Orchid oil production whole-course tracing and quality control management system and method

Abstract

The invention discloses an orchid oil production whole-course tracing and quality control management system and method, and the method comprises the steps: marking raw materials through an RFID technology or a two-dimensional code, and recording a production place, a supplier and a quality detection report. When raw materials are warehoused, a sensor collects basic data and uploads the basic data to a cloud database; in the production process, the Internet of Things equipment collects production data in real time and transmits the production data to the cloud platform for analysis, and if abnormity occurs, the system automatically gives an alarm and prompts an operator to adjust; finished products are subjected to multi-dimensional analysis through an intelligent quality detection module, quality data and production data are compared and analyzed, and if unqualified products exist, the system automatically adjusts production parameters and notifies quality management personnel to intervene; production, detection and storage data of each batch are registered through the block chain, and a consumer scans the two-dimensional code to check information of the whole process of the product from raw materials to finished products, including production date, environment and quality reports.

Description

Technical Field

[0001] The present invention belongs to the field of orchid oil production full-process traceability and quality control management, and in particular relates to an orchid oil production full-process traceability and quality control management system and method. Background Art

[0002] The Orchid Oil Production Traceability and Quality Control Management System is an integrated information system designed to ensure that every step of the orchid oil production process, from raw material procurement, production and processing, quality testing to final product delivery, can be traced and effectively controlled. The system records detailed information from raw material planting and harvesting, processing, storage, and transportation, ensuring that each batch of orchid oil products can be traced back to the source. Consumers or regulatory agencies can query the product's production history and related information by scanning QR codes or barcodes. The system conducts real-time monitoring and quality testing of each link in the production process to ensure that each production link meets standard requirements. Quality control measures include raw material inspection, automated control during production, production environment monitoring, and final quality testing of finished products. Sensors and automated equipment collect various data from the production process, and the system automatically analyzes them to promptly identify potential quality risks or operational anomalies, allowing measures to ensure product quality stability. Through information management, the system centrally manages and analyzes all data from the entire production process, thereby improving production efficiency, reducing costs, enhancing product quality, and strengthening the company's market competitiveness.

[0003] However, the full-process traceability and quality control management system for orchid oil production may face some technical and management challenges during its implementation. Traditional orchid oil production systems have problems in quality control and traceability, such as incomplete data collection, insufficient real-time monitoring, lack of intelligence in quality control links, difficulty in supply chain traceability, and low system integration. These defects lead to unstable quality of orchid oil products, and it is difficult to trace the source in a timely manner when quality problems occur. Summary of the Invention

[0004] In response to the deficiencies in the existing technology, the purpose of the present invention is to provide a full-process traceability and quality control management system and method for orchid oil production, which ensures the quality stability and traceability of the orchid oil production process by integrating advanced data collection, real-time monitoring, intelligent analysis, and supply chain traceability technologies.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] The orchid oil production traceability and quality control management system includes:

[0007] Data acquisition module: collects raw material sources, processing temperature, humidity, processing time, and equipment operating status parameters in real time during the production process through IoT devices and sensors;

[0008] Real-time monitoring and abnormal alarm module: Through intelligent algorithms, various data in the production process are analyzed in real time, and impending quality problems are predicted and early warning signals are issued;

[0009] Blockchain traceability module: In the procurement, processing, inspection and storage of raw materials, blockchain technology is used to create an unalterable record for each product or batch;

[0010] Intelligent quality control module: Analyzes historical data and real-time monitoring data through artificial intelligence technology to assess potential quality risks in the production process.

[0011] As a preferred option, the data acquisition module automatically uploads data to the cloud; the real-time monitoring and abnormal alarm module controls quality by providing real-time feedback on the equipment operating status and environmental temperature and humidity parameters during the production process; the blockchain traceability module allows consumers or regulatory agencies to access the complete production chain information of the product by scanning the QR code; the intelligent quality control module automatically adjusts production parameters and optimizes the production process through machine learning models.

[0012] The whole-process traceability and quality control management method of orchid oil production includes the following steps:

[0013] Step 1: Use RFID technology or QR codes to identify raw materials and record the production location, supplier, and quality inspection report of the raw materials. When the raw materials are put into storage, the basic data of the raw materials is collected through sensors and uploaded to the cloud database;

[0014] Step 2: During the production process, IoT devices are used to collect production data in real time, such as temperature, humidity, time, and pressure process parameters. The data is then uploaded to the cloud platform via wireless transmission for processing. The system uses algorithms to analyze the data in real time. If any anomalies occur, the system automatically generates an alarm and prompts the operator to make adjustments.

[0015] Step 3: The finished product undergoes multi-dimensional analysis through the intelligent quality inspection module, including sensory testing, chemical composition analysis, and microbiological testing. The quality inspection data is compared and analyzed with production data. If any unqualified product is produced, the system automatically adjusts production parameters and notifies quality management personnel to intervene.

