Feed quality online production monitoring method based on big data analysis

Abstract

The invention belongs to the technical field of feed quality online production monitoring, and discloses a feed quality online production monitoring method based on big data analysis. According to the method, the processing procedure quality and the processed finished product quality of the target feed production process are comprehensively analyzed, and the quality condition from the production process to the final product can be comprehensively grasped through the analysis mode. By strictly monitoring the processing procedures, problems in the production process can be found and corrected in time, the problems are prevented from being amplified in subsequent links, and therefore the stability of the quality of processed finished products is ensured. According to the method, the processes of burdening, crushing, stirring and the like are analyzed, then the burdening procedure quality evaluation condition and the processing procedure quality evaluation condition are obtained, the analysis mode can comprehensively grasp the quality condition of the whole processing flow, potential quality risks and potential safety hazards can be found in time, and corresponding prevention measures are taken. And the risk in the production process is reduced, and the product quality is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of online production monitoring of feed quality, and relates to an online production monitoring method of feed quality based on big data analysis. Background Art

[0002] Feed is a product used to feed animals and plays a vital role in modern animal husbandry. Feed production usually includes processes such as raw material preparation, crushing, mixing and granulation. Feed production quality monitoring is related to the health of farmed animals. It can avoid problems such as nutritional deficiencies, indigestion or toxic substances that affect animal growth and cause diseases, and ensure the benefits of farming. At the same time, it can improve animal growth rate and production performance, reduce the incidence of diseases, and ensure the quality of livestock products. Therefore, online production monitoring of feed quality based on big data analysis is of great significance.

[0003] In the prior art, there are also related solutions for feed quality monitoring. For example, an invention patent application with publication number CN115809556A is for a feed formula optimization method, system, device and storage medium. It determines the feed formula raw materials and the constraints corresponding to the raw materials; constructs a feed formula optimization problem model and determines the optimization target of the model; processes the feed formula optimization problem model to obtain the final feed formula.

[0004] The invention patent application of feed raw material nutrient remote analysis and online intelligent storage database system with publication number CN106446234B includes an information acquisition unit, an information transmission unit, an information analysis unit and a central server; the information acquisition unit collects samples through a multifunctional data acquisition device to obtain a data file; the data file is transmitted and detected by the information transmission unit; the information analysis unit includes an information service platform and a feed spectrum database; the feed information data and the feed spectrum database are quickly traced and verified online. The present invention collects samples through a multifunctional data acquisition device to obtain a data file, uses a wireless multi-hop data transmission method to detect and transmit the transmitted data, realizes remote data collection, and makes information decisions and early warnings by analyzing the collected data. It has good real-time performance and is convenient for unified management and analysis.

[0005] Although the above scheme proposes some solutions for feed quality monitoring, it still has certain limitations. The above scheme mainly discovers problems of feed raw materials and optimizes raw materials through product testing through online testing of feed raw materials, but it cannot trace the source well. For feed that does not meet the quality requirements, it cannot trace the specific ingredients or process impact, etc., which has limitations. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, an online production monitoring method for feed quality based on big data analysis is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: an online production monitoring method for feed quality based on big data analysis, comprising: S1, batching process quality analysis: monitoring the batching process of the target feed production process, obtaining the batching process quality data of the target feed production process, and analyzing the batching process quality evaluation of the target feed production process.

[0008] S2. Processing procedure quality analysis: monitor the processing process of the target feed production process, obtain the processing procedure quality data of the target feed production process, and analyze the processing procedure quality evaluation of the target feed production process.

[0009] S3. Processed product quality analysis: Monitor the quality of the processed products in the target feed production process, obtain the quality data of the processed products in the target feed production process, and analyze the quality evaluation of the processed products in the target feed production process.

[0010] S4. Identification of quality anomalies: Based on the quality evaluation of the ingredient process, the quality evaluation of the processing process and the quality evaluation of the finished product in the target feed production process, determine whether there are quality anomalies, and optimize the target feed production process based on the judgment results.

