MON-A-10. The backend uses the Python FastAPI framework, data is stored in a PostgreSQL database, and the early warning engine module is configured with an AsyncIOScheduler timed task scheduler. The environmental data acquisition channel is superimposed with Gaussian white noise with a signal-to-noise ratio of 20dB and power frequency interference harmonics at a frequency of 50Hz to simulate measurement fluctuations of the station's temperature and humidity sensors during actual data acquisition.

[0064] After receiving the generation instruction containing data scale parameters, the system settings and test data management module generates a spare parts file with a globally unique code through the end-to-end test data automatic construction unit. Basic information such as spare parts name, structure type, and nominal shelf life is written into the gas turbine spare parts basic information model. Based on the primary key of the generated spare parts file, batch inventory is randomly allocated among multiple warehouse locations. Data such as batch number, inventory quantity, expiration date, and location identifier are written into the batch-level inventory record model. Simultaneously, the safety stock threshold for each spare part is calculated. Inbound and outbound documents containing completed and pending statuses are then generated. An inventory change log is generated in chronological order, showing the inventory quantity before, changed, and after the change, satisfying an arithmetic identity. This log is written into the inventory change ledger model. Environmental monitoring points generate continuously distributed temperature and humidity time-series data according to the set collection intervals. The collection timestamp, monitoring point code, real-time temperature value, real-time humidity value, and exceedance marker are written into the environmental record model. When temperature and humidity data meet exceedance conditions, hierarchical early warning data associated with the corresponding batch is generated synchronously, thus forming a closed test dataset with complete association between all data objects.

[0065] The test subject was an interstage rubber seal from a compressor, with a unique spare part code of SP-GT-2026-001 and a nominal shelf life of 24 months. The test group operated the complete processing flow of this system, and the structural sensitivity coefficient was measured. The value is set to 0.15, and inventory quantity verification and transaction locking based on arithmetic identity are enabled in the outbound deduction database transaction. Control group 1 uses a traditional static warehouse management system, does not perform dynamic depreciation recalculation of batch expiration dates, and does not perform transaction lock verification; Control group 2 retains batch management and transaction lock verification, but disconnects the linkage between the temperature and humidity data collected at the storage location and the batch expiration date, and the structural sensitivity coefficient is [not specified]. The value is set to 0.00; control groups 3a and 3b are used to examine the boundary values ​​of the structural sensitivity coefficient. They were set to 0.05 and 0.30 respectively.

[0066] The testing period was 30 days. During the test, each group continuously endured a high-temperature environment exceeding the limit of 32°C and received 100 concurrent data entry requests per second. The duration of continuous exceeding the limit was [not specified]. After 240 hours, the shelf-life reduction coefficient for each group was recorded. The following metrics were considered: the number of days until the early warning trigger expires, the error rate of concurrent requisition ledgers, and the rate of outbound shipments of spare parts with hidden failures.

[0067] In the experimental group, the environmental monitoring module calculated the shelf-life reduction coefficient based on a structural sensitivity coefficient of 0.15 and a continuous exceedance time of 240 hours. The value is 0.95. Based on this, the data storage module updates the original expiration date from July 21, 2028 to July 3, 2028. When the current system date advances to June 20, 2028, the updated expiration date differs from the current system date by 13 days. The warning trigger is recorded as 13.0 days from the expiration date. The warning engine module generates a general-level expiration warning record. The inventory management module relocks the deduction priority according to the updated expiration date and adjusts batch LOT-20260801 to the first deduction order. During high-concurrency requisition, the inventory management module verifies the arithmetic identity between the inventory quantity before the change, the changed quantity, and the changed inventory quantity under transaction lock protection. If the verification fails, the database transaction is rolled back. Therefore, the concurrent requisition ledger error rate of the test group is 0.00%, and the hidden failure spare parts issuance rate is 0.00%.

[0068] In control group one, the system did not update the batch expiration date and shelf-life reduction factor based on environmental exceedance data. The value remains at 1.00. The number of days until the warning is triggered is recorded as -5.2 days. Due to the lack of transaction locks and arithmetic identity verification in the outbound deduction process, the concurrent requisition ledger error rate is 4.82% under the influence of 100 concurrent requests per second. The batch deduction order has not been adjusted according to environmental influences, and the outbound rate of hidden failure spare parts is 12.35%.