[0016] Step 4: The data of each batch’s production, testing, and storage links are registered through blockchain technology. Consumers can obtain information on the entire process from raw materials to finished products, including production date, production environment, and quality inspection report, by scanning the QR code on the product packaging.

[0017] Preferably, raw materials are identified by RFID technology or QR codes, and the production location, supplier, and quality inspection report information of the raw materials are recorded. When the raw materials are put into storage, the basic data of the raw materials are collected by sensors and uploaded to the cloud database. The method is as follows:

[0018] The raw materials are identified by ID, which records the following information through RFID technology or QR code:

[0019] Place of production: location;

[0020] Supplier: supplier;

[0021] Quality inspection report: quality_report;

[0022] The formula for identifying and recording raw materials is:

[0023] RawMaterialData(ID)=(location,supplier,quality_report)

[0024] When raw materials are put into storage, sensors collect basic data of raw materials, including temperature T, humidity H, weight W and other related parameters P;

[0025] These data are collected in real time through the sensor module, and the collection formula is:

[0026] SensorData(ID)=(T,H,W,P)

[0027] The collected basic data and identification information of raw materials are uploaded to the cloud database. The upload interface of the cloud database is set as UploadCloud(), and the data is organized into a structure or record. The upload process is expressed by the following formula:

[0028] RawMaterialRecord(ID)=(RawMaterialData(ID),SensorData(ID))

[0029] The formula for uploading to the cloud database is:

[0030] CloudDataUpload(RawMaterialRecord(ID))→CloudDatabase

[0031] Among them, CloudDatabase is the database for storing cloud data, and all raw material information and sensor data will be transmitted and stored through this interface.

[0032] As a preferred method, during the production process, IoT devices are used to collect production data in real time, such as temperature, humidity, time, and pressure process parameters, and uploaded to the cloud platform for processing via wireless transmission. The system uses algorithms to analyze the data in real time. If an abnormality occurs, the system automatically alarms and generates prompts for the operator to make adjustments. The method is as follows:

[0033] The data collected by IoT devices include the following process parameters:

[0034] Temperature: T;

[0035] Humidity: H;

[0036] Time: t;

[0037] Pressure: P;

[0038] These data are collected by sensors, and the collection function is set up as:

[0039] SensorData(t)=(T(t),H(t),P(t))

[0040] Through wireless transmission, data is uploaded to the cloud platform in real time:

[0041] CloudDataUpload(SensorData(t))→CloudPlatform

[0042] In the cloud platform, the system performs real-time analysis on the collected data according to the set process standards and thresholds. The set process standards include temperature, humidity, and pressure ranges as follows:

[0043] Temperature range: [T min , T max ]

[0044] Humidity range: [H min , H max ]

[0045] Pressure range: [P min , P max ]

[0046] The system performs anomaly detection algorithm on the data. The formula is as follows:

[0047]

[0048] When the system detects an anomaly, it triggers the alarm mechanism, generates an alarm signal, and sends a prompt message to the operator. The alarm formula is as follows:

[0049]

[0050] Once the alarm signal is triggered, the operator makes corresponding adjustments based on the prompt information provided by the system. At this time, the system records the adjustment timestamp and collects data again for verification:

[0051] AdjustmentFeedback(t)=SensorData(t)after adjustment

[0052] As a preferred option, finished products undergo multi-dimensional analysis through an intelligent quality inspection module, including sensory testing, chemical composition analysis, and microbiological testing. The quality inspection data will be compared and analyzed with production data. If unqualified products are produced, the system will automatically adjust production parameters and notify quality management personnel to intervene. The following methods are used:

[0053] The finished product undergoes three testing modules: sensory testing, chemical composition analysis, and microbiological testing. Each module evaluates the quality of the finished product based on different indicators.

[0054] Set up the following data:

[0055] Sensory test results: S;

[0056] Chemical composition analysis results: C = (C1, C2, ..., C n );

[0057] Microbiological test results: M;

[0058] The system integrates these data and establishes the test results of each module as S(t), C(t), and M(t), which constitute the quality data of the finished product:

[0059] QualityData(t)=(S(t),C(t),M(t))

[0060] To identify and determine whether the finished product is qualified, the system compares and analyzes the quality inspection data with the production parameters collected during the production process, and sets the parameters of the production process as follows:

[0061] Temperature: T p ;

[0062] Humidity: H p ;

[0063] Pressure: P p ;

[0064] By comparing production parameters with quality inspection data, abnormality detection and qualification judgment are carried out:

[0065]

[0066] If the system identifies a product as unqualified, it will automatically adjust the production parameters. The adjustment of production parameters is based on the deviation between the current value and the standard value. The formula is:

[0067]

[0068] Among them, T target , H target , P target is the preset target value, k1, k2, k3 are adjustment coefficients;

[0069] Once a product is identified as unqualified and production parameters are adjusted, the system automatically generates a notification to the quality management personnel. The notification includes:

[0070] Reasons for failure: sensory, chemical, microbiological;

[0071] Adjusted production parameters: temperature, humidity, pressure;

[0072] Current Status: Conducting intervention;

[0073] The formula is:

[0074] .

