Peanut oil quality detection and tracing system

Abstract

The invention discloses a peanut oil quality detection and tracing system, which relates to the technical field of data analysis, and comprises an acquisition module, an analysis module and a tracing module, and is characterized in that a sampling proportion is set, detection data is obtained, first standard data is compared with the detection data of peanut oil, first operation is executed, and first data is matched; and obtaining to-be-analyzed data according to the problem detection data, screening problem first data, and sending an early warning signal. Through an automatic acquisition module, detection data of peanut oil is rapidly obtained, the detection efficiency is improved, through comparison of the detection data and standard data, it is ensured that the product quality meets the specified standard, the traceability of the product is enhanced, through a transparent traceability system, traceability information of edible oil is conveniently inquired, the confidence of consumers on the product quality is improved, and the economic benefits of the consumers are improved. Problem data are screened out through an analysis result, and an early warning signal is sent, so that food safety problems can be prevented.

Description

Technical Field

[0001] The invention relates to the technical field of data analysis, and in particular to a peanut oil quality detection and tracing system. Background Art

[0002] In recent years, the peanut oil industry is developing towards a refined stage. Healthy, nutritious and delicious have become the high-quality demands of more consumers, which has promoted the healthy upgrade of the entire edible oil industry. Peanut oil quality inspection and traceability technology are constantly developing and improving to meet the market demand for high-quality peanut oil and ensure consumer health.

[0003] At present, a Chinese invention patent with publication number CN116612123B discloses a method for visual inspection of peanut oil processing quality. The method obtains the gray value convergence area of ​​the oil area in the pipeline oil output image by frame difference method, and obtains the impurity area according to the speed of change of the centroid position of the gray value convergence area; quality inspection is performed based on the impurity area, but the related technology does not analyze the quality of peanut oil according to different production processes and different supply links, lacks comprehensiveness and completeness of the analysis, does not trace back and warn different quality problems, is not conducive to correcting specific problems in the supply chain, and is prone to causing erroneous production and waste of resources. Summary of the invention

[0004] The technical problem solved by the present invention is that the related art does not analyze the quality of peanut oil according to different production processes and different supply links, lacks comprehensiveness and completeness of the analysis, does not trace the source of different quality problems and does not issue early warnings, is not conducive to correcting specific problems in the supply chain, easily causes erroneous production, and easily leads to waste of resources.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a peanut oil quality detection and traceability system, comprising a collection module, an analysis module and a traceability module;

[0006] The acquisition module obtains the same batch of peanut oil, sets a sampling ratio, and detects the peanut oil according to the sampling ratio to obtain detection data;

[0007] The analysis module selects the test data of any sampled peanut oil and its corresponding first standard data, compares the first standard data with the test data of the peanut oil to obtain a comparison result, performs a first operation according to the comparison result, matches the first data according to the first operation, and transmits the first data to the traceability module;

[0008] The traceability module obtains the supply chain links, classifies the first data into the supply chain links, gives the supply chain links a first number, gives the first data corresponding to the supply chain links a second number, obtains the data to be analyzed based on the problem detection data, analyzes the data to be analyzed to obtain the analysis results, screens the problem first data based on the analysis results, and sends an early warning signal based on the problem first data.

[0009] As a preferred solution of the peanut oil quality detection and traceability system of the present invention, wherein: the acquisition module obtains the same batch of peanut oil, sets the first value as a sampling ratio, and the sampling ratio is obtained through historical detection experience;

[0010] Testing peanut oil according to the thermal experiment operation method and the sampling ratio to obtain test data;

[0011] The detection data includes color value data, insoluble impurity content data, peroxide content data and free fatty acid content data;

[0012] The detection data is obtained by detecting the peanut oil after the heat experiment through various sensors.

[0013] As a preferred solution of the peanut oil quality detection and traceability system of the present invention, wherein: the analysis module obtains the pressing process of the peanut oil, retrieves the quality database, inputs the pressing process into the quality database, and matches the first standard data corresponding to the pressing process;

[0014] The pressing process includes cold pressing, microwave preheating pressing and hot pressing;

[0015] The first standard data includes standard color value data, standard insoluble impurity content data, standard free fatty acid content data, standard peroxide content data and standard free fatty acid content data, and the standard data is expressed as the maximum value of the detection data.

