A method and system for optimizing the production of animal blood protein powder

Abstract

The invention discloses an animal blood protein powder production optimization method and system, which specifically relates to the field of production monitoring technology, and is used to solve the problem that it is difficult to effectively identify the potential hidden dangers of the animal blood protein powder production process caused by cavitation effect caused by ultrasound; it is by capturing the harmonic characteristics of ultrasonic signals and combining with a spectrum analysis algorithm to identify acoustic energy intensive areas; constructing a turbulence disturbance intensity graph model, and combining with a graph embedding learning algorithm to analyze the changes in dissolved gas distribution characteristics; based on the first and second potential risk areas identified above, determining the cavitation potential risk area; in the cavitation potential risk area, by analyzing the complexity of the bubble interaction kinetic energy distribution and the degree of diffusion of local high temperature, evaluating the comprehensive influence of the bubble group synergy effect and thermal effect; finally, by calculating the cavitation influence coefficient, determining the cavitation hidden danger caused by ultrasound, and optimizing the production process parameters according to the determination result.

Description

Technical Field

[0001] The present invention relates to the technical field of production monitoring, and more specifically, to a method and system for optimizing the production of animal blood protein powder. Background Art

[0002] Animal blood is an important by-product produced during the slaughter of livestock and poultry. The protein in the blood has good nutritional value and functional properties and is widely used in food processing, feed additives and other fields. In the production process of animal blood protein powder, while using ultrasonic treatment technology to improve production efficiency, there is also a cavitation effect; the cavitation effect refers to the formation and collapse of bubbles due to local pressure changes in liquid under the action of ultrasound. In the existing technology, the cavitation effect may cause uneven sound field distribution and turbulent disturbances during the production process, resulting in regional differences in the treatment effect.

[0003] If the impact of ultrasonic cavitation on the production of animal blood protein powder is not discovered in time, it may lead to insufficient processing of the target area or over-processing of the non-target area, thereby destroying the structural stability, gelation and functional characteristics of the protein. At the same time, local high temperature diffusion may cause protein denaturation or product performance degradation, affecting the production efficiency and product quality consistency of animal blood protein powder.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an animal blood protein powder production optimization method and system to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for optimizing the production of animal blood protein powder comprises the following steps:

[0008] Capture the harmonic characteristics of ultrasonic signals and use spectrum analysis algorithms to evaluate areas with dense acoustic energy, identifying the first potential risk area;

[0009] A turbulence disturbance intensity graph model is constructed, and the changes in the dissolved gas distribution characteristics in the turbulence disturbance intensity graph model are analyzed through a graph embedding learning algorithm to identify the second potential risk area.

[0010] Determining a cavitation potential risk area based on the first potential risk area and the second potential risk area;

[0011] By analyzing the complexity of the distribution of bubble interaction kinetic energy in the potential cavitation risk area, the cumulative impact of the synergistic effect of the bubble group on the target area is identified;

[0012] By analyzing the extent of the spread of local high temperature when bubbles collapse in the potential risk area of ​​cavitation, the possibility of thermal effects spreading to non-target areas can be identified;

[0013] Through a comprehensive analysis of the cumulative impact of the synergistic effect of bubble groups on the target area and the possibility of thermal effects diffusing to non-target areas, the hidden dangers of ultrasound-induced cavitation effect on the production process of animal blood protein powder are evaluated, and the production process of animal blood protein powder is optimized based on the evaluation results.

[0014] In a preferred embodiment, the harmonic characteristics of the ultrasonic signal are captured, and the acoustic energy intensive area is evaluated in combination with a spectrum analysis algorithm to identify the first potential risk area, specifically:

[0015] Collect the ultrasonic signal in the ultrasonic treatment process in real time to obtain the original data of the ultrasonic signal;

[0016] Perform signal processing on the raw data to extract the harmonic characteristics of the ultrasonic signal, which include the fundamental frequency, the amplitude of each harmonic and the phase of each harmonic;

[0017] Apply spectrum analysis algorithm to perform spectrum analysis on harmonic characteristics and calculate the energy distribution of ultrasonic signals at different frequencies;

[0018] According to the energy distribution of ultrasonic signals at different frequencies, the acoustic energy intensive area is obtained, and the distribution of acoustic energy in space is determined;

[0019] A first potential risk area is identified and marked based on the spatial distribution of acoustic energy.

[0020] In a preferred embodiment, a turbulence disturbance intensity graph model is constructed, and the changes in the dissolved gas distribution characteristics in the turbulence disturbance intensity graph model are analyzed by a graph embedding learning algorithm to identify the second potential risk area, specifically:

[0021] Obtaining dissolved gas concentrations at various sampling points in the liquid medium during animal blood processing as raw data on dissolved gas distribution;

[0022] The turbulence disturbance intensity of each sampling point is calculated based on the dissolved gas concentration to form a turbulence disturbance intensity data set;

[0023] The turbulence disturbance intensity graph model is constructed by treating each sampling point as a node, and the connection relationship is established according to the difference in turbulence disturbance intensity between nodes;

[0024] The graph embedding learning algorithm is used to analyze the turbulence disturbance intensity graph model, extract the intrinsic correlation between turbulence disturbance and dissolved gas distribution, and generate the feature representation of each node;

[0025] According to the characteristic representation of each node, the nodes are grouped using a clustering algorithm to identify and mark the second potential risk area.

[0026] In a preferred embodiment, the calculation formula for turbulence disturbance intensity is: ;in, Indicates The turbulence intensity at each sampling point is represents the total number of sampling time points, Indicates sampling points at time The dissolved gas concentration at Indicates The average dissolved gas concentration at each sampling point during the entire sampling period is Indicates The sampling time, Indicates the number of the sampling time.

