Intelligent management and scheduling system and method for milk powder production workshop

Abstract

The invention relates to the technical field of food production supervision, in particular to an intelligent management and scheduling system and method for a milk powder production workshop. The system comprises a data acquisition and processing unit which acquires milk pulp data, workshop environment data and equipment operation data in real time; the milk slurry drying monitoring unit is based on milk slurry data and workshop environment data, adopts a milk slurry multi-component variable-order concentration adjusting algorithm and combines a spray drying tower dynamic airflow control algorithm to jointly adjust the milk slurry concentration in real time and optimize spray drying tower airflow distribution and equipment operation data. The lactose crystallization monitoring unit monitors the crystallization trend of lactose in real time; and the information management tracing unit collects and stores all data in the milk powder production process and carries out data report analysis. By introducing a multi-component variable-order concentration adjusting algorithm and a spray drying tower dynamic airflow control algorithm, dynamic coupling adjustment of the milk slurry concentration and the drying airflow is achieved, and the problems that high-viscosity milk slurry spraying is not uniform and lactose crystallization is out of control are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of food production supervision, and particularly relates to an intelligent management and scheduling system and method for a milk powder production workshop. Background Art

[0002] During the milk powder production process, the spray drying process, as a key link connecting liquid milk slurry and solid milk powder products, has a decisive impact on the solubility, particle size distribution, lactose crystallization state, and overall quality of the milk powder. In current industrial practices, milk powder production enterprises generally use a variety of controllers and PLC systems to achieve single-point adjustment of equipment such as milk slurry concentration, sprayer operating parameters, and fan speed, and there are still significant deficiencies in the coordination and intelligent scheduling of milk powder drying; In the actual milk powder production workshop environment, the stability of milk slurry drying is affected by the combined effects of milk slurry concentration, milk slurry viscosity, temperature, and lactose content. High-viscosity milk slurry usually first affects the fluidity and spraying effect of the milk slurry during the spray drying process. If the milk slurry viscosity is too high, the milk slurry will become difficult to spray through the nozzle, and the uneven spraying effect makes the temperature, humidity, and air flow in the spray tower uneven, forming local air flow dead zones or vortex zones in the spray tower. In the later stage of drying, the lactose crystallization rate is directly affected by the temperature, humidity, milk slurry viscosity, and air flow in the drying tower. Due to the uneven air flow and temperature and humidity, some milk slurry droplets may not be fully dried, resulting in high humidity, and lactose begins to crystallize and precipitate and form crystals inside the particles. High-viscosity milk slurry will reduce the efficiency of lactose dissolution, making lactose more likely to crystallize; Then, due to the high-concentration milk slurry affecting the uneven distribution of temperature and humidity and the air flow stability in the drying tower, which in turn affects the milk slurry evaporation rate, ultimately leading to the problem that the high-concentration milk slurry in some areas of the drying tower cannot be fully dried and will precipitate to form large particle crystals. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent management and scheduling system and method for a milk powder production workshop to solve the problem proposed in the above background art that due to the high-concentration milk slurry affecting the uneven distribution of temperature and humidity and the air flow stability in the drying tower, which in turn affects the milk slurry evaporation rate, ultimately leading to the problem that the high-concentration milk slurry in some areas of the drying tower cannot be fully dried and will precipitate to form large particle crystals.

[0004] To achieve the above purpose, the present invention aims to provide an intelligent management and scheduling system for a milk powder production workshop, including: A data acquisition and processing unit, which uses sensors and monitoring devices to collect milk slurry data, workshop environment data, and equipment operation data in real time, and preliminarily processes all the collected data; The milk slurry drying monitoring unit, which is based on milk slurry data and workshop environmental data, uses the milk slurry multi-fraction variable-order concentration adjustment algorithm and combines it with the spray drying tower dynamic air flow control algorithm to jointly and real-time adjust the milk slurry concentration, optimize the air flow distribution in the spray drying tower, and the equipment operation data; The lactose crystallization monitoring unit, which is used to monitor the lactose crystallization trend in real time; The information management and traceability unit, which is used to collect and store all data during the milk powder production process and conduct data report analysis.

[0005] Preferably, the milk slurry data includes: milk slurry viscosity , milk slurry temperature , milk slurry concentration and lactose content ; The workshop environmental data includes: workshop temperature , workshop humidity and air flow rate ; The equipment operation data includes: the fan speed of the spray drying tower and injection pressure ; Among them, is time.