[0069] In control group two, the structural sensitivity coefficient The value is 0.00. Temperature and humidity data collected at the storage location are not included in the calculation of the batch expiration date; the resulting expiration period depreciation factor is... The value is 1.00, the warning trigger is triggered in -2.1 days, the group retains the arithmetic identity verification in the outbound deduction database transaction, and the concurrent requisition ledger error rate is 0.00%; however, the batch expiration date and deduction priority are not updated according to the continuous temperature and humidity exceeding the limits, and the outbound rate of hidden failure spare parts is 8.60%.

[0070] In control group 3a, the structural sensitivity coefficient Taking a value of 0.05, and substituting this coefficient with the 240-hour continuous exceedance duration into the established calculation relationship, the expiration date reduction coefficient is determined. The error rate was 0.98, the warning was triggered 6.2 days before the due date, the group performed transaction lock and arithmetic identity verification, and the concurrent requisition ledger error rate was 0.00%; because the reduction of the due date was smaller than that of the experimental group, the batch affected by the environment entered the priority deduction order relatively late, and the hidden failure spare parts outbound rate was 3.10%.

[0071] In control group 3b, the structural sensitivity coefficient The expiration date reduction factor is set to 0.30, calculated based on the same continuous over-limit period. The error rate was 0.90, the warning trigger was 28.5 days before expiry, the concurrent requisition ledger error rate for this group was 0.00%, and the hidden failure spare parts issuance rate was 0.00%. Due to the large reduction in the expiry date, the system generated an expiry date warning earlier, causing spare parts that were still in safe use to trigger the near-expiration warning earlier.

[0072] In the above data, the error rate of concurrent requisition ledger in Control Group 1 stemmed from the lack of transaction-level inventory quantity verification under high concurrency requests. The experimental group, Control Group 2, Control Group 3a, and Control Group 3b all controlled this error rate to 0.00% through transaction locks and arithmetic identity verification. Regarding environmental expiration handling, the expiration loss coefficients for Control Group 1 and Control Group 2 were both 1.00, and the batch expiration dates did not change with the continuous overdue period, resulting in implicit failure spare parts issuance rates of 12.35% and 8.60%, respectively. Control Group 3a adjusted its expiration loss coefficient... After the structural sensitivity coefficient was reduced to 0.98, the outbound rate dropped to 3.10%. The experimental group used a structural sensitivity coefficient of 0.15, adjusted the expiration period reduction coefficient to 0.95, and re-locked the deduction priority based on the updated expiration date, so that the heat-affected batches were given priority to be outbound before failure. When the control group 3b used a structural sensitivity coefficient of 0.30, the expiration period reduction coefficient was further reduced to 0.90. Although the outbound rate of spare parts with hidden failures remained at 0.00%, the warning was triggered earlier, 28.5 days before the expiration date, which increased the number of spare parts that were still in safe use period entering the scope of near-expiration disposal.

[0073] Under the test conditions of this embodiment, the temperature and humidity data collected at the storage location are used to update the batch expiration date after the continuous over-limit duration is statistically analyzed. The update results are further used for expiration warning and deduction priority adjustment. The outbound deduction database transaction controls the inventory flow writing through transaction locks and arithmetic identity verification. The data of each group shows that the environmental expiration recalculation and outbound deduction transaction verification are respectively applied to the batch usage status and inventory quantity consistency. The two processes are used together for the inbound and outbound management of gas turbine spare parts.

[0074] Example 3: After the compressor station of the national pipeline natural gas long-distance transmission line enters the high-temperature supply guarantee stage in summer, the environmental data acquisition equipment at cargo position LOC-A-01-02 reports the real-time temperature at a fixed data acquisition interval of 300 seconds. and real-time humidity The environmental monitoring module receives temperature and humidity data from the storage location and displays the real-time temperature data. The upper limit of storage temperature recorded in the basic information model of gas turbine spare parts In comparison, the upper limit of the storage temperature corresponding to the compressor stage rubber seal SP-GT-2026-001 is 25℃; after the air conditioning cooling failure occurs, the real-time temperature rises to 32℃, and the environmental monitoring module initiates abnormal sampling and judgment accordingly.

