Dynamic sterilization optimization control method and device for sterilized milk based on efficient production

Abstract

The application discloses a sterilized milk dynamic sterilization optimization control method and device based on efficient production, relates to the related field of dairy product dynamic sterilization optimization control, and comprises the following steps: respectively connecting temperature, flow, valve feedback and steam control signal links of a target sterilized milk production line in series with a digital potentiometer through an SPI control interface to obtain a digital potentiometer array; performing dynamic sterilization working condition signal time sequence monitoring on the temperature, flow, valve feedback and steam control signal links; performing time sequence memory integration on a multi-source dynamic sterilization working condition signal set sequence; performing digital potentiometer array switching action mapping analysis based on multi-source dynamic sterilization working condition signal state characteristics, and distributing to the digital potentiometer array for dynamic sterilization optimization control. The application solves the technical problems of insufficient sterilization control precision and dynamic response capability of the existing sterilized milk dynamic sterilization control, and achieves the technical effect of improving the sterilization control precision and dynamic response capability.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of dynamic sterilization optimization control of dairy products, and particularly relates to a dynamic sterilization optimization control method and device for sterilized milk based on efficient production. BACKGROUND

[0002] In the industrial production of sterilized milk, the sterilization process is a core link, and its control precision and dynamic response capability directly determine the safety quality, nutrient retention rate and production energy efficiency of the product. At present, the industry generally adopts a single-loop constant value control strategy with a PLC / PID controller as the core, and independently adjusts each actuator by presetting fixed set points of temperature, flow and other parameters. However, due to the strong coupling relationship between temperature, flow, pressure and other variables in the production line, and the slow time-varying of sterilization conditions caused by equipment fouling, changes in raw material properties and other factors, this control method based on fixed parameters and isolated loops is difficult to perceive and adapt to the dynamic correlation characteristics and overall energy efficiency of the system, often showing control lag, overshoot or continuous oscillation, resulting in insufficient or excessive sterilization, high steam energy consumption, and even system instability due to parameter mismatch.

[0003] At present, the dynamic sterilization control of sterilized milk in the related art has the technical problems of insufficient sterilization control precision and dynamic response capability. SUMMARY

[0004] The present application provides a dynamic sterilization optimization control method and device for sterilized milk based on efficient production, which adopts a digital potentiometer array formed by connecting four types of control signal links of the target sterilized milk production line through an SPI control interface and a digital potentiometer in series, performs dynamic sterilization process signal timing monitoring on the four types of links, determines a multi-source dynamic sterilization process signal set sequence, integrates the sequence timing memory, determines the signal state characteristics, analyzes the digital potentiometer array switching action based on the characteristics and distributes it, and performs dynamic sterilization optimization control. The technical means solve the technical problems of insufficient sterilization control precision and dynamic response capability of the existing dynamic sterilization control of sterilized milk, and achieve the technical effect of improving the precision and dynamic response capability of sterilization control.

[0005] The application provides a dynamic sterilization optimization control method for high-efficiency production-based sterilized milk, comprising the following steps: connecting a temperature control signal link, a flow control signal link, a valve feedback control signal link and a steam control signal link of a target sterilized milk production line through SPI control interfaces and digital potentiometers in series to obtain a digital potentiometer array; performing dynamic sterilization working condition signal timing monitoring on the temperature control signal link, the flow control signal link, the valve feedback control signal link and the steam control signal link to determine a multi-source dynamic sterilization working condition signal set sequence; performing timing memory integration on the multi-source dynamic sterilization working condition signal set sequence to determine a multi-source dynamic sterilization working condition signal state feature; and performing switching action mapping analysis of the digital potentiometer array based on the multi-source dynamic sterilization working condition signal state feature and distributing the digital potentiometer array to perform dynamic sterilization optimization control.