[0006] Preferably, the milk slurry drying monitoring unit includes a milk slurry concentration adjustment module and a drying tower air flow optimization module; Among them, the milk slurry concentration adjustment module is based on the milk slurry data and workshop environmental data, uses the milk slurry multi-fraction variable-order concentration adjustment algorithm to combine with real-time dynamic adjustment of the milk slurry concentration before drying, and outputs the adjusted milk slurry concentration and the milk slurry concentration control residual to the drying tower air flow optimization module; The drying tower air flow optimization module is based on the adjusted milk slurry concentration, workshop environmental data and equipment operation data, and uses the spray drying tower dynamic air flow control algorithm to optimize the air flow distribution in the spray drying tower.

[0007] Preferably, the milk slurry multi-fraction variable-order concentration adjustment algorithm is realized based on the multi-fractal dynamic fluctuation modeling method and the variable-order fractional differential response mechanism, and is used to real-time dynamically adjust the milk slurry concentration before drying. The specific method of the milk slurry multi-fraction variable-order concentration adjustment algorithm is as follows: S2.1.1. Input the milk slurry data and workshop environmental data into the variable-order differential neural operator, calculate the quasi-variable-order quasi-fractal derivatives of all milk slurry data and workshop environmental data , and combine and embed all the quasi-variable-order quasi-fractal derivatives to obtain a variable-order differential feature ; S2.1.2. Use the Moran multi-fractal mapping for the variable-order differential feature Perform non-linear fusion and calculate the milk slurry fluctuation intensity index ; S2.1.3. Calculate the automatic adjustment control speed of the milk slurry based on the workshop environment data ; S2.1.4. Use the variable-order Caputo derivative model to construct an adaptive milk slurry concentration control equation; S2.1.5. According to the prediction result of the adaptive milk slurry concentration control equation, adjust the actual milk slurry concentration to the target milk slurry concentration in real time and output the adjusted milk slurry concentration and the milk slurry concentration control residual , is an increment of a time period.

[0008] Preferably, in S2.1.4, the adaptive milk slurry concentration control equation is the core equation of the milk slurry multi-fraction variable-order concentration adjustment algorithm, constructed based on the variable-order Caputo derivative model, and used to dynamically predict the trend of the milk slurry concentration. The specific form of the adaptive milk slurry concentration control equation is as follows: ; where is the Caputo variable-order fractional derivative, representing the dynamic change rate of the milk slurry concentration at time ; is the automatic adjustment control speed of the milk slurry; is the milk slurry concentration; is the target milk slurry concentration at time ; is the control gain coefficient; is the milk slurry fluctuation intensity index; is the fractal excitation gain coefficient.

[0009] Preferably, the dynamic air flow control algorithm for the spray drying tower is an air flow control algorithm based on persistent homology topological analysis and discrete differential manifold modeling, used to regulate the air flow distribution in the spray drying tower. The specific form of the dynamic air flow control algorithm for the spray drying tower is as follows: S2.2.1. Input the adjusted milk slurry concentration as well as the workshop environment data and equipment operation data into the lightweight CFD neural operator to construct a three-dimensional velocity vector field in the space of the spray drying tower ; S2.2.2. Perform persistent homology analysis on the three-dimensional velocity vector field and calculate the topological flux entropy ; S2.2.3. According to the topological flux entropy , the adjusted milk slurry concentration , lactose content and the residual of milk slurry concentration control , the adjusted fan speed and nozzle pressure are calculated; S2.2.4. Based on topological flux entropy and the workshop environment data, the automatic adjustment control speed of the milk slurry is weighted and updated .

[0010] Preferably, in the above S2.2.3, according to the topological flux entropy , the adjusted milk slurry concentration , lactose content and the residual of milk slurry concentration control , the adjusted fan speed and nozzle pressure are calculated. The specific method is as follows: ; ; wherein, is the fan speed at the adjusted time ; is the nozzle pressure at the adjusted time ; is the weight of the influence of fan change; is the weight of milk slurry concentration deviation; is the coupling weight of fan concentration adjustment residual; is the weight of the influence of nozzle change; is the weight of lactose deviation; is the coupling weight of nozzle concentration adjustment residual; is the change rate of topological flux entropy; is the adjusted milk slurry concentration; is the time of the target milk slurry concentration; time of the lactose content; is the time of the target lactose content.