[0075] As a preferred method, the data of each batch of production, testing, and storage links are registered through blockchain technology. Consumers can obtain the whole process information from raw materials to finished products by scanning the QR code on the product packaging, including the production date, production environment, and quality inspection report.

[0076] The data for each production link includes:

[0077] Production DateD prod ;

[0078] Production environment parameters E prod =(T, H, P);

[0079] Quality inspection report Q report =(S, C, M);

[0080] The blockchain record format is as follows:

[0081] Block n =(BatchID,D prod ,E prod ,Q report ,Timestamp,Hash n1 )

[0082] Among them, BatchID is the unique identifier of the current batch;

[0083] D prod is the production date;

[0084] E prod Production environment parameters

[0085] Q report Provide data for quality inspection reports;

[0086] Timestamp is the record timestamp;

[0087] Hash n1 is the hash value of the previous block;

[0088] Whenever data is generated during production, testing, or storage, a corresponding block is added to the blockchain, establishing that a block is created at the beginning of the production process:

[0089]

[0090] As production progresses, new blocks are generated in the detection and storage stages respectively:

[0091]

[0092] Each product package is printed with a unique QR code. After scanning the QR code, consumers can obtain the complete production information of the product.

[0093] The content of the QR code is:

[0094] QRCode=BatchID(with link to blockchain query system)

[0095] After the consumer scans the QR code, the system queries the data on the blockchain. The blockchain query is performed by providing the BatchID:

[0096] Through the blockchain query API, the consumer system enters the BatchID into the query interface;

[0097] The system returns all block data related to the BatchID, including production date, production environment and quality inspection report;

[0098] The consumer query formula is:

[0099] ProductInfo=BlockchainQuery(BatchID)={D prod ,E prod ,Q report}

[0100] Among them, D prod is the production date;

[0101] E prod =(T, H, P) are production environment parameters;

[0102] Q report =(S, C, M) is the quality inspection report.

[0103] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and run on the processor. When the processor executes the program, it realizes the full-process traceability and quality control management system and method for orchid oil production as described in any of the above.

[0104] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium on which a computer program is stored, which, when executed by a processor, implements a full-process traceability and quality control management system and method for orchid oil production.

[0105] The beneficial effects of the present invention are:

[0106] Raw materials are identified through RFID technology or QR codes, and key information such as their production location, supplier, and quality inspection report is recorded to ensure the traceability of the source of raw materials. From raw material procurement to the production process and then to the quality inspection of the finished product, each step is recorded and uploaded to the cloud to form a complete production record. Process parameters such as temperature, humidity, and pressure in production are collected in real time through IoT devices to ensure that the production process is carried out under optimal conditions. Data is analyzed in real time through intelligent algorithms. If any abnormality is found, the system will automatically generate an alarm and prompt the operator to adjust the production parameters. The finished product undergoes multi-dimensional quality inspection, including sensory inspection, chemical composition analysis, and microbiological inspection. Blockchain technology is used to register data from each batch of production, inspection, and storage links to ensure the data is tamper-proof and transparent. Through automated data collection and processing, companies can reduce manual operations and intermediate links and improve supply chain management efficiency. Real-time monitoring and analysis can effectively avoid cost losses due to production anomalies or quality problems. Through transparent supply chain management and high-quality products, companies can enhance consumer trust and loyalty to the brand and enhance their brand image. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 The figure is a flow chart of the whole-process traceability and quality control management system for orchid oil production according to the present invention. DETAILED DESCRIPTION

[0108] The principles and features of the present invention are described below. The examples provided are intended to illustrate the present invention only and are not intended to limit the scope of the present invention. The following paragraphs describe the present invention in more detail by way of example. The advantages and features of the present invention will become more apparent from the following description and claims.

[0109] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0110] Example

[0111] The technical solution adopted by the present invention to solve its technical problem is:

[0112] The orchid oil production traceability and quality control management system includes:

[0113] Data acquisition module: collects raw material sources, processing temperature, humidity, processing time, and equipment operating status parameters in real time during the production process through IoT devices and sensors;

[0114] Real-time monitoring and abnormal alarm module: Through intelligent algorithms, various data in the production process are analyzed in real time, and impending quality problems are predicted and early warning signals are issued;

[0115] Blockchain traceability module: In the procurement, processing, inspection and storage of raw materials, blockchain technology is used to create an unalterable record for each product or batch;

[0116] Intelligent quality control module: Analyzes historical data and real-time monitoring data through artificial intelligence technology to assess potential quality risks in the production process.