[0006] In a possible implementation, the temperature control signal link, the flow control signal link, the valve feedback control signal link and the steam control signal link of the target sterilized milk production line are connected through SPI control interfaces and digital potentiometers in series to obtain a digital potentiometer array, and the following processing is performed: a first digital potentiometer is connected through a first SPI control interface and connected in series between a thermocouple signal and an amplifier feedback loop in the temperature control signal link; a second digital potentiometer is connected through a second SPI control interface and connected in series between an RC filter node and a PLC analog input in the flow control signal link; a third digital potentiometer is connected through a third SPI control interface and connected in series between an output of a valve position transmitter and a PLC analog input in the valve feedback control signal link; a fourth digital potentiometer is connected through a fourth SPI control interface and connected in series between an AO output of a PLC and an I / P control input in the steam control signal link; and the first digital potentiometer, the second digital potentiometer, the third digital potentiometer and the fourth digital potentiometer are summarized to obtain a digital potentiometer array.

[0007] In a possible implementation, the timing memory integration is performed on the multi-source dynamic sterilization working condition signal set sequence to determine a multi-source dynamic sterilization working condition signal state feature, and the following processing is performed: the multi-source dynamic sterilization working condition signal set sequence is subjected to adjacent signal pre-memory association verification to obtain a memory association verification result sequence; a multi-source dynamic sterilization working condition signal set corresponding to a memory association verification result that fails in verification in the memory association verification result sequence is taken as a segmentation node, and the multi-source dynamic sterilization working condition signal set sequence is segmented to obtain Q multi-source dynamic sterilization working condition signal set subsequences, where Q is a positive integer; the Q multi-source dynamic sterilization working condition signal set subsequences are iterated to perform timing memory integration to determine Q integrated multi-source dynamic sterilization working conditions; and the Q integrated multi-source dynamic sterilization working conditions are subjected to signal state analysis to determine the multi-source dynamic sterilization working condition signal state feature.

[0008] In a possible implementation, the set sequence of the multi-source dynamic sterilization condition signals is subjected to adjacent signal pre-sequence memory correlation verification to obtain a sequence of memory correlation verification results, and the following processing is performed: a first multi-source dynamic sterilization condition signal set and a second multi-source dynamic sterilization condition signal set are extracted from the set sequence of the multi-source dynamic sterilization condition signals; an inner product mapping of adjacent signals of the first multi-source dynamic sterilization condition signal set and the second multi-source dynamic sterilization condition signal set is performed to obtain a first adjacent signal inner product mapping result; when the first adjacent signal inner product mapping result is less than a preset threshold, a first memory correlation verification result corresponding to the second multi-source dynamic sterilization condition signal set is verification failure; when the first adjacent signal inner product mapping result is greater than or equal to the preset threshold, the first memory correlation verification result corresponding to the second multi-source dynamic sterilization condition signal set is verification success; inner product mappings of adjacent multi-source dynamic sterilization condition signal sets in the set sequence of the multi-source dynamic sterilization condition signals are performed, and the results are compared with the preset threshold to obtain the sequence of the memory correlation verification results.

[0009] In a possible implementation, the Q multi-source dynamic sterilization condition signal set subsequences are traversed for time sequence memory integration to determine Q integrated multi-source dynamic sterilization condition signals, and the following processing is performed: a first multi-source dynamic sterilization condition signal set subsequence is extracted from the Q multi-source dynamic sterilization condition signal set subsequences; a first time sequence memory is obtained by performing time sequence memory integration on a first multi-source dynamic sterilization condition signal set and a second multi-source dynamic sterilization condition signal set in the first multi-source dynamic sterilization condition signal set subsequence; the first time sequence memory is used to perform time sequence memory integration on a third multi-source dynamic sterilization condition signal set in the first multi-source dynamic sterilization condition signal set subsequence, and the same is repeated, and according to a time sequence memory integration result each time, a next multi-source dynamic sterilization condition signal set in the first multi-source dynamic sterilization condition signal set subsequence is subjected to time sequence memory integration until a last multi-source dynamic sterilization condition signal set in the first multi-source dynamic sterilization condition signal set subsequence is reached to obtain a first integrated multi-source dynamic sterilization condition signal; and the first integrated multi-source dynamic sterilization condition signal is added to the Q integrated multi-source dynamic sterilization condition signals.