[0011] Preferably, the lactose crystallization monitoring unit is used to monitor the lactose crystallization trend in real time, specifically as follows: Set the critical interval of lactose content as , the critical milk slurry concentration fluctuation threshold and the critical milk slurry temperature fluctuation threshold ; At time , if the current lactose content is within the critical interval of lactose content, the current milk slurry concentration fluctuation rate is greater than the critical milk slurry concentration fluctuation threshold, and the current milk slurry temperature fluctuation rate is greater than the critical milk slurry temperature fluctuation threshold, and the continuous duration exceeds the manually set duration, the production work is immediately stopped.

[0012] Preferably, the information management and traceability unit is used to collect and store all data in the milk powder production process, including the original milk slurry data, workshop environment data, and equipment operation data in the data acquisition and processing unit, and also including the adjusted milk slurry concentration, milk slurry concentration control residual, topological flux entropy, and milk slurry automatic adjustment control speed in the milk slurry drying monitoring unit, and also including the set lactose content critical range, critical milk slurry concentration fluctuation threshold, and critical milk slurry temperature fluctuation threshold in the lactose crystallization monitoring unit.

[0013] On the other hand, the present invention provides a method for intelligent management and scheduling of a milk powder production workshop, which is used for the above-mentioned intelligent management and scheduling system of a milk powder production workshop, and includes the following steps: S10.1. Use sensors and monitoring devices to collect milk slurry data, workshop environment data, and equipment operation data in real time, and perform preliminary processing on all the collected data; S10.2. Based on the milk slurry data and workshop environment data, adopt the milk slurry multi-variable order concentration adjustment algorithm and combine it with the dynamic air flow control algorithm of the spray drying tower to jointly adjust the milk slurry concentration in real time, optimize the air flow distribution of the spray drying tower, and equipment operation data; S10.3. Monitor the lactose crystallization trend in real time; S10.4. Collect and store all data in the milk powder production process, and perform data report analysis.

[0014] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects: 1. In the present invention, the milk slurry multi-variable order concentration adjustment algorithm is used to perceive the influence of milk slurry viscosity, temperature, and lactose content on the milk slurry concentration in real time, and adaptively adjust the milk slurry concentration under different workshop environment parameters, realizing high-precision and low-latency adjustment of the milk slurry concentration, thereby solving the problem of uneven spray distribution caused by poor fluidity of high-viscosity milk slurry in the initial stage of drying; 2. In the present invention, through the dynamic air flow control algorithm of the spray drying tower, real-time quantification and control prediction adjustment of the complexity of the air flow topological structure in the spray drying tower are realized, and the milk slurry concentration control residual is dynamically coupled with the topological flux entropy, so that the fan speed and nozzle pressure can be adjusted in real-time linkage with the physical state of the milk slurry, alleviating the problem of non-uniform crystallization and large particle precipitation formed by high-concentration milk slurry in the tail section of drying. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a principle block diagram of an embodiment proposed by the present invention; Reference numerals: 1, data acquisition and processing unit; 2, milk slurry drying monitoring unit; 21, milk slurry concentration adjustment module; 22, drying tower air flow optimization module; 3, lactose crystallization monitoring unit; 4, information management and traceability unit. Detailed implementation manners

[0016] Example 1 is as follows Figure 1 As shown, an intelligent management and scheduling system for a milk powder production workshop is provided, including: A data acquisition and processing unit 1, which uses sensors and monitoring devices to collect milk slurry data, workshop environment data, and equipment operation data in real time, and preliminarily processes all the collected data; The milk slurry data includes: milk slurry viscosity , milk slurry temperature , milk slurry concentration and lactose content ; The workshop environment data includes: workshop temperature , workshop humidity and air flow rate ; The equipment operation data includes: the fan speed and injection pressure of the spray drying tower; Among them, is time; In this embodiment, temperature sensors are installed in front of the milk slurry conveying pipeline and the spray drying tower to monitor and collect the milk slurry temperature in real time, viscosity sensors are used to monitor and collect the milk slurry viscosity, and lactose content sensors are used to monitor the lactose content in the milk slurry in real time; then temperature and humidity sensors are installed at multiple positions in the workshop to monitor the environmental temperature and humidity in the workshop in real time; an air flow velocity sensor is installed in the drying tower to monitor the air flow rate in real time to ensure uniform air flow distribution in the spray drying tower; a fan operation state sensor is used to monitor the working state of the spray drying tower fan, including wind speed and rotation speed; all sensors and monitoring devices upload the collected data to the PLC system in real time, and the data collection period is 50 ms; Noise in the collected data is removed through a filtering algorithm, and the collected data is checked in real time. Outliers are automatically identified and removed. Outlier detection is performed through statistical algorithms and threshold determination to ensure that the data used subsequently is not affected by outliers, and the collected data is standardized; The milk slurry viscosity directly affects the spray drying effect, the milk slurry temperature affects the lactose solubility and drying efficiency, and the lactose content affects the lactose solubility and crystallization.