[0075] To eliminate single-point instantaneous temperature fluctuations caused by environmental noise, the environmental monitoring module is configured with a sliding confirmation window spanning three consecutive sampling cycles, totaling 900 seconds. If the real-time temperature exceeds 25℃ for all three sampling cycles, the current storage location is confirmed to be in an over-temperature state, and the timestamp of the first over-temperature sample is recorded as [data missing]. And begin accumulating consecutive overtime periods. During the period of exceeding the limit, the environmental monitoring module uses the latest exceeding the limit sampling timestamp. and The time difference between them updates the duration of continuous overruns, that is: ,in, For continuous overtime, This is the latest timestamp for exceeding the sampling limit. The first sampling time stamp is used to collect data for the first out-of-limit sample. After the temperature control fault is cleared, the real-time temperature recovers to 10℃ to 25℃ and is maintained for 3 consecutive sampling cycles. The environmental monitoring module confirms that the out-of-limit state has ended, stops the timing, and clears the continuous out-of-limit timer to avoid incorrect expiration due to instantaneous noise.

[0076] When multiple discontinuous temperature and humidity exceedances occur in the same storage location, the environmental monitoring module processes each exceedance event segment by segment according to the state transition between the exceedance state and the recovery state. After each exceedance event ends, the validity period reduction factor is first calculated based on the duration of the continuous exceedance, and then applied to the remaining valid time at that time. The data storage module calculates and writes the new expiration date, and at the same time, the continuous exceedance timer is cleared. When an exceedance occurs again, the current system date at the time of the event and the previously updated expiration date are used as the new calculation basis to re-accumulate the continuous exceedance duration and complete the next reduction, so that the impact of each exceedance event on the remaining valid time accumulates sequentially.

[0077] Temperature control malfunctions have been occurring continuously in storage location LOC-A-01-02, with the duration exceeding the limit for an extended period. When the cycle reaches 240 hours, the environmental monitoring module reads the structural sensitivity coefficient based on the structural type of the compressor stage rubber seal. The value is 0.15, and the expiration date reduction factor is calculated. The value is 0.95, which is the product of 30 days and 24 hours per day corresponding to the baseline constant of 720 hours. It is used to convert the continuous over-limit duration into the time proportion within the monthly assessment cycle. The structural sensitivity coefficient of 0.15 comes from the offline aging test calibration of rubber sealing materials at over-limit temperatures, and represents the proportion of the effective period of the rubber seal within a complete monthly over-limit cycle.

[0078] The environmental monitoring module will include the expiration date reduction factor. The expiration date change instruction is sent to the data storage module, which then reads the original expiration date of batch LOT-20260801. and current system date The original expiration date was July 21, 2028, and the current system date is August 1, 2027. The updated expiration date is calculated according to the following formula. : ,in, The updated expiration date. The current system date. The original expiration date is calculated to be July 3, 2028. The data storage module writes this date into the batch-level inventory record model. The environmental monitoring module then pushes an expiration date change instruction to the inventory management module. Upon receiving the instruction, the inventory management module retrieves all batch-level inventory records for spare part SP-GT-2026-001 across the entire warehouse. It then re-locks the deduction priority in ascending order from earliest to latest according to the updated expiration date, and adjusts batch LOT-20260801 to the first deduction order.

[0079] When the compressor unit undergoes emergency maintenance, two terminals simultaneously submit outbound application forms OUT-20280805-01 and OUT-20280805-02, requesting 15 and 25 compressor stage rubber seals respectively from storage location LOC-A-01-02. After receiving the two outbound requests, the inventory management module initiates database transactions and applies a row-level exclusive lock to the batch-level inventory record corresponding to batch LOT-20260801.

