Intelligent feed processing quality control method and system

Abstract

The present application relates to the technical field of feed quality control, in particular to an intelligent feed processing quality control method and system.In the present application, the running parameters of the production equipment and the environmental temperature and humidity are acquired in real time, and the environmental and equipment state parameter set is established, so that the influencing factors of the production conditions can be quantified, and accurate basis is provided for subsequent processing process parameter adjustment.In the production process, based on the running stability of the equipment and the variation trend of the raw material components, the adaptive processing process threshold is calculated, so as to avoid the unstable product quality caused by the fluctuation of raw materials or the change of equipment state.Combining the change of steam absorption rate and granulation pressure, it is dynamically judged whether the key indicators in the production process are in the adaptive range, so as to ensure that the processing process control is more precise.Aiming at the possible trend deviation in the production process, the stability of the production process is analyzed by analyzing the equipment load level, energy consumption change synchronism and abnormal trend of the processing process parameters.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of feed quality control, and in particular to an intelligent feed processing quality control method and system. BACKGROUND

[0002] The technical field of feed quality control includes systematic management and monitoring of raw material screening, ratio adjustment, production process optimization, and finished product testing during the production process of feed. The core content of this technical field includes the use of sensing detection, data analysis, and control strategies to ensure the accuracy of the feed formula, the stability of the processing process, and the pass rate of the finished product.

[0003] Among them, the intelligent feed processing quality control method refers to the dynamic monitoring and adjustment of the quality of each link of feed production based on intelligent detection means and data analysis technology, to ensure the stability and safety of the feed product. It includes real-time component detection of raw materials, formula proportion calculation, automatic adjustment of processing parameters, and finished product quality analysis.

[0004] In traditional feed processing quality control, the production process mainly relies on fixed parameter setting, lacking real-time monitoring of equipment running state and environmental factors, resulting in low matching degree of equipment running state and processing process parameters. Due to the inevitable fluctuation of raw material composition, existing technology usually relies on historical experience for adjustment, lacking quantitative analysis of raw material quality changes, making the adaptability of processing process parameters uncertain. In addition, during the production process, adjustments are usually made only after quality problems occur, lacking trend analysis of the production process, making it difficult to identify potential quality risks in advance, resulting in a large impact of product quality by sudden factors. The equipment load condition cannot be accurately matched with the current processing process demand, which may cause high energy consumption or unstable equipment operation, thereby affecting the finished product quality of the feed. In terms of finished product quality testing, it often relies on static evaluation of final hardness, moisture content and other indicators, without considering dynamic factors in the production process, resulting in lagging production adjustment, which cannot quickly respond to quality fluctuations, affecting the uniformity and durability of the feed pellets. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art and to provide an intelligent feed processing quality control method and system.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: an intelligent feed processing quality control method, comprising the following steps:

[0007] S1: Obtain the current batch feed production equipment running parameters and the current environmental temperature and humidity, calculate the equipment running stability coefficient and the current environmental influence coefficient, and construct the environmental and equipment state parameter set;

[0008] S2: Obtain the pelleting pressure and steam absorption rate in the current batch of feed production process, and the moisture content and protein content of the current batch of feed raw materials, calculate the pelleting pressure floating range, steam absorption rate adjustment amount and feed raw material component variation coefficient combined with the environment and equipment state parameter set, and set the processing flow parameter threshold interval;

[0009] S3: Based on the processing flow parameter threshold interval, respectively judge the pelleting pressure deviation state, the steam absorption rate adaptation state and the moisture content adaptation state, and obtain the processing flow parameter adaptation analysis result;

[0010] S4: Based on the processing flow parameter adaptation analysis result, analyze whether the processing flow parameter has an abnormal trend, and whether the equipment load level matches the current processing flow demand, and obtain the production process stability analysis result;

[0011] S5: Based on the production process stability analysis result, calculate the current batch of feed particle hardness change amount, judge whether the current processing flow parameter meets the feed processing quality control standard, and obtain the feed processing quality control result.

[0012] As a further scheme of the present application, the environment and equipment state parameter set includes environment influence coefficient and equipment operation stability coefficient, the processing flow parameter threshold interval includes pelleting pressure floating range, steam absorption rate adjustment amount and raw material component variation coefficient, the processing flow parameter adaptation analysis result includes pelleting pressure deviation state, steam absorption rate adaptation state and moisture content adaptation state, the production process stability analysis result includes processing flow parameter abnormal trend, equipment operation and energy consumption change synchronization and equipment load level matching condition, and the feed processing quality control result includes pelleting particle uniformity, steam condition adaptation degree and influence range of raw material moisture change on particle durability.

[0013] As a further scheme of the present application, the environment and equipment state parameter set is obtained by the following steps:

[0014] S111: Obtain the running load and energy consumption level of the current batch of feed production equipment, call the equipment real-time monitoring data, extract the power consumption data of the equipment and the total energy consumption in a specified time, obtain the instantaneous load rate of the equipment, and collect the current environment temperature and humidity, extract the temperature and humidity sensor data, record the measurement time and measurement point position, and establish the equipment energy consumption and environment data set;

[0015] S112: Based on the equipment energy consumption and environment data set, the formula is:

[0016] ;

[0017] Calculate the environment influence coefficient ;

[0018] wherein, is the current temperature, is the optimum temperature, is the temperature sensitivity parameter, is the current humidity, is the optimum humidity, is the humidity sensitivity parameter, is the natural logarithm function with base e;

[0019] S113: based on the device energy consumption and environmental data set, using the formula:

[0020] ;

[0021] calculating the device running stability coefficient ;

[0022] wherein, represents the device power consumption at the i-th moment, represents the mean value of the device power consumption, represents the number of sampling points;

[0023] S114: normalizing the environmental influence coefficient and the device running stability coefficient, and constructing the environmental and device state parameter set.