[0010] In a possible implementation, the first multi-source dynamic sterilization process signal set sub-sequence and the second multi-source dynamic sterilization process signal set are subjected to time sequence memory integration to obtain a first time sequence memory, and the following processing is performed: from the four dimensions of the temperature control signal, the flow control signal, the valve feedback control signal, and the steam control signal, the fine-grained correlation similarity of the first multi-source dynamic sterilization process signal set and the second multi-source dynamic sterilization process signal set is calculated to obtain a first fine-grained correlation similarity set; the first fine-grained correlation similarity set is subjected to adjacency matrix processing to obtain a first adjacency matrix; the second multi-source dynamic sterilization process signal set is subjected to convolution integration by using the first adjacency matrix to obtain the first time sequence memory.

[0011] In a possible implementation, the first fine-grained correlation similarity set is subjected to adjacency matrix processing to obtain a first adjacency matrix, and the following processing is performed: the first fine-grained correlation similarity set is subjected to standardization processing to obtain a first fine-grained correlation similarity standard value set; the first fine-grained correlation similarity standard value set is added to an initially empty matrix to construct the first adjacency matrix.

[0012] In a possible implementation, the switching action mapping analysis based on the multi-source dynamic sterilization process signal state feature is performed and distributed to the digital potentiometer array for dynamic sterilization optimization control, and the following processing is performed: a switching action mapping analyzer is pre-constructed, and the multi-source dynamic sterilization process signal state feature is transmitted to the switching action mapping analyzer for analysis by using the digital potentiometer array to obtain a digital potentiometer switching action array; the digital potentiometer switching action array is distributed to the digital potentiometer array for dynamic sterilization optimization control.

[0013] In a possible implementation, the following processing is performed: the resolution of the digital potentiometer is greater than or equal to 8 bits, and an end-to-end resistance of 10 kΩ-50 kΩ is adopted.

[0014] The application also provides a dynamic sterilization optimization control device for sterilized milk based on efficient production, comprising: a digital potentiometer array acquisition module, configured to respectively connect a temperature control signal link, a flow control signal link, a valve feedback control signal link and a steam control signal link of a target sterilized milk production line in series with a digital potentiometer through an SPI control interface, and obtain a digital potentiometer array; a dynamic sterilization working condition signal time sequence monitoring module, configured to traverse the temperature control signal link, the flow control signal link, the valve feedback control signal link and the steam control signal link to perform dynamic sterilization working condition signal time sequence monitoring, and determine a multi-source dynamic sterilization working condition signal set sequence; a time sequence memory integration module, configured to perform time sequence memory integration on the multi-source dynamic sterilization working condition signal set sequence, and determine a multi-source dynamic sterilization working condition signal state feature; and a dynamic sterilization optimization control module, configured to perform switching action mapping analysis of the digital potentiometer array based on the multi-source dynamic sterilization working condition signal state feature, and distribute to the digital potentiometer array for dynamic sterilization optimization control.

[0015] The dynamic sterilization optimization control method and device for sterilized milk based on efficient production provided in the application first connect a temperature control signal link, a flow control signal link, a valve feedback control signal link and a steam control signal link of a target sterilized milk production line in series with a digital potentiometer through an SPI control interface, and obtain a digital potentiometer array, then traverse the temperature control signal link, the flow control signal link, the valve feedback control signal link and the steam control signal link to perform dynamic sterilization working condition signal time sequence monitoring, determine a multi-source dynamic sterilization working condition signal set sequence, then perform time sequence memory integration on the multi-source dynamic sterilization working condition signal set sequence, determine a multi-source dynamic sterilization working condition signal state feature, and finally perform switching action mapping analysis of the digital potentiometer array based on the multi-source dynamic sterilization working condition signal state feature, and distribute to the digital potentiometer array for dynamic sterilization optimization control. Through the above process, the method and device provided in the application achieve the technical effect of improving the precision and dynamic response capability of sterilization control. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings of the embodiments of the application will be briefly introduced as follows. In the present application, a flow chart is used to illustrate the operations performed by the device according to the embodiments of the application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously according to needs. Meanwhile, other operations can be added to these processes, or a step or several steps can be removed from these processes.

[0017] Figure 1 The flowchart of the dynamic sterilization optimization control method for sterilized milk based on efficient production provided in the embodiments of the application is shown.

