Unconventional feed quality evaluation and grading method and system

Abstract

The invention discloses an unconventional feed quality evaluation grading method and system, and particularly relates to the technical field of feed quality evaluation. Conventional nutrients, functional nutrients and harmful substance components are comprehensively analyzed through a high-precision detection technology; constructing a multi-factor synergistic toxicity evaluation model, quantifying the synergistic effect of the various harmful substances based on the toxicity equivalent factors and a matrix interaction analysis method, and outputting a synergistic toxicity index; the overall quality of feed samples is evaluated by combining detection results and toxicity indexes, the feed samples are classified into high-quality, qualified and unqualified grades, targeted application suggestions and risk control measures are provided, a cloud database is constructed to record evaluation data, and an Internet of Things sensor is used for monitoring supply chain quality fluctuation in real time, automatically identifying abnormities and triggering early warning. Dynamic management of feed quality is achieved, scientificity and accuracy of quality evaluation of unconventional feed are effectively improved, potential safety hazards are reduced, and safe popularization and efficient application of the system in the breeding industry are promoted.

Description

Technical Field

[0001] The invention relates to the technical field of feed quality assessment, and in particular to a non-conventional feed quality assessment and grading method and system. Background Art

[0002] With the rapid development of the breeding industry, the problem of feed resource shortage has become increasingly prominent. Unconventional feeds (such as agricultural and sideline products, industrial by-products and wild plant resources) have gradually become an important supplement to the feed industry due to their abundant resources and low cost. However, compared with traditional feeds, unconventional feeds have complex ingredients, large fluctuations in nutritional value, and may contain harmful substances such as heavy metals, toxins and anti-nutritional factors. These characteristics make the quality evaluation and grading of unconventional feeds an important topic in the field of feed science. It is urgent to establish a systematic and scientific evaluation method to ensure the health of farmed animals and the stability of production performance.

[0003] At present, the quality assessment of unconventional feeds is mainly based on conventional nutrition (such as protein, energy, minerals, etc.) and functional nutrition (such as immune enhancement, antioxidant, etc.) indicators, and at the same time, it is necessary to focus on the impact of heavy metals, toxins and anti-nutritional factors (such as phytates, tannins, etc.) that may be contained in them. However, due to the complex interactions between these indicators and the significant differences in the composition of unconventional feeds from different sources, the existing evaluation system is difficult to fully reflect its comprehensive quality and potential risks, which affects the development and application of unconventional feeds.

[0004] The prior art has the following deficiencies:

[0005] Existing unconventional feed quality assessment methods are unable to accurately evaluate the combined effects of complex ingredients on animal health and production performance, especially the lack of effective evaluation mechanisms for interactions between different feed ingredients and the cumulative effects of toxic factors. For example, when certain unconventional feeds contain low doses of heavy metals (such as lead and cadmium) and toxins (such as aflatoxin) at the same time, these substances may produce synergistic toxic effects. In addition, if existing technologies cannot accurately predict or quantify their long-term harm to animals. This technical limitation may not only lead to potential safety hazards in the use of feed, but also limit the widespread promotion of unconventional feeds in the industry. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for evaluating and grading the quality of unconventional feeds, so as to solve the shortcomings of the background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a method for evaluating and grading the quality of unconventional feed, comprising the following steps:

[0008] S1: Collect unconventional feed samples from industrial by-products, crop by-products and wild plant raw materials, pre-treat the samples using low-temperature drying technology, use high-precision crushing equipment to crush the samples to a particle diameter of less than 1 mm, and mix them evenly through a three-dimensional mixer;

[0009] S2: The pre-treated samples are tested for ingredients, including three categories of indicators: conventional nutrition, functional nutrition and harmful substances. The protein, fat, immune enhancement factor and antioxidant components in the samples are tested respectively. For heavy metals, toxins and anti-nutritional factors, ICP-MS, GC-MS and HPLC technologies are used to accurately determine their contents.

[0010] S3: Construct a multi-factor synergistic toxicity assessment model, calculate the toxicity contribution of a single toxin based on the toxic equivalent factor, use the matrix interaction analysis method to simulate the synergistic effect of multiple harmful substances, and output the synergistic toxicity index;

[0011] S4: Evaluate the overall quality of feed samples based on conventional nutrition, functional nutrition and synergistic toxicity index. According to the evaluation results, classify unconventional feed into three levels: high quality, qualified and unqualified. Provide corresponding application suggestions for each level, including scope of application and risk control measures.

[0012] S5: Build a cloud database to record the evaluation and grading data of unconventional feeds in real time, and use IoT sensors to monitor feed quality fluctuations in the raw material supply chain in real time, automatically identify abnormal samples and trigger early warnings.