[0011] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention comprehensively analyzes the quality of the processing procedures and the quality of the finished product in the target feed production process. This analysis method can comprehensively grasp the quality status from the production process to the final product. Through strict monitoring of the processing procedures, problems in the production process can be discovered and corrected in a timely manner to prevent these problems from being amplified in subsequent links, thereby ensuring the stability of the quality of the finished product. The ability to discover and take measures to adjust quality problems in a timely manner greatly reduces the lag time for quality problems to be discovered. By analyzing the quality of the processing procedures, possible quality problems of the finished product can be predicted in advance.

[0012] (2) The present invention analyzes the processes of batching, crushing and mixing, and then obtains the quality evaluation of the batching process and the quality evaluation of the processing process. This analysis method can fully grasp the quality status of the entire processing flow, timely discover potential quality risks and safety hazards, and take corresponding preventive measures. This helps to reduce risks in the production process and ensure production safety and product quality. This analysis method also provides a basis for continuous improvement and innovation. Through in-depth understanding and evaluation of the processing procedures, potential room for improvement can be discovered, promoting technological innovation and process optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0014] Figure 1 It is a schematic diagram for implementing the steps of the method of the present invention.

[0015] Figure 2 A schematic diagram of an embodiment of the present invention is provided.

[0016] Figure 3 A logical flow chart for determining the need for optimizing the target feed production process according to the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0018] See also Figure 1 As shown, the present invention provides an online production monitoring method for feed quality based on big data analysis, including: S1, batching process quality analysis: monitoring the batching process of the target feed production process, obtaining the batching process quality data of the target feed production process, and analyzing the batching process quality evaluation of the target feed production process.

[0019] It is necessary to explain the reasons for monitoring the batching process of the target feed production process: 1. The batching process is the key link in mixing various raw materials according to the formula ratio. By monitoring the batching process, it can be ensured that the amount of each raw material added is accurate, so as to ensure that the feed can meet the nutritional needs of animals and promote the healthy growth and development of animals. Accurate batching is the basis for ensuring the stability of feed product quality. By monitoring the batching process, batching errors can be discovered and corrected in a timely manner to ensure that each batch of feed has consistent quality. 2. In the batching process, if effective monitoring is not carried out, incorrect or excessive addition of raw materials may occur, resulting in waste of raw materials.

[0020] See also Figure 2 As shown, it needs to be further explained that the batching process quality data of the target feed production process includes the raw material weight compliance and the raw material quality compliance. The analysis process of the raw material weight compliance is as follows: the weight of each raw material is obtained by weighing each raw material using a weighing device, which is recorded as ,in Indicates the raw material number, , Indicates the quantity of raw materials.

[0021] It should be noted that the reasons for selecting raw material weight compliance and raw material quality compliance as the batching process quality data of the target feed production process are: 1. The feed formula is carefully designed, and the weight ratio of each raw material is crucial to meet the nutritional needs of animals. Raw material weight compliance directly reflects the degree of closeness between the amount of various raw materials added in the actual ingredients and the formula requirements. 2. Raw material quality compliance includes the detection of harmful substances that may exist in the raw materials. Selecting raw materials that meet quality standards can effectively reduce the risk of animals ingesting harmful substances.

[0022] Exemplarily, the raw materials may be: corn, soybean, meat and bone meal and limestone powder.

[0023] Using the formula Analyze and obtain the raw material weight compliance of the target feed production process ,in Indicates the preset Reference required weight of raw materials, Respectively represent the preset weight factors.

[0024] For example, .

[0025] It needs to be further explained that the analysis process of the raw material quality compliance is as follows: each raw material is conveyed separately through a conveyor belt, the apparent quality image of each raw material is collected by a high-definition camera, and the apparent quality image of each raw material is divided into color areas by image processing software to obtain various color areas, and the chromaticity values ​​of each color area corresponding to the apparent quality image of each raw material are compared with the pre-stored standard chromaticity values ​​of each raw material to obtain various abnormal color areas corresponding to the apparent quality image of each raw material.