[0027] In a preferred embodiment, based on the first potential risk area and the second potential risk area, the cavitation potential risk area is determined, specifically:

[0028] The union of the first potential risk area and the second potential risk area is marked as a cavitation potential risk area.

[0029] In a preferred embodiment, the cumulative impact of the synergistic effect of the bubble group on the target area is identified by analyzing the complexity of the distribution of the kinetic energy of the bubble interaction in the potential risk area of ​​cavitation, specifically:

[0030] Obtaining physical property data of bubbles in the potential cavitation risk area, the physical property data including the mass and velocity of each bubble, and recording the distance and relative velocity between the bubbles;

[0031] Calculate the kinetic energy of each bubble based on its mass and velocity;

[0032] The interaction energy between bubbles is calculated based on the interaction coefficient, the relative speed and distance between bubbles;

[0033] Calculate the interaction kinetic energy distribution complexity index: ;in, is the interaction kinetic energy distribution complexity index, Indicates Bubbles and The interaction energy between the bubbles, represents the average value of the interaction energy between bubbles, Indicates the total number of bubbles in the potential cavitation risk area, Indicates the potential risk area for cavitation. bubbles, Indicates the potential risk area for cavitation. bubbles.

[0034] In a preferred embodiment, the possibility of thermal effects spreading to non-target areas is identified by analyzing the degree of diffusion of local high temperature when bubbles collapse in the potential risk area of ​​cavitation, specifically:

[0035] Collect the instantaneous temperature peak near each bubble in real time to obtain the highest temperature value generated when the bubble collapses;

[0036] By analyzing the attenuation of the high temperature generated by bubble collapse in the surrounding medium, the diffusion radius of the bubble high temperature is calculated;

[0037] Among them, high temperature is the transient maximum temperature measured in the vicinity of the bubble at the moment of bubble collapse;

[0038] The cumulative temperature change in the non-target area is obtained by superposition method;

[0039] Compare the accumulated temperature change with the temperature change threshold to determine which positions in the non-target area have temperature changes exceeding the critical value; the set of positions where the accumulated temperature change is greater than the temperature change threshold represents the affected area of ​​the thermal effect;

[0040] For the overall impact on non-target areas, calculate the diffuse impact ratio: ;in, represents the diffusion impact ratio, represents the total spatial extent of the non-target area, Indicates the area affected by thermal effects.

[0041] In a preferred embodiment, the potential risks of ultrasonic-induced cavitation on the production process of animal blood protein powder are evaluated by comprehensively analyzing the cumulative impact of the synergistic effect of the bubble group on the target area and the possibility of the thermal effect diffusing to the non-target area, specifically:

[0042] The interaction kinetic energy distribution complexity index and diffusion influence ratio are dimensionlessly processed and assigned weight coefficients respectively, and the cavitation influence coefficient is calculated.

[0043] Set the cavitation impact threshold and compare the cavitation impact coefficient with the cavitation impact threshold:

[0044] When the cavitation influence coefficient is greater than the cavitation influence threshold, it is determined that a large cavitation hidden danger signal is generated;

[0045] When the cavitation influence coefficient is less than or equal to the cavitation influence threshold, it is determined that a normal signal of cavitation hidden danger is generated;

[0046] When a large cavitation hazard signal is generated, the animal blood protein powder production process is optimized, including adjusting the ultrasonic power, changing the ultrasonic frequency, improving the flow state of the liquid medium, and reconfiguring the sensor layout in the production equipment.

[0047] On the other hand, the present invention provides an animal blood protein powder production optimization system, including a harmonic feature capture module, a turbulence intensity analysis module, a cavitation region determination module, a synergistic effect evaluation module, a thermal effect identification module, and a cavitation hidden danger evaluation module;

[0048] Harmonic feature capture module: captures the harmonic features of ultrasonic signals, and combines spectrum analysis algorithms to evaluate acoustic energy-intensive areas, identifying the first potential risk area;

[0049] Turbulence intensity analysis module: constructs a turbulence disturbance intensity graph model, analyzes the changes in the dissolved gas distribution characteristics in the turbulence disturbance intensity graph model through a graph embedding learning algorithm, and identifies the second potential risk area;

[0050] A cavitation area determination module: determining a cavitation potential risk area based on the first potential risk area and the second potential risk area;

[0051] Synergistic effect evaluation module: By analyzing the complexity of the distribution of bubble interaction kinetic energy in the potential cavitation risk area, the cumulative impact of the synergistic effect of the bubble group on the target area is identified;

[0052] Thermal effect identification module: By analyzing the diffusion degree of local high temperature when bubbles collapse in the potential cavitation risk area, the possibility of thermal effect diffusion to non-target areas is identified;

[0053] Cavitation hazard assessment module: Through a comprehensive analysis of the cumulative impact of the synergistic effect of bubble groups on the target area and the possibility of thermal effects diffusing to non-target areas, the hazard of ultrasonic-induced cavitation effect on the production process of animal blood protein powder is assessed, and the production process of animal blood protein powder is optimized based on the assessment results.

[0054] Technical effects and advantages of the animal blood protein powder production optimization method and system of the present invention:

[0055] 1. The present invention captures the harmonic characteristics of ultrasonic signals and combines them with a spectrum analysis algorithm to accurately evaluate areas with dense acoustic energy and identify the first potential risk area. At the same time, the turbulence disturbance intensity graph model and graph embedding learning algorithm are used to conduct an in-depth analysis of the dissolved gas distribution characteristics to identify the second potential risk area. Based on the first potential risk area and the second potential risk area, the cavitation potential risk area is determined. This combined evaluation method realizes a comprehensive analysis of the acoustic field and flow field characteristics, makes up for the limitations of single-dimensional identification, greatly improves the recognition accuracy of potential risk areas for cavitation effects, ensures full coverage of cavitation areas, and provides a reliable basis for subsequent hidden danger assessments.