[0024] As a further scheme of the present application, the obtaining step of the processing flow parameter threshold interval is specifically:

[0025] S211: obtaining the pelleting pressure and steam absorption rate in the current batch of feed production process, calling the raw material detection record of the current batch of feed, obtaining the moisture content and protein content of the raw material of the current batch of feed, calculating the pelleting pressure floating range based on the device running stability coefficient in the environmental and device state parameter set, calculating the steam absorption rate adjustment amount based on the environmental influence coefficient in the environmental and device state parameter set, and calculating the raw material component variation coefficient relative to the historical production data according to the moisture content and protein content of the raw material of the current batch of feed;

[0026] S212: combining the pelleting pressure floating range, the steam absorption rate adjustment amount and the raw material component variation coefficient, and setting the processing flow parameter threshold interval.

[0027] As a further scheme of the present application, the obtaining step of the processing flow parameter adaptation analysis result is specifically:

[0028] S311: Based on the processing flow parameter threshold interval, the current batch granulation pressure is compared with the granulation pressure floating range, the pressure values at all time points in the granulation process are extracted, the deviation rates at multiple times are compared, and if the deviation rate at a certain time exceeds the preset deviation threshold, it is judged that the granulation pressure exceeds the normal range, and the granulation pressure deviation state is obtained;

[0029] S312: Based on the processing flow parameter threshold interval, the current steam absorption rate is compared with the steam absorption rate adjustment amount, the current batch steam absorption rate is calculated and compared with the steam absorption rate adjustment amount, if the current absorption rate is within the interval of the steam absorption rate adjustment amount, it is judged as the adaptation state, if it exceeds the interval, it is judged as the deviation state, the steam absorption rate adaptation state is obtained;

[0030] S313: Based on the processing flow parameter threshold interval, the current moisture content is compared with the raw material component variation coefficient, the moisture content data of the current batch is extracted, and compared with the variation range of the current batch moisture content, if the current moisture variation value is less than the set variation coefficient, it is judged that the moisture content is adapted, if it exceeds the variation coefficient, it is judged as the deviation state, the moisture content adaptation state is obtained, combined with the granulation pressure deviation state and the steam absorption rate adaptation state, the processing flow parameter adaptation analysis result is obtained.

[0031] As a further scheme of the present application, the production process stability analysis result obtaining step is specifically:

[0032] S411: Based on the processing flow parameter adaptation analysis result, the key processing flow parameters in the current batch production process are obtained, including the granulation pressure, the moisture content, the steam absorption rate, and the abnormal trend of the processing flow parameters is analyzed to obtain the processing flow parameter abnormal trend;

[0033] S412: According to the moisture content adaptation state and the equipment operation stability coefficient, the equipment operation and energy consumption change synchronism and whether the equipment load level matches the current processing flow demand are analyzed, and the production process stability analysis result is obtained combined with the processing flow parameter abnormal trend.

[0034] As a further scheme of the present application, the feed processing quality control result obtaining step is specifically:

[0035] S511: Based on the production process stability analysis result, the formula:

[0036] ;

[0037] The current batch feed particle hardness change amount is calculated ;

[0038] Wherein, is the the hardness value of the measurement point after granulation, the hardness value of the measurement point before granulation, the average value of the hardness values of all measurement points before granulation, the average value of the hardness values of all measurement points after granulation, the total number of measurement points after granulation, the total number of measurement points before granulation;

[0039] S512: According to the hardness change amount of the current batch of feed particles, analyze the influence of production process stability on the uniformity of granulated particles, combine the moisture change range of raw materials, judge whether the current processing flow parameters meet the feed processing quality control standard, if not, adjust the granulation pressure setting, and obtain the feed processing quality control result.

[0040] An intelligent feed processing quality control system is used to execute the above-mentioned intelligent feed processing quality control method, and the system comprises:

[0041] The environmental and equipment state monitoring module obtains the current batch of feed production equipment operation parameters and the current environmental temperature and humidity, calculates the equipment operation stability coefficient and the current environmental influence coefficient, and constructs the environmental and equipment state parameter set;

[0042] The processing flow parameter setting module obtains the granulation pressure and steam absorption rate in the current batch of feed production process, and the moisture content and protein content of the current batch of feed raw materials, combines the environmental and equipment state parameter set to calculate the granulation pressure floating range, the steam absorption rate adjustment amount and the feed raw material component variation coefficient, and sets the processing flow parameter threshold interval;

[0043] The processing flow parameter adaptation analysis module judges the granulation pressure deviation state, the steam absorption rate adaptation state and the moisture content adaptation state based on the processing flow parameter threshold interval, and obtains the processing flow parameter adaptation analysis result;

[0044] The production process stability evaluation module analyzes whether the processing flow parameters have abnormal trends and whether the equipment load level matches the current processing flow requirements based on the processing flow parameter adaptation analysis result, and obtains the production process stability analysis result;

[0045] The feed processing quality control module calculates the hardness change amount of the current batch of feed particles based on the production process stability analysis result, judges whether the current processing flow parameters meet the feed processing quality control standard, and obtains the feed processing quality control result.