[0013] Preferably, in S3, a multi-factor synergistic toxicity assessment model is constructed, the toxicity contribution of a single toxin is calculated based on the toxic equivalent factor, the matrix interaction analysis method is used to simulate the synergistic effect of multiple harmful substances, and the synergistic toxicity index is output, specifically:

[0014] Determine the toxicity equivalent factor TEF, which indicates the relative toxicity intensity of a substance to a reference substance. The toxicity contribution of a single toxin is calculated as: T i =C i ×TEF i Where, T i is the toxicity contribution value of toxin i, C i is the concentration of toxin i; TEF i is the toxicity equivalent factor of toxin i, and T is calculated for each toxin i. i And summarize to form a list of single toxin toxicity values, define the synergy matrix M, where the elements M ij represents the synergistic effect between substances i and j; M ij >0: synergistic effect, indicating that the toxicity of the two substances is enhanced; M ij=0: no synergistic effect, indicating that the toxicity of the two substances is independent; M ij <0: Antagonistic effect, indicating that the toxicity of the two substances is weakened. The synergistic effect calculation formula is: T total is the comprehensive toxicity value, T i is the toxicity contribution value of a single toxin i, n is the total number of toxin types, and the normalized comprehensive toxicity value is: STI is the synergistic toxicity index, ranging from 0 to 100; T max It is the reference maximum toxicity value used for normalization.

[0015] Preferably, in S4, the overall quality of the feed sample is evaluated based on conventional nutrition, functional nutrition and synergistic toxicity index, specifically:

[0016] Collect ingredient test results, including conventional nutrition, functional nutrition and synergistic toxicity index, and normalize each index to a value between 0 and 1: X norm is the normalized index value; X is the original index value; X min and X max The minimum and maximum values ​​of the indicators are as follows: weights are set according to the importance of the indicators: weight of conventional nutrition indicators: 50%; weight of functional nutrition indicators: 30%; weight of synergistic toxicity index: 20%; the comprehensive score calculation formula is: Q = 0.5 Q 常规营养 +0.3 Q 功能营养 -0.2 STI norm .

[0017] Preferably, in S4, Q is the comprehensive quality score of the feed sample; Q 常规营养 It is a comprehensive score of conventional nutritional indicators. W i is the weight of the ith nutritional indicator; N i is the normalized value of the ith nutritional index; b is the number of conventional nutritional indicators; Q 功能营养 It is a comprehensive score of functional nutrition index; STI norm is the normalized synergistic toxicity index; Q 功能营养 is the comprehensive score of functional nutrition indicators, expressed as: W j is the weight of the jth functional nutrition index, F j is the normalized value of the jth functional nutritional index; m is the number of functional nutritional indicators.

[0018] Preferably, in S4, according to the evaluation results, the unconventional feed is divided into three levels: high quality, qualified and unqualified, specifically:

[0019] According to the range of the comprehensive score Q, the feed is divided into three levels: high quality Q≥0.8: comprehensive nutritional ingredients, strong functionality, and low synergistic toxicity; qualified 0.5≤Q<0.8: nutritional indicators meet the requirements, but the functionality or toxicity is insufficient; unqualified Q<0.5: low nutritional value and high synergistic toxicity, not suitable for use.

[0020] Preferably, in S5, the feed quality fluctuation in the raw material supply chain is monitored in real time by IoT sensors, abnormal samples are automatically identified and early warnings are triggered, specifically:

[0021] IoT sensors collect real-time raw material characteristic data R at supply chain nodes, including: temperature R T 、Humidity R H 、Volatile organic compounds R VOC , weight R w ; Assume that the historical range of raw material characteristic data is [R i,min ,R i,max ], real-time data R i The abnormal judgment conditions are: R i is a characteristic data collected in real time; R i,min , R i,max The historical upper and lower limits of the characteristic data. The output value 1 indicates abnormality, and 0 indicates normality.

[0022] When multiple data indicators are collected at the same time, a comprehensive scoring method is used to determine the degree of sample abnormality: In the formula, a is the total number of monitoring indicators, R i,mean is the historical mean of the characteristic data, R i,std is the standard deviation of the feature data; the anomaly score is higher than the set threshold.

[0023] Preferably, the real-time monitoring results and abnormal conditions are recorded in the cloud database, and the historical data range is updated. i,min ,R i,max ] and mean and standard deviation;

[0024] Update history includes: In the formula, is the updated historical minimum value, indicating that the current real-time data R i The historical minimum R i,min After comparison, take the smaller value. is the updated historical maximum value, indicating that the current real-time data R i The historical maximum R i,max After comparison, take the larger value;

[0025] Update statistical parameters: In the formula, R i,kis the value of the i-th characteristic data in the k-th sample, G is the number of new samples, is the updated historical mean, which represents the average value of the characteristic data in G historical samples; is the updated historical standard deviation, which indicates the degree of fluctuation of the characteristic data relative to the mean in G samples.