[0016] As a preferred solution of the peanut oil quality detection and traceability system of the present invention, the analysis module obtains any sampled peanut oil, obtains corresponding detection data and first standard data, compares the detection data with the first standard data, and obtains a comparison result, wherein the comparison result includes that the detection data is greater than the first standard data, and the detection data is less than or equal to the first standard data;

[0017] Perform a first operation according to the comparison result, wherein the first operation includes obtaining the code of peanut oil and jumping to the next peanut oil;

[0018] The setting logic of the first operation includes:

[0019] Obtain the comparison result. When the comparison result is that the detection data is greater than the first standard data, set the first operation to obtain the encoding of the peanut oil. When the comparison result is that the detection data is less than or equal to the first standard data, set the first operation to jump to the next peanut oil.

[0020] As a preferred solution of the peanut oil quality detection and traceability system of the present invention, the analysis module sets the detection data greater than the first standard data as problem detection data, matches the supply chain related data according to the peanut oil coding, records it as the first data, and transmits the problem detection data and the first data to the traceability module, wherein the supply chain related data includes planting data, drying data, pressing data, transportation data and storage data;

[0021] The planting data includes average fertilizer application amount data and average pesticide application amount data, wherein the average fertilizer application amount data and the average pesticide application amount data are expressed as average fertilizer application amount data and average pesticide application amount data in a time period after sowing and before harvesting;

[0022] The drying data includes average drying density data, average light intensity data and average light duration data, wherein the average drying density data is represented by the distribution density of peanut fruits per square meter within the drying time period, with the second time period as the collection period, and the second time period is obtained through big data;

[0023] The squeezing data includes average operating efficiency data of a crusher, average operating efficiency data of a flaking mill, and average temperature data of a heat treatment, wherein the operating efficiency data of the crusher and the operating efficiency data of the flaking mill are expressed as a ratio of output power to input power;

[0024] The transport data includes average transport temperature data and average transport humidity data;

[0025] The storage data includes average storage temperature data and average storage humidity data, wherein the average storage temperature data and the average storage humidity data are expressed as the average storage temperature data and the average storage humidity data from the storage of peanut fruits at the end of the transportation link to the storage of peanut fruits before the production link;

[0026] The logic for obtaining the first data includes:

[0027] Obtain a picture of the outer packaging of peanut oil, the picture of the outer packaging of peanut oil being accompanied by a code of the peanut oil, connect to a network, identify the code of the peanut oil, obtain supply chain related data, and record the supply chain related data as first data.

[0028] As a preferred solution of the peanut oil quality detection and traceability system of the present invention, the traceability module retrieves the production database, inputs the peanut oil code into the production database, and matches the supply chain link corresponding to the peanut oil code, wherein the supply chain link includes the raw material link, the transportation link, the production link and the warehousing link;

[0029] The first data is classified into the supply chain link, and the classification logic of the first data includes:

[0030] The planting data and drying data are classified into the raw material link, the transportation data is classified into the transportation link, the pressing data is classified into the production link, and the warehousing data is classified into the warehousing link. The planting data, drying data, transportation data, pressing data and warehousing data are set as the first-level data, and the data in the planting data, drying data, transportation data, pressing data and warehousing data are set as sub-data.

[0031] As a preferred solution of the peanut oil quality detection and traceability system of the present invention, the traceability link first numbers the supply chain links according to the order of the supply chain, and second numbers the first data corresponding to the supply chain links, and the order of the supply chain from first to last is the raw material link, the transportation link, the warehousing link and the production link;

[0032] The first number is represented by X i , where i is a natural number, X i Represented as the i-th supply chain link;

[0033] The second number is represented by X ijr , where j is a natural number, r is a natural number, and X ijr It is represented as the rth child data of the jth parent data of the i-th supply chain link;

[0034] The data to be analyzed is obtained according to the problem detection data, the data to be analyzed is analyzed to obtain the analysis result, the first problem data is screened according to the analysis result, and a warning signal is sent according to the first problem data, wherein the warning signal includes a first warning signal, a second warning signal, a third warning signal and a fourth warning signal.