[0046] Compared with the prior art, the advantages and positive effects of the present application are: ​

[0047] In the present application, by acquiring the running parameters of the production equipment and the environmental temperature and humidity in real time, the environmental and equipment state parameter set is established, so that the influencing factors of the production conditions can be quantified, and accurate basis is provided for subsequent processing process parameter adjustment. In the production process, based on the running stability of the equipment and the fluctuation trend of the raw material composition, the adaptive processing process threshold is calculated, so as to avoid the unstable product quality caused by raw material fluctuation or equipment state change. Combined with the change of steam absorption rate and granulation pressure, it is dynamically judged whether the key indicators in the production process are in the adaptive range, so as to ensure that the processing process control is more fine. For the possible trend deviation in the production process, by analyzing the equipment load level, energy consumption change synchronicity and abnormal trend of the processing process parameters, the stability of the production process is analyzed, so that the adjustment is no longer limited to result feedback, but can actively intervene, and the influence of production fluctuation is reduced. In the finished feed stage, combined with the calculation of the change amount of particle hardness, it is judged whether the processing process meets the quality control requirements, and the granulation pressure is adjusted according to the quality control standard, so as to improve the uniformity and durability of the particles. Through this whole set of dynamic adjustment and real-time optimization strategy, the feed processing quality is effectively guaranteed, the dependence on manual adjustment is reduced, the automation level of production is improved, and the stability and safety of the product are ensured. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 It is a workflow diagram of the present application;

[0049] Figure 2 It is a flowchart of step S1 of the present application;

[0050] Figure 3 It is a flowchart of step S2 of the present application;

[0051] Figure 4 It is a flowchart of step S3 of the present application;

[0052] Figure 5 It is a flowchart of step S4 of the present application;

[0053] Figure 6 It is a flowchart of step S5 of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0055] Please refer to Figure 1 The present application provides a technical scheme: an intelligent feed processing quality control method, comprising the following steps:

[0056] S1: Obtain the running load and energy consumption level of the current batch of feed production equipment, collect the current environmental temperature and humidity, calculate the current environmental influence coefficient based on the environmental temperature and humidity, calculate the equipment running stability coefficient based on the equipment running load and energy consumption level, combine the environmental influence coefficient and the equipment running stability coefficient to construct an environment and equipment state parameter set;

[0057] S2: Obtain the pelleting pressure and steam absorption rate in the current batch of feed production process, as well as the moisture content and protein content of the current batch of feed raw materials, calculate the pelleting pressure floating range based on the equipment running stability coefficient in the environment and equipment state parameter set, calculate the steam absorption rate adjustment amount based on the environmental influence coefficient, combine the current batch of moisture content and protein content to calculate the raw material composition variation coefficient relative to historical production data, combine the pelleting pressure floating range, the steam absorption rate adjustment amount and the raw material composition variation coefficient to set the processing flow parameter threshold interval;

[0058] S3: Based on the processing flow parameter threshold interval, compare the current batch of pelleting pressure with the pelleting pressure floating range to determine the pelleting pressure deviation state, compare the current steam absorption rate with the steam absorption rate adjustment amount to determine the steam absorption rate adaptation state, compare the current moisture content with the raw material composition variation coefficient to determine the moisture content adaptation state, and obtain the processing flow parameter adaptation analysis result;

[0059] S4: Based on the processing flow parameter adaptation analysis result, calculate the cross-validation value of the raw material parameter and the processing flow parameter, analyze whether there is an abnormal trend in the processing flow parameter, analyze the synchronization of equipment running and energy consumption change according to the moisture content adaptation state and the equipment running stability coefficient, and whether the equipment load level matches the current processing flow demand, to obtain the production process stability analysis result;

[0060] S5: Based on the production process stability analysis result, calculate the current batch of feed particle hardness change amount, analyze the influence of production process stability on the uniformity of pelleting particles, including the adaptation degree of steam conditions to the uniformity of pelleting particles and the influence range of raw material moisture change on the durability of particles, determine whether the current processing flow parameter meets the feed processing quality control standard, if not, adjust the pelleting pressure setting, and obtain the feed processing quality control result;

[0061] The environmental and equipment state parameter set includes environmental influence coefficients and equipment operation stability coefficients, the processing flow parameter threshold interval includes granulation pressure floating range, steam absorption rate adjustment amount, and raw material component variation coefficient, the processing flow parameter adaptation analysis result includes granulation pressure deviation state, steam absorption rate adaptation state, and moisture content adaptation state, the production process stability analysis result includes processing flow parameter abnormal trend, equipment operation and energy consumption change synchronicity, and equipment load level matching condition, and the feed processing quality control result includes granulation particle uniformity, steam condition adaptation degree, and influence range of raw material moisture change on particle durability.

[0062] Please refer to Figure 2 The obtaining step of the environmental and equipment state parameter set is specifically as follows:

[0063] S111: Obtain the running load and energy consumption level of the current batch of feed production equipment, call the equipment real-time monitoring data, extract the power consumption data of the equipment and the total energy consumption in a specified time, obtain the instantaneous load rate of the equipment, and collect the current environmental temperature and humidity, extract the temperature and humidity sensor data, record the measurement time and measurement point position, and establish the equipment energy consumption and environmental data set.

[0064] First, install a power sensor on the feed production equipment. The sensor can measure the instantaneous power of the equipment in real time and record the energy consumption data at a set time interval, for example, with a 10-second monitoring period, the power value (unit: kilowatt) of the equipment is obtained once every period. These data are stored in a local database or cloud system. The obtained data set includes time stamp, equipment number, and power reading at the corresponding time. For example, in a certain batch of feed production, the initial power consumption of each equipment is 5.2 kW, the maximum power consumption monitored is 7.8 kW, and the minimum power consumption is 4.9 kW. Based on this data set, the total energy consumption per unit time can be calculated by the following calculation method: in each time interval, the energy consumption can be calculated by the product of power and time, for example, if the power value is 6.5 kW in a 10-second sampling period, the energy consumption of this time period is The total energy consumption of the equipment in the batch production process is obtained by accumulating the energy consumption of all time intervals, such as the total energy consumption which can be calculated as wherein, represents the power consumption of the equipment at the ith moment. The total energy consumption data can be used as the basis for energy consumption optimization analysis, and the instantaneous load rate can be calculated by the ratio of the current power consumption to the rated power of the equipment, for example, if the rated power of the equipment is 8.0 kW, the load rate at a certain moment can be calculated as follows: , which represents the load level of the device at this moment, when collecting the temperature and humidity of the environment, using the temperature and humidity sensor installed in the production workshop, recording the temperature and humidity values every 30 seconds, for example, the temperature recorded at a certain moment is 28.5°C, and the humidity is 65.2%, combining the device power consumption data and environmental data, forming the device power consumption and environmental data set.