[0117] The data acquisition module collects data such as raw material sources, processing parameters, and equipment operating status in real time to ensure that every link from raw materials to finished products can be traced; the intelligent quality control module uses artificial intelligence to analyze historical data and real-time monitoring data to evaluate potential quality risks in the production process; the real-time monitoring and abnormal alarm module uses intelligent algorithms to analyze production data in real time, predict and warn of impending quality problems, such as excessive temperature and inappropriate humidity; blockchain technology is used to create tamper-proof records in the production, processing, inspection and storage links to ensure the authenticity and integrity of all production data; data collection and real-time monitoring help production managers grasp the production status in real time and identify potential bottlenecks and optimization points; through efficient quality control and full traceability capabilities, companies can improve the market competitiveness of their products, attract consumers who value quality and traceability, and enhance their brand image and market reputation.

[0118] The data acquisition module automatically uploads data to the cloud; the real-time monitoring and abnormal alarm module controls quality by providing real-time feedback on the equipment operating status and environmental temperature and humidity parameters during the production process; the blockchain traceability module allows consumers or regulatory agencies to access the complete production chain information of the product by scanning the QR code; the intelligent quality control module automatically adjusts production parameters and optimizes the production process through machine learning models.

[0119] The real-time monitoring and anomaly alarm module provides real-time feedback on key parameters during the production process, such as equipment operating status, ambient temperature and humidity, and enables timely quality adjustments based on this feedback. The blockchain traceability module ensures that all data from the production process is recorded in an immutable distributed ledger. The intelligent quality control module uses machine learning models to analyze historical and real-time production data and automatically adjust production parameters, thereby continuously optimizing the production process. This solution relies on an automated system to collect data, monitor the production process, and adjust production parameters, reducing manual intervention and human error, enhancing production automation, and improving production efficiency and accuracy. Thanks to the use of blockchain technology, data from all key production links can be traced and verified for authenticity. In an increasingly competitive market, consumers are increasingly concerned about the source and quality of products. Through blockchain traceability and intelligent quality control, companies can not only improve production quality, but also establish a positive brand image in the market, strengthen consumer trust, and enhance their market competitiveness.

[0120] The whole-process traceability and quality control management method of orchid oil production includes the following steps:

[0121] Step 1: Use RFID technology or QR codes to identify raw materials and record the production location, supplier, and quality inspection report of the raw materials. When the raw materials are put into storage, the basic data of the raw materials is collected through sensors and uploaded to the cloud database;

[0122] Step 2: During the production process, IoT devices are used to collect production data in real time, such as temperature, humidity, time, and pressure process parameters. The data is then uploaded to the cloud platform via wireless transmission for processing. The system uses algorithms to analyze the data in real time. If any anomalies occur, the system automatically generates an alarm and prompts the operator to make adjustments.

[0123] Step 3: The finished product undergoes multi-dimensional analysis through the intelligent quality inspection module, including sensory testing, chemical composition analysis, and microbiological testing. The quality inspection data is compared and analyzed with production data. If any unqualified product is produced, the system automatically adjusts production parameters and notifies quality management personnel to intervene.

[0124] Step 4: The data of each batch’s production, testing, and storage links are registered through blockchain technology. Consumers can obtain information on the entire process from raw materials to finished products, including production date, production environment, and quality inspection report, by scanning the QR code on the product packaging.

[0125] The use of RFID technology or QR codes ensures the recording of key information, such as the source of each batch of raw materials and quality inspection reports. Powered by blockchain technology, consumers can easily access complete product information from raw materials to finished product by scanning a QR code. IoT devices collect real-time process parameters (such as temperature, humidity, pressure, and time) during the production process, enabling the system to analyze this data in real time. The finished product quality inspection module comprehensively assesses product quality through multi-dimensional analysis (sensory testing, chemical composition analysis, microbiological testing, etc.). If a product is detected as non-compliant, the system immediately adjusts production parameters or notifies quality management personnel for intervention. The system compares and analyzes various production process data with quality inspection results, providing data support for quality management. The system's automated data collection, real-time monitoring, quality analysis, and production adjustment functions significantly reduce the need for manual intervention and improve production efficiency. Furthermore, automated early warning and adjustment mechanisms reduce the risk of human error, ensuring a smooth and stable production process. By using blockchain technology for data registration, data from each batch's production, testing, and storage stages are recorded on the blockchain, forming an immutable chain of evidence.