[0056] 2. In the potential risk area of ​​cavitation, by analyzing the complexity of the distribution of kinetic energy of bubble interactions, the cumulative impact of the synergistic effect of bubble groups on the target area is identified; at the same time, the degree of diffusion of local high temperature when the bubbles collapse is analyzed, and the possibility of thermal effects diffusing to non-target areas is evaluated. By comprehensively evaluating the bubble synergistic effect and the diffusion behavior of the thermal effect, the cavitation influence coefficient is calculated, the level of hidden dangers of the cavitation effect caused by ultrasound to the production process is quantified, and corresponding signals are generated based on the evaluation results to guide the optimization of the production process. Through this comprehensive dynamic evaluation and feedback mechanism, the present invention can significantly improve the precise control capability of the process and reduce the adverse effects of the cavitation effect on non-target areas, thereby achieving a comprehensive improvement in the quality stability and efficiency of the production of animal blood protein powder. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a schematic diagram of an optimization method for producing animal blood protein powder according to the present invention;

[0058] Figure 2 The present invention is a structural schematic diagram of an animal blood protein powder production optimization system. DETAILED DESCRIPTION

[0059] 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.

[0060] Embodiment 1: Figure 1 The present invention provides an animal blood protein powder production optimization method, which comprises the following steps:

[0061] The harmonic characteristics of the ultrasonic signal are captured and combined with the spectrum analysis algorithm to evaluate the acoustic energy-intensive area, and the first potential risk area is identified.

[0062] A turbulence disturbance intensity graph model is constructed, and the changes in the dissolved gas distribution characteristics in the turbulence disturbance intensity graph model are analyzed through a graph embedding learning algorithm to identify the second potential risk area.

[0063] Based on the first potential risk area and the second potential risk area, a cavitation potential risk area is determined.

[0064] By analyzing the complexity of the distribution of bubble interaction kinetic energy in the potential cavitation risk area, the cumulative impact of the synergistic effect of bubble groups on the target area is identified.

[0065] By analyzing the extent of the spread of local high temperature when bubbles collapse in potential cavitation risk areas, the possibility of thermal effects spreading to non-target areas can be identified.

[0066] Through a comprehensive analysis of the cumulative impact of the synergistic effect of bubble groups on the target area and the possibility of thermal effects diffusing to non-target areas, the hidden dangers of ultrasound-induced cavitation effect on the production process of animal blood protein powder are evaluated, and the production process of animal blood protein powder is optimized based on the evaluation results.

[0067] Capture the harmonic characteristics of the ultrasonic signal, and combine the spectrum analysis algorithm to evaluate the acoustic energy intensive area, and identify the first potential risk area, specifically:

[0068] The ultrasonic signal in the ultrasonic processing process is collected in real time to obtain the original data of the ultrasonic signal:

[0069] In the production process of animal blood protein powder, ultrasound propagates in the liquid medium to form a specific sound field distribution to affect the structural characteristics of the protein. In order to accurately evaluate the acoustic effect caused by ultrasound, it is necessary to use high-precision acoustic sensors to collect the ultrasonic signals generated during the processing process in real time.

[0070] Among them, "specific sound field distribution" refers to a sound pressure and sound intensity pattern with stable spatial distribution formed by the ultrasonic waves emitted by the ultrasonic transducer after propagation, reflection, refraction and mutual interference in the blood or plasma medium of the animal during the ultrasonic treatment process. Specifically, the sound field distribution is manifested as an obvious standing wave structure inside the liquid, in which high sound pressure areas (anti-nodes) and low sound pressure areas (nodes) are formed, as well as a complex energy superposition pattern generated by the synergistic action of multiple transducers.

[0071] In the production process of animal blood protein powder, ultrasonic waves are emitted by the transducer and enter the blood or plasma medium. Their propagation is affected by the physical properties of the medium (such as density, viscosity and sound speed) and the geometric shape of the container. When ultrasonic waves propagate in liquids, they encounter the container wall or the uneven interface in the liquid and are reflected, refracted and scattered. The superposition of multiple wavefronts produces interference effects, thus forming a standing wave or semi-standing wave field with a fixed spatial distribution, which is manifested as a local high sound pressure area (anti-node) and a low sound pressure area (node). In addition, the complexity of the multi-transducer arrangement or the internal structure of the container will also cause the focusing, diffusion and multiple superposition of the ultrasonic beam, further modulating the sound field distribution. The above factors work together to determine the "specific sound field distribution" formed by ultrasonic waves in liquid media, that is, the sound pressure and sound intensity distribution with clear spatial distinction characteristics.

[0072] The sensors are arranged in the core area and the edge area of ​​the animal blood processing equipment to ensure that the collected signals can fully reflect the spatial characteristics of the sound field distribution.

[0073] The raw data of ultrasonic signals include time domain signals, which serve as the basis for subsequent processing and need to be further analyzed to extract useful acoustic features.

[0074] The original data is processed to extract the harmonic characteristics of the ultrasonic signal. The harmonic characteristics include the fundamental frequency, the amplitude of each harmonic, and the phase of each harmonic:

[0075] In order to accurately capture the characteristics of ultrasonic signals, the collected time domain signals are processed. Fast Fourier Transform (FFT) is used to convert the time domain signals into frequency domain signals and extract their harmonic features.

[0076] The calculation formula of fast Fourier transform is: ;in, represents the amplitude of the frequency domain signal, represents the instantaneous amplitude of the time domain signal, represents the time variable, Indicates frequency, represents the complex exponential kernel function used for frequency domain transformation.