[0065] S112: Based on the device energy consumption and environmental data set, the formula is used:

[0066] ;

[0067] Calculate the environmental impact coefficient ;

[0068] Where, is the current temperature (°C); is the optimal temperature (°C), which is the most favorable temperature for the production process; is the temperature sensitivity parameter, which represents the sensitivity of the temperature deviation from the optimal temperature to the processing flow; is the current humidity (%); is the optimal humidity (%), which is the most favorable humidity for the production process; is the humidity sensitivity parameter, which represents the sensitivity of the humidity change to the production, is the natural logarithm function with e as the base.

[0069] First, define the following parameters:

[0070] is the current measured environmental temperature, for example, 30°C.

[0071] is the optimal temperature, assumed to be 25°C.

[0072] is the temperature sensitivity parameter, assumed to be 5°C.

[0073] is the current measured environmental humidity, for example, 75%.

[0074] is the optimal humidity, assumed to be 60%.

[0075] is the humidity sensitivity parameter, assumed to be 10%.

[0076] Calculate the environmental impact coefficient :

[0077] ;

[0078] Put the numerical values into the formula:

[0079] ;

[0080] Calculate each part:

[0081] Temperature part:

[0082] ;

[0083] Humidity part:

[0084] ;

[0085] Final calculation :

[0086] ;

[0087] The resulting environmental impact coefficient is approximately 0.5557, which indicates the degree of deviation of the current environmental conditions from the optimal conditions. A lower value indicates a smaller environmental impact, while a higher value suggests the need to adjust environmental or equipment parameters to meet production requirements.

[0088] S113: Based on the device energy consumption and environmental data set, use the formula:

[0089] ;

[0090] Calculate the device operation stability coefficient ;

[0091] where represents the device power consumption at the i-th moment, represents the average device power consumption, represents the number of sampling points.

[0092] Suppose in a certain batch production, power data is monitored every 10 seconds, resulting in a power consumption data set (unit: kW), calculate the average power consumption : . Then calculate the power consumption deviation rate at each moment: , which indicates the stability of the device operation. A higher value indicates greater fluctuations in power consumption, which may indicate problems such as uneven device load. Finally, the device operation stability coefficient is obtained.

[0093] S114: Normalize the environmental impact coefficient and the device operation stability coefficient to construct the environmental and device state parameter set;

[0094] First, determine the environmental impact coefficient and the device operation stability coefficient​ the reference range of the environmental influence coefficient is determined through historical production data analysis, based on data from multiple production batches, the relationship between temperature and humidity deviation and production stability is statistically analyzed, when the environmental influence coefficient is less than 0.2, the production and processing flow fluctuation is small, and the feed quality remains stable, therefore, the range of 0-0.2 is set as the reference range of the equipment operation stability coefficient Through the analysis of the fluctuation of equipment energy consumption, the power consumption fluctuation rate is calculated by monitoring the operation data of multiple batches of equipment, when is less than 0.1, the equipment operation state is stable, and the power fluctuation will not have a significant impact on production, therefore, the range of 0-0.1 is selected as the reference range of the equipment operation stability coefficient.

[0095] Then the environmental influence coefficient and the equipment operation stability coefficient are normalized, the calculation method is as follows:

[0096] ;

[0097] wherein, denotes the normalized environmental influence coefficient, denotes the normalized equipment operation stability coefficient, the normalization processing makes the data of different dimensions can be directly compared, the calculation result is substituted into:

[0098] ;

[0099] Then the comprehensive influence factor of environment and equipment is calculated, the average value of the two is adopted:

[0100] ;

[0101] This value is used to measure the overall influence of the current environment and equipment operation state, if exceeds a certain threshold value, for example, 0.8, it may be necessary to adjust the environmental parameters or optimize the equipment operation state, and finally the environment and equipment state parameter set is constructed.

[0102] Please refer to Figure 3 , the acquisition steps of the processing flow parameter threshold interval are as follows:

[0103] S211: Obtain the pelleting pressure and steam absorption rate in the current batch of feed production process, call the raw material detection record of the current batch of feed, based on the equipment operation stability coefficient in the environment and equipment state parameter set, adopt the formula:

[0104] ;

[0105] Floating range of pelleting pressure ;

[0106] Wherein, represents the average value of pelleting pressure, represents the equipment operation stability coefficient.

[0107] Obtain the pelleting pressure and steam absorption rate in the current batch of feed production process, as well as the moisture content and protein content of the current batch of feed raw materials, call the pressure sensor data of the feed pelletizer, extract the pelleting pressure in different time periods, and calculate the average value and fluctuation range of the pressure. In actual production process, the collection of pelleting pressure usually adopts high-precision sensor, collecting data once per second. For example, in the production process of a certain batch, the pressure sensor of a certain pelleting machine records the following data:

[0108] Table 1 Pelleting pressure data record table

[0109] ;

[0110] Calculate the data to obtain the average value of pressure: At the same time, calculate the fluctuation range between the maximum value and the minimum value, get the maximum pressure MPa, the minimum pressure MPa, the fluctuation range: Finally, combined with the equipment operation stability coefficient Calculate the floating range of pelleting pressure, using the formula: Finally, the floating range of pelleting pressure is obtained.

[0111] Based on the environmental influence coefficient in the set of environmental and equipment state parameters, using the formula:

[0112] ;

[0113] Calculate the steam absorption rate adjustment amount ;

[0114] Wherein, represents the standard steam absorption rate.