[0126] Raw materials are identified using RFID technology or QR codes, and their production location, supplier, and quality inspection report information are recorded. When raw materials enter the warehouse, basic data of the raw materials is collected through sensors and uploaded to the cloud database as follows:

[0127] The raw materials are identified by ID, which records the following information through RFID technology or QR code:

[0128] Place of production: location;

[0129] Supplier: supplier;

[0130] Quality inspection report: quality_report;

[0131] The formula for identifying and recording raw materials is:

[0132] RawMaterialData(ID)=(location,supplier,quality_report)

[0133] When raw materials are put into storage, sensors collect basic data of raw materials, including temperature T, humidity H, weight W and other related parameters P;

[0134] These data are collected in real time through the sensor module, and the collection formula is:

[0135] SensorData(ID)=(T,H,W,P)

[0136] The collected basic data and identification information of raw materials are uploaded to the cloud database. The upload interface of the cloud database is set as UploadToCloud(), and the data is organized into a structure or record. The upload process is expressed by the following formula:

[0137] RawMaterialRecord(ID)=(RawMaterialData(ID),SensorData(ID))

[0138] The formula for uploading to the cloud database is:

[0139] CloudDataUpload(RawMaterialRecord(ID))→CloudDatabase

[0140] Among them, CloudDatabase is the database for storing cloud data, and all raw material information and sensor data will be transmitted and stored through this interface.

[0141] By combining raw material identification with detailed information, every link from raw material procurement to final product can be traced; using sensors to collect basic raw material data in real time, such as temperature, humidity and weight, can help with real-time monitoring of the production process; uploaded data can be analyzed and stored in a cloud database, which enables production managers to access and analyze relevant data on raw materials and production processes at any time, thereby better optimizing the production process, improving efficiency and reducing waste; combining sensor data with other production information and uploading it to a cloud database helps in big data analysis, discovering potential problems and making decisions based on historical data; by recording and tracking each batch of raw materials in detail, companies can understand the flow of raw materials in the supply chain in real time; consumers can access the complete production record from raw materials to finished products by scanning the QR code or RFID tag on the product. This transparency enhances consumer trust in the product and improves brand value.

[0142] During the production process, IoT devices are used to collect production data in real time, such as temperature, humidity, time, and pressure process parameters, and upload them to the cloud platform for processing via wireless transmission. The system uses algorithms to analyze the data in real time. If an abnormality occurs, the system automatically alarms and generates prompts for the operator to make adjustments. The following methods are used:

[0143] The data collected by IoT devices include the following process parameters:

[0144] Temperature: T;

[0145] Humidity: H;

[0146] Time: t;

[0147] Pressure: P;

[0148] These data are collected by sensors, and the collection function is set up as:

[0149] SensorData(t)=(T(t),H(t),P(t))

[0150] Through wireless transmission, data is uploaded to the cloud platform in real time:

[0151] CloudDataUpload(SensorData(t))→CloudPlatform

[0152] In the cloud platform, the system performs real-time analysis on the collected data according to the set process standards and thresholds. The set process standards include temperature, humidity, and pressure ranges as follows:

[0153] Temperature range: [T min , T max ]

[0154] Humidity range: [H min , H max ]

[0155] Pressure range: [P min , P max ]

[0156] The system performs anomaly detection algorithm on the data. The formula is as follows:

[0157]

[0158] When the system detects an anomaly, it triggers the alarm mechanism, generates an alarm signal, and sends a prompt message to the operator. The alarm formula is as follows:

[0159]

[0160] Once the alarm signal is triggered, the operator makes corresponding adjustments based on the prompt information provided by the system. At this time, the system records the adjustment timestamp and collects data again for verification:

[0161] AdjustmentFeedback(t)=SensorData(t)after adjustment.

[0162] By collecting production data in real time through IoT devices and uploading it to the cloud platform, the system can monitor process parameters in real time to ensure that the production process is always under control; the system can automatically analyze data and perform anomaly detection based on the set process standards; the system can quickly identify anomalies and promptly notify operators to adjust parameters, thereby reducing failures or quality problems in the production process and improving production efficiency and product quality; automated data analysis and alarm mechanisms reduce interference from human factors and avoid production problems caused by inaccurate human judgment or negligence; by collecting and storing large amounts of production data, the cloud platform can perform data analysis and provide decision support, such as optimizing production processes and predicting equipment maintenance time; the system records the timestamp and operation process of each adjustment to provide data support for subsequent equipment maintenance and process adjustments; the system records detailed production process data, including abnormal alarms and operator adjustment information, to ensure the traceability of product quality and contribute to the transparency and controllability of the production process.

[0163] Finished products undergo multi-dimensional analysis through the intelligent quality inspection module, including sensory testing, chemical composition analysis, and microbiological testing. The quality inspection data is compared and analyzed with production data. If unqualified products are produced, the system automatically adjusts production parameters and notifies quality management personnel to intervene. The following methods are used:

[0164] The finished product undergoes three testing modules: sensory testing, chemical composition analysis, and microbiological testing. Each module evaluates the quality of the finished product based on different indicators.