[0080] Transaction 1 first acquires a row-level exclusive lock and reads the inventory quantity before the change. The quantity will be changed to 50 pieces. Recorded as -15 units, the changed inventory quantity is calculated. There are 35 items, and the arithmetic identity is verified in the database transaction: After successful verification, the inventory change ledger module writes the outbound transaction record to the inventory change ledger model. The inventory management module commits transaction one and releases the row-level exclusive lock. Transaction two then acquires the lock and reads the updated inventory quantity before the change. The requested quantity of 25 items is less than the available inventory of 35 items. The inventory management module will adjust the quantity accordingly. Recorded as -25 units, the changed inventory quantity is calculated. The quantity is 10. After the arithmetic identity verification passes, the corresponding outbound transaction is written and transaction two is submitted. If the outbound quantity requested by transaction two is 40, which is greater than the available inventory quantity of 35 read after it acquires the lock, the inventory management module will not perform inventory deduction and will roll back the database transaction to prevent the batch inventory quantity from dropping to a negative value or generating an outbound transaction with no corresponding inventory change.

[0081] After the two outbound transactions are completed, the inventory management module calculates the current total inventory quantity of spare part SP-GT-2026-001 across the entire warehouse. Ten pieces were obtained, and the system read the safety stock threshold calculated based on the historical requisition data of this spare part over the past six months. The value is 45.28 pieces. The current total inventory is 10 pieces, which is less than the safety stock threshold of 45.28 pieces. The inventory management module queries the Warning table. If there is no low inventory warning record for this spare part and the status is pending, a low inventory trigger signal is pushed to the warning engine module. The warning engine module receives the signal, generates a low inventory warning record with the warning level of Warning, writes it into the Warning table, and pushes it to the front-end workbench so that the material management personnel can start the procurement and replenishment process.

[0082] Example 4: This example combines Figures 1 to 2 This document describes the automated management system and methods for the inbound and outbound operations of gas turbine spare parts, such as... Figure 1 As shown, step S1 stores the basic information model of gas turbine spare parts, the batch-level inventory record model, and the inventory change ledger model through the data storage module. Step S2 configures the early warning rule model, which includes the rule day threshold, through the early warning rule management module. Step S3 obtains the temperature and humidity data of the storage location through the environmental monitoring module. When the data continuously exceeds the threshold range, the duration of continuous exceedance is counted. The expiration period depreciation coefficient is calculated based on the duration and structure type to update the batch expiration date and push the expiration date change instruction. Step S4 responds to the expiration date change instruction through the early warning engine module, reads the rule day threshold, obtains the difference between the updated expiration date and the system date, and generates an expiration period early warning record when the difference is less than or equal to the threshold. Step S5 responds to the change instruction through the inventory management module, relocks the batch deduction priority in ascending order of the updated expiration date, and verifies the arithmetic identity of inventory change in the outbound transaction. If it is not true, the transaction is rolled back.

[0083] like Figure 2 As shown, the environmental monitoring module pushes expiration date change instructions to the inventory management module and the early warning engine module respectively, and synchronously writes them to the data storage module. The data storage module stores the basic information model of gas turbine spare parts, the batch-level inventory record model, and the inventory change ledger model. The inventory management module retrieves the total available inventory from the data storage module and performs arithmetic identity verification with the inventory change ledger module. The inventory change ledger module writes transaction records to the data storage module. The early warning engine module reads the rule day threshold from the early warning rule management module.

[0084] Example 5: Before the deployment of the national pipeline network compressor station storage system, the structural sensitivity coefficients of gas turbine spare parts of different structural types were assessed. Offline calibration tests were conducted, and corresponding parameter baselines were established in the WarningRule model, the basic information model of gas turbine spare parts, and the SparePart model. When calibrating rubber seals, multiple sets of compressor interstage rubber seal samples were placed in a constant temperature and humidity test chamber. The test temperatures were set to 28℃, 32℃, and 36℃, and the relative humidity was set to 70%. During the test, the elongation at break of the rubber samples was detected every 24 hours using a thermogravimetric analyzer and an electronic universal tensile testing machine. When the elongation at break dropped to 50% of the original value, this moment was taken as the critical time when the sample reached the physical failure state.

[0085] Linear regression was performed based on the continuous over-limit duration and effective life decay data recorded at 32℃ to obtain the structural sensitivity coefficient corresponding to the rubber seal. The value was 0.15. For the fastener and casing samples, no physical degradation of metal grain boundaries was detected under the same temperature gradient, and the measured deformation attenuation rate was 0. The corresponding structural sensitivity coefficient was 0. The value is 0.00. Based on the structural type of the gas turbine spare parts, the system writes the calibrated structural sensitivity coefficient into the basic information model of the gas turbine spare parts, which is then used by the environmental monitoring module to calculate the depreciation coefficient over the service life. Read at any time.