[0017] A milk slurry drying monitoring unit 2, which is based on the milk slurry data and workshop environment data, and uses a milk slurry multi-variable order concentration adjustment algorithm and combines with a spray drying tower dynamic air flow control algorithm to jointly adjust the milk slurry concentration in real time, optimize the air flow distribution in the spray drying tower, and equipment operation data; The milk slurry drying monitoring unit 2 includes a milk slurry concentration adjustment module 21 and a drying tower air flow optimization module 22; Among them, the milk slurry concentration adjustment module 21, based on the milk slurry data and the workshop environment data, uses the milk slurry multi-order variable concentration adjustment algorithm to combine with real-time dynamic adjustment of the milk slurry concentration before drying, and outputs the adjusted milk slurry concentration and the milk slurry concentration control residual to the drying tower air flow optimization module 22; The drying tower air flow optimization module 22, based on the adjusted milk slurry concentration, as well as the workshop environment data and the equipment operation data, uses the spray drying tower dynamic air flow control algorithm to optimize the air flow distribution in the spray drying tower.

[0018] In this embodiment, the milk slurry multi-order variable concentration adjustment algorithm uses the variable-order differential neural operator to extract the multi-scale perturbations in the milk slurry data and the environment data, forms a 128-dimensional feature vector, and constructs the milk slurry fluctuation intensity index through the Moran multifractal mapping to reflect the non-linear intensity of the physical property fluctuations such as the milk slurry viscosity and temperature; The spray drying tower dynamic air flow control algorithm uses the lightweight CFD neural operator to predict the three-dimensional velocity field in the tower, and extracts its topological structure to calculate the topological flux entropy; The milk slurry multi-order variable concentration adjustment algorithm is based on the multifractal dynamic fluctuation modeling method and the variable-order fractional differential response mechanism, and is used to dynamically adjust the milk slurry concentration before drying in real time. The specific method of the milk slurry multi-order variable concentration adjustment algorithm is as follows: In this embodiment, the multifractal dynamic fluctuation modeling method is a method used to describe the fluctuation structure at multiple scales in a complex system, and uses a set of exponential spectra of power-law distributions to describe the perturbation signal; The variable-order fractional differential response mechanism is a variable-order fractional differential response mechanism constructed on the basis of the traditional fractional Caputo derivative model, which further allows the derivative order to change with time, and is used to dynamically describe the control system with memory effect; By combining the multifractal dynamic fluctuation modeling method and the variable-order fractional differential response mechanism, a milk slurry multi-order variable concentration adjustment algorithm for intelligently perceiving and controlling the milk slurry concentration is constructed in the present invention; S2.1.1. Input the milk slurry data and the workshop environment data into the variable-order differential neural operator, and calculate the quasi-variable-order quasi-fractal derivatives of all the milk slurry data and the workshop environment data and combine and embed all the quasi-variable-order quasi-fractal derivatives to obtain a variable-order differential feature ; Extract the multi-scale dynamic change patterns of the original milk slurry data and the workshop environment data through the variable-order quasi-fractal derivative, and construct a mathematical representation form of non-linear perturbation, cross-scale fluctuation and instantaneous response characteristics, as the perception basis of the concentration adjustment system. The quasi-variable-order quasi-fractal derivative It is a high-order dynamic feature extraction tool that integrates fractional-order derivatives and fractal differentials; and due to the non-stationarity and environmental dependence of variables such as milk slurry concentration, the milk slurry viscosity fluctuates smoothly at high temperatures and oscillates violently at low temperatures, and the concentration regulation response is slow when the environmental humidity suddenly rises. Fixed orders cannot accommodate the changes in fluctuation characteristics under different physical processes, so variable orders are used ; For each variable data, calculate the quasi-variable-order quasi-fractal derivative respectively After that, the characteristic structures at different time windows and scales need to be considered. All the quasi-variable-order quasi-fractal derivative results are encoded into a set of feature dimensions through a set of learnable embedding mappings, which are the variable-order differential features ; In this embodiment, the variable-order differential features are 128-dimensional, specifically as follows: The milk slurry data includes four types of data: milk slurry viscosity , milk slurry temperature , milk slurry concentration and lactose content ; The workshop environmental data includes three types of data: workshop temperature , workshop humidity and air flow rate ; There are a total of 7 types of data, that is, the variable-order differential features take dimensions; S2.1.2. Use Moran multifractal mapping to perform non-linear fusion on the variable-order differential features and calculate the milk slurry fluctuation intensity index ; Moran multifractal mapping is a non-linear fusion method used to measure the intensity of multi-scale perturbation structures in complex systems. Based on the idea of multifractal spectrum analysis in statistical physics, it maps the local drastic changes and global structural differences in the variable-order differential features into a single-valued index representing the perturbation intensity of the system, which is the milk slurry fluctuation intensity index , specifically as follows: ; Among them, is the index of the quasi-variable-order quasi-fractal derivative of each data; is the weight of the variable-order differential feature of the th data; is the Moran power exponent, which is used to amplify drastic perturbations; is the perturbation amplitude of the quasi-variable-order quasi-fractal derivative of the th data; S2.1.3. Calculate the automatic adjustment control speed of the milk slurry based on the workshop environment data ; S2.1.4. Construct an adaptive milk slurry concentration control equation using the variable-order Caputo derivative model; S2.1.5. According to the prediction result of the adaptive milk slurry concentration control equation, adjust the actual milk slurry concentration to the target milk slurry concentration in real time, and output the adjusted milk slurry concentration and the milk slurry concentration control residual , where is an increment of a time period.