[0077] Among them, the fundamental frequency is the main frequency of the ultrasonic signal, which represents the basic vibration frequency; the amplitude of each harmonic is the intensity of each harmonic component, reflecting the energy distribution of the signal; the phase of each harmonic is the phase offset of each harmonic component, representing the time characteristics of the signal.

[0078] Apply spectrum analysis algorithm to perform spectrum analysis on harmonic characteristics and calculate the energy distribution of ultrasonic signals at different frequencies:

[0079] The purpose of spectral analysis is to identify areas of concentrated acoustic energy during ultrasonic processing.

[0080] Calculate the frequency of the ultrasonic signal The corresponding acoustic energy is expressed as: ;in, Indicates the frequency of the ultrasonic signal The corresponding acoustic energy is represents the amplitude of each harmonic, is the ordinal number of the harmonic.

[0081] The spectrum analysis results are presented in the form of the relationship between frequency and energy, revealing the energy distribution characteristics of ultrasonic signals in different frequency ranges.

[0082] According to the energy distribution of ultrasonic signals at different frequencies, the acoustic energy intensive area is obtained, and the distribution of acoustic energy in space is determined:

[0083] Based on the results of spectrum analysis, combined with the spatial position of the sensor and the characteristics of the sound field, the spatial distribution of the acoustic energy intensive area is determined. The spatial distribution of acoustic energy is achieved by mapping the frequency domain information to the physical space. The mapping rules are as follows:

[0084] Calculate the spatial coordinates of each sensor and its corresponding acoustic energy; determine the location where the acoustic energy is concentrated based on the gradient of the energy distribution.

[0085] The acoustic energy intensive area is expressed as: ;in, represents the frequency range of the area where acoustic energy is dense, is the chosen threshold value for energy.

[0086] The energy threshold is used to distinguish whether there is cavitation effect. When the energy of the ultrasonic signal exceeds the threshold, it means that cavitation effect may occur, which may affect the production process of animal blood protein powder. By setting the appropriate energy threshold, the risk area of ​​potential cavitation effect can be effectively identified.

[0087] Identify and mark the first potential risk area based on the spatial distribution of acoustic energy:

[0088] According to the spatial distribution of acoustic energy, the first potential risk area is marked. The marking of the risk area is based on the following rules: select the spatial location where the acoustic energy reaches the peak; determine the area with a large acoustic energy gradient as the risk area; record the spatial coordinates of the risk area.

[0089] The marking result is expressed in the form of a three-dimensional space coordinate set: ;in, represents the spatial coordinate set of the first potential risk area, represents the distribution of acoustic energy in three-dimensional space, , and They respectively represent the three-dimensional spatial coordinates of acoustic energy distribution.

[0090] Using the harmonic characteristics and spectrum analysis results of ultrasonic signals, the first potential risk area was identified and marked, providing a scientific basis for the subsequent analysis of the cavitation effect in the production process of animal blood protein powder. The first potential risk area represents the area with dense acoustic energy during ultrasonic treatment, which is usually the area where cavitation effect caused by ultrasound may occur.

[0091] A turbulence disturbance intensity graph model is constructed, and the changes in the dissolved gas distribution characteristics in the turbulence disturbance intensity graph model are analyzed through a graph embedding learning algorithm to identify the second potential risk area, specifically:

[0092] The dissolved gas concentration at each sampling point in the liquid medium during the animal blood processing process is obtained as the raw data of dissolved gas distribution:

[0093] In the production process of animal blood protein powder, the effect of ultrasound on the liquid medium will cause changes in the distribution of dissolved gas. These changes are closely related to the strength of the cavitation effect and its range of action.

[0094] The collection of dissolved gas distribution is completed by multiple high-precision sensors arranged in the equipment. These sensors are evenly distributed in the processing area and regularly record the dissolved gas concentration at each sampling point in the liquid. In order to ensure the accuracy of the data, the sampling frequency of the sensor is set ten times higher than the ultrasonic frequency to avoid signal folding. The data at each sampling point includes the instantaneous value of the dissolved gas concentration, which constitutes the original data set of the dissolved gas distribution.

[0095] The turbulence intensity at each sampling point is calculated based on the dissolved gas concentration to form a turbulence intensity data set:

[0096] The change of dissolved gas concentration in liquid medium is not only related to the diffusion of dissolved gas itself, but also significantly affected by the turbulent disturbance caused by ultrasound.

[0097] The turbulence disturbance intensity is defined as the standard deviation of dissolved gas concentration over time, which is used to reflect the disturbance intensity of the liquid at different sampling points. The calculation formula is: ;in, Indicates The turbulence intensity at each sampling point is represents the total number of sampling time points, Indicates sampling points at time The dissolved gas concentration at Indicates The average dissolved gas concentration at each sampling point during the entire sampling period is Indicates The sampling time, Indicates the number of the sampling time.

[0098] Based on the turbulence disturbance intensity of all sampling points, a turbulence disturbance intensity dataset is formed.

[0099] The turbulence disturbance intensity graph model is constructed by treating each sampling point as a node, and the connection relationship is established according to the difference in turbulence disturbance intensity between nodes:

[0100] Based on the turbulence disturbance intensity dataset, a turbulence disturbance intensity graph model is constructed. The nodes of the graph model correspond to the sampling points in the liquid medium, and the attribute of each node is its turbulence disturbance intensity. The connection relationship between the nodes is established according to the difference in turbulence disturbance intensity, and the connection weight reflects the degree of disturbance correlation between different sampling points.

[0101] The calculation formula of connection weight is: ;in, Representation Node With Node The connection weight between them is used to quantify the correlation degree of the turbulence disturbance intensity of these two nodes; and Respectively represent nodes and nodes The turbulence intensity; It is an adjustment parameter used to control the decay rate of the weight; Represents a node in the turbulence disturbance intensity graph model, corresponding to the identifier of a sampling point in the liquid medium; Represents another node in the turbulence disturbance intensity graph model, corresponding to the identifier of another sampling point in the liquid medium.