[0035] As a preferred solution of the peanut oil quality detection and traceability system of the present invention, the first data after the second number is represented as the sub-data of the supply chain link after the second number, and the acquisition logic of the analysis result includes:

[0036] Acquire problem detection data, and acquire data to be analyzed according to the problem detection data, wherein the data to be analyzed is acquired through historical detection data;

[0037] When the problem detection data is color value data, the sub-data of the production link is obtained; when the problem detection data is insoluble impurity content data, the sub-data of the planting link is obtained; when the problem detection data is peroxide content data, the sub-data of the transportation link is obtained; when the problem detection data is free fatty acid content data, the sub-data of the transportation link is obtained;

[0038] The data to be analyzed is obtained, a production database is retrieved, a pressing process is input into the production database, and corresponding second standard data is matched, wherein the second standard data includes standard average fertilizer application amount data, standard average pesticide amount data, standard average drying density data, standard average light intensity data, standard average light duration data, standard crusher average operation efficiency data, standard embryo rolling machine average operation efficiency data, standard heat treatment average temperature data, standard average transportation temperature data, standard average transportation humidity data, standard average storage temperature data, and standard average storage humidity data, wherein the second standard data is represented as a theoretical distribution interval of the corresponding data to be analyzed, and the data to be analyzed is compared with the second standard data to obtain an analysis result.

[0039] As a preferred solution of the peanut oil quality detection and traceability system described in the present invention, when the data to be analyzed is distributed in the second standard data, jump to the next data to be analyzed; when the data to be analyzed is not distributed in the second standard data, set the data to be analyzed as the problematic first data.

[0040] As a preferred solution of the peanut oil quality detection and tracing system of the present invention, when the first data in question is sub-data of the planting link, a first warning signal is sent, and a second code of the first data in question is displayed;

[0041] When the first data in question is sub-data of the transport link, a second warning signal is sent and the second code of the first data in question is displayed;

[0042] When the first data in question is sub-data of the warehousing link, a third warning signal is sent and the second code of the first data in question is displayed;

[0043] When the problematic first data is sub-data of a production link, a fourth warning signal is sent and the second code of the problematic first data is displayed.

[0044] The beneficial effects of the present invention are as follows: through the automated acquisition module, the test data of peanut oil can be quickly acquired, the test efficiency can be improved, and by comparing the test data with the standard data, it can be ensured that the product quality meets the prescribed standards, so that the data of each link can be tracked, and the traceability of the product is enhanced. Through the transparent traceability system, it is convenient to query the traceability information of edible oil, and the confidence of consumers in the product quality is improved. Through the analysis results, the problem data is screened out, and the early warning signal is sent, which is helpful to prevent the occurrence of food safety problems. Through effective supervision and detection, it is helpful to curb the aflatoxin B1 contamination phenomenon. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A basic flow chart of a peanut oil quality detection and traceability system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0047] Example, see Figure 1 , as an embodiment of the present invention, provides a peanut oil quality detection and traceability system, including a collection module, an analysis module and a traceability module;

[0048] The acquisition module obtains the same batch of peanut oil, sets a sampling ratio, and detects the peanut oil according to the sampling ratio to obtain detection data;

[0049] The analysis module selects the test data of any sampled peanut oil and its corresponding first standard data, compares the first standard data with the test data of the peanut oil to obtain a comparison result, performs a first operation according to the comparison result, matches the first data according to the first operation, and transmits the first data to the traceability module;

[0050] The traceability module obtains the supply chain links, classifies the first data into the supply chain links, gives the supply chain links a first number, gives the first data corresponding to the supply chain links a second number, obtains the data to be analyzed based on the problem detection data, analyzes the data to be analyzed to obtain the analysis results, screens the problem first data based on the analysis results, and sends an early warning signal based on the problem first data.

[0051] The present invention uses an automated collection module to quickly acquire the test data of peanut oil, thereby improving the test efficiency. By comparing the test data with the standard data, it is ensured that the product quality meets the prescribed standards, so that the data of each link can be tracked, thereby enhancing the traceability of the product. Through a transparent traceability system, it is convenient to query the traceability information of edible oil, thereby enhancing consumers' confidence in product quality. Problem data can be screened out through analysis results, and early warning signals can be sent, thereby helping to prevent the occurrence of food safety problems. Through effective supervision and detection, it is helpful to curb the aflatoxin B1 contamination phenomenon.

[0052] The collection module obtains the same batch of peanut oil and sets the first value as a sampling ratio, wherein the sampling ratio is obtained through historical testing experience;

[0053] Testing peanut oil according to the thermal experiment operation method and the sampling ratio to obtain test data;

[0054] The detection data includes color value data, insoluble impurity content data, peroxide content data and free fatty acid content data;

[0055] The detection data is obtained by detecting the peanut oil after the heat experiment through various sensors.