[0165] Set up the following data:

[0166] Sensory test results: S;

[0167] Chemical composition analysis results: C = (C1, C2, ..., C n );

[0168] Microbiological test results: M;

[0169] The system integrates these data and establishes the test results of each module as S(t), C(t), and M(t), which constitute the quality data of the finished product:

[0170] QualityData(t)=(S(t),C(t),M(t))

[0171] To identify and determine whether the finished product is qualified, the system compares and analyzes the quality inspection data with the production parameters collected during the production process, and sets the parameters of the production process as follows:

[0172] Temperature: T p ;

[0173] Humidity: H p ;

[0174] Pressure: P p ;

[0175] By comparing production parameters with quality inspection data, abnormality detection and qualification judgment are carried out:

[0176]

[0177] If the system identifies a product as unqualified, it will automatically adjust the production parameters. The adjustment of production parameters is based on the deviation between the current value and the standard value. The formula is:

[0178]

[0179]

[0180] Among them, T target , H target , P target is the preset target value, k1, k2, k3 are adjustment coefficients;

[0181] Once a product is identified as unqualified and production parameters are adjusted, the system automatically generates a notification to the quality management personnel. The notification includes:

[0182] Reasons for failure: sensory, chemical, microbiological;

[0183] Adjusted production parameters: temperature, humidity, pressure;

[0184] Current Status: Conducting intervention;

[0185] The formula is:

[0186]

[0187] The intelligent quality inspection module is used to comprehensively evaluate the finished products to ensure that the product quality meets the standards; the system can automatically adjust the production parameters according to the quality inspection results, eliminate the delay of manual intervention, and improve production efficiency and response speed; by automatically adjusting the production parameters, the system can maintain the consistency of product quality and avoid unqualified products caused by human factors or production fluctuations; when the system detects unqualified products, it automatically sends a notification to the quality management personnel to help them intervene in time and make corresponding treatment to ensure the rapid recovery of the production line; through data analysis and automatic adjustment, the number of unqualified products caused by improper production parameters is reduced, the pass rate is improved, and losses are reduced; through the comparison and analysis of comprehensive quality inspection data and production parameters, data support is provided for subsequent production optimization, process improvement and quality management; the system can accurately identify the quality of each batch of products, ensure that each finished product meets the preset quality standards, and enhance customer confidence in product quality.

[0188] The data of each batch of production, testing, and storage links are registered through blockchain technology. Consumers can obtain information on the entire process from raw materials to finished products by scanning the QR code on the product packaging, including production date, production environment, and quality inspection report. The method is as follows:

[0189] The data for each production link includes:

[0190] Production DateD prod ;

[0191] Production environment parameters E prod =(T, H, P);

[0192] Quality inspection report Q report =(S, C, M);

[0193] The blockchain record format is as follows:

[0194] Block n =(BatchID,D prod ,E prod ,Q report ,Timestamp,Hash n1 )

[0195] Among them, BatchID is the unique identifier of the current batch;

[0196] D prod is the production date;

[0197] E prod Production environment parameters

[0198] Q report Provide data for quality inspection reports;

[0199] Timestamp is the record timestamp;

[0200] Hash n1 is the hash value of the previous block;

[0201] Whenever data is generated during production, testing, or storage, a corresponding block is added to the blockchain, establishing that a block is created at the beginning of the production process:

[0202]

[0203] As production progresses, new blocks are generated in the detection and storage stages respectively:

[0204]

[0205] Each product package is printed with a unique QR code. After scanning the QR code, consumers can obtain the complete production information of the product.

[0206] The content of the QR code is:

[0207] QRCode=BatchID(with link to blockchain query system)

[0208] After the consumer scans the QR code, the system queries the data on the blockchain. The blockchain query is performed by providing the BatchID:

[0209] Through the blockchain query API, the consumer system enters the BatchID into the query interface;

[0210] The system returns all block data related to the BatchID, including production date, production environment and quality inspection report;

[0211] The consumer query formula is:

[0212] ProductInfo=BlockchainQuery(BatchID)={D prod ,E prod ,Q report}

[0213] Among them, D prod is the production date;

[0214] E prod =(T, H, P) are production environment parameters;

[0215] Q report =(S, C, M) is the quality inspection report.

[0216] Blockchain technology ensures that data from all production, testing, and storage links can be recorded and traced, allowing consumers to easily access detailed product information and ensuring transparency in product quality. The decentralized and tamper-proof nature of blockchain means that once registered, data for each batch cannot be modified or deleted, ensuring the authenticity and reliability of the information. By scanning a QR code, consumers can directly access the product's production process, environmental conditions, and quality inspection reports, enhancing their trust in product quality. Blockchain can automatically record data at every production link, reducing the cost of manual recording and management, lowering the possibility of human error, and improving the efficiency and accuracy of the production process. Because blockchain uses encryption technology, data security is effectively guaranteed, and only authorized users can access relevant data, protecting the privacy of manufacturers and consumers. Blockchain not only records data from all production links but also shares data from each link with other stakeholders in the supply chain, thereby improving the efficiency and transparency of supply chain collaboration. Consumers can clearly understand the production, testing, and storage processes of each product, effectively preventing counterfeit and shoddy products from entering the market and protecting brand and consumer rights.