[0019] In S2.1.4, the adaptive milk slurry concentration control equation is the core equation of the milk slurry multi-fraction variable-order concentration adjustment algorithm. It is constructed based on the variable-order Caputo derivative model and is used to dynamically predict the trend of the milk slurry concentration. The specific form of the adaptive milk slurry concentration control equation is as follows: ; where is the Caputo variable-order fractional derivative, representing the dynamic change rate of the milk slurry concentration at time ; is the automatic adjustment control speed of the milk slurry; is the milk slurry concentration; is the target milk slurry concentration at time ; is the control gain coefficient; is the milk slurry fluctuation intensity index; is the fractal excitation gain coefficient.

[0020] In this embodiment, the Caputo derivative model is a fractional calculus tool used to describe dynamic processes with memory effects and non-locality. The variable-order Caputo derivative model further extends the Caputo derivative model, and its order is the automatic adjustment control speed of the milk slurry , which changes dynamically with time, indicating different degrees of response inertia or memory strength at different moments; Under the control system of the milk slurry concentration, the milk slurry concentration disturbance is non-stationary. A fixed control coefficient cannot adapt to the characteristics of fast and slow fluctuations, and there is an inertial memory effect in milk slurry control. The first derivative has no memory, so the variable-order Caputo derivative model is used; In the formula of the adaptive milk slurry concentration control equation, represents the steady-state attraction of traditional closed-loop regulation: if the current concentration deviates from the target concentration, it will be pulled back; represents the disturbance response enhancement term: if the system is in a strong disturbance environment at this moment, such as viscosity mutation, humidity fluctuation, etc., the adjustment should be increased; Final output: adjusted milk slurry concentration and the residual of milk slurry concentration control .