[0026] The present invention also provides a non-conventional feed quality assessment and grading system, including a sample collection and pretreatment module, a component detection and analysis module, a collaborative toxicity assessment module, a quality assessment and grading module and a management module:

[0027] Sample collection and pretreatment module: collect unconventional feed samples from industrial by-products, crop by-products and wild plant raw materials, pre-treat the samples using low-temperature drying technology, use high-precision crushing equipment to crush the samples to a particle diameter of less than 1 mm, and mix them evenly through a three-dimensional mixer;

[0028] Component detection and analysis module: Component detection is performed on the pre-treated samples. Component detection includes three categories of indicators: conventional nutrition, functional nutrition and harmful substances. The protein, fat, immune enhancement factor and antioxidant components in the samples are detected respectively. For heavy metals, toxins and anti-nutritional factors, ICP-MS, GC-MS and HPLC technologies are used to accurately determine their content;

[0029] Synergistic toxicity assessment module: construct a multi-factor synergistic toxicity assessment model, calculate the toxicity contribution of a single toxin based on the toxic equivalent factor, use the matrix interaction analysis method to simulate the synergistic effect of multiple harmful substances, and output the synergistic toxicity index;

[0030] Quality assessment and grading module: Evaluate the overall quality of feed samples based on conventional nutrition, functional nutrition and synergistic toxicity index. Based on the evaluation results, classify unconventional feed into three levels: high quality, qualified and unqualified. Provide corresponding application suggestions for each level, including scope of application and risk control measures;

[0031] Management module: Build a cloud database to record the evaluation and grading data of unconventional feeds in real time, and use IoT sensors to monitor the feed quality fluctuations in the raw material supply chain in real time, automatically identify abnormal samples and trigger early warnings.

[0032] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0033] 1. The present invention adopts standardized sample pretreatment technology (low temperature drying, fine grinding and uniform mixing), combined with high-precision detection technologies such as ICP-MS, GC-MS and HPLC, to achieve a comprehensive analysis of conventional nutrition, functional nutrition and harmful substances. By constructing a multi-factor synergistic toxicity assessment model, the toxic equivalent factor and matrix interaction analysis method are used to quantify the synergistic effects of multiple harmful substances, and a synergistic toxicity index (STI) is generated to scientifically evaluate the potential risks of the feed. Combined with the nutritional score and toxicity index, the quality grading of the sample is achieved through comprehensive weight calculation, and application suggestions and risk control measures are provided to ensure the safety and applicability of the feed.

[0034] 2. The present invention realizes dynamic management of feed quality by constructing a cloud database and an Internet of Things monitoring system. By using sensors to monitor quality fluctuations in the raw material supply chain in real time, and combining anomaly detection algorithms to identify risk samples and trigger early warnings, potential hidden dangers in feed production and use can be effectively prevented. The system also supports dynamic updating of historical data ranges and statistical parameters to ensure that the model is highly adaptable and the monitoring results are reliable. In summary, the present invention significantly improves the scientificity, accuracy and management efficiency of unconventional feed quality assessment, and provides technical support for its safe promotion and efficient application. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0036] Figure 1 The figure is a flow chart of the method of the present invention.

[0037] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Example 1, please refer to Figure 1 As shown, the unconventional feed quality assessment and grading method described in this embodiment comprises the following steps:

[0040] S1: Collect unconventional feed samples from industrial by-products, crop by-products and wild plant raw materials, pre-treat the samples using low-temperature drying technology, use high-precision crushing equipment to crush the samples to a particle diameter of less than 1 mm, and mix them evenly through a three-dimensional mixer;

[0041] S2: The pre-treated samples are tested for ingredients, including three categories of indicators: conventional nutrition, functional nutrition and harmful substances. The protein, fat, immune enhancement factor and antioxidant components in the samples are tested respectively. For heavy metals, toxins and anti-nutritional factors, ICP-MS, GC-MS and HPLC technologies are used to accurately determine their contents.

[0042] S3: Construct a multi-factor synergistic toxicity assessment model, calculate the toxicity contribution of a single toxin based on the toxic equivalent factor, use the matrix interaction analysis method to simulate the synergistic effect of multiple harmful substances, and output the synergistic toxicity index;

[0043] S4: Evaluate the overall quality of feed samples based on conventional nutrition, functional nutrition and synergistic toxicity index. According to the evaluation results, classify unconventional feed into three levels: high quality, qualified and unqualified. Provide corresponding application suggestions for each level, including scope of application and risk control measures.

[0044] S5: Build a cloud database to record the evaluation and grading data of unconventional feeds in real time, and use IoT sensors to monitor feed quality fluctuations in the raw material supply chain in real time, automatically identify abnormal samples and trigger early warnings.