[0102] The constructed turbulence disturbance intensity graph model is represented in the form of nodes and edges for subsequent graph embedding learning and analysis.

[0103] The graph embedding learning algorithm is used to analyze the turbulence disturbance intensity graph model, extract the intrinsic correlation between turbulence disturbance and dissolved gas distribution, and generate the feature representation of each node:

[0104] Based on the constructed turbulence disturbance intensity graph model, a graph embedding learning algorithm is used to extract the feature representation of nodes in the graph and explore the correlation between turbulence disturbance and dissolved gas distribution.

[0105] The goal of graph embedding learning is to map the high-dimensional graph structure to a low-dimensional embedding space while preserving the topological relationship and attribute characteristics of the nodes in the graph. The result of embedding learning is the feature vector of each node, which has a lower dimension than the original data dimension, making it easier to analyze later.

[0106] According to the characteristic representation of each node, the nodes are grouped using a clustering algorithm to identify and mark the second potential risk area:

[0107] Based on the generated node feature vectors, a clustering algorithm is used to group the nodes in the graph. The clustering algorithm divides the nodes into several groups based on the similarity of node features, and each group corresponds to a region with similar turbulent disturbance characteristics.

[0108] The areas with abnormal disturbances in the clustering results are marked as the second potential risk areas. The marking results are expressed in three-dimensional spatial coordinates to guide subsequent production optimization; the second potential risk areas represent areas corresponding to abnormal turbulence disturbance intensity or significant changes in dissolved gas distribution characteristics.

[0109] Based on the first potential risk area and the second potential risk area, the cavitation potential risk area is determined, specifically:

[0110] The first potential risk area is an assessment based on the acoustic energy-intensive area, reflecting the local characteristics of the sound field distribution during ultrasonic treatment; the second potential risk area is obtained by analyzing the changes in the turbulence disturbance intensity and the dissolved gas distribution characteristics, focusing on the fluid dynamics effects caused by ultrasound. These two analysis methods locate the cavitation effect from the two dimensions of the sound field and the flow field, respectively, and each has certain limitations. Marking the union of the first potential risk area and the second potential risk area as the cavitation potential risk area can integrate the characteristics of the sound field and the flow field to ensure that all possible high-risk areas for cavitation effects can be accurately identified, avoiding the omission of risk areas caused by a single analysis dimension.

[0111] Therefore, the union of the first potential risk area and the second potential risk area is marked as a cavitation potential risk area.

[0112] By marking the union of the first potential risk area and the second potential risk area as the cavitation potential risk area, the characteristics of the two dimensions of the acoustic field and the flow field can be effectively combined to comprehensively cover all possible high-risk areas. This method improves the recognition accuracy of cavitation effect risk areas, helps to optimize the ultrasonic parameters in the production of animal blood protein powder, and reduces the adverse effects on product performance.

[0113] In the process of ultrasonic processing of animal blood protein powder, cavitation effect (i.e., the formation and collapse of bubbles caused by ultrasound) has a significant impact on the production process. In order to minimize the impact of cavitation effect on product quality during production, it is necessary to accurately evaluate the interaction of bubbles in the potential risk area of ​​cavitation, especially the synergistic effect between bubble groups.

[0114] By analyzing the complexity of the distribution of bubble interaction kinetic energy in the potential cavitation risk area, the cumulative impact of the synergistic effect of the bubble group on the target area is identified, specifically:

[0115] The bubble interaction kinetic energy in the potential cavitation risk area refers to the kinetic energy changes caused by collision, interaction or group behavior between bubbles in these areas.

[0116] The physical property data of the bubbles in the potential cavitation risk area are obtained, the physical property data including the mass and velocity of each bubble, and the distance and relative velocity between the bubbles are recorded.

[0117] The kinetic energy of each bubble is calculated based on the mass and velocity of the bubble. The kinetic energy is determined by the kinetic energy formula and will not be described in detail.

[0118] For the synergistic effect of bubble groups, the interaction between each pair of bubbles needs to be calculated. The interaction energy between bubbles is mainly affected by the relative speed and proximity between the bubbles.

[0119] The interaction energy between bubbles is calculated based on the interaction coefficient, the relative speed and distance between bubbles. The interaction energy is determined by the following formula: ;in, Indicates Bubbles and The interaction energy between the bubbles, represents the interaction coefficient between bubbles, Indicates Bubbles and The distance between bubbles, Indicates Bubbles and The relative speed between the bubbles, Indicates the potential risk area for cavitation. bubbles, Indicates the potential risk area for cavitation. bubbles (with bubbles to match).

[0120] in, A regulating parameter describing the strength of the interaction between bubbles due to distance and relative velocity, whose value depends on the properties of the liquid medium and the intensity of the ultrasonic field.

[0121] The interaction kinetic energy distribution of a bubble group is highly complex because it is not only related to the physical properties of each bubble, but also affected by the ultrasonic intensity, frequency, and spatial distribution between bubbles.

[0122] Calculate the interaction kinetic energy distribution complexity index, which is determined by the following formula: ;in, is the interaction kinetic energy distribution complexity index, which indicates the complexity of the bubble interaction kinetic energy distribution; represents the average value of the interaction energy between bubbles; Indicates the total number of bubbles in the potential risk area for cavitation.