[0021] The dynamic air flow control algorithm for the spray drying tower is an air flow control algorithm based on persistent homology topological analysis and discrete differential manifold modeling, which is used to regulate the air flow distribution in the spray drying tower. The specific dynamic air flow control algorithm for the spray drying tower is as follows: In this embodiment, persistent homology analysis is a computational topology data analysis technique in topology. In the spray drying tower, the three-dimensional air flow velocity field is a tensor field, which includes complex forms such as local swirling eddy current structures, local air flow dead zones or fracture surfaces, and asymmetric air mass channels. Persistent homology analysis can be used to extract the following topological features: the number of connected components, one-dimensional holes, and two-dimensional cavities. By calculating the difference between the birth time and the death time of different features, the topological complexity quantification index of the three-dimensional air flow velocity field can be obtained, which is the topological flux entropy; Discrete differential manifold modeling transforms continuous differential geometry definitions into numerical forms that can be calculated on discrete grids, and is used to deal with the evolution law of complex physical fields in discrete spaces. Inside the spray drying tower, there are: conical lower closing geometry, multi-nozzle angle distribution, and non-uniform boundary heat transfer and reflection. These will all lead to the geometric non-linear propagation and local structural deformation of the air flow in the spray drying tower. Compared with traditional CFD which is a black box simulation, this method is more interpretable in structure; By combining persistent homology analysis and discrete differential manifold modeling to construct the dynamic air flow control algorithm for the spray drying tower, not only can the local structural features of the air flow velocity field inside the spray drying tower be extracted and prediction modeled, but also the complexity and imbalance of the flow field can be quantitatively measured from the global topological structure level, realizing the linkage prediction scheduling of the fan and nozzle control, and avoiding problems such as air flow dead zones, uneven heat, or premature generation of lactose crystal nuclei; S2.2.1. Input the adjusted milk slurry concentration as well as the workshop environment data and equipment operation data into the lightweight CFD neural operator to construct a three-dimensional velocity vector field in the spray drying tower space ; The lightweight CFD neural operator is more suitable for real-time feedback control compared to traditional CFD solvers. By using the lightweight CFD neural operator to construct the air flow velocity field at any position inside the spray drying tower, that is, the three-dimensional velocity vector field , the air flow information in any area of the drying tower space can be obtained in real time; S2.2.2. Perform persistent homology analysis on the three-dimensional velocity vector field and calculate the topological flux entropy ; The three-dimensional velocity vector field It is regarded as a three-dimensional tensor field, and its topological structure characteristics are analyzed by persistent homology. The Betti numbers are calculated, including the number of connected components, one-dimensional holes, and two-dimensional cavities, and the topological flux entropy is constructed. : ; Among them, is the index of topological structure characteristics; is the topological structure characteristic in the proportion of the three-dimensional velocity vector field; S2.2.3. According to the topological flux entropy , the adjusted milk slurry concentration , the lactose content and the milk slurry concentration control residual , the adjusted fan speed and nozzle pressure are calculated; S2.2.4. Based on the topological flux entropy and the workshop environment data, the automatic adjustment control speed of the milk slurry is weighted and updated .

[0022] In this embodiment, based on the topological flux entropy and the workshop environment data, the automatic adjustment control speed of the milk slurry is weighted and updated , specifically as follows: ; Among them, is the control speed reference value; is the temperature difference influence weight coefficient; is the humidity influence weight coefficient; is the topological entropy coupling coefficient; is the workshop temperature; is the milk slurry temperature; is the workshop humidity; In S2.2.3, according to the topological flux entropy , the adjusted milk slurry concentration , the lactose content and the milk slurry concentration control residual , the adjusted fan speed and nozzle pressure are calculated. The specific method is as follows: ; ; Among them, is the fan speed at the adjusted time ; is the nozzle pressure at the adjusted time ; is the influence weight of fan change; is the weight of milk slurry concentration deviation; is the residual coupling weight of the fan concentration adjustment; is the influence weight of the nozzle change; is the lactose deviation weight; is the residual coupling weight of the nozzle concentration adjustment; is the change rate of the topological flux entropy; is the adjusted milk slurry concentration; is time of the target milk slurry concentration; time of the lactose content; is time of the target lactose content.

[0023] The lactose crystallization monitoring unit 3 is used to monitor the lactose crystallization trend in real time; In this embodiment, the lactose crystallization monitoring unit 3 is used to monitor the lactose crystallization trend in real time, specifically as follows: Set the lactose content critical interval as , the critical milk slurry concentration fluctuation threshold and the critical milk slurry temperature fluctuation threshold ; At time if the current lactose content is within the lactose content critical interval, the current milk slurry concentration fluctuation rate is greater than the critical milk slurry concentration fluctuation threshold, and the current milk slurry temperature fluctuation rate is greater than the critical milk slurry temperature fluctuation threshold, and the continuous duration exceeds the manually set duration, then immediately stop the production work.

[0024] The information management and traceability unit 4 is used to collect and store all data in the milk powder production process and perform data report analysis; In this embodiment, the information management and traceability unit 4 is used to collect and store all data in the milk powder production process, including the original milk slurry data, workshop environment data, and equipment operation data in the data acquisition and processing unit 1, and also including the adjusted milk slurry concentration, milk slurry concentration control residual, topological flux entropy, and milk slurry automatic adjustment control speed in the milk slurry drying monitoring unit 2, and also including the set lactose content critical interval, critical milk slurry concentration fluctuation threshold, and critical milk slurry temperature fluctuation threshold in the lactose crystallization monitoring unit 3.