[0018] Figure 2 The structural schematic diagram of the dynamic sterilization optimization control device for sterilized milk based on efficient production provided by the embodiment of the present application is shown.

[0019] The reference numerals are explained: a digital potentiometer array acquisition module 10, a dynamic sterilization working condition signal timing monitoring module 20, a timing memory integration module 30, and a dynamic sterilization optimization control module 40. DETAILED DESCRIPTION

[0020] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined application purposes, the specific embodiments, structures, features and effects according to the present application are described in detail as follows in combination with the drawings and preferred embodiments.

[0021] The embodiment of the present application provides a dynamic sterilization optimization control method for sterilized milk based on efficient production, as shown in the following formula: Figure 1 The method comprises the following steps:

[0022] In step S100, the temperature control signal link, the flow control signal link, the valve feedback control signal link and the steam control signal link of the target sterilized milk production line are respectively connected in series with the digital potentiometer through the SPI control interface, so as to obtain a digital potentiometer array. The resolution of the digital potentiometer is greater than or equal to 8 bits, and the end-to-end resistance is 10kΩ-50kΩ.

[0023] Specifically, the hardware basis of the system is modified. The digital potentiometer is connected in series on the existing production line key control loop. The digital potentiometer is a resistor device controlled by digital signals. These digital potentiometers are connected with the central controller through the serial peripheral interface SPI communication protocol to form a digital potentiometer array. In this way, the central controller can dynamically change the resistance value of these digital potentiometers by sending digital instructions, so as to fine-tune the current or voltage signal flowing through these loops, thereby indirectly controlling the temperature, flow, valve and steam. The digital potentiometer with a resolution greater than or equal to 8 bits is selected. The resolution greater than or equal to 8 bits means that each potentiometer has at least 256 adjustable gears, which has sufficient adjustment accuracy. The resistance range of 10kΩ-50kΩ is used to match the signal level of the industrial sensor and the controller, so as to ensure that the signal can be effectively adjusted and the original circuit is not overloaded.

[0024] For example, in a temperature control loop, the weak voltage signal generated by the thermocouple needs to be amplified by an amplifier before it can be recognized by the PLC. After the digital potentiometer is connected here, the central controller can slightly increase or decrease the resistance of the digital potentiometer through the SPI, so as to slightly increase or decrease the signal voltage input into the amplifier, and finally realize the fine adjustment of the set temperature, so as to ensure that the sterilization temperature is always stable at the optimal value.

[0025] In a possible implementation, the temperature control signal link, the flow control signal link, the valve feedback control signal link and the steam control signal link of the target sterilization milk production line are respectively connected in series with the digital potentiometer through the SPI control interface, to obtain a digital potentiometer array, and step S100 further includes step S110 of connecting the first digital potentiometer in series to the temperature control signal link between a thermocouple signal and an amplifier feedback loop through the first SPI control interface. Specifically, one digital potentiometer is selected and connected in series between a temperature sensor, i.e. a thermocouple, and a subsequent signal amplifier. Since the thermocouple signal is a weak voltage at a millivolt level, a resistor is connected at this position, and the original voltage signal can be finely biased or attenuated by changing the resistance value. According to the temperature control requirement, the resistance value and the adjustment range of the digital potentiometer are set. For example, a 12-bit resolution, 20kΩ digital potentiometer is selected and connected in series to the temperature control signal link, to adjust the amplification multiple of the thermocouple signal.

[0026] Step S120 connects the second digital potentiometer in series to the RC filter node and the PLC analog input in the flow control signal link through the second SPI control interface. Specifically, the digital potentiometer is connected in the path of the flow transmitter output signal after RC filtering and before entering the PLC analog input interface, where the RC filter is used to eliminate high-frequency noise. According to the flow control requirement, the resistance value and the adjustment range of the digital potentiometer are set. For example, a 10-bit resolution, 15kΩ digital potentiometer is selected and connected in series to the flow control signal link, to adjust the output signal of the RC filter node.