[0123] The larger the complexity index of the interaction kinetic energy distribution, the greater the complexity of the bubble interaction kinetic energy distribution, indicating that the synergistic effect of the bubble group caused by the cavitation effect is more significant, which may lead to local excessive energy accumulation. High complexity will trigger stronger high pressure and high temperature areas, posing a threat to the stability and structural integrity of the protein, which may cause protein denaturation or gel property failure. In this case, the natural conformation of the protein molecules may be destroyed, ultimately affecting the functional properties of animal blood protein powder. Increased complexity will aggravate the randomness and unevenness of the cavitation effect, which may lead to uncontrollable phenomena in the processing process, such as local over-processing or excessive equipment load, which will not only reduce the consistency of product quality, but also increase equipment loss or energy consumption, further affecting production efficiency and cost.

[0124] During the ultrasonic treatment of animal blood protein powder, local high temperature will be generated when the bubbles collapse. If this high temperature effect spreads to non-target areas, it may have an adverse effect on the protein structure and functional properties during the production process.

[0125] By analyzing the diffusion degree of local high temperature when bubbles collapse in the potential risk area of ​​cavitation, the possibility of thermal effects spreading to non-target areas is identified, specifically:

[0126] In the potential cavitation risk area, the bubble collapse caused by ultrasonic action generates local transient high temperature. In order to quantify the impact of this high temperature, a high-precision temperature sensor is used to collect the instantaneous temperature peak near each bubble in real time, so as to obtain the highest temperature value generated when the bubble collapses.

[0127] The diffusion range is determined by analyzing the attenuation of the high temperature generated by bubble collapse in the surrounding medium, and the diffusion radius of the bubble high temperature is calculated. The diffusion radius of the bubble high temperature can be determined by the following formula: ;in, Indicates The diffusion radius of the high temperature bubble, Indicates The maximum temperature value generated when a bubble collapses is Indicates the threshold of temperature change (defined as the critical temperature change value at which high temperature affects non-target areas).

[0128] The diffusion range is proportional to the maximum temperature value. The smaller the threshold of temperature change, the larger the diffusion range.

[0129] In the potential risk area of ​​cavitation, the high temperature diffusion ranges of multiple bubbles may overlap, and the evaluation of the overall high temperature effect is particularly important. For the diffusion effect of multiple bubbles, the cumulative temperature change in the non-target area can be obtained by superposition method, and its expression is: ;in, Indicates a point in the non-target area The cumulative temperature change at Used to indicate the The coordinates of the bubbles, Used to represent the coordinates of the calculation points in the non-target area. Indicates The Euclidean distance between each bubble and the calculation point.

[0130] Will and Compare and determine which locations in the non-target area have temperature changes exceeding the critical value. Greater than The location set represents the influence area of ​​the thermal effect, and the influence area of ​​the thermal effect is represented by express.

[0131] For the overall impact on non-target areas, calculate the diffuse impact ratio: ;in, It represents the diffusion impact ratio, which is used to evaluate the possibility of thermal effects spreading to non-target areas; represents the total spatial extent of the non-target area; The volume element used to represent integration is used to represent a tiny volume element in space (meaning the accumulation of all tiny volumes affected by high temperatures point by point).

[0132] The greater the diffusion effect ratio, the greater the possibility that the thermal effect will diffuse to non-target areas. This high-temperature diffusion will significantly increase the hidden dangers of ultrasonic-induced cavitation effect in the production of animal blood protein powder. Specifically, high-temperature diffusion may destroy the protein molecular structure in non-target areas, leading to protein denaturation, loss of gelation or reduced emulsification ability, which directly affects the functional characteristics and quality of the final product.

[0133] Among them, in the production of animal blood protein powder, ultrasonic treatment must be precisely concentrated on the target area, while avoiding energy diffusion to non-target areas; among them, the target area refers to the core area of ​​ultrasonic treatment, which is usually the area where the cavitation effect is expected to be concentrated, and its purpose is to use the cavitation effect of ultrasound to optimize the functional properties of protein, such as improving emulsification, gelation or solubility; non-target area refers to the area where the ultrasonic cavitation effect is not expected to have a significant effect, which may include the edge of the processing device or the liquid outside the core area, and the non-target area may be adversely affected by the cavitation effect.

[0134] Through a comprehensive analysis of the cumulative impact of the synergistic effect of the bubble group on the target area and the possibility of the thermal effect spreading to the non-target area, the hidden dangers of the cavitation effect caused by ultrasound on the production process of animal blood protein powder are evaluated, specifically:

[0135] The interaction kinetic energy distribution complexity index and diffusion influence ratio were dimensionlessly processed and assigned weight coefficients respectively, and the cavitation influence coefficient was calculated.

[0136] Among them, the cavitation influence coefficient can be calculated by the following formula: ;in, represents the cavitation influence coefficient, and are the weight coefficients of the interaction kinetic energy distribution complexity index and the diffusion influence ratio, and are greater than 0, and They are the complexity index of interaction kinetic energy distribution and diffusion influence ratio after dimensionless processing.

[0137] The greater the cavitation influence coefficient, the greater the hidden danger of the cavitation effect caused by ultrasound to the production process of animal blood protein powder.

[0138] Set the cavitation impact threshold and compare the cavitation impact coefficient with the cavitation impact threshold:

[0139] When the cavitation influence coefficient is greater than the cavitation influence threshold, a large cavitation hazard signal is generated. This indicates that the cumulative impact of the cavitation effect on the target area and the combined impact of the thermal effect diffusion to the non-target area are beyond the acceptable range, indicating that the current ultrasonic treatment parameters may cause protein denaturation, gel property failure or other quality problems, posing a significant hazard to the production process.

[0140] When the cavitation influence coefficient is less than or equal to the cavitation influence threshold, it is determined that a normal cavitation hazard signal is generated. This indicates that the influence of the cavitation effect is within a controllable range, indicating that the current ultrasonic treatment parameters have a good effect on the target area and have little negative impact on the non-target area, and the production process is safe and stable.