[0115] The steam supply data of the current batch measured by the flow meter is as follows:

[0116] Table 2 Steam supply and absorption record table

[0117] ;

[0118] Calculate the standard steam absorption rate: ;

[0119] Combining the environmental impact coefficient , the steam absorption rate adjustment amount is calculated by using the formula: , and finally the steam absorption rate adjustment amount is obtained.

[0120] According to the moisture content and protein content of the current batch of feed raw materials, the formula is used:

[0121] ;

[0122] Calculate the raw material component variation coefficient relative to the historical production data ;

[0123] Among them, represents the moisture content or protein content of the first batch of raw materials, represents the maximum value, represents the minimum value, represents the sample size. Call the current batch of production data, and the recorded moisture content and protein content data are as follows:

[0124] Table 3 Raw material component content record table

[0125]

[0126] ; Calculate the raw material component variation coefficient by using the formula:

[0127] ; The coefficient of variation of moisture content is calculated as follows: ; The coefficient of variation of protein content is calculated as follows: ; and finally the raw material component variation coefficient is obtained. S212: Combine the granulation pressure floating range, the steam absorption rate adjustment amount and the raw material component variation coefficient to set the processing flow parameter threshold interval;

[0128]

[0129] ​In combination with the granulation pressure floating range, the steam absorption rate adjustment amount, and the raw material component variation coefficient, the processing flow parameter threshold interval is set. First, based on the granulation pressure floating range, the allowable pressure range is set. In the process of feed granulation, the pressure directly affects the particle density and strength. If the pressure is too low (less than 1.16 MPa), the particles may not be formed, resulting in an increase in the powder rate. If the pressure is too high (higher than 1.36 MPa), the particle hardness may be too large, affecting the palatability of the feed. Therefore, the interval [1.16, 1.36] MPa is set to maintain the stability of the particle quality. Second, based on the steam absorption rate adjustment amount, the target steam flow range is set. The steam absorption rate reflects the moisture and heat acquisition in the feed conditioning process. If the absorption rate is too low (less than 0.78), the feed temperature may be insufficient, affecting the starch gelatinization rate and thus the particle strength. If the absorption rate is too high (higher than 0.83), the moisture content of the feed may exceed the standard, increasing the risk of mold during storage. Therefore, the absorption rate interval [0.78, 0.83] is set. Finally, based on the raw material component variation coefficient, the allowable error range of the raw material ratio is adjusted. The moisture content variation coefficient 0.0328 indicates that the current batch moisture fluctuation range is small. Therefore, the control range of the moisture content is set to [11.8%, 12.6%]. The protein content variation coefficient 0.0261 indicates that the protein fluctuation is small. The allowable range of the protein is set to [15.0%, 15.6%]. The setting of this interval ensures that the nutritional components of the feed fluctuate within a reasonable range. Finally, all parameters are integrated to establish the processing flow parameter threshold interval.

[0130] Referring to Figure 4 , the obtaining step of the processing flow parameter adaptation analysis result is specifically:

[0131] S311: Based on the processing flow parameter threshold interval, the current batch granulation pressure is compared with the granulation pressure floating range. The pressure values at all time points in the granulation process are extracted. The deviation rates at multiple times are compared. If the deviation rate at a certain time exceeds the preset deviation threshold, it is judged that the granulation pressure exceeds the normal range, and the granulation pressure deviation state is obtained.

[0132] Based on the processing flow parameter threshold interval, the current batch granulation pressure is compared with the granulation pressure floating range. The current batch granulation pressure data is obtained, and the pressure values at each time point in the granulation process are extracted. For example, the pressure record of a certain batch production is as follows:

[0133] Table 4 Current batch granulation pressure data record table

[0134] ;

[0135] The current batch data is compared with the granulation pressure floating range [1.16, 1.36] MPa to judge the granulation pressure deviation state. The method of calculating the deviation rate is adopted. The deviation rate formula is as follows:

[0136] ;

[0137] wherein, MPa, calculate the offset rate at a certain time (for example, 10s time point):

[0138] ;

[0139] wherein, represent the offset rate of the granulation pressure, represent the current time of the granulation pressure, represent the mean value of the granulation pressure floating range, which is used to judge whether the current pressure is out of the set range. If the offset rate at a certain time exceeds the preset offset threshold, for example, 0.1, it is judged that the granulation pressure is out of the normal range, and if all time points are within the range, it is judged that the pressure is adapted, and the granulation pressure offset state is finally obtained.

[0140] S312: Based on the processing flow parameter threshold interval, compare the current steam absorption rate with the steam absorption rate adjustment amount, calculate the current batch steam absorption rate and compare it with the steam absorption rate adjustment amount, if the current absorption rate is within the interval of the steam absorption rate adjustment amount, it is judged as the adaptation state, if it exceeds the interval, it is judged as the deviation state, and the steam absorption rate adaptation state is obtained;

[0141] Compare the current steam absorption rate with the steam absorption rate adjustment amount to judge the steam absorption rate adaptation state, extract the current batch steam absorption rate, calculate the actual absorption of steam by feed during the production process of the batch, and the steam absorption rate of the current batch is calculated as follows:

[0142] Table 5 Current batch steam absorption rate data record table

[0143] ;

[0144] Calculate the steam absorption rate of the current batch:

[0145] ;

[0146] ;

[0147] wherein, represent the current steam absorption rate, represent the steam absorption amount at the k time point, represent the steam supply amount at the k time point, and the summation symbol represents the cumulative steam data of 10 time points, which is used to calculate the overall steam absorption rate.

[0148] The value is compared with the steam absorption rate adjustment amount [0.78, 0.83], and if the current absorption rate is within the interval, it is judged to be in the adaptive state, and if it is out of the interval, it is judged to be in the deviation state, and finally the steam absorption rate adaptive state is obtained.