[0026] It should be added that the specific analysis steps for obtaining the abnormal color areas corresponding to the apparent quality images of each raw material are as follows: the chromaticity values ​​of each color area corresponding to the apparent quality images of each raw material are compared with the pre-stored standard chromaticity values ​​of each raw material. If the chromaticity value of a color area corresponding to the apparent quality image of a raw material is inconsistent with the standard chromaticity value of the raw material, then the color area corresponding to the apparent quality image of the raw material is judged to be an abnormal color area.

[0027] It should be explained that the appearance of the abnormal color areas may be caused by the growth of mold or other fungi.

[0028] The number of regions of each abnormal color region corresponding to the apparent quality image of each raw material is counted, and the area of ​​each abnormal color region corresponding to the apparent quality image of each raw material is obtained by image processing software, which is recorded as ,in Indicates the number of each abnormal color area, , Indicates the number of abnormal color areas.

[0029] Using the formula Analyze and obtain the raw material quality compliance of the target feed production process ,in Represents the area of ​​the apparent mass image of each raw material.

[0030] It should be further explained that the quality evaluation of the batching process of the target feed production process is specifically as follows: Extract the weight compliance of the raw materials in the target feed production process Compliance with raw material quality , using the formula Analyze and obtain the ingredient quality index of the target feed production process ,in They respectively represent the weight factors of the raw material weight compliance and raw material quality compliance of the preset target feed production process.

[0031] It should be noted that the weight factors of the raw material weight compliance and raw material quality compliance of the target feed production process are set based on the following: from the perspective of the impact on feed quality, raw material weight deviation may affect the proportion of nutrients and physical properties, while raw material quality is related to feed safety and nutritional value. If it contains harmful substances, it will harm animal health, while high-quality raw materials are easy to digest and absorb. In terms of the controllability of the production process, raw material weight control is relatively easy, but if there are factors that cause fluctuations, its weight needs to be increased; raw material quality control is more complex and is greatly affected by uncontrollable factors, so it needs to be given a higher weight.

[0032] For example, .

[0033] The batching process quality index of the target feed production process is compared with the preset batching process quality index threshold. If the batching process quality index of the target feed production process is greater than or equal to the batching process quality index threshold, it is judged that the batching process quality of the target feed production process meets the production requirements; if the batching process quality index of the target feed production process is less than the batching process quality index threshold, it is judged that the batching process quality of the target feed production process does not meet the production requirements.

[0034] It should be noted that the threshold value of the quality index of the batching process is set based on the following: first, the nutritional needs of animals must be met. According to the formula requirements, the feed must meet the nutritional needs of animals at different growth stages. At the same time, the appropriate threshold value must be determined in combination with the growth performance of the animals to ensure their growth. Secondly, the stability of feed quality must be ensured, and production process control must be considered to ensure that the quality fluctuation of each batch of feed is within a certain range. It is also necessary to refer to quality inspection standards to ensure that safety requirements are met. Furthermore, cost-effectiveness must be considered, taking into account both raw material costs and production efficiency, and minimizing costs and maximizing efficiency while ensuring quality. Finally, the actual situation of the enterprise, such as production equipment and technical level and quality management system, must be combined, and the threshold value must be adjusted according to the enterprise's capabilities to ensure smooth production and improve product quality levels.

[0035] For example, the batching process quality index threshold is .

[0036] S2. Processing procedure quality analysis: monitor the processing process of the target feed production process, obtain the processing procedure quality data of the target feed production process, and analyze the processing procedure quality evaluation of the target feed production process.

[0037] It should be noted that the reasons for monitoring the processing of the target feed production process are: on the one hand, it can ensure product quality, ensure the stability of nutrients, control physical properties and prevent contamination and deterioration. On the other hand, it can improve production efficiency, optimize processing technology, reduce energy consumption and reduce production costs. At the same time, it can also meet regulatory requirements, ensure product safety and standardize production behavior.