[0141] Among them, the cavitation impact threshold is set comprehensively based on experimental data and process requirements. By analyzing the distribution law of the cavitation impact coefficient under different ultrasonic parameters, combined with the protein functional characteristics of the target area and the allowable impact range of the non-target area, a critical value is selected as the threshold to ensure production efficiency and product quality.

[0142] Based on the evaluation results, the production process of animal blood protein powder was optimized, specifically:

[0143] When a large cavitation risk signal is generated, the animal blood protein powder production process is optimized, including:

[0144] Adjust the ultrasonic power: reduce the ultrasonic intensity to reduce the local high temperature and turbulence disturbance intensity when the bubble collapses;

[0145] Changing the ultrasonic frequency: By adjusting the frequency distribution, the intensity of the cavitation effect can be reduced or the position of the cavitation area can be changed;

[0146] Improve the flow state of liquid media: optimize the fluid flow rate, evenly distribute the cavitation effect area, and reduce local over-processing.

[0147] Reconfigure the sensor layout in production equipment: strengthen the monitoring of potential risk areas of cavitation and dynamically adjust processing parameters.

[0148] Example 2: The difference between Example 2 of the present invention and Example 1 is that this example introduces an animal blood protein powder production optimization system.

[0149] Figure 2 A structural schematic diagram of an animal blood protein powder production optimization system of the present invention is given, which includes a harmonic feature capture module, a turbulence intensity analysis module, a cavitation area determination module, a synergistic effect evaluation module, a thermal effect identification module and a cavitation hazard evaluation module.

[0150] Harmonic feature capture module: captures the harmonic features of ultrasonic signals, and combines the spectrum analysis algorithm to evaluate the acoustic energy-intensive area to identify the first potential risk area.

[0151] Turbulence intensity analysis module: Construct a turbulence disturbance intensity graph model, analyze the changes in the dissolved gas distribution characteristics in the turbulence disturbance intensity graph model through a graph embedding learning algorithm, and identify the second potential risk area.

[0152] The cavitation area determination module determines the cavitation potential risk area based on the first potential risk area and the second potential risk area.

[0153] Synergistic effect assessment module: By analyzing the complexity of the kinetic energy distribution of bubble interactions in the potential cavitation risk area, the cumulative impact of the synergistic effect of bubble groups on the target area is identified.

[0154] Thermal effect identification module: By analyzing the diffusion degree of local high temperature when bubbles collapse in the potential cavitation risk area, the possibility of thermal effect spreading to non-target areas is identified.

[0155] Cavitation hazard assessment module: Through a comprehensive analysis of the cumulative impact of the synergistic effect of bubble groups on the target area and the possibility of thermal effects diffusing to non-target areas, the hazard of ultrasonic-induced cavitation effect on the production process of animal blood protein powder is assessed, and the production process of animal blood protein powder is optimized based on the assessment results.

[0156] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0157] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0158] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0159] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0160] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0161] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0162] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0163] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0164] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the production of animal blood protein powder, characterized in that: The steps include: Capture the harmonic characteristics of ultrasonic signals and use spectrum analysis algorithms to evaluate areas with dense acoustic energy, identifying the first potential risk area; A turbulence disturbance intensity graph model is constructed, and the changes in the dissolved gas distribution characteristics in the turbulence disturbance intensity graph model are analyzed through a graph embedding learning algorithm to identify the second potential risk area. Determining a cavitation potential risk area based on the first potential risk area and the second potential risk area; By analyzing the complexity of the distribution of bubble interaction kinetic energy in the potential cavitation risk area, the cumulative impact of the synergistic effect of the bubble group on the target area is identified; By analyzing the extent of the spread of local high temperature when bubbles collapse in the potential risk area of ​​cavitation, the possibility of thermal effects spreading to non-target areas can be identified; Through a comprehensive analysis of the cumulative impact of the synergistic effect of bubble groups on the target area and the possibility of thermal effects diffusing to non-target areas, the hidden dangers of ultrasound-induced cavitation effect on the production process of animal blood protein powder are evaluated, and the production process of animal blood protein powder is optimized based on the evaluation results.

2. The method for optimizing the production of animal blood protein powder according to claim 1, characterized in that: Capture the harmonic characteristics of the ultrasonic signal, and combine the spectrum analysis algorithm to evaluate the acoustic energy intensive area, and identify the first potential risk area, specifically: Collect the ultrasonic signal in the ultrasonic treatment process in real time to obtain the original data of the ultrasonic signal; Perform signal processing on the raw data to extract the harmonic characteristics of the ultrasonic signal, which include the fundamental frequency, the amplitude of each harmonic and the phase of each harmonic; Apply spectrum analysis algorithm to perform spectrum analysis on harmonic characteristics and calculate the energy distribution of ultrasonic signals at different frequencies; According to the energy distribution of ultrasonic signals at different frequencies, the acoustic energy intensive area is obtained, and the distribution of acoustic energy in space is determined; A first potential risk area is identified and marked based on the spatial distribution of acoustic energy.

3. The method for optimizing the production of animal blood protein powder according to claim 2, characterized in that: A turbulence disturbance intensity graph model is constructed, and the changes in the dissolved gas distribution characteristics in the turbulence disturbance intensity graph model are analyzed through a graph embedding learning algorithm to identify the second potential risk area, specifically: Obtaining dissolved gas concentrations at various sampling points in the liquid medium during animal blood processing as raw data on dissolved gas distribution; The turbulence disturbance intensity of each sampling point is calculated based on the dissolved gas concentration to form a turbulence disturbance intensity data set; The turbulence disturbance intensity graph model is constructed by treating each sampling point as a node, and the connection relationship is established according to the difference in turbulence disturbance intensity between nodes; The graph embedding learning algorithm is used to analyze the turbulence disturbance intensity graph model, extract the intrinsic correlation between turbulence disturbance and dissolved gas distribution, and generate the feature representation of each node; According to the characteristic representation of each node, the nodes are grouped using a clustering algorithm to identify and mark the second potential risk area.