[0149] S313: Based on the processing flow parameter threshold interval, the current moisture content is compared with the raw material component variation coefficient. The moisture content data of the current batch is extracted, and compared with the variation range of the moisture content of the current batch. If the current moisture variation value is less than the set variation coefficient, it is judged that the moisture content is adaptive, and if it exceeds the variation coefficient, it is judged to be in the deviation state. The moisture content adaptive state is obtained, combined with the granulation pressure offset state and the steam absorption rate adaptive state, to obtain the processing flow parameter adaptive analysis result;

[0150] The current moisture content is compared with the raw material component variation coefficient to judge the moisture content adaptive state. The moisture content data of the current batch is extracted, and compared with the variation range of the moisture content of the current batch. Assuming that the current batch moisture content record is as follows:

[0151] Table 6 Current batch moisture content data record table

[0152] ;

[0153] The variation range of the moisture content of the current batch is calculated:

[0154] ;

[0155] ;

[0156] Wherein, represents the variation coefficient of the moisture content of the current batch, represents the moisture content of the kth batch, represents the maximum moisture content, represents the minimum moisture content, represents the number of sample batches, and the formula calculates the fluctuation of the moisture content of the current batch and judges its adaptability.

[0157] The value is compared with the raw material component variation coefficient [0.0328], and if the current moisture variation value is less than the set variation coefficient, it is judged that the moisture content is adaptive, and if it exceeds the value, it is judged to be in the deviation state, and finally the moisture content adaptive state is obtained, and combined with all parameters, the processing flow parameter adaptive analysis result is obtained.

[0158] Please refer to Figure 5 , the steps of obtaining the production process stability analysis result are as follows:

[0159] S411: Based on the results of the processing flow parameter adaptation analysis, obtain the key processing flow parameters in the current batch production process, including granulation pressure, moisture content, and steam absorption rate, analyze whether there are abnormal trends in the processing flow parameters, and obtain the abnormal trends of the processing flow parameters.

[0160] Historical data of key processing parameters during the current batch production process are obtained, including granulation pressure, moisture content, and steam absorption rate. Their changing trends across different production batches are calculated. The processing parameters for the current batch are extracted, and the rate of change of these parameters is calculated using a moving average method. A threshold range is set to determine if there are any abnormal trends in the current processing parameters, using the following formula:

[0161] ;

[0162] ;

[0163] in, This represents the offset rate of the processing parameters. This represents the processing parameter value for the current batch. This represents the processing parameter value for batch g. The summation symbol represents the number of sample batches. The summation symbol indicates that the data from q batches are used to calculate the overall trend. This formula is used to determine whether the current processing parameters exceed the normal range of variation.

[0164] If the M offset exceeds the preset threshold (e.g., 0.03), it is determined that there is an abnormal trend in the processing parameters, and the abnormal trend status of the processing parameters is finally obtained.

[0165] S412: Based on the moisture content adaptation status and equipment operation stability coefficient, analyze the synchronicity of equipment operation and energy consumption changes, and whether the equipment load level matches the current processing flow requirements. Combine the abnormal trends of processing flow parameters to obtain the production process stability analysis results.

[0166] Extract the power consumption data of the current batch of equipment and, in conjunction with the changes in moisture content, calculate the impact of moisture content adaptation on equipment operation using the following formula:

[0167] ;

[0168] ;

[0169] in, This represents the synchronicity between equipment operation and changes in energy consumption. This represents the power consumption of the current batch of equipment. This represents the average power consumption of the equipment. This represents the moisture content of the current batch. This represents the average moisture content. This formula is used to assess the impact of moisture content fluctuations on equipment operation and to determine whether the equipment load level matches the processing requirements, ultimately establishing the results of the production process stability analysis.

[0170] If Z synchronization exceeds the set threshold (e.g., 0.05), it indicates that the equipment operation and energy consumption changes are not synchronized. At the same time, the equipment load level offset rate is calculated. If the equipment load offset rate exceeds the preset range, it is determined that the equipment operation status is abnormal, and the final production process stability analysis result is obtained.

[0171] Please see Figure 6 The specific steps for obtaining feed processing quality control results are as follows:

[0172] S511: Based on the results of the production process stability analysis, the following formula is used:

[0173] ;

[0174] Calculate the change in pellet hardness for the current batch of feed. ;

[0175] in, It is the first The hardness value at each measurement point after granulation. It is the first The hardness value at each measurement point before granulation. It is the average hardness value of all measured points before granulation. It is the average of the hardness values ​​measured at all points after granulation. This is the total number of measurement points after granulation. This represents the total number of measurement points before granulation.

[0176] Given the measurement data:

[0177] Hardness value before granulation: N;

[0178] Hardness value after granulation: N;

[0179] Calculate the average:

[0180] ;

[0181] ;

[0182] calculate :

[0183] ;

[0184] This result The change amount of hardness in the pelleting process indicates the change of hardness, which provides scientific data support for quality monitoring and process optimization in production process.

[0185] S512: According to the change amount of the current batch of feed pellet hardness, analyze the influence of production process stability on the uniformity of pelleted particles, and judge whether the current processing process parameters meet the feed processing quality control standard combined with the moisture content range of raw materials. If not, adjust the pelleting pressure setting to obtain the feed processing quality control result;

[0186] First, the steam condition data needs to be obtained, including steam pressure (MPa) and temperature (℃). Assuming that the measured steam pressure is MPa, and the steam temperature is Then, based on the steam condition data, the distribution interval of pellet hardness is calculated. The adaptive interval of pellet hardness under different steam parameters is set as follows:

[0187] Table 7 Steam pressure and feed pellet hardness distribution

[0188] ;

[0189] As shown in Table 1, the hardness change range under different steam pressures shows a clear trend. The measured hardness change amount N under the current processing process conditions is consistent with the hardness change amount under the steam conditions corresponding to MPa in the table, indicating that the steam adaptation degree is high.