[0038] It needs to be further explained that the processing procedure quality data of the target feed production process includes the crushing particle size compliance and the mixing uniformity compliance, wherein the analysis steps of the crushing particle size compliance are as follows: a real-time particle size detection device is installed on the feed production line to obtain the particle size distribution data corresponding to each crushed raw material after crushing, and the weight of each crushed raw material corresponding to each particle size is obtained, and the weight of each crushed raw material corresponding to each particle size is compared with the pre-set target particle size range of each crushed raw material to obtain the qualified weight of each crushed raw material.

[0039] It should be noted that the reasons for selecting crushing particle size compliance and mixing uniformity compliance as the processing procedure quality data of the target feed production process are: 1. Crushing particle size compliance is crucial. Appropriate crushing particle size can affect the digestion and absorption of feed by animals. Too coarse particle size may cause digestion difficulties for animals and reduce nutrient utilization; too fine particle size may increase processing costs and may cause problems such as respiratory diseases in animals. 2. Mixing uniformity compliance is also indispensable. Good mixing uniformity can ensure that the proportions of various nutrients in each feed are consistent, avoiding the impact of unbalanced nutritional intake on the growth, development and health of animals. If the mixing is not uniform, some animals may ingest too much or too little specific nutrients, causing a series of problems.

[0040] The total weight of each crushed raw material is obtained, and the qualified weight of each crushed raw material is calculated as a ratio to the total weight of each crushed raw material to obtain the crushing particle size compliance of each crushed raw material, and then the average is calculated to obtain the crushing particle size compliance of the target feed production process.

[0041] It needs to be further explained that the analysis steps of the mixing uniformity compliance are as follows: stirring the crushed raw materials to obtain a mixture, collecting samples of the mixture based on the weight of the mixture to obtain a number of sample mixtures, and then obtaining the weight of each crushed raw material in each sample mixture, calculating the difference between the weight of each crushed raw material in each sample mixture and the pre-set weight of each reference crushed raw material to obtain the weight deviation of each sample mixture corresponding to each crushed raw material, and then calculating the ratio of the absolute value of the weight deviation of each sample mixture corresponding to each crushed raw material and the weight of each reference crushed raw material to obtain the weight deviation degree of each sample mixture corresponding to each crushed raw material.

[0042] The weight deviation of each sample mixture corresponding to each crushed raw material is averaged to obtain the mixing uniformity abnormality index of each sample mixture, and then the reciprocal calculation is performed to obtain the mixing uniformity compliance of the target feed production process.

[0043] It should be further explained that the specific steps of analyzing the quality evaluation of the processing steps of the target feed production process are as follows: extracting the crushing particle size compliance and mixing uniformity compliance of the target feed production process, which are recorded as , , using the formula Analyze and obtain the processing quality index of the target feed production process ,in Indicates the pre-set reference crushing particle size compliance, Indicates the preset reference mixing uniformity compliance, They respectively represent the weight factors of the crushing particle size compliance and mixing uniformity compliance of the target feed production process.

[0044] It should be noted that the reference crushing particle size compliance and the reference mixing uniformity compliance are set based on the following: 1. On the one hand, the needs of different animal species and growth stages should be considered. For example, young animals usually require finer crushing particle sizes to facilitate digestion and absorption, while adult animals may have relatively loose requirements on particle size. On the other hand, in combination with the characteristics of feed raw materials, some raw materials may require specific crushing particle sizes to achieve the best nutritional value. 2. Important nutrients require higher mixing uniformity to ensure balanced animal intake. In addition, the mixing capacity of the production process and equipment should also be considered to ensure that the set mixing uniformity standards can be achieved in actual production.