[0045] In S1, representative sample sources are selected based on the potential application scenarios of unconventional feeds, including but not limited to: industrial by-products: such as distiller's grains, soybean meal, cottonseed cake, and sugarcane bagasse; agricultural by-products: such as corn stalks, rice husks, and fruit and vegetable waste; wild plant raw materials: such as wild forage grass, seaweed, and weed seeds.

[0046] Use a multi-point random sampling method to collect several samples from different batches, areas or raw material piles to ensure representativeness; samples from each sampling point are mixed to form an initial comprehensive sample.

[0047] Low-temperature drying technology is used to remove moisture, reduce the risk of sample corruption, and avoid high temperature damage to active ingredients (such as vitamins and antioxidants). Use a vacuum freeze dryer to dry the sample at a temperature range of -50°C to -30°C until its moisture content is less than 10%; if there is no vacuum equipment, a forced air drying oven can be used with the temperature controlled below 40°C to ensure uniform drying and stable activity of the ingredients.

[0048] Use high-precision crushing equipment (such as high-speed crusher or vibration mill) to ensure the refinement of sample particles, which is convenient for the uniformity and representativeness of subsequent testing. Put the dried samples into the crushing equipment in batches, adjust the speed to more than 10,000 rpm; control the crushing time to 1-3 minutes until the sample particle diameter is less than 1 mm (verified using a standard sieve); prevent partial damage to the sample components due to overheating of the equipment, and crush intermittently.

[0049] Through the mixing process, the components of each part of the sample are evenly distributed, eliminating the detection errors caused by differences in particle size or distribution. Use a three-dimensional mixer to place the crushed sample into the rotating mixing chamber; set the mixing time to 15-20 minutes to ensure that the sample is fully flipped and mixed in the three-dimensional direction; after the mixing is completed, take part of the sample for uniformity testing to ensure the consistency of the sample in the distribution of nutrients and harmful substances. Put the processed samples into moisture-proof and oxidation-proof sealed bags, mark the batch, source and date; store the samples in a low temperature environment (below 4°C) to prevent changes in the sample composition before subsequent testing.

[0050] S2: The pretreated samples are subjected to component testing, which includes three categories of indicators: conventional nutrition, functional nutrition and harmful substances. The protein, fat, immune enhancing factors and antioxidant components in the samples are tested respectively. For heavy metals, toxins and anti-nutritional factors, ICP-MS, GC-MS and HPLC techniques are used to accurately determine their content.

[0051] Protein detection: Use a fully automatic near-infrared spectrometer (NIR) for rapid detection to directly determine the protein content of the sample; if further verification is required, the Kjeldahl method can be used to calculate the protein content (total nitrogen content multiplied by 6.25). Fat detection: Extract crude fat from the sample by Soxhlet extraction, and calculate the fat content using the residual amount after solvent evaporation; or directly perform rapid detection through NIR to achieve accurate analysis. Fiber content: Use the acid-base digestion method (Weende method) to detect crude fiber content; Energy value: Based on the chemical composition results, use a near-infrared prediction model or calculation formula (such as the total digestible nutrient method) to estimate metabolic energy. Minerals: Use flame photometry to determine potassium and sodium content, and use atomic absorption spectrometry to determine minerals such as calcium and phosphorus; Vitamins: Use spectrophotometry to detect the content of vitamin A, vitamin E, and vitamin C in the sample.

[0052] Enzyme-linked immunosorbent assay (ELISA) was used to quantitatively detect immune-enhancing factors such as β-glucan and nucleotides in the samples; the results were corrected using standard curves to ensure that the data were accurate and reliable. The carotenoid and vitamin E contents in the samples were determined by high-performance liquid chromatography (HPLC); the peak areas of antioxidant substances were detected and compared with the standards to calculate the concentration levels. Inductively coupled plasma mass spectrometry (ICP-MS) was used to detect heavy metals such as lead, cadmium, arsenic, and mercury; the samples were wet-digested (such as nitric acid-hydrogen peroxide system) to ensure the complete release of heavy metals. Gas chromatography-mass spectrometry (GC-MS) was used to analyze aflatoxin, ochratoxin, and vomitoxin in the samples; the internal standard calibration method was used to improve the sensitivity and accuracy of the detection. The contents of phytate and tannin were detected by high-performance liquid chromatography (HPLC); after appropriate extraction, the samples were injected into the HPLC instrument for separation and quantitative analysis combined with standards.

[0053] All test results are collated and entered into the data management system, divided into three categories: conventional nutrition, functional nutrition and harmful substances. Statistical methods (such as mean and variance analysis) are used to verify the data to ensure the repeatability and reliability of the test results.