[0086] During the calibration process, the initial thermal decomposition temperature threshold of the polymer rubber material was first determined by thermogravimetric analysis, and the upper limit of the safe temperature for accelerated aging tests was determined accordingly to avoid thermal decomposition of the samples at the test temperature. The mechanical property degradation of the rubber seals was measured using an electronic universal tensile testing machine. Standard tensile stress was applied to the samples under temperature control at 28℃, 32℃, and 36℃, and the change curve of the elongation at break retention rate over time was continuously recorded. The time corresponding to the elongation at break retention rate decreasing to 50% was used as the physical failure index. The test data obtained reflected the correspondence between the changes in the crosslinking network caused by thermo-oxidative aging of polymer segments and the macroscopic elongation at break of the rubber material under normal temperature and mild heating conditions, and served as the calibration basis for the structural sensitivity coefficient of 0.15.

[0087] When deploying environmental monitoring point MON-A-01 at storage location LOC-A-01-02 in the warehouse, on-site calibration and zero-point drift compensation were first performed. A high-precision standard temperature and humidity calibrator was placed next to the physical measurement point corresponding to MON-A-01. Time-series temperature data from both the environmental monitoring point and the standard temperature and humidity calibrator were collected synchronously over 3600 seconds. Calculations showed that the root mean square deviation between the temperature reported by the sensor and the reading from the standard temperature and humidity calibrator was 0.3℃. The system used this root mean square deviation as the zero-point compensation operator. Write the calibration parameters corresponding to the environmental monitoring points.

[0088] After zero-point calibration, a 10°C step temperature disturbance is applied to the sensor. When the sensor output reaches 90% of the step amplitude, the corresponding response delay time is 120 seconds. Based on this, the system sets 120 seconds as the sampling response filtering time window for the environmental monitoring point. Before writing the real-time temperature data into the environmental recording model, zero-point drift correction is performed according to the following formula: ,in, For the real-time temperature after calibration, The real-time temperature reported by the sensor. As a zero-point compensation operator, the environmental monitoring module uses the calibrated real-time temperature to determine the temperature and humidity threshold range and the duration of continuous exceedances. The cumulative total.

[0089] Within a 120-second sampling response filtering time window, the system uses a sliding weighted average method to process multiple continuously collected raw temperature and humidity data points, smooths the data within the window, and removes transient high-frequency spikes caused by electromagnetic interference. The processed temperature and humidity values ​​are used as valid temperature and humidity data of the cargo location, written into the environmental recording model, and used by the environmental monitoring module for subsequent statistics on continuous over-limit duration and calculation of expiration period.

[0090] Example 6: When the local area network of the national pipeline warehouse is down, or the temperature and humidity sensor experiences intermittent data collection interruption, the environmental monitoring module continuously checks the environmental time-series records of the environmental monitoring point corresponding to storage location LOC-A-01-02. If no data is received from the sensor for two consecutive collection cycles, totaling 600 seconds, or if the received real-time temperature exceeds the physical extreme range of -40℃ to 80℃, the environmental monitoring module marks the environmental monitoring point as a degraded operation state.

[0091] After entering the degraded operation state, the environmental monitoring module initiates spatial proximity interpolation processing. It reads the calibrated real-time temperature of adjacent environmental monitoring point MON-A-02 in the same area and, combined with the temperature change trend of the target storage location in the 24 hours before the interruption, calculates the estimated temperature of storage location LOC-A-01-02. In specific processing, it first selects multiple environmental monitoring points in the same warehouse area that are closest to the target storage location in terms of physical distance and are in normal data collection status. It assigns weights according to the reciprocal of the physical distance between each environmental monitoring point and the target storage location, and sums them up to obtain the spatial weighted average temperature. It reads the historical temperature change curve of the target storage location in the 24 hours before the interruption, obtains the temperature at the same time in history, and calculates the historical temperature change relative to the previous sampling time. It then superimposes the spatial weighted average temperature and the historical temperature change according to the preset configurable fusion weights to obtain the estimated temperature of the faulty storage location. The fusion weights can be configured in the system parameter table. If there are no environmental monitoring points in the same warehouse area that are in a normal data collection state, the environmental monitoring point corresponding to the storage location will be marked as having an unpredictable failure state, and an alarm for complete failure of environmental data collection will be pushed to the upper level. The estimated temperature will no longer be output. Based on this, the environmental monitoring module will push an expiration risk warning signal to the inventory management module.