[0025] Embodiment 2, the present invention proposes a method for intelligent management and scheduling of a milk powder production workshop, which is used for an intelligent management and scheduling system of a milk powder production workshop in the above Embodiment 1, and includes the following steps: S10.1. Use sensors and monitoring devices to collect milk slurry data, workshop environment data, and equipment operation data in real time, and perform preliminary processing on all the collected data; S10.2. Based on the milk slurry data and the workshop environment data, adopt the milk slurry multi-point variable-order concentration adjustment algorithm and combine it with the dynamic air flow control algorithm of the spray drying tower to jointly and real-time adjust the milk slurry concentration, optimize the air flow distribution of the spray drying tower and the equipment operation data; S10.3. Monitor the lactose crystallization trend in real time; S10.4. Collect and store all data during the milk powder production process, and conduct data report analysis.

[0026] The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made without departing from the spirit of the present invention within the scope of knowledge possessed by those skilled in the art to which the present invention pertains.

Claims

1. An intelligent management and scheduling system for a milk powder production workshop, characterized in that: include: The data acquisition and processing unit (1) uses sensors and monitoring equipment to collect milk slurry data, workshop environment data and equipment operation data in real time, and performs preliminary processing on all collected data; The milk slurry drying monitoring unit (2) is based on milk slurry data and workshop environment data, adopts a milk slurry multi-stage concentration adjustment algorithm and combines it with a spray drying tower dynamic airflow control algorithm to jointly adjust the milk slurry concentration in real time and optimize the spray drying tower airflow distribution and equipment operation data; A lactose crystallization monitoring unit (3), the lactose crystallization monitoring unit (3) is used to monitor the lactose crystallization trend in real time; The information management and tracing unit (4) is used to collect and store all data in the milk powder production process and to perform data report analysis.

2. The intelligent management and scheduling system for milk powder production workshop according to claim 1, characterized in that: The milk slurry data includes: milk slurry viscosity , milk temperature , milk concentration and lactose content ; Workshop environment data includes: workshop temperature , Workshop humidity and air velocity ; Equipment operation data includes: fan speed of spray drying tower and injection pressure ; in, For time.

3. The intelligent management and scheduling system for milk powder production workshop according to claim 2, characterized in that: The milk slurry drying monitoring unit (2) comprises a milk slurry concentration adjustment module (21) and a drying tower airflow optimization module (22); The milk slurry concentration adjustment module (21) uses a milk slurry multi-step concentration adjustment algorithm based on milk slurry data and workshop environment data to dynamically adjust the milk slurry concentration before drying, and outputs the adjusted milk slurry concentration and milk slurry concentration control residual to the drying tower airflow optimization module (22); The drying tower airflow optimization module (22) optimizes the airflow distribution in the spray drying tower by using a spray drying tower dynamic airflow control algorithm based on the adjusted milk slurry concentration as well as the workshop environment data and the equipment operation data.

4. The intelligent management and scheduling system for milk powder production workshop according to claim 3, characterized in that: The multi-division variable-order concentration adjustment algorithm for milk slurry is based on a multi-fractal dynamic fluctuation modeling method and a variable-order fractional differential response mechanism, and is used to dynamically adjust the milk slurry concentration before drying in real time. The specific method of the multi-division variable-order concentration adjustment algorithm for milk slurry is as follows: S2.1.

1. Input the milk slurry data and workshop environment data into the variable-order differential neural operator to calculate the quasi-variable-order quasi-fractal derivatives of all milk slurry data and workshop environment data. , and embed all quasi-variable-order quasi-fractal derivatives into a variable-order differential feature ; S2.1.

2. Using Moran multifractal mapping for variable-order differential features Perform nonlinear fusion to calculate the milk slurry fluctuation intensity index ; S2.1.

3. Automatically adjust the control speed of milk paste based on workshop environment data ; S2.1.4, using the variable-order Caputo derivative model to construct the adaptive milk slurry concentration control equation; S2.1.

5. According to the prediction results of the adaptive milk slurry concentration control equation, the actual milk slurry concentration is adjusted to the target milk slurry concentration in real time, and the adjusted milk slurry concentration is output. and milk concentration control residual , Increment of one time period.