[0027] Step S130 connects the third digital potentiometer in series to the output of the valve position transmitter and the PLC analog input in the valve feedback control signal link through the third SPI control interface. Specifically, similar to step S120, the digital potentiometer is connected in series between the valve position transmitter and the PLC, where the valve position transmitter is used to report the actual opening degree of the valve. According to the valve feedback control requirement, the resistance value and the adjustment range of the digital potentiometer are set. For example, an 8-bit resolution, 10kΩ digital potentiometer is selected and connected in series to the valve feedback control signal link, to adjust the output signal of the valve position transmitter.

[0028] Step S140, the fourth digital potentiometer is connected in series between the AO output of the PLC and the I / P control input of the steam control signal link through the fourth SPI control interface. Specifically, the digital potentiometer is connected in series between the analog output module of the PLC and the current / pneumatic pressure converter. The PLC outputs a control signal to the I / P converter through the AO module, and the I / P converter converts it into a pneumatic pressure signal in proportion to drive the steam valve. According to the steam control requirement, the resistance value and adjustment range of the digital potentiometer are set. For example, a 12-bit resolution, 25kΩ digital potentiometer is selected and connected in series in the steam control signal link to adjust the AO output signal of the PLC.

[0029] Step S150, the first digital potentiometer, the second digital potentiometer, the third digital potentiometer and the fourth digital potentiometer are aggregated to obtain a digital potentiometer array. Specifically, the above four independently installed digital potentiometers are logically and physically assembled into a unified array that can be centrally addressed and managed by the central controller. The controller can send control commands to any digital potentiometer in the array simultaneously or separately. For example, using the SPI interface manager, the four digital potentiometers are aggregated into a digital potentiometer array and controlled uniformly through the SPI interface.

[0030] Step S200, dynamic sterilization process signal timing monitoring is performed on the temperature control signal link, the flow control signal link, the valve feedback control signal link and the steam control signal link to determine a multi-source dynamic sterilization process signal set sequence.

[0031] Specifically, real-time data is continuously and cyclically collected from the above four control links with implanted digital potentiometers. These data are recorded in chronological order to form a sequence set that changes with time and contains temperature, flow, valve opening and steam pressure signals. The data at each time point is a set containing four signal values, and a series of time point sets constitute a multi-source dynamic sterilization process signal set sequence.

[0032] For example, data is collected once every second. At T1, the following data is collected: temperature = 135.2°C, flow = 1000L / h, valve opening = 65%, steam pressure = 0.55MPa; at T2, the following data is collected: temperature = 135.5°C, flow = 998L / h, valve opening = 66%, steam pressure = 0.56MPa. Combining these sets arranged in time order, a multi-source signal sequence reflecting the dynamic changes of the entire sterilization process is formed.

[0033] Step S300, the multi-source dynamic sterilization process signal set sequence is integrated by timing memory to determine the multi-source dynamic sterilization process signal state characteristics.

[0034] Specifically, a large amount of time series data collected in step S200 is processed. The original data sequence is lengthy and contains noise, and direct use for control is inefficient. Through time series memory integration, useful information in a long time sequence is extracted and fused into a state feature that can represent the overall working condition in this time period, which is used to describe the current state of the signal. For example, the collected temperature, flow and other signals are subjected to time series memory integration using an LSTM long short-term memory network, and then the state feature is determined through a feature extraction algorithm.

[0035] In one possible implementation, the multi-source dynamic sterilization working condition signal set sequence is subjected to time series memory integration to determine the multi-source dynamic sterilization working condition signal state feature, and step S300 further includes step S310 of performing adjacent signal pre-sequence memory correlation verification on the multi-source dynamic sterilization working condition signal set sequence to obtain a memory correlation verification result sequence. Specifically, the similarity index between the signal sets of two adjacent time points is calculated. If the similarity is very high, it means that the working condition is stable; if the similarity suddenly becomes very low, it means that a significant change has occurred, such as device start-stop, raw material switching, etc. By performing adjacent signal pre-sequence memory correlation verification, the mutation points in the sequence are found.