[0054] S3: Construct a multi-factor synergistic toxicity assessment model, calculate the toxicity contribution of a single toxin based on the toxic equivalent factor, use the matrix interaction analysis method to simulate the synergistic effect of multiple harmful substances, and output the synergistic toxicity index.

[0055] Determine the toxicity equivalent factor (TEF), which indicates the relative toxicity intensity of a substance relative to a reference substance (such as aflatoxin B1) and is derived from an authoritative database or experimental results. TEF example: If the TEF of a toxin is 2, it is twice as toxic as the reference substance. The formula for calculating the toxicity contribution of a single toxin is: T i =C i ×TEF i Where, T i is the toxicity contribution value of toxin i, C i is the concentration of toxin i (e.g. mg / kg); TEF i is the toxicity equivalent factor of toxin i, and T is calculated for each toxin i. i And summarize to form a list of single toxin toxicity values, define the synergy matrix M, where the elements M ij represents the synergistic effect between substances i and j; M ij >0: synergistic effect, indicating that the toxicity of the two substances is enhanced; M ij =0: no synergistic effect, indicating that the toxicity of the two substances is independent; M ij <0: Antagonistic effect, indicating that the toxicity of the two substances is weakened. The synergistic effect calculation formula is: T total is the comprehensive toxicity value, T i is the toxicity contribution value of a single toxin i, n is the total number of toxin types, and the normalized comprehensive toxicity value is: STI is the synergistic toxicity index, ranging from 0 to 100; T max It is the reference maximum toxicity value used for normalization.

[0056] S4: Evaluate the overall quality of feed samples based on conventional nutrition, functional nutrition and synergistic toxicity index. Based on the evaluation results, classify unconventional feed into three levels: high quality, qualified and unqualified. Provide corresponding application recommendations for each level, including scope of application and risk control measures.

[0057] Collect ingredient test results, including conventional nutrition, functional nutrition and synergistic toxicity index, and normalize each index to a value between 0 and 1: X norm is the normalized index value; X is the original index value; X min and X max are the minimum and maximum values ​​of the indicator.

[0058] The weights are set according to the importance of the indicators: weight of conventional nutrition indicators: 50%; weight of functional nutrition indicators: 30%; weight of synergistic toxicity index: 20%. The comprehensive score calculation formula is: Q = 0.5·Q 常规营养 +0.3 Q 功能营养 -0.2 STI norm ; In the formula, Q is the comprehensive quality score of the feed sample; Q 常规营养 It is a comprehensive score of conventional nutritional indicators. W i is the weight of the ith nutritional indicator; N i is the normalized value of the ith nutritional index; b is the number of conventional nutritional indicators (such as protein, fat, etc.). The weight can be set according to actual needs. For example: protein: W 1 =0.4, fat: W 2 =0.2, fiber: W 3 =0.2, minerals: W 4 =0.1, vitamin: W 5 =0.1. Q 功能营养 It is a comprehensive score of functional nutrition index; STI norm Q is the normalized synergistic toxicity index. 功能营养 is the comprehensive score of functional nutrition indicators, expressed as: W j is the weight of the jth functional nutrition index, F jis the normalized value of the jth functional nutritional index; m is the number of functional nutritional indexes (such as immune enhancement factors, antioxidants, etc.). Weights can be assigned according to the importance of functional nutritional indexes. For example: β-glucan: W 1 =0.6; Nucleotide: W 2 =0.2; Antioxidant: W 3 =0.2.

[0059] According to the range of the comprehensive score Q, the feed is divided into three levels: high quality Q ≥ 0.8: comprehensive nutritional ingredients, strong functionality, and extremely low synergistic toxicity; qualified 0.5 ≤ Q < 0.8: nutritional indicators meet basic requirements, but the functionality or toxicity is slightly insufficient; unqualified Q < 0.5: low nutritional value and high synergistic toxicity, unsuitable for use. The grading results are marked for each feed sample, and the grading basis is attached, including conventional nutrition, functional nutrition and synergistic toxicity data.

[0060] Application recommendations and risk control measures include: High-quality feed (Q≥0.8) applicable scope: high-end farms, efficient production models; Risk control measures: only routine quality monitoring is required, no special risk intervention is required.

[0061] Scope of application of qualified feed (0.5≤Q<0.8): ordinary breeding environment, can be used as regular feed; risk control measures: limit the usage ratio, avoid long-term single feeding, and strengthen the dynamic monitoring of synergistic toxicity indicators.

[0062] Scope of application of unqualified feed (Q<0.5): Direct use is not recommended; Risk control measures: It is recommended to abandon it or optimize the raw materials, and combine it with detoxification treatment (such as adsorbents or detoxification processes) when necessary.

[0063] The output evaluation report includes: Quality grade: mark the grade of the sample (high quality, qualified, unqualified). Data support: provide detailed test data and the calculation process of the comprehensive score. Recommendations and measures: add specific usage recommendations and risk control plans for samples of different grades.