[0217] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the orchid oil production full-process traceability and quality control management system and method as described above is implemented.

[0218] This embodiment also provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the orchid oil production full-process traceability and quality control management system and method as described above are implemented.

[0219] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0220] Those skilled in the art will clearly understand that for the sake of convenience and brevity in description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0221] The above embodiments of the present invention are not intended to limit the scope of protection of the present invention, and the implementation methods of the present invention are not limited thereto. All other modifications, replacements or changes made to the above structures of the present invention based on the above contents of the present invention, in accordance with common technical knowledge and customary means in this field, without departing from the above basic technical ideas of the present invention, should fall within the scope of protection of the present invention.

Claims

1. The whole-process traceability and quality control management system for orchid oil production is characterized by: Includes: Data acquisition module: collects raw material sources, processing temperature, humidity, processing time, and equipment operating status parameters in real time during the production process through IoT devices and sensors; Real-time monitoring and abnormal alarm module: Through intelligent algorithms, various data in the production process are analyzed in real time, and impending quality problems are predicted and early warning signals are issued; Blockchain traceability module: In the procurement, processing, inspection and storage of raw materials, blockchain technology is used to create an unalterable record for each product or batch; Intelligent quality control module: Analyzes historical data and real-time monitoring data through artificial intelligence technology to assess potential quality risks in the production process.

2. The orchid oil production full-process traceability and quality control management system according to claim 1, characterized in that: The data acquisition module automatically uploads data to the cloud; the real-time monitoring and abnormal alarm module controls quality by providing real-time feedback on the equipment operating status and environmental temperature and humidity parameters during the production process; the blockchain traceability module allows consumers or regulatory agencies to access the complete production chain information of the product by scanning the QR code; the intelligent quality control module automatically adjusts production parameters and optimizes the production process through machine learning models.

3. The whole-process traceability and quality control management method for orchid oil production is characterized by: The following steps are involved: Step 1: Use RFID technology or QR codes to identify raw materials and record the production location, supplier, and quality inspection report of the raw materials. When the raw materials are put into storage, the basic data of the raw materials is collected through sensors and uploaded to the cloud database; Step 2: During the production process, IoT devices are used to collect production data in real time, such as temperature, humidity, time, and pressure process parameters. The data is then uploaded to the cloud platform via wireless transmission for processing. The system uses algorithms to analyze the data in real time. If any anomalies occur, the system automatically generates an alarm and prompts the operator to make adjustments. Step 3: The finished product undergoes multi-dimensional analysis through the intelligent quality inspection module, including sensory testing, chemical composition analysis, and microbiological testing. The quality inspection data is compared and analyzed with production data. If any unqualified product is produced, the system automatically adjusts production parameters and notifies quality management personnel to intervene. Step 4: The data of each batch’s production, testing, and storage links are registered through blockchain technology. Consumers can obtain information on the entire process from raw materials to finished products, including production date, production environment, and quality inspection report, by scanning the QR code on the product packaging.

4. The orchid oil production full-process traceability and quality control management method according to claim 3, characterized in that: Raw materials are identified using RFID technology or QR codes, and their production location, supplier, and quality inspection report information are recorded. When raw materials enter the warehouse, basic data of the raw materials is collected through sensors and uploaded to the cloud database. The method is as follows: The raw materials are identified by ID, which records the following information through RFID technology or QR code: Place of production: location; Supplier: supplier; Quality inspection report: quality_report; The formula for identifying and recording raw materials is: RawMaterialData(ID)=(location,supplier,quality_report) When raw materials are put into storage, sensors collect basic data of raw materials, including temperature T, humidity H, weight W and other related parameters P; These data are collected in real time through the sensor module, and the collection formula is: SensorData(ID)=(T,H,W,P) The collected basic data and identification information of raw materials are uploaded to the cloud database. The upload interface of the cloud database is set as UploadToCloud(), and the data is organized into a structure or record. The upload process is expressed by the following formula: RawMateialRecord(ID)=(RawMaterialData(ID), SensorData(ID)) The formula for uploading to the cloud database is: CloudDataUpload(RawMaterialRecord(ID))→CloudDatabase Among them, CloudDatabase is the database for storing cloud data, and all raw material information and sensor data will be transmitted and stored through this interface.