[0045] It should be noted that the weight factors for the compliance of the crushing particle size and the compliance of the mixing uniformity in the target feed production process are set based on the following: On the one hand, considering the impact on animal growth and health, different animal species and growth stages have different requirements for crushing particle size. The appropriate particle size helps digestion and absorption, while mixing uniformity ensures that the animal's nutritional intake is balanced to avoid the impact of nutritional imbalance on growth and development. On the other hand, from the perspective of production technology, crushing particle size affects processing costs and equipment performance. Too fine particle size may increase energy consumption and equipment loss. Mixing uniformity is related to production technology and equipment mixing capacity, and it is necessary to ensure that the set standards are met in actual production. At the same time, it is also necessary to combine the company's business strategy and market demand. Companies that focus on product quality may pay more attention to mixing uniformity compliance to meet high-end market needs, while companies that focus on cost control need to balance the two.

[0046] For example, .

[0047] The processing procedure quality index of the target feed production process is compared with the pre-set processing procedure quality index threshold. If the processing procedure quality index of the target feed production process is greater than or equal to the processing procedure quality index threshold, it is judged that the processing procedure quality of the target feed production process meets the production requirements. If the processing procedure quality index of the target feed production process is less than the processing procedure quality index threshold, it is judged that the processing procedure quality of the target feed production process does not meet the production requirements.

[0048] It should be noted that the threshold of the processing quality index is set based on the following: First, the nutritional needs of animals must be met to ensure that the nutritional content of feed is stable during processing. For example, appropriate crushing particle size and good mixing uniformity can affect the digestion, absorption and utilization of nutrients by animals. The threshold is determined according to the needs of different animal species and growth stages and feed formula requirements to ensure the growth performance of animals. Secondly, the stability of feed quality must be ensured, and production process control must be considered, such as the equipment operation and process parameters during processing, to ensure that the quality of each batch of feed fluctuates within a certain range. At the same time, relevant quality inspection standards and industry specifications must be referred to to ensure compliance with safety requirements.

[0049] Exemplarily, the processing quality index threshold is .

[0050] It should be noted that the present invention analyzes the processes of batching, crushing and stirring, and then obtains the batching process quality evaluation and the processing process quality evaluation. This analysis method can fully grasp the quality status of the entire processing flow, can timely discover potential quality risks and safety hazards, and take corresponding preventive measures. This helps to reduce the risks in the production process and ensure production safety and product quality. This analysis method also provides a basis for continuous improvement and innovation. Through in-depth understanding and evaluation of the processing procedures, potential room for improvement can be discovered to promote technological innovation and process optimization.

[0051] S3. Processed product quality analysis: Monitor the quality of the processed products in the target feed production process, obtain the quality data of the processed products in the target feed production process, and analyze the quality evaluation of the processed products in the target feed production process.

[0052] It should be further explained that the quality data of the processed product of the target feed production process includes particle hardness, particle size and moisture content.

[0053] It should be noted that the reasons for selecting particle hardness, particle size and moisture content as the quality data of the processed finished product in the target feed production process are: 1. Particle hardness has an important impact on the feeding and digestion of animals. Appropriate particle hardness can make it easier for animals to chew and swallow during feeding, thereby increasing feed intake. 2. Particle size is directly related to the digestion and absorption efficiency of animals. Different animal species and growth stages have different requirements for particle size. Reasonable particle size can improve the digestibility of feed in animals, thereby improving feed utilization and reducing breeding costs. 3. Moisture content is a key factor affecting feed quality and storage stability. Excessive moisture content can easily cause feed mold and deterioration, reduce the nutritional value of feed, and may even produce toxic and harmful substances, posing a threat to animal health.

[0054] It should be further explained that the specific steps of analyzing the quality evaluation of the finished product of the target feed production process are as follows: extract the particle size hardness, particle size and moisture content of the finished product of the target feed production process, and record them as , , .

[0055] It should be added that the analysis method of the particle size hardness of the finished product of the target feed production process is: randomly extract a number of particle hardness monitoring samples from the target feed production process, use a particle hardness meter to obtain the particle hardness of each particle hardness monitoring sample, and then perform mean calculation to obtain the particle size hardness of the finished product of the target feed production process.

[0056] It should be noted that the analysis method of the particle size and moisture content of the finished product of the target feed production process refers to the analysis method of the particle size and hardness of the finished product of the target feed production process.