5. The intelligent management and scheduling system for milk powder production workshop according to claim 4, characterized in that: In S2.1.4, the adaptive milk slurry concentration control equation is the core equation of the milk slurry multi-division variable order concentration adjustment algorithm, which is constructed based on the variable order Caputo derivative model and is used to dynamically predict the milk slurry concentration trend. The adaptive milk slurry concentration control equation is as follows: ； in, is the Caputo variable-order fractional derivative, which represents the milk concentration In time The dynamic rate of change; Automatically adjust the control speed for milk mix; is the milk concentration; For in time Target milk concentration; is the control gain coefficient; It is an indicator of milk slurry fluctuation intensity; is the fractal excitation gain coefficient.

6. The intelligent management and scheduling system for milk powder production workshop according to claim 5, characterized in that: The spray drying tower dynamic airflow control algorithm is an airflow control algorithm based on persistent coherent topology analysis and discrete differential manifold modeling, which is used to control the airflow distribution in the spray drying tower. The specific details of the spray drying tower dynamic airflow control algorithm are as follows: S2.2.

1. Adjust the milk slurry concentration The workshop environment data and equipment operation data are input into the lightweight CFD neural operator to construct the three-dimensional velocity vector field in the spray drying tower space. ; S2.2.

2. Three-dimensional velocity vector field Perform persistent homology analysis and calculate topological flux entropy ; S2.2.

3. According to the topological flux entropy , Adjusted milk concentration , lactose content and the residual of milk concentration control , calculate and obtain the adjusted fan speed and nozzle pressure; S2.2.

4. Based on topological flux entropy Weighted update of milk slurry and workshop environment data to automatically adjust the control speed .

7. The intelligent management and scheduling system for milk powder production workshop according to claim 6, characterized in that: In S2.2.3, according to the topological flux entropy , Adjusted milk concentration , lactose content and the residual of milk concentration control , calculate the adjusted fan speed and nozzle pressure, the specific method is as follows: ； ； in, After adjustment The fan speed under After adjustment Nozzle pressure under is the weight of wind turbine change impact; is the milk slurry concentration deviation weight; Adjust the residual coupling weights for fan concentration; Weights for nozzle changes; is the lactose bias weight; Adjust the residual coupling weights for nozzle concentration; is the rate of change of topological flux entropy; is the adjusted milk slurry concentration; For time Target milk concentration; time lactose content; For time target lactose content.

8. The intelligent management and scheduling system for milk powder production workshop according to claim 7, characterized in that: The lactose crystallization monitoring unit (3) is used to monitor the lactose crystallization trend in real time, as follows: Set the critical range of lactose content to , Critical milk concentration fluctuation threshold Critical milk temperature fluctuation threshold ; In time If the current lactose content is in the critical range of lactose content, the current milk slurry concentration fluctuation rate is greater than the critical milk slurry concentration fluctuation threshold, and the current milk slurry temperature fluctuation rate is greater than the critical milk slurry temperature fluctuation threshold, and the duration exceeds the manually set duration, production work will be stopped immediately.

9. The intelligent management and scheduling system for milk powder production workshop according to claim 8, characterized in that: The information management and tracing unit (4) is used to collect and store all data in the milk powder production process, including the original milk slurry data, workshop environment data and equipment operation data in the data acquisition and processing unit (1), and also includes the adjusted milk slurry concentration, milk slurry concentration control residual, topological flux entropy and milk slurry automatic adjustment control speed in the milk slurry drying monitoring unit (2), and also includes the lactose content critical range, critical milk slurry concentration fluctuation threshold and critical milk slurry temperature fluctuation threshold set in the lactose crystallization monitoring unit (3).

10. An intelligent management and scheduling method for a milk powder production workshop, used in an intelligent management and scheduling system for a milk powder production workshop as claimed in any one of claims 1 to 9, characterized in that: The steps include: S10.

1. Use sensors and monitoring equipment to collect milk slurry data, workshop environment data and equipment operation data in real time, and perform preliminary processing on all collected data; S10.

2. Based on the milk slurry data and workshop environment data, the milk slurry multi-stage concentration adjustment algorithm is used in combination with the spray drying tower dynamic airflow control algorithm to jointly adjust the milk slurry concentration in real time and optimize the spray drying tower airflow distribution and equipment operation data; S10.3, real-time monitoring of lactose crystallization trend; S10.

4. Collect and store all data in the milk powder production process, and conduct data report analysis.

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