5. The orchid oil production full-process traceability and quality control management method according to claim 4, characterized in that: During the production process, IoT devices are used to collect production data in real time, such as temperature, humidity, time, and pressure process parameters, and upload them to the cloud platform for processing via wireless transmission. The system uses algorithms to analyze the data in real time. If an abnormality occurs, the system automatically alarms and generates prompts for the operator to make adjustments. The following methods are used: The data collected by IoT devices include the following process parameters: Temperature: T; Humidity: H; Time: t; Pressure: P; These data are collected by sensors, and the collection function is set up as: SensorData(t)=(T(t),H(t),P(t)) Through wireless transmission, data is uploaded to the cloud platform in real time: CloudDataUpload(SensorData(t))→CloudPlatform In the cloud platform, the system performs real-time analysis on the collected data according to the set process standards and thresholds. The set process standards include temperature, humidity, and pressure ranges as follows: Temperature range: [T min ,T max ] Humidity range: [H min , H max ] Pressure range: [P min , P max ] The system performs anomaly detection algorithm on the data. The formula is as follows: When the system detects an anomaly, it triggers the alarm mechanism, generates an alarm signal, and sends a prompt message to the operator. The alarm formula is as follows: Once the alarm signal is triggered, the operator makes corresponding adjustments based on the prompt information provided by the system. At this time, the system records the adjustment timestamp and collects data again for verification: AdjustmentFeedback(t)=SensorData(t)after adjustment.

6. The orchid oil production full-process traceability and quality control management method according to claim 5, characterized in that: Finished products undergo multi-dimensional analysis through the intelligent quality inspection module, including sensory testing, chemical composition analysis, and microbiological testing. The quality inspection data is compared and analyzed with production data. If unqualified products are produced, the system automatically adjusts production parameters and notifies quality management personnel to intervene. The following methods are used: The finished product undergoes three testing modules: sensory testing, chemical composition analysis, and microbiological testing. Each module evaluates the quality of the finished product based on different indicators. Set up the following data: Sensory test results: S; Chemical composition analysis results: C = (C1, C2, ..., C n ); Microbiological test results: M; The system integrates these data and establishes the test results of each module as S(t), C(t), and M(t), which constitute the quality data of the finished product: QualityData(t)=(S(t),C(t),M(t)) To identify and determine whether the finished product is qualified, the system compares and analyzes the quality inspection data with the production parameters collected during the production process, and sets the parameters of the production process as follows: Temperature: T p ; Humidity: H p ; Pressure: P p ; By comparing production parameters with quality inspection data, abnormality detection and qualification judgment are carried out: If the system identifies a product as unqualified, it will automatically adjust the production parameters. The adjustment of production parameters is based on the deviation between the current value and the standard value. The formula is: Among them, T target , H target , P target is the preset target value, k1, k2, k3 are adjustment coefficients; Once a product is identified as unqualified and production parameters are adjusted, the system automatically generates a notification to the quality management personnel. The notification includes: Reasons for failure: sensory, chemical, microbiological; Adjusted production parameters: temperature, humidity, pressure; Current status: Conducting intervention; The formula is:

7. The orchid oil production full-process traceability and quality control management method according to claim 6, characterized in that: The data of each batch of production, testing, and storage links are registered through blockchain technology. Consumers can obtain information on the entire process from raw materials to finished products by scanning the QR code on the product packaging, including production date, production environment, and quality inspection report. The method is as follows: The data for each production link includes: Production DateD prod ; Production environment parameters E prod =(T, H, P); Quality inspection report Q report =(S, C, M); The blockchain record format is as follows: Block n =(BatchID,D prod ,E prod ,Q report ,Timestamp,Hash n 1 ) Among them, BatchID is the unique identifier of the current batch; D prod is the production date; E prod Production environment parameters Q report Provide data for quality inspection reports; Timestamp is the record timestamp; Hash n 1 is the hash value of the previous block; Whenever data is generated during production, testing, or storage, a corresponding block is added to the blockchain, establishing that a block is created at the beginning of the production process: As production progresses, new blocks are generated in the detection and storage stages respectively: Each product package is printed with a unique QR code. After scanning the QR code, consumers can obtain the complete production information of the product. The content of the QR code is: QRCode=BatchID(with link to blockchain query system) After the consumer scans the QR code, the system queries the data on the blockchain. The blockchain query is performed by providing the BatchID: Through the blockchain query API, the consumer system enters the BatchID into the query interface; The system returns all block data related to the BatchID, including production date, production environment and quality inspection report; The consumer query formula is: ProductInfo=BlockchainQuery(BatchID)={D prod ,E prod ,Q report } Among them, D prod is the production date; E prod =(T, H, P) are production environment parameters; Q report =(S, C, M) is the quality inspection report.

8. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for full-process traceability and quality control management of orchid oil production as described in any one of claims 3 to 7 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the orchid oil production full-process traceability and quality control management method as described in any one of claims 3-7 is implemented.

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