[0056] The analysis module obtains the pressing process of the peanut oil, retrieves the quality database, inputs the pressing process into the quality database, and matches the first standard data corresponding to the pressing process;

[0057] The pressing process includes cold pressing, microwave preheating pressing and hot pressing;

[0058] The first standard data includes standard color value data, standard insoluble impurity content data, standard free fatty acid content data, standard peroxide content data and standard free fatty acid content data, and the standard data is expressed as the maximum value of the detection data.

[0059] In specific implementation, the sampling ratio determined by historical testing experience can more accurately reflect the quality status of the entire batch of peanut oil, improve the representativeness and accuracy of the test results, and use historical data to determine the sampling ratio, making the sampling process more scientific and reasonable, avoiding the deviation that may be caused by random sampling. The test data covers multiple key indicators such as color value, insoluble impurity content, peroxide content and free fatty acid content, which comprehensively controls the quality of peanut oil. By testing the key quality indicators of peanut oil, problems can be discovered in time to enhance food safety. Through continuous testing and data analysis, problems in the production process can be discovered to promote the improvement of production processes and the improvement of product quality.

[0060] The analysis module obtains any sampled peanut oil, obtains corresponding test data and first standard data, compares the test data with the first standard data, and obtains a comparison result, wherein the comparison result includes that the test data is greater than the first standard data, and the test data is less than or equal to the first standard data;

[0061] Perform a first operation according to the comparison result, wherein the first operation includes obtaining the code of peanut oil and jumping to the next peanut oil;

[0062] The setting logic of the first operation includes:

[0063] Obtain the comparison result. When the comparison result is that the detection data is greater than the first standard data, set the first operation to obtain the encoding of the peanut oil. When the comparison result is that the detection data is less than or equal to the first standard data, set the first operation to jump to the next peanut oil.

[0064] In specific implementation, when the test data exceeds the standard, the peanut oil code is automatically obtained to facilitate tracking of problematic products. For peanut oil that meets the standards, the system will skip detailed inspection and directly analyze the next sample, saving resources. Through coding tracking, the transparency of the supply chain is improved and consumer trust in the product is enhanced.

[0065] The analysis module sets the detection data greater than the first standard data as problem detection data, matches the supply chain related data according to the peanut oil code, records it as the first data, and transmits the problem detection data and the first data to the traceability module, wherein the supply chain related data includes planting data, drying data, pressing data, transportation data and storage data;

[0066] The planting data includes average fertilizer application amount data and average pesticide application amount data, wherein the average fertilizer application amount data and the average pesticide application amount data are expressed as average fertilizer application amount data and average pesticide application amount data in a time period after sowing and before harvesting;

[0067] The drying data includes average drying density data, average light intensity data and average light duration data, wherein the average drying density data is represented by the distribution density of peanut fruits per square meter within the drying time period, with the second time period as the collection period, and the second time period is obtained through big data;

[0068] The squeezing data includes average operating efficiency data of a crusher, average operating efficiency data of a flaking mill, and average temperature data of a heat treatment, wherein the operating efficiency data of the crusher and the operating efficiency data of the flaking mill are expressed as a ratio of output power to input power;

[0069] The transport data includes average transport temperature data and average transport humidity data;

[0070] The storage data includes average storage temperature data and average storage humidity data, wherein the average storage temperature data and the average storage humidity data are expressed as the average storage temperature data and the average storage humidity data from the storage of peanut fruits at the end of the transportation link to the storage of peanut fruits before the production link;

[0071] The logic for obtaining the first data includes:

[0072] Obtain a picture of the outer packaging of peanut oil, the picture of the outer packaging of peanut oil being accompanied by a code of the peanut oil, connect to a network, identify the code of the peanut oil, obtain supply chain related data, and record the supply chain related data as first data.

[0073] In the specific implementation, through the coding system, each link of the supply chain of the problematic peanut oil can be accurately traced, which improves the pertinence and efficiency of problem handling. The tracking of problem detection data can timely discover and deal with food safety issues and protect the health of consumers. The data of planting, drying, pressing, transportation and warehousing are integrated to provide data support for the comprehensive analysis of the causes of the problems. Through the monitoring of transportation and warehousing data, the environmental conditions of the products during transportation and storage can be better controlled to reduce the impact of environmental factors on product quality. The recording of planting data helps to analyze and optimize the use of fertilizers and pesticides and promote sustainable agricultural development. The collection and analysis of supply chain data supports data-driven decision-making and improves the scientificity and effectiveness of decision-making.