[0064] S5: Build a cloud database to record the evaluation and grading data of unconventional feeds in real time, and use IoT sensors to monitor feed quality fluctuations in the raw material supply chain in real time, automatically identify abnormal samples and trigger early warnings.

[0065] The system will evaluate the data of non-conventional feeds, including Q 常规营养 , Q 功能营养 And STI are uploaded to the cloud database through the network interface. Each data includes: sample number ID, timestamp T, conventional nutrition score Q 常规营养 , Functional Nutrition Score Q 功能营养 , synergistic toxicity index STI, grading result L (high quality, qualified, unqualified).

[0066] Data is stored in a relational database or NoSQL database: Table structure example: SampleData(ID, T, Q 常规营养 ,Q 功能营养 ,STI,L).

[0067] IoT sensors collect real-time data on raw material characteristics at key supply chain nodes: Temperature R T 、Humidity R H , gas concentration (such as volatile organic matter R VOC ), weight R w The data is transmitted to the cloud via wireless network for real-time analysis.

[0068] Assume that the historical range of raw material characteristic data is [R i,min ,R i,max ], real-time data R i The abnormal judgment conditions are: R i is a characteristic data collected in real time; R i,min , R i,max The historical upper and lower limits of the characteristic data (set according to historical data or industry standards). The output value 1 indicates abnormality, and 0 indicates normality.

[0069] When multiple data indicators are collected at the same time, a comprehensive scoring method is used to determine the degree of sample abnormality: In the formula, a is the total number of monitoring indicators, R i,mean is the historical mean of the characteristic data, R i,std is the standard deviation of the characteristic data; an early warning is triggered when the anomaly score is higher than the set threshold (such as 3).

[0070] When the abnormal conditions are met, the system automatically generates an early warning message and sends it to the management end: early warning message = {timestamp, ID, abnormality type, abnormality score}; after receiving it, the management end can take measures, such as suspending the use of raw materials or further analyzing the cause of the abnormality.

[0071] Record real-time monitoring results and abnormal conditions to the cloud database and update historical data range [R i,min ,R i,max ] and statistical parameters (such as mean and standard deviation).

[0072] Update history includes: max(R i ,R i,max );where, is the updated historical minimum value, indicating that the current real-time data R i The historical minimum R i,minAfter comparison, the smaller value is taken to dynamically adjust the historical lower limit range to reflect new trends in the data. is the updated historical maximum value, indicating that the current real-time data R i The historical maximum R i,max After comparison, the larger value is taken to dynamically adjust the historical upper limit range and capture new peak changes in the data; update statistical parameters: In the formula, R i,k is the value of the i-th characteristic data in the k-th sample, G is the number of new samples, is the updated historical mean, which represents the average value of the characteristic data in G historical samples; is the updated historical standard deviation, which indicates the degree of fluctuation of the characteristic data relative to the mean in G samples.

[0073] In this embodiment, first, samples are collected from industrial by-products, crop by-products and wild plant raw materials, and pre-treated by low-temperature drying, fine grinding and three-dimensional mixing technology; then, the samples are tested for composition, covering conventional nutrition (such as protein, fat), functional nutrition (such as immune enhancing factors, antioxidants) and harmful substances (such as heavy metals, toxins, anti-nutritional factors), and ICP-MS, GC-MS and HPLC technologies are used for precise analysis; then, a multi-factor synergistic toxicity assessment model is constructed, the toxic contribution of a single toxin is calculated, the synergistic effect is simulated using the matrix interaction analysis method, and the synergistic toxicity index is output; then, the overall quality of the sample is evaluated by combining conventional nutrition, functional nutrition and synergistic toxicity index, and the feed is divided into three levels: high quality, qualified and unqualified, and application suggestions and risk control measures are provided; finally, a cloud database is constructed to record evaluation and grading data in real time, and the Internet of Things sensors are combined to monitor feed quality fluctuations in the supply chain, automatically identify anomalies and trigger early warnings, and ensure dynamic management and risk control of feed quality.