[0190] Further analysis combined with the moisture content range of raw materials, assuming that the moisture content of the current batch of raw materials before and after pelleting is 12% and 10% respectively, the interval of the influence of moisture change on pellet durability is calculated. The influence of moisture on durability can be expressed as:

[0191] ;

[0192] Where, : The influence of moisture change on pellet durability (unit: %), indicating the influence degree of moisture change in the pelleting process on pellet quality and stability. : Moisture content of raw materials before pelleting (unit: %), indicating the proportion of water in raw materials before pelleting, the current measurement value is 12%. : Moisture content of pellets after pelleting (unit: %), indicating the proportion of water in feed pellets after pelleting, the current measurement value is 10%.

[0193] If the collected parameters are , , the calculation is:

[0194] ;

[0195] The durability impact is 16.67%, indicating that the reduction in raw material moisture content has a certain impact on particle durability. The granulation pressure setting needs to be adjusted in conjunction with processing parameters. If the current pressure is too low, resulting in uneven hardness distribution, the pressure can be appropriately adjusted. The pressure (MPa) will be monitored, and feed processing quality will continue to be monitored to obtain the final feed processing quality control results. The impact of a 16.67% decrease in moisture on pellet durability needs to be analyzed in conjunction with the effect of moisture on the physical properties of feed pellets. Moisture plays a role in binding, lubrication, and plasticization during feed pelleting, affecting the hardness, durability, and disintegration rate of the pellets. Excessive or insufficient moisture reduction may affect the final product quality. The following is a hypothetical analysis: When the moisture reduction is moderate (usually within the range of 10%-20%), the binding properties of the feed pellets can still be maintained, the pellet hardness is relatively stable, and it is not easily broken. If the moisture reduction is too large (>25%), it may lead to a decrease in the internal binding force of the pellets, making the pellets brittle, reducing durability, and making them prone to breakage during transportation or storage. If the moisture reduction is too small (<5%), the pellet moisture content is too high, which may lead to softening of the pellets, or even spoilage due to the growth of mold or bacteria during storage.

[0196] An intelligent feed processing quality control system is provided, which is used to execute the above-mentioned intelligent feed processing quality control method. The system includes:

[0197] The environmental and equipment status monitoring module acquires the operating parameters of the current batch of feed production equipment and the current ambient temperature and humidity, calculates the equipment operating stability coefficient and the current environmental impact coefficient, and constructs a set of environmental and equipment status parameters.

[0198] The processing parameters setting module obtains the pelleting pressure and steam absorption rate during the production process of the current batch of feed, as well as the moisture content and protein content of the feed raw materials in the current batch. It calculates the pelleting pressure fluctuation range, steam absorption rate adjustment amount, and feed raw material composition variation coefficient in combination with the environmental and equipment status parameter set, and sets the processing parameters threshold range.

[0199] The processing flow parameter adaptation analysis module determines the granulation pressure deviation state, steam absorption rate adaptation state, and moisture content adaptation state based on the threshold range of processing flow parameters, and obtains the processing flow parameter adaptation analysis results.

[0200] The production process stability assessment module analyzes whether there are abnormal trends in the processing parameters and whether the equipment load level matches the current processing requirements based on the processing parameters adaptation analysis results, and obtains the production process stability analysis results.

[0201] The feed processing quality control module calculates a current batch feed pellet hardness variation amount based on production process stability analysis results, judges whether the current processing flow parameters meet the feed processing quality control standards, and obtains a feed processing quality control result.

[0202] The above merely describes the preferred embodiments of the present application, but is not intended to limit the present application in other forms. Any person skilled in the art can modify or change the above disclosed technical content or apply it to other fields as equivalent embodiments with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution content of the present application and in accordance with the technical essence of the present application still belongs to the protection scope of the technical solution of the present application.

Claims

1. A method of intelligent feed processing quality control, characterized by, Comprise the following steps: S1: Obtain the current batch feed production equipment operating parameters and current environmental temperature and humidity, calculate the equipment running stability coefficient and the current environmental influence coefficient, and construct the environment and equipment state parameter set; S2: Obtain the pelleting pressure and steam absorption rate in the current batch feed production process, and the moisture content and protein content of the current batch feed raw materials, calculate the pelleting pressure floating range, steam absorption rate adjustment amount and feed raw material component variation coefficient combined with the environment and equipment state parameter set, and set the processing flow parameter threshold interval; S3: Based on the processing flow parameter threshold interval, respectively judge the pelleting pressure deviation state, steam absorption rate adaptation state and moisture content adaptation state, and obtain the processing flow parameter adaptation analysis result; S4: Based on the processing flow parameter adaptation analysis result, analyze whether the processing flow parameter has an abnormal trend, and whether the equipment load level matches the current processing flow demand, and obtain the production process stability analysis result; S5: Based on the production process stability analysis result, calculate the current batch feed pellet hardness change amount, judge whether the current processing flow parameter meets the feed processing quality control standard, and obtain the feed processing quality control result.

2. The intelligent feed processing quality control method according to claim 1, characterized in that, The environment and equipment state parameter set includes environmental influence coefficient and equipment running stability coefficient, the processing flow parameter threshold interval includes pelleting pressure floating range, steam absorption rate adjustment amount and raw material component variation coefficient, the processing flow parameter adaptation analysis result includes pelleting pressure deviation state, steam absorption rate adaptation state and moisture content adaptation state, the production process stability analysis result includes processing flow parameter abnormal trend, equipment running and energy consumption change synchronization and equipment load level matching condition, and the feed processing quality control result includes pelleting particle uniformity, steam condition adaptation degree and influence range of raw material moisture change on pellet durability.