[0074] The traceability module retrieves the production database, inputs the code of the peanut oil into the production database, and matches the supply chain link corresponding to the code of the peanut oil, wherein the supply chain link includes the raw material link, the transportation link, the production link and the warehousing link;

[0075] The first data is classified into the supply chain link, and the classification logic of the first data includes:

[0076] The planting data and drying data are classified into the raw material link, the transportation data is classified into the transportation link, the pressing data is classified into the production link, and the warehousing data is classified into the warehousing link. The planting data, drying data, transportation data, pressing data and warehousing data are set as the first-level data, and the data in the planting data, drying data, transportation data, pressing data and warehousing data are set as sub-data.

[0077] The traceability link assigns a first number to the supply chain link according to the order of the supply chain, and assigns a second number to the first data corresponding to the supply chain link, wherein the order of the supply chain is, from first to last, the raw material link, the transportation link, the warehousing link, and the production link;

[0078] The first number is represented by Xi, where i is a natural number and Xi represents the i-th supply chain link;

[0079] The second number is represented by Xijr, where j is a natural number, r is a natural number, and Xijr represents the rth child data of the jth parent data of the i-th supply chain link;

[0080] The data to be analyzed is obtained according to the problem detection data, the data to be analyzed is analyzed to obtain the analysis result, the first problem data is screened according to the analysis result, and a warning signal is sent according to the first problem data, wherein the warning signal includes a first warning signal, a second warning signal, a third warning signal and a fourth warning signal.

[0081] In the specific implementation, by accurately matching the peanut oil code with the supply chain links, the source of the problem can be quickly located and the accuracy of traceability can be improved. By analyzing the data of each link, the production process can be optimized, the product quality can be improved, and the responsibilities of each link can be clarified to facilitate accountability when problems arise. By accurately locating the problem, unnecessary inspections and processing can be reduced, costs can be reduced, and by optimizing supply chain management, the efficiency and response speed of the entire supply chain can be improved.

[0082] The first data after the second number is represented as the sub-data of the supply chain link after the second number, and the logic for obtaining the analysis result includes:

[0083] Acquire problem detection data, and acquire data to be analyzed according to the problem detection data, wherein the data to be analyzed is acquired through historical detection data;

[0084] When the problem detection data is color value data, the sub-data of the production link is obtained; when the problem detection data is insoluble impurity content data, the sub-data of the planting link is obtained; when the problem detection data is peroxide content data, the sub-data of the transportation link is obtained; when the problem detection data is free fatty acid content data, the sub-data of the transportation link is obtained;

[0085] The data to be analyzed is obtained, a production database is retrieved, a pressing process is input into the production database, and corresponding second standard data is matched, wherein the second standard data includes standard average fertilizer application amount data, standard average pesticide amount data, standard average drying density data, standard average light intensity data, standard average light duration data, standard crusher average operation efficiency data, standard embryo rolling machine average operation efficiency data, standard heat treatment average temperature data, standard average transportation temperature data, standard average transportation humidity data, standard average storage temperature data, and standard average storage humidity data, wherein the second standard data is represented as a theoretical distribution interval of the corresponding data to be analyzed, and the data to be analyzed is compared with the second standard data to obtain an analysis result.

[0086] When the data to be analyzed is distributed in the second standard data, jump to the next data to be analyzed; when the data to be analyzed is not distributed in the second standard data, set the data to be analyzed as the problematic first data.

[0087] In the specific implementation, based on the nature of the problem detection data, sub-data of related links are obtained in a targeted manner, which improves the pertinence and accuracy of problem analysis and reduces cost losses caused by quality problems, such as recalls and compensation. By analyzing historical detection data, potential room for improvement is discovered, promoting continuous improvement of product quality and production processes.

[0088] When the first data in question is sub-data of the planting stage, a first warning signal is sent and a second code of the first data in question is displayed;

[0089] When the first data in question is sub-data of the transport link, a second warning signal is sent and the second code of the first data in question is displayed;

[0090] When the first data in question is sub-data of the warehousing link, a third warning signal is sent and the second code of the first data in question is displayed;

[0091] When the problematic first data is sub-data of a production link, a fourth warning signal is sent and the second code of the problematic first data is displayed.