[0057] Specifically, a number of particle size monitoring samples are randomly extracted from the target feed production process, and a three-dimensional scanning device is used to obtain the particle volume of each particle size monitoring sample, and then the mean is calculated to obtain the particle size of the finished product of the target feed production process.

[0058] A number of moisture content monitoring samples are randomly extracted from the target feed production process, and the moisture content of each moisture content monitoring sample is obtained using a moisture content monitoring device, and then the mean is calculated to obtain the moisture content of the finished product of the target feed production process.

[0059] Using the formula Analyze and obtain the quality index of the finished product of the target feed production process ,in Indicates the preset reference particle hardness, Indicates the preset reference particle size, Indicates the preset reference moisture content, Indicates the allowable difference between the pre-set particle size hardness of the finished product and the reference particle hardness. It indicates the allowable difference between the preset particle size of the finished product and the reference particle size. Indicates the allowable difference between the preset moisture content of the finished product and the reference moisture content.

[0060] The quality index of the finished product of the target feed production process is compared with the pre-set finished product quality index threshold. If the quality index of the finished product of the target feed production process is greater than or equal to the finished product quality index threshold, it is judged that the quality of the finished product of the target feed production process meets the production requirements. If the quality index of the finished product of the target feed production process is less than the finished product quality index threshold, it is judged that the quality of the finished product of the target feed production process does not meet the production requirements.

[0061] It should be noted that the threshold of the quality index of processed finished products is set based on: first, to meet the needs of animals, adapt to the feeding habits of different animals to increase feed intake, and ensure nutrient absorption efficiency. Secondly, to ensure product quality stability, so as to control the production process, so that the quality of each batch of feed products is stable and meets relevant quality standards and industry specifications. It is also necessary to consider cost-effectiveness, balance production costs, avoid excessive resource investment due to excessive quality requirements, and reduce product failure rates and waste caused by unreasonable thresholds.

[0062] For example, the finished product quality index threshold is .

[0063] S4. Identification of quality anomalies: Determine whether there are quality anomalies based on the quality evaluation of the ingredients process, the processing process and the finished product in the target feed production process. Figure 3 As shown, if there are any quality abnormalities, the target feed production process will be optimized.

[0064] It needs to be further explained that the specific steps for determining whether there are any quality abnormalities are as follows: extracting the quality evaluation of the ingredient process, the quality evaluation of the processing process and the quality evaluation of the finished product in the target feed production process, and then determining whether there are any quality abnormalities.

[0065] It should be added that the specific steps for determining whether there is a quality abnormality are as follows: if the quality of the ingredient process in the target feed production process does not meet the production requirements, the quality of the processing process does not meet the production requirements, or the quality of the processed product does not meet the production requirements, then it is determined that there is a quality abnormality; otherwise, it is determined that there is no quality abnormality.

[0066] It should be added that the specific steps for optimizing the target feed production process are as follows: When the target feed production process has quality abnormalities, the production process can be optimized by determining the root cause of the problem, formulating targeted solutions, implementing optimization plans, monitoring the effects, and continuously improving these steps. First, the ingredients, processing procedures, and finished products should be inspected and analyzed separately to find out the problems. For example, the ingredients process may be related to raw material procurement, quality inspection, metering equipment, and operation. The processing procedures may involve problems such as crushing particle size and mixing uniformity caused by improper equipment or parameters. The finished product problems may be related to the process links related to particle hardness, particle size, and moisture content. Then, optimization plans are formulated for different problems, including optimizing raw material procurement, strengthening inspections, calibrating equipment, adjusting process parameters, replacing equipment, etc. Then strictly implement the optimization plan and establish a real-time monitoring system, regularly evaluate the results, and make timely adjustments if they are not ideal.

[0067] It should be noted that the present invention comprehensively analyzes the quality of the processing procedures and the quality of the finished product in the target feed production process. This analysis method can fully grasp the quality status from the production process to the final product. Through strict monitoring of the processing procedures, problems in the production process can be discovered and corrected in time to avoid these problems from being amplified in subsequent links, thereby ensuring the stability of the quality of the finished product. It can be discovered in time and measures can be taken to adjust, greatly reducing the lag time for quality problems to be discovered. Through the analysis of the quality of the processing procedures, possible quality problems of the finished product can be predicted in advance.