[0074] Example 2, please refer to Figure 2 As shown, the unconventional feed quality assessment and grading system described in this embodiment includes a sample collection and pretreatment module, a component detection and analysis module, a collaborative toxicity assessment module, a quality assessment and grading module and a management module:

[0075] Sample collection and pretreatment module: collect unconventional feed samples from industrial by-products, crop by-products and wild plant raw materials, pre-treat the samples using low-temperature drying technology, use high-precision crushing equipment to crush the samples to a particle diameter of less than 1 mm, and mix them evenly through a three-dimensional mixer;

[0076] Component detection and analysis module: Component detection is performed on the pre-treated samples. Component detection includes three categories of indicators: conventional nutrition, functional nutrition and harmful substances. The protein, fat, immune enhancement factor and antioxidant components in the samples are detected respectively. For heavy metals, toxins and anti-nutritional factors, ICP-MS, GC-MS and HPLC technologies are used to accurately determine their content;

[0077] Synergistic toxicity assessment module: construct a multi-factor synergistic toxicity assessment model, calculate the toxicity contribution of a single toxin based on the toxic equivalent factor, use the matrix interaction analysis method to simulate the synergistic effect of multiple harmful substances, and output the synergistic toxicity index;

[0078] Quality assessment and grading module: Evaluate the overall quality of feed samples based on conventional nutrition, functional nutrition and synergistic toxicity index. Based on the evaluation results, classify unconventional feed into three levels: high quality, qualified and unqualified. Provide corresponding application suggestions for each level, including scope of application and risk control measures;

[0079] Management module: Build a cloud database to record the evaluation and grading data of unconventional feeds in real time, and use IoT sensors to monitor the feed quality fluctuations in the raw material supply chain in real time, automatically identify abnormal samples and trigger early warnings.

[0080] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0081] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0082] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0083] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for evaluating and grading the quality of unconventional feeds, characterized in that: The following steps are involved: S1: Collect unconventional feed samples from industrial by-products, crop by-products and wild plant raw materials, pre-treat the samples using low-temperature drying technology, use high-precision crushing equipment to crush the samples to a particle diameter of less than 1 mm, and mix them evenly through a three-dimensional mixer; S2: The pre-treated samples are tested for ingredients, including three categories of indicators: conventional nutrition, functional nutrition and harmful substances. The protein, fat, immune enhancement factor and antioxidant components in the samples are tested respectively. For heavy metals, toxins and anti-nutritional factors, ICP-MS, GC-MS and HPLC technologies are used to accurately determine their contents. S3: Construct a multi-factor synergistic toxicity assessment model, calculate the toxicity contribution of a single toxin based on the toxic equivalent factor, use the matrix interaction analysis method to simulate the synergistic effect of multiple harmful substances, and output the synergistic toxicity index; S4: Evaluate the overall quality of feed samples based on conventional nutrition, functional nutrition and synergistic toxicity index. According to the evaluation results, classify unconventional feed into three levels: high quality, qualified and unqualified. Provide corresponding application suggestions for each level, including scope of application and risk control measures. S5: Build a cloud database to record the evaluation and grading data of unconventional feeds in real time, and use IoT sensors to monitor feed quality fluctuations in the raw material supply chain in real time, automatically identify abnormal samples and trigger early warnings.

2. The method for evaluating and grading the quality of unconventional feed according to claim 1, characterized in that: In S3, a multi-factor synergistic toxicity assessment model was constructed to calculate the toxicity contribution of a single toxin based on the toxic equivalent factor, and the matrix interaction analysis method was used to simulate the synergistic effect of multiple harmful substances to output the synergistic toxicity index, which is as follows: Determine the toxicity equivalent factor TEF, which indicates the relative toxicity intensity of a substance to a reference substance. The toxicity contribution of a single toxin is calculated as: T i =C i ×TEF i Where, T i is the toxicity contribution value of toxin i, C i is the concentration of toxin i; TEF i is the toxicity equivalent factor of toxin i, and T is calculated for each toxin i. i And summarize to form a list of single toxin toxicity values, define the synergy matrix M, where the elements M ij represents the synergistic effect between substances i and j; M ij >0: synergistic effect, indicating that the toxicity of the two substances is enhanced; M ij =0: no synergistic effect, indicating that the toxicity of the two substances is independent; M ij <0: Antagonistic effect, indicating that the toxicity of the two substances is weakened. The synergistic effect calculation formula is: T total is the comprehensive toxicity value, T i is the toxicity contribution value of a single toxin i, n is the total number of toxin types, and the normalized comprehensive toxicity value is: STI is the synergistic toxicity index, ranging from 0 to 100; T max It is the reference maximum toxicity value used for normalization.

3. The method for evaluating and grading the quality of unconventional feed according to claim 2, characterized in that: In S4, the overall quality of the feed samples was evaluated based on conventional nutrition, functional nutrition and synergistic toxicity index, specifically: Collect ingredient test results, including conventional nutrition, functional nutrition and synergistic toxicity index, and normalize each index to a value between 0 and 1: X norm is the normalized index value; X is the original index value; X min and X max The minimum and maximum values ​​of the indicators; weights are set according to the importance of the indicators: weight of conventional nutrition indicators: 50%; weight of functional nutrition indicators: 30%; Synergistic toxicity index weight: 20%; The comprehensive score calculation formula is: Q = 0.5 Q 常规营养 +0.3 Q 功能营养 -0.2 STI norm .