[0092] In specific implementation, by sending early warning signals, the attention of relevant personnel can be quickly attracted, and timely measures can be taken to deal with potential problems. Different early warning signals are sent according to different links, making the response measures more targeted and enhancing the response capabilities of the entire supply chain.

[0093] The present invention uses an automated collection module to quickly acquire the test data of peanut oil, thereby improving the test efficiency. By comparing the test data with the standard data, it is ensured that the product quality meets the prescribed standards, so that the data of each link can be tracked, thereby enhancing the traceability of the product. Through a transparent traceability system, it is convenient to query the traceability information of edible oil, thereby enhancing consumers' confidence in product quality. Problem data can be screened out through analysis results, and early warning signals can be sent, thereby helping to prevent the occurrence of food safety problems. Through effective supervision and detection, it is helpful to curb the aflatoxin B1 contamination phenomenon.

[0094] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Wherein, the storage medium is implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions of the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A peanut oil quality detection and traceability system, characterized in that: It includes collection module, analysis module and traceability module; The acquisition module obtains the same batch of peanut oil, sets a sampling ratio, and detects the peanut oil according to the sampling ratio to obtain detection data; The analysis module selects the test data of any sampled peanut oil and its corresponding first standard data, compares the first standard data with the test data of the peanut oil to obtain a comparison result, performs a first operation according to the comparison result, matches the first data according to the first operation, and transmits the first data to the traceability module; The traceability module obtains the supply chain links, classifies the first data into the supply chain links, gives the supply chain links a first number, gives the first data corresponding to the supply chain links a second number, obtains the data to be analyzed based on the problem detection data, analyzes the data to be analyzed to obtain the analysis results, screens the problem first data based on the analysis results, and sends an early warning signal based on the problem first data.

2. A peanut oil quality detection and tracing system as claimed in claim 1, characterized in that: The collection module obtains the same batch of peanut oil and sets the first value as a sampling ratio, wherein the sampling ratio is obtained through historical testing experience; Testing peanut oil according to the thermal experiment operation method and the sampling ratio to obtain test data; The detection data includes color value data, insoluble impurity content data, peroxide content data and free fatty acid content data; The detection data is obtained by detecting the peanut oil after the heat experiment through various sensors.

3. A peanut oil quality detection and tracing system as claimed in claim 1, characterized in that: The analysis module obtains the pressing process of the peanut oil, retrieves the quality database, inputs the pressing process into the quality database, and matches the first standard data corresponding to the pressing process; The pressing process includes cold pressing, microwave preheating pressing and hot pressing; The first standard data includes standard color value data, standard insoluble impurity content data, standard free fatty acid content data, standard peroxide content data and standard free fatty acid content data, and the standard data is expressed as the maximum value of the detection data.

4. A peanut oil quality detection and tracing system as claimed in claim 3, characterized in that: The analysis module obtains any sampled peanut oil, obtains corresponding test data and first standard data, compares the test data with the first standard data, and obtains a comparison result, wherein the comparison result includes that the test data is greater than the first standard data, and the test data is less than or equal to the first standard data; Perform a first operation according to the comparison result, wherein the first operation includes obtaining the code of peanut oil and jumping to the next peanut oil; The setting logic of the first operation includes: Obtain the comparison result. When the comparison result is that the detection data is greater than the first standard data, set the first operation to obtain the encoding of the peanut oil. When the comparison result is that the detection data is less than or equal to the first standard data, set the first operation to jump to the next peanut oil.

5. A peanut oil quality detection and tracing system as claimed in claim 4, characterized in that: The analysis module sets the detection data greater than the first standard data as problem detection data, matches the supply chain related data according to the peanut oil code, records it as the first data, and transmits the problem detection data and the first data to the traceability module, wherein the supply chain related data includes planting data, drying data, pressing data, transportation data and storage data; The planting data includes average fertilizer application amount data and average pesticide application amount data, wherein the average fertilizer application amount data and the average pesticide application amount data are expressed as average fertilizer application amount data and average pesticide application amount data in a time period after sowing and before harvesting; The drying data includes average drying density data, average light intensity data and average light duration data, wherein the average drying density data is represented by the distribution density of peanut fruits per square meter within the drying time period, with the second time period as the collection period, and the second time period is obtained through big data; The squeezing data includes average operating efficiency data of a crusher, average operating efficiency data of a flaking mill, and average temperature data of a heat treatment, wherein the operating efficiency data of the crusher and the operating efficiency data of the flaking mill are expressed as a ratio of output power to input power; The transport data includes average transport temperature data and average transport humidity data; The storage data includes average storage temperature data and average storage humidity data, wherein the average storage temperature data and the average storage humidity data are expressed as the average storage temperature data and the average storage humidity data from the storage of peanut fruits at the end of the transportation link to the storage of peanut fruits before the production link; The logic for obtaining the first data includes: Obtain a picture of the outer packaging of peanut oil, the picture of the outer packaging of peanut oil being accompanied by a code of the peanut oil, connect to a network, identify the code of the peanut oil, obtain supply chain related data, and record the supply chain related data as first data.