[0068] It should be noted that the present invention identifies the existence of quality abnormalities. If quality abnormalities exist, the target feed production process is optimized. Otherwise, the next round of online production monitoring of feed quality is carried out.

[0069] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A method for online production monitoring of feed quality based on big data analysis, characterized in that: include: S1. Quality analysis of batching process: monitor the batching process of the target feed production process, obtain the batching process quality data of the target feed production process, and analyze the batching process quality evaluation of the target feed production process; S2. Processing process quality analysis: monitor the processing process of the target feed production process, obtain the processing process quality data of the target feed production process, and analyze the processing process quality evaluation of the target feed production process; S3. Processed product quality analysis: monitor the quality of the processed products in the target feed production process, obtain the quality data of the processed products in the target feed production process, and analyze the quality evaluation of the processed products in the target feed production process; S4. Identification of quality anomalies: Based on the quality evaluation of the ingredient process, the processing process and the finished product in the target feed production process, determine whether there are quality anomalies. If there are quality anomalies, optimize the target feed production process.

2. The method for online production monitoring of feed quality based on big data analysis according to claim 1, characterized in that: The batching process quality data of the target feed production process includes raw material weight compliance and raw material quality compliance. The analysis process of the raw material weight compliance is as follows: Weigh each raw material using a weighing device to obtain the weight of each raw material, recorded as ,in Indicates the raw material number, , Indicates the quantity of raw materials; Using the formula Analyze and obtain the raw material weight compliance of the target feed production process ,in Indicates the preset Reference required weight of raw materials, Respectively represent the preset weight factors.

3. The method for online production monitoring of feed quality based on big data analysis according to claim 2, characterized in that: The analysis process of the raw material quality compliance is as follows: Each raw material is conveyed through a conveyor belt, and an apparent quality image of each raw material is collected by a high-definition camera. The apparent quality image of each raw material is divided into color regions by image processing software to obtain various color regions, and the chromaticity values ​​of each color region corresponding to the apparent quality image of each raw material are compared with the pre-stored standard chromaticity values ​​of each raw material to obtain various abnormal color regions corresponding to the apparent quality image of each raw material; The number of regions of each abnormal color region corresponding to the apparent quality image of each raw material is counted, and the area of ​​each abnormal color region corresponding to the apparent quality image of each raw material is obtained by image processing software, which is recorded as ,in Indicates the number of each abnormal color area, , Indicates the number of abnormal color areas; Using the formula Analyze and obtain the raw material quality compliance of the target feed production process ,in Represents the area of ​​the apparent mass image of each raw material.

4. The method for online production monitoring of feed quality based on big data analysis according to claim 3, characterized in that: The quality evaluation of the batching process of the target feed production process is as follows: Extraction of raw material weight compliance in target feed production process Compliance with raw material quality , using the formula Analyze and obtain the ingredient quality index of the target feed production process ,in Respectively represent the weight factors of the raw material weight compliance and raw material quality compliance of the preset target feed production process; The batching process quality index of the target feed production process is compared with the preset batching process quality index threshold. If the batching process quality index of the target feed production process is greater than or equal to the batching process quality index threshold, it is judged that the batching process quality of the target feed production process meets the production requirements; if the batching process quality index of the target feed production process is less than the batching process quality index threshold, it is judged that the batching process quality of the target feed production process does not meet the production requirements.

5. The method for online production monitoring of feed quality based on big data analysis according to claim 1, characterized in that: The processing quality data of the target feed production process includes the crushing particle size compliance and the mixing uniformity compliance, wherein the analysis steps of the crushing particle size compliance are as follows: Install real-time particle size detection equipment on the feed production line to obtain the particle size distribution data of each raw material after being crushed, obtain the weight of each raw material corresponding to each particle size, and compare the weight of each raw material corresponding to each particle size with the preset target particle size range of each raw material to obtain the qualified weight of each raw material; The total weight of each crushed raw material is obtained, and the qualified weight of each crushed raw material is calculated as a ratio to the total weight of each crushed raw material to obtain the crushing particle size compliance of each crushed raw material, and then the average is calculated to obtain the crushing particle size compliance of the target feed production process.