4. The method for evaluating and grading the quality of unconventional feed according to claim 3, characterized in that: In S4, Q is the comprehensive quality score of the feed sample; Q 常规营养 It is a comprehensive score of conventional nutritional indicators. W i is the weight of the ith nutritional indicator; N i is the normalized value of the ith nutritional index; b is the number of conventional nutritional indicators; Q 功能营养 It is a comprehensive score of functional nutrition index; STI norm is the normalized synergistic toxicity index; Q 功能营养 is the comprehensive score of functional nutrition indicators, expressed as: W j is the weight of the jth functional nutrition index, F j is the normalized value of the jth functional nutritional index; m is the number of functional nutritional indicators.

5. The method for evaluating and grading the quality of unconventional feed according to claim 3, characterized in that: In S4, based on the evaluation results, unconventional feeds are divided into three levels: high quality, qualified and unqualified. Specifically: According to the range of the comprehensive score Q, the feed is divided into three levels: high quality Q≥0.8: comprehensive nutritional ingredients, strong functionality, and low synergistic toxicity; qualified 0.5≤Q<0.8: nutritional indicators meet the requirements, but the functionality or toxicity is insufficient; unqualified Q<0.5: low nutritional value and high synergistic toxicity, not suitable for use.

6. The method for evaluating and grading the quality of unconventional feed according to claim 1, characterized in that: In S5, IoT sensors are used to monitor feed quality fluctuations in the raw material supply chain in real time, automatically identify abnormal samples and trigger early warnings. Specifically: IoT sensors collect real-time raw material characteristic data R at supply chain nodes, including: temperature R T 、Humidity R H , Volatile organic compounds R VOC , weight R w ; Assume that the historical range of raw material characteristic data is [R i,min ,R i,max ], real-time data R i The abnormal judgment conditions are: R i is a characteristic data collected in real time; R i,min , R i,max The historical upper and lower limits of the characteristic data. The output value 1 indicates abnormality, and 0 indicates normality. When multiple data indicators are collected at the same time, a comprehensive scoring method is used to determine the degree of sample abnormality: In the formula, a is the total number of monitoring indicators, R i,mean is the historical mean of the characteristic data, R i,std is the standard deviation of the feature data; the anomaly score is higher than the set threshold.

7. The method for evaluating and grading the quality of unconventional feed according to claim 6, characterized in that: Record real-time monitoring results and abnormal conditions to the cloud database and update historical data range [R i,min ,R i,max ] and mean and standard deviation; Update history includes: In the formula, is the updated historical minimum value, indicating that the current real-time data R i The historical minimum R i,min After comparison, take the smaller value. is the updated historical maximum value, indicating that the current real-time data R i The historical maximum R i,max After comparison, take the larger value; Update statistical parameters: In the formula, R i,k is the value of the i-th characteristic data in the k-th sample, G is the number of new samples, is the updated historical mean, which represents the average value of the characteristic data in G historical samples; is the updated historical standard deviation, which indicates the degree of fluctuation of the characteristic data relative to the mean in G samples.

8. A non-conventional feed quality assessment and grading system, used to implement a non-conventional feed quality assessment and grading method according to any one of claims 1 to 7, characterized in that: It includes sample collection and pretreatment module, component detection and analysis module, collaborative toxicity assessment module, quality assessment and grading module and management module: Sample collection and pretreatment module: collect unconventional feed samples from industrial by-products, crop by-products and wild plant raw materials, pre-treat the samples using low-temperature drying technology, use high-precision crushing equipment to crush the samples to a particle diameter of less than 1 mm, and mix them evenly through a three-dimensional mixer; Component detection and analysis module: Component detection is performed on the pre-treated samples. Component detection includes three categories of indicators: conventional nutrition, functional nutrition and harmful substances. The protein, fat, immune enhancement factor and antioxidant components in the samples are detected respectively. For heavy metals, toxins and anti-nutritional factors, ICP-MS, GC-MS and HPLC technologies are used to accurately determine their content; Synergistic toxicity assessment module: construct a multi-factor synergistic toxicity assessment model, calculate the toxicity contribution of a single toxin based on the toxic equivalent factor, use the matrix interaction analysis method to simulate the synergistic effect of multiple harmful substances, and output the synergistic toxicity index; Quality assessment and grading module: Evaluate the overall quality of feed samples based on conventional nutrition, functional nutrition and synergistic toxicity index. Based on the evaluation results, classify unconventional feed into three levels: high quality, qualified and unqualified. Provide corresponding application suggestions for each level, including scope of application and risk control measures; Management module: Build a cloud database to record the evaluation and grading data of unconventional feeds in real time, and use IoT sensors to monitor the feed quality fluctuations in the raw material supply chain in real time, automatically identify abnormal samples and trigger early warnings.

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