[0036] Step S320 takes the multi-source dynamic sterilization working condition signal set corresponding to the memory correlation verification result of the verification failure in the memory correlation verification result sequence as a segmentation node, segments the multi-source dynamic sterilization working condition signal set sequence, and obtains Q multi-source dynamic sterilization working condition signal set sub-sequences, where Q is a positive integer. Specifically, the mutation points found in step S310, i.e., the points of verification failure, are used as boundaries to cut the original time sequence into multiple shorter sub-sequences with relatively stable internal states. Each sub-sequence represents an independent working condition stage. For example, if the entire sequence has verification failures at T3 and T15, the sequence is segmented into three sub-sequences: sub-sequence 1: T1-T2 warming stage, sub-sequence 2: T4-T14 holding stage, and sub-sequence 3: T16-Tn cooling stage, where n is the last time.

[0037] Step S330 traverses the Q multi-source dynamic sterilization working condition signal set sub-sequences to perform time series memory integration and determine Q integrated multi-source dynamic sterilization working condition signals. Specifically, for each segmented sub-sequence with a stable internal state, a recursive fusion method is used to fuse the data at all time points in the sub-sequence into a single, information-rich integrated signal. This integrated signal contains all the time series memories of the sub-sequence.

[0038] Step S340, signal state analysis is performed on the Q integrated multi-source dynamic sterilization process signals to determine the multi-source dynamic sterilization process signal state characteristics. Specifically, final feature extraction is performed on each integrated signal generated in step S330, including calculating statistical characteristics such as mean, variance, frequency domain characteristics, or determining the state category to which it belongs through a classifier / regressor, such as the optimal sterilization state, slight oscillation state, deviation from the set state, etc. Finally, the multi-source dynamic sterilization process signal state characteristics are output to determine the current state of the signal. For example, for the integrated signal of the holding phase subsequence, it is found after analysis that the temperature mean is stable at 135°C, but the flow rate has small periodic fluctuations. Therefore, the state characteristics of this phase can be marked as: state: steady-state holding, health: good, attention: slight flow fluctuation.

[0039] In one possible implementation, the multi-source dynamic sterilization process signal set sequence is subjected to adjacent signal pre-sequence memory correlation verification, and a memory correlation verification result sequence is obtained. Step S310 further includes step S311 of extracting a first multi-source dynamic sterilization process signal set and a second multi-source dynamic sterilization process signal set from the multi-source dynamic sterilization process signal set sequence. Specifically, two adjacent data sets at two time points are sequentially taken out from the complete signal sequence arranged in chronological order for analysis. The first multi-source dynamic sterilization process signal set refers to the data at the previous time point, and the second multi-source dynamic sterilization process signal set refers to the data at the immediately subsequent time point. The two sets constitute a pair of adjacent data that needs to be verified for correlation.

[0040] Step S312, inner product mapping of the adjacent signals of the first multi-source dynamic sterilization process signal set and the second multi-source dynamic sterilization process signal set is performed to obtain a first adjacent signal inner product mapping result. Specifically, the similarity between the two signal sets is calculated and quantitatively compared using vector inner product. First, each signal set containing four values of temperature, flow rate, valve opening, and steam pressure is taken as a vector in a multi-dimensional space. Then, the inner product of the two vectors, i.e., the first multi-source dynamic sterilization process signal set vector and the second multi-source dynamic sterilization process signal set vector, is calculated. The size of the inner product value reflects the overall similarity of the two vectors in direction and amplitude. The larger the inner product value, the more similar the process at the two time points and the more stable the change; the smaller the inner product value, the more significant the change.

[0041] Step S313, when the first adjacent signal inner product mapping result is less than the preset threshold, the first memory correlation verification result corresponding to the second multi-source dynamic sterilization working condition signal set is verification failure. Specifically, the calculated inner product result is compared with the pre-set threshold, when the inner product result is less than the threshold, it indicates that there is a significant difference between the signal sets of two adjacent time points, and the working condition has changed. Therefore, it is determined that the correlation verification of the second multi-source dynamic sterilization working condition signal set and the first multi-source dynamic sterilization working condition signal set fails, and this point is marked as a cut point.

[0042] Step S314, when the first adjacent signal inner product mapping result is greater than or equal to the preset threshold, the first memory correlation verification result corresponding to the second multi-source dynamic sterilization working condition signal set is verification success. Specifically, based on the comparison of the inner product and the threshold, when the inner product result is greater than or equal to the preset threshold, it indicates that the signal sets of two adjacent time points are highly similar, the working condition is stable and continuous, and no mutation occurs. Therefore, it is determined that the correlation verification of the second multi-source dynamic sterilization working condition signal set and the first multi-source dynamic sterilization working condition signal set is successful.