[0092] After receiving the risk warning signal, the inventory management module reads the safety stock downgrade coefficient of 1.20, multiplies the original safety stock threshold by this coefficient to obtain the downgraded safety stock threshold, and writes the calculation result into the batch-level inventory record model. The safety stock downgrade coefficient of 1.20 is set according to the risk assessment results under the condition of station communication interruption. During the period of sensor data collection interruption, the system cannot continuously obtain the temperature and humidity data of the storage location, so the safety stock threshold is increased by 20% to increase the corresponding spare parts reserve in the warehouse during the period of environmental monitoring interruption.

[0093] During the interruption of warehouse LAN communication, the warehouse handheld terminal enables offline inbound and outbound transaction logs and records each inventory change in local storage in chronological order of operation. Each offline transaction record includes the inventory quantity before the change, the quantity changed, the inventory quantity after the change, the spare parts unique code, and the storage location identifier, and also writes a local timestamp and data verification code.

[0094] After the local area network communication is restored, the system settings and test data management module starts the asynchronous offline data synchronization service, which sends the offline inbound and outbound transaction logs to the backend PostgreSQL database in batches. The inventory management module replays the offline transactions one by one according to the offline timestamp from earliest to latest, and verifies the arithmetic identity between the inventory quantity before the change, the changed quantity and the changed inventory quantity under the protection of the database lock.

[0095] During the replay process, the inventory management module compares the pre-change inventory quantity in each offline transaction record with the current inventory quantity in the database. If they match, the inventory change is executed according to the offline transaction record, and the corresponding transaction is written to the inventory change ledger model. If they do not match, the writing of the current abnormal transaction is stopped, the transaction is isolated to the concurrent conflict pending verification queue, and a verification work order is pushed to the front-end workbench. Offline transaction records isolated to the pending verification queue need to be manually reviewed and confirmed by the administrator. If the review is approved, the transaction will participate in the replay again. If the review is rejected, the offline transaction is invalidated and will no longer participate in inventory changes. Subsequently, the current inventory quantity in the database is used as the pre-change inventory quantity for subsequent offline transactions. Combined with the change quantity of each offline transaction record, the corresponding post-change inventory quantity is corrected in turn, and the verification and replay of subsequent transactions continue.

[0096] After all offline logs have been verified and replayed, the system settings and test data management module clears the offline log cache in the warehouse handheld terminal and removes the completed flags from the concurrent conflict check queue. After the environmental monitoring module restores real-time communication with the environmental monitoring point MON-A-01, the inventory management module cancels the downgraded safety stock threshold and restores the safety stock threshold to 45.28 pieces.

[0097] In the above process, the environmental monitoring module supplements the storage location temperature data during the sensor interruption by using adjacent environmental monitoring points and historical temperature change trends. After communication is restored, the inventory management module replays the inventory change flow according to the offline timestamp and handles concurrent conflicts by checking database locks and arithmetic identities. Under this abnormal working condition, the error rate of accounting and actual inventory is 0.00%.

[0098] During system operation, environmental monitoring points collect temperature and humidity data of the storage locations. The environmental monitoring module counts the duration of continuous exceedances and calculates the expiration date depreciation coefficient. The early warning engine module generates graded early warning records according to the scanning cycle. The inventory management module re-locks the deduction priority based on the updated expiration date and verifies the changes in inventory quantity in the database transaction. Each module transmits data and status through data interfaces and expiration date change instructions, so that on-site data collection, expiration date calculation, early warning generation, and inventory deduction are sequentially connected.