6. The method for online production monitoring of feed quality based on big data analysis according to claim 5, characterized in that: The analysis steps of the mixing uniformity compliance are as follows: Performing a stirring operation on each crushed raw material after crushing to obtain a mixture, collecting samples of the mixture based on the weight of the mixture to obtain a number of sample mixtures, and then obtaining the weight of each crushed raw material in each sample mixture, performing a difference calculation between the weight of each crushed raw material in each sample mixture and the weight of each preset reference crushed raw material to obtain a weight deviation of each sample mixture corresponding to each crushed raw material, and then performing a ratio calculation between the absolute value of the weight deviation of each sample mixture corresponding to each crushed raw material and the weight of each reference crushed raw material to obtain a weight deviation degree of each sample mixture corresponding to each crushed raw material; The weight deviation of each sample mixture corresponding to each crushed raw material is averaged to obtain the mixing uniformity abnormality index of each sample mixture, and then the reciprocal calculation is performed to obtain the mixing uniformity compliance of the target feed production process.

7. The method for online production monitoring of feed quality based on big data analysis according to claim 6, characterized in that: The specific steps of analyzing the quality evaluation of the processing steps in the target feed production process are as follows: The particle size compliance and mixing uniformity compliance of the target feed production process are extracted and recorded as , , using the formula Analyze and obtain the processing quality index of the target feed production process ,in Indicates the pre-set reference crushing particle size compliance, Indicates the preset reference mixing uniformity compliance, The weight factors representing the compliance of the crushing particle size and the compliance of the mixing uniformity in the target feed production process respectively; The processing procedure quality index of the target feed production process is compared with the pre-set processing procedure quality index threshold. If the processing procedure quality index of the target feed production process is greater than or equal to the processing procedure quality index threshold, it is judged that the processing procedure quality of the target feed production process meets the production requirements. If the processing procedure quality index of the target feed production process is less than the processing procedure quality index threshold, it is judged that the processing procedure quality of the target feed production process does not meet the production requirements.

8. The method for online production monitoring of feed quality based on big data analysis according to claim 1, characterized in that: The quality data of the processed product of the target feed production process include particle hardness, particle size and moisture content.

9. The method for online production monitoring of feed quality based on big data analysis according to claim 8, characterized in that: The specific steps of analyzing the quality evaluation of the finished product of the target feed production process are as follows: The particle size hardness, particle size and moisture content of the finished product of the target feed production process are extracted and recorded as , , ; Using the formula Analyze and obtain the quality index of the finished product of the target feed production process ,in Indicates the preset reference particle hardness, Indicates the preset reference particle size, Indicates the preset reference moisture content, Indicates the allowable difference between the pre-set particle size hardness of the finished product and the reference particle hardness. It indicates the allowable difference between the preset particle size of the finished product and the reference particle size. Indicates the allowable difference between the preset moisture content of the finished product and the reference moisture content; The quality index of the finished product of the target feed production process is compared with the pre-set finished product quality index threshold. If the quality index of the finished product of the target feed production process is greater than or equal to the finished product quality index threshold, it is judged that the quality of the finished product of the target feed production process meets the production requirements. If the quality index of the finished product of the target feed production process is less than the finished product quality index threshold, it is judged that the quality of the finished product of the target feed production process does not meet the production requirements.

10. The method for online production monitoring of feed quality based on big data analysis according to claim 1, characterized in that: The specific steps for determining whether there is a quality abnormality are as follows: The quality evaluation of the ingredient process, the quality evaluation of the processing process and the quality evaluation of the finished product in the target feed production process are extracted to determine whether there are any quality abnormalities.

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