3. The intelligent feed processing quality control method according to claim 2, wherein, The acquisition step of the environment and equipment state parameter set is specifically: S111: Obtain the running load and energy consumption level of the current batch feed production equipment, call the equipment real-time monitoring data, extract the power consumption data and total energy consumption in a specified time of the equipment, obtain the instantaneous load rate of the equipment, and collect the current environmental temperature and humidity, extract the temperature and humidity sensor data, record the measurement time and measurement point position, and establish the equipment energy consumption and environment data set; S112: Based on the equipment energy consumption and environment data set, the formula is: ； Computing environmental impact coefficient ; wherein, is the current temperature, is the optimum temperature, is the current temperature is the sensitivity parameter of the current temperature, is the current humidity, is the optimum humidity, is the current humidity is the sensitivity parameter of the current humidity, is the natural logarithm function with base e; S113: Based on the equipment energy consumption and environment data set, the formula is: ； Computing device running stability coefficient ; wherein, representing the first device power consumption at the time instant, representing the mean value of the device power consumption, representing the number of sampling points; S114: Normalize the environmental influence coefficient and equipment running stability coefficient to construct the environment and equipment state parameter set.

4. The intelligent feed processing quality control method according to claim 3, wherein, The acquisition step of the processing flow parameter threshold interval is specifically: S211: Obtain the pelleting pressure and steam absorption rate in the current batch of feed production process, call the raw material detection record of the current batch of feed, obtain the moisture content and protein content of the raw material of the current batch of feed, calculate the pelleting pressure floating range based on the equipment running stability coefficient in the environment and equipment state parameter set, calculate the steam absorption rate adjustment amount based on the environmental influence coefficient in the environment and equipment state parameter set, calculate the raw material ingredient variation coefficient relative to the historical production data according to the moisture content and protein content of the raw material of the current batch of feed; S212: Combine the pelleting pressure floating range, steam absorption rate adjustment amount and raw material ingredient variation coefficient to set the processing flow parameter threshold interval.

5. The intelligent feed processing quality control method according to claim 4, wherein, The acquisition step of the processing flow parameter adaptation analysis result is specifically: S311: Based on the processing flow parameter threshold interval, compare the current batch pelleting pressure with the pelleting pressure floating range, extract the pressure value at all time points in the pelleting process, compare the offset rates at multiple times, if the offset rate at a certain time exceeds the preset offset threshold, it is judged that the pelleting pressure exceeds the normal range, and the pelleting pressure offset state is obtained; S312: Based on the processing flow parameter threshold interval, compare the current steam absorption rate with the steam absorption rate adjustment amount, calculate the current batch steam absorption rate and compare it with the steam absorption rate adjustment amount, if the current absorption rate is within the interval of the steam absorption rate adjustment amount, it is judged as an adaptation state, if it exceeds the interval, it is judged as a deviation state, and the steam absorption rate adaptation state is obtained; S313: Based on the processing flow parameter threshold interval, compare the current moisture content with the raw material ingredient variation coefficient, compare the current batch moisture content data with the variation range of the current batch moisture content, if the current moisture variation value is less than the set variation coefficient, it is judged that the moisture content is adapted, if it exceeds the variation coefficient, it is judged as a deviation state, the moisture content adaptation state is obtained, and the processing flow parameter adaptation analysis result is obtained by combining the pelleting pressure offset state and the steam absorption rate adaptation state.

6. The intelligent feed processing quality control method according to claim 5, wherein, The acquisition step of the production process stability analysis result is specifically: S411: Based on the processing flow parameter adaptation analysis result, obtain the key processing flow parameters in the current batch production process, including the pelleting pressure, moisture content and steam absorption rate, analyze whether there is an abnormal trend of the processing flow parameter, and obtain the processing flow parameter abnormal trend; S412: According to the moisture content adaptation state and the equipment running stability coefficient, analyze the synchronization of equipment running and energy consumption change, and whether the equipment load level matches the current processing flow demand, combine the processing flow parameter abnormal trend to obtain the production process stability analysis result.

7. The intelligent feed processing quality control method according to claim 6, wherein, The acquisition step of the feed processing quality control result is specifically: S511: Based on the production process stability analysis result, use the formula: ； calculating a current batch feed pellet hardness change amount ; wherein, is the hardness value of the first measurement point after granulation, is the hardness value of the first measurement point before granulation, is the average of the hardness values of all measurement points before granulation, is the average of the hardness values of all measurement points after granulation, is the total number of measurement points after granulation, is the total number of measurement points before granulation; S512: According to the current batch of feed particle hardness change amount, analyze the influence of production process stability on the uniformity of granulation particles, combine the moisture content variation range of raw materials, judge whether the current processing flow parameter meets the feed processing quality control standard, if not, adjust the granulation pressure setting, and get the feed processing quality control result.

8. An intelligent feed processing quality control system, characterized by, The intelligent feed processing quality control method according to any one of claims 1-7, the system comprises: The environmental and equipment state monitoring module obtains the current batch of feed production equipment operating parameters and the current environmental temperature and humidity, calculates the equipment operation stability coefficient and the current environmental influence coefficient, and constructs the environmental and equipment state parameter set; The processing flow parameter setting module obtains the granulation pressure and steam absorption rate in the current batch of feed production process, as well as the moisture content and protein content of the current batch of feed raw materials, combines the environmental and equipment state parameter set to calculate the granulation pressure floating range, the steam absorption rate adjustment amount and the feed raw material component variation coefficient, and sets the processing flow parameter threshold interval; The processing flow parameter adaptation analysis module judges the granulation pressure deviation state, the steam absorption rate adaptation state and the moisture content adaptation state based on the processing flow parameter threshold interval, respectively, and obtains the processing flow parameter adaptation analysis result; The production process stability evaluation module analyzes whether the processing flow parameter has an abnormal trend and whether the equipment load level matches the current processing flow demand based on the processing flow parameter adaptation analysis result, and obtains the production process stability analysis result; The feed processing quality control module calculates the current batch of feed particle hardness change amount based on the production process stability analysis result, judges whether the current processing flow parameter meets the feed processing quality control standard, and obtains the feed processing quality control result.

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