[0099] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application 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 under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. An automated warehouse management system for gas turbine spare parts, characterized by: include: The data storage module is used to store the basic information model of gas turbine spare parts, the batch-level inventory record model, and the inventory change ledger model. The early warning rule management module is used to configure early warning rule models that include rule day thresholds; The environmental monitoring module is used to acquire temperature and humidity data of the storage location. When the temperature and humidity data continuously exceed the temperature and humidity threshold range, the duration of continuous exceedance is counted. Based on the duration of continuous exceedance and the structural type of the gas turbine spare parts, the expiration date of the corresponding batch of spare parts in the batch-level inventory record model is updated, and the expiration date change instruction is pushed. The early warning engine module is used to respond to the expiration date change command, read the rule day threshold in the early warning rule management module, obtain the difference between the updated expiration date and the current system date, and generate an expiration warning record when the difference is less than or equal to the rule day threshold; The inventory management module is used to respond to expiration date change instructions, re-lock the deduction priority of the corresponding batch of spare parts in ascending order of the updated expiration date, and verify the arithmetic identity in the outbound deduction database transaction that the changed inventory quantity is equal to the sum of the inventory quantity before the change and the changed quantity. If the arithmetic identity is not true, the database transaction is rolled back.

2. The automated inbound and outbound management system for gas turbine spare parts according to claim 1, characterized in that: inventory The management module obtains the average daily usage quantity, procurement lead time, service level safety factor, and standard deviation of the average daily usage quantity of gas turbine spare parts over the past 6 months, and calculates the safety stock threshold for gas turbine spare parts. After deducting outbound items, the inventory management module calculates the current total inventory quantity of gas turbine spare parts across the entire warehouse. When the current total inventory quantity is less than or equal to the safety stock threshold and there are no pending low inventory warnings, it pushes a low inventory trigger signal to the warning engine module to generate a low inventory warning record.

3. The automated warehouse management system for gas turbine spare parts as described in claim 1, characterized in that: The structural types of gas turbine spare parts include casing, turbine, compressor, fasteners and rubber seals; when the temperature and humidity exceed the limits in the warehouse location corresponding to the rubber seals, the environmental monitoring module starts the cumulative timing of the continuous exceedance duration; The environmental monitoring module reduces the remaining difference between the expiration date of the corresponding batch of spare parts in the batch-level inventory record model and the current system date based on the expiration factor, and obtains the updated expiration date. The updated expiration date is then synchronously written into the batch-level inventory record model in the data storage module.

4. The automated warehouse management system for gas turbine spare parts as described in claim 1, characterized in that: The early warning rule management module stores a data table containing rule codes, rule names, early warning types, early warning levels, and parameter key-value pairs; The early warning engine module is equipped with an asynchronous coroutine-based task scheduler, which reads the early warning rules enabled in the early warning rule management module according to the scheduled scanning cycle. In the stagnant inventory early warning scan, the early warning engine module queries the most recent change timestamp of the corresponding spare parts in the inventory change ledger model. When the time difference between the current system time and the most recent change timestamp is greater than the stagnant days threshold configured in the data table and there are no pending stagnant warnings, an stagnant inventory early warning record at the prompt level is generated.

5. The automated warehouse management system for gas turbine spare parts as described in claim 1, characterized in that: When the inventory management module receives an outbound application, it retrieves the total available inventory of the corresponding spare parts under the target storage location from the data storage module. When the total available inventory is greater than or equal to the quantity requested for shipment in the shipment requisition form, the inventory management module deducts the inventory quantity of each batch of spare parts in the batch-level inventory record model in order of the expiration date from earliest to latest, until the cumulative deduction quantity equals the quantity requested for shipment; when the inventory quantity of a certain batch of spare parts after deduction is equal to 0, the inventory management module marks the batch record in the batch-level inventory record model as cancelled.

6. The automated warehouse management system for gas turbine spare parts as described in claim 1, characterized in that: The arithmetic logic used by the environmental monitoring module to calculate the shelf-life reduction factor is as follows: ,in, This is the expiration date reduction factor. The sensitivity coefficient is determined based on the structural type of the gas turbine spare parts. When the structural type is a rubber seal, Take 0.15, when the structure type is a metal part. Take 0, For the duration of continuous overrun, 720 is the conversion benchmark constant corresponding to 720h; the environmental monitoring module sends the expiration period depreciation coefficient and the expiration date change instruction to the data storage module together to calculate the updated expiration date.