4. The method for optimizing the production of animal blood protein powder according to claim 3, characterized in that: The calculation formula for turbulence disturbance intensity is: ;in, Indicates The turbulence intensity at each sampling point is represents the total number of sampling time points, Indicates sampling points at time The dissolved gas concentration at Indicates The average dissolved gas concentration at each sampling point during the entire sampling period is Indicates The sampling time, Indicates the number of the sampling time.

5. The method for optimizing the production of animal blood protein powder according to claim 4, characterized in that: Based on the first potential risk area and the second potential risk area, the cavitation potential risk area is determined, specifically: The union of the first potential risk area and the second potential risk area is marked as a cavitation potential risk area.

6. The method for optimizing the production of animal blood protein powder according to claim 5, characterized in that: By analyzing the complexity of the distribution of bubble interaction kinetic energy in the potential cavitation risk area, the cumulative impact of the synergistic effect of the bubble group on the target area is identified, specifically: Obtaining physical property data of bubbles in the potential cavitation risk area, the physical property data including the mass and velocity of each bubble, and recording the distance and relative velocity between the bubbles; Calculate the kinetic energy of each bubble based on its mass and velocity; The interaction energy between bubbles is calculated based on the interaction coefficient, the relative speed and distance between bubbles; Calculate the interaction kinetic energy distribution complexity index: ;in, is the interaction kinetic energy distribution complexity index, Indicates Bubbles and The interaction energy between the bubbles, represents the average value of the interaction energy between bubbles, Indicates the total number of bubbles in the potential cavitation risk area, Indicates the potential risk area for cavitation. bubbles, Indicates the potential risk area for cavitation. bubbles.

7. The method for optimizing the production of animal blood protein powder according to claim 6, characterized in that: By analyzing the diffusion degree of local high temperature when bubbles collapse in the potential risk area of ​​cavitation, the possibility of thermal effects spreading to non-target areas is identified, specifically: Collect the instantaneous temperature peak near each bubble in real time to obtain the highest temperature value generated when the bubble collapses; By analyzing the attenuation of the high temperature generated by bubble collapse in the surrounding medium, the diffusion radius of the bubble high temperature is calculated; Among them, high temperature is the transient maximum temperature measured in the vicinity of the bubble at the moment of bubble collapse; The cumulative temperature change in the non-target area is obtained by superposition method; Compare the accumulated temperature change with the temperature change threshold to determine which positions in the non-target area have temperature changes exceeding the critical value; the set of positions where the accumulated temperature change is greater than the temperature change threshold represents the affected area of ​​the thermal effect; For the overall impact on non-target areas, calculate the diffuse impact ratio: ;in, represents the diffusion impact ratio, represents the total spatial extent of the non-target area, Indicates the area affected by thermal effects.

8. The method for optimizing the production of animal blood protein powder according to claim 7, characterized in that: Through a comprehensive analysis of the cumulative impact of the synergistic effect of the bubble group on the target area and the possibility of the thermal effect spreading to the non-target area, the hidden dangers of the cavitation effect caused by ultrasound on the production process of animal blood protein powder are evaluated, specifically: The interaction kinetic energy distribution complexity index and diffusion influence ratio are dimensionlessly processed and assigned weight coefficients respectively, and the cavitation influence coefficient is calculated. Set the cavitation impact threshold and compare the cavitation impact coefficient with the cavitation impact threshold: When the cavitation influence coefficient is greater than the cavitation influence threshold, it is determined that a large cavitation hidden danger signal is generated; When the cavitation influence coefficient is less than or equal to the cavitation influence threshold, it is determined that a normal signal of cavitation hidden danger is generated; When a large cavitation hazard signal is generated, the animal blood protein powder production process is optimized, including adjusting the ultrasonic power, changing the ultrasonic frequency, improving the flow state of the liquid medium, and reconfiguring the sensor layout in the production equipment.

9. An animal blood protein powder production optimization system, used to implement the animal blood protein powder production optimization method according to any one of claims 1 to 8, characterized in that: It includes harmonic feature capture module, turbulence intensity analysis module, cavitation area determination module, synergy effect evaluation module, thermal effect identification module and cavitation hazard assessment module; Harmonic feature capture module: captures the harmonic features of ultrasonic signals, and combines spectrum analysis algorithms to evaluate acoustic energy-intensive areas, identifying the first potential risk area; Turbulence intensity analysis module: constructs a turbulence disturbance intensity graph model, analyzes the changes in the dissolved gas distribution characteristics in the turbulence disturbance intensity graph model through a graph embedding learning algorithm, and identifies the second potential risk area; A cavitation area determination module: determining a cavitation potential risk area based on the first potential risk area and the second potential risk area; Synergistic effect evaluation module: By analyzing the complexity of the distribution of bubble interaction kinetic energy in the potential cavitation risk area, the cumulative impact of the synergistic effect of the bubble group on the target area is identified; Thermal effect identification module: By analyzing the diffusion degree of local high temperature when bubbles collapse in the potential cavitation risk area, the possibility of thermal effect diffusion to non-target areas is identified; Cavitation hazard assessment module: Through a comprehensive analysis of the cumulative impact of the synergistic effect of bubble groups on the target area and the possibility of thermal effects diffusing to non-target areas, the hazard of ultrasonic-induced cavitation effect on the production process of animal blood protein powder is assessed, and the production process of animal blood protein powder is optimized based on the assessment results.

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