[0043] Step S315, the inner product mapping of the adjacent signals is performed on all adjacent multi-source dynamic sterilization working condition signal sets in the multi-source dynamic sterilization working condition signal set sequence, and the results are compared with the preset threshold to obtain the memory correlation verification result sequence. Specifically, a loop program is used to sequentially process each pair of adjacent signal sets in the sequence according to steps S312 to S314. Each comparison will produce a success or failure label. Finally, all these labels are arranged in chronological order to form a memory correlation verification result sequence corresponding to the original data sequence.

[0044] For example, for a sequence containing 100 time points, through the above process, a sequence containing 99 verification results is generated, which marks all the time points of state mutation in the entire sterilization process, and is used to provide boundaries for data cutting.

[0045] In a possible implementation, the Q multi-source dynamic sterilization working condition signal set sub-sequences are traversed to perform time sequence memory integration, and Q integrated multi-source dynamic sterilization working condition signals are determined, and step S330 further includes step S331 of extracting a first multi-source dynamic sterilization working condition signal set sub-sequence from the Q multi-source dynamic sterilization working condition signal set sub-sequences. Specifically, from all the sub-sequences cut in step S320, an arbitrary sub-sequence is selected as the current processing object, that is, the first multi-source dynamic sterilization working condition signal set sub-sequence. The global data processing task is decomposed into independent processing tasks for multiple local sub-sequences with stable internal states.

[0046] Step S332, the first multi-source dynamic sterilization condition signal set and the second multi-source dynamic sterilization condition signal set of the first multi-source dynamic sterilization condition signal set sub-sequence are time sequence memory integrated to obtain a first time sequence memory. Specifically, from the sub-sequence currently being processed, the data of the first two time points, i.e., the first multi-source dynamic sterilization condition signal set and the second multi-source dynamic sterilization condition signal set, are taken out, and a fusion method such as weighted average or gated recurrent unit calculation based on attention mechanism is used to fuse the two discrete data points into a new comprehensive data representation containing information of the previous two time points. The comprehensive representation is the first time sequence memory.

[0047] Step S333, the third multi-source dynamic sterilization condition signal set of the first multi-source dynamic sterilization condition signal set sub-sequence is time sequence memory integrated using the first time sequence memory, and the next multi-source dynamic sterilization condition signal set in the first multi-source dynamic sterilization condition signal set sub-sequence is time sequence memory integrated according to the time sequence memory integration result each time, until the last multi-source dynamic sterilization condition signal set in the first multi-source dynamic sterilization condition signal set sub-sequence is reached to obtain a first integrated multi-source dynamic sterilization condition signal. Specifically, the time sequence memory integration is recursively iterated, and the time sequence memory generated in the last step is fused with the data of the next time point in the sub-sequence, such as the third, fourth, and so on until the last. Each fusion combines the new instantaneous data with the current memory to update the memory to contain information of a longer time span. The memory is constantly enriched and updated as each new data point is processed until the last data point of the sub-sequence is processed. Finally, the first integrated multi-source dynamic sterilization condition signal is obtained, which is a complete representation of all information of the entire sub-sequence.

[0048] Step S334, the first integrated multi-source dynamic sterilization condition signal is added to the Q integrated multi-source dynamic sterilization condition signals. Specifically, a list or array set is created to store the final result, and the first integrated multi-source dynamic sterilization condition signal representing all information of the first multi-source dynamic sterilization condition signal set sub-sequence obtained in step S333 is stored in the set. The set finally contains the integrated signals of all Q sub-sequences.

[0049] In a possible implementation, the first multi-source dynamic sterilization process signal set and the second multi-source dynamic sterilization process signal set of the first multi-source dynamic sterilization process signal set subsequence are time sequence memory integrated to obtain a first time sequence memory. Step S332 further includes step S3321. The first multi-source dynamic sterilization process signal set and the second multi-source dynamic sterilization process signal set are calculated in four dimensions of temperature control signals