7. The automated warehouse management system for gas turbine spare parts as described in claim 1, characterized in that: The system settings and test data management module provides a data clearing mechanism based on dual verification of administrator credentials and short-term memory verification codes. When the system settings and test data management module receives a verification code retrieval request from an administrator, it generates a 6-digit random verification code and binds the verification code with the administrator's identity to the memory cache, setting the verification code to have a validity period of 5 minutes. After receiving a reset request containing the login password and verification code, the system settings and test data management module verifies the correctness of the login password, the existence of the verification code, and the consistency of the verification code value in sequence. When all three verifications pass, the module clears the warning records, environment records, inventory change ledgers, inbound and outbound documents, inventory records, and spare parts files in reverse order of foreign key dependency constraints.

8. The automated warehouse management system for gas turbine spare parts as described in claim 1, characterized in that: The system settings and test data management module includes an automatic construction unit for end-to-end test data, which receives generation instructions containing data scale parameters; The end-to-end test data automatic construction unit generates spare parts files with globally unique codes based on data scale parameters, randomly allocates batch inventory in multiple warehouse locations, calculates the corresponding safety stock threshold, generates inbound and outbound documents containing completed and pending statuses and inventory change ledgers that conform to arithmetic identities, and simultaneously generates supporting environmental monitoring points, environmental time-series records and hierarchical early warning data to construct a closed test dataset.

9. The automated warehouse management system for gas turbine spare parts as described in claim 1, characterized in that: The inventory change ledger module writes a logistic record to the inventory change ledger model each time there is an inventory change. This record includes the inventory quantity before the change, the inventory quantity after the change, the changed quantity, the change type, the change timestamp, and the identifier of the associated business document. The change type is taken from inbound, outbound, inventory adjustment, inventory reduction, and scrap. When the change type is inbound or inventory adjustment, the changed quantity is positive; when the change type is outbound, inventory reduction, or scrap, the changed quantity is negative. The inventory change ledger module uses an arithmetic identity to lock the linear algebraic relationship between the inventory quantity before the change, the inventory quantity after the change, and the changed quantity. The environmental monitoring module includes environmental monitoring points bound to warehouse locations. These monitoring points acquire real-time temperature and humidity values ​​at configured collection intervals, generating environmental monitoring records that include collection timestamps, monitoring point codes, real-time temperature and humidity values, and exceedance markers. These records are then stored in the environmental record model within the data storage module. When the real-time temperature or humidity value of the same monitoring point continuously exceeds the configured temperature and humidity threshold range within multiple consecutive collection timestamps, the environmental monitoring module calculates the difference between the timestamp of the first exceedance record and the timestamp of the latest exceedance record as the duration of continuous exceedance.

10. An automated management method for the inbound and outbound of gas turbine spare parts, used to operate the automated management system for the inbound and outbound of gas turbine spare parts as described in claim 1, characterized in that it includes: Step S1: Store the basic information model of gas turbine spare parts, the batch-level inventory record model, and the inventory change ledger model through the data storage module; Step S2: Configure an early warning rule model that includes a rule day threshold through the early warning rule management module; Step S3: Obtain temperature and humidity data of the storage location through the environmental monitoring module. When the temperature and humidity data continuously exceed the temperature and humidity threshold range, calculate the duration of continuous exceedance. Calculate the expiration period depreciation coefficient based on the duration of continuous exceedance and the structural type of the gas turbine spare parts. Update the expiration date of the corresponding batch of spare parts in the batch-level inventory record model and push the expiration date change instruction. Step S4: In response to the expiration date change instruction, the early warning engine module reads the rule day threshold in the early warning rule management module, obtains the difference between the updated expiration date and the current system date, and generates an expiration warning record when the difference is less than or equal to the rule day threshold. Step S5: In response to the expiration date change instruction, the inventory management module re-locks the deduction priority of the corresponding batch of spare parts in ascending order of the updated expiration date from earliest to latest. In the outbound deduction database transaction, the arithmetic identity is verified that the inventory quantity after the change is equal to the sum of the inventory quantity before the change and the changed quantity. If the arithmetic identity is not true, the database transaction is rolled back.

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