6. A peanut oil quality detection and tracing system as claimed in claim 1, characterized in that: The traceability module retrieves the production database, inputs the code of the peanut oil into the production database, and matches the supply chain link corresponding to the code of the peanut oil, wherein the supply chain link includes the raw material link, the transportation link, the production link and the warehousing link; The first data is classified into the supply chain link, and the classification logic of the first data includes: The planting data and drying data are classified into the raw material link, the transportation data is classified into the transportation link, the pressing data is classified into the production link, and the warehousing data is classified into the warehousing link. The planting data, drying data, transportation data, pressing data and warehousing data are set as the first-level data, and the data in the planting data, drying data, transportation data, pressing data and warehousing data are set as sub-data.

7. A peanut oil quality detection and tracing system as claimed in claim 6, characterized in that: The traceability link assigns a first number to the supply chain link according to the order of the supply chain, and assigns a second number to the first data corresponding to the supply chain link, wherein the order of the supply chain is, from first to last, the raw material link, the transportation link, the warehousing link, and the production link; The first number is represented by X i , where i is a natural number, X i Represented as the i-th supply chain link; The second number is represented by X ijr , where j is a natural number, r is a natural number, and X ijr It is represented as the rth child data of the jth parent data of the i-th supply chain link; The data to be analyzed is obtained according to the problem detection data, the data to be analyzed is analyzed to obtain the analysis result, the first problem data is screened according to the analysis result, and a warning signal is sent according to the first problem data, wherein the warning signal includes a first warning signal, a second warning signal, a third warning signal and a fourth warning signal.

8. A peanut oil quality detection and tracing system as claimed in claim 7, characterized in that: The first data after the second number is represented as the sub-data of the supply chain link after the second number, and the logic for obtaining the analysis result includes: Acquire problem detection data, and acquire data to be analyzed according to the problem detection data, wherein the data to be analyzed is acquired through historical detection data; When the problem detection data is color value data, the sub-data of the production link is obtained; when the problem detection data is insoluble impurity content data, the sub-data of the planting link is obtained; when the problem detection data is peroxide content data, the sub-data of the transportation link is obtained; when the problem detection data is free fatty acid content data, the sub-data of the transportation link is obtained; The data to be analyzed is obtained, a production database is retrieved, a pressing process is input into the production database, and corresponding second standard data is matched, wherein the second standard data includes standard average fertilizer application amount data, standard average pesticide amount data, standard average drying density data, standard average light intensity data, standard average light duration data, standard crusher average operation efficiency data, standard embryo rolling machine average operation efficiency data, standard heat treatment average temperature data, standard average transportation temperature data, standard average transportation humidity data, standard average storage temperature data, and standard average storage humidity data, wherein the second standard data is represented as a theoretical distribution interval of the corresponding data to be analyzed, and the data to be analyzed is compared with the second standard data to obtain an analysis result.

9. A peanut oil quality detection and tracing system as claimed in claim 8, characterized in that: When the data to be analyzed is distributed in the second standard data, jump to the next data to be analyzed; when the data to be analyzed is not distributed in the second standard data, set the data to be analyzed as the problematic first data.

10. A peanut oil quality detection and tracing system as claimed in claim 9, characterized in that: When the first data in question is sub-data of the planting stage, a first warning signal is sent and a second code of the first data in question is displayed; When the first data in question is sub-data of the transport link, a second warning signal is sent and the second code of the first data in question is displayed; When the first data in question is sub-data of the warehousing link, a third warning signal is sent and the second code of the first data in question is displayed; When the problematic first data is sub-data of a production link, a fourth warning signal is sent and the second code of the problematic first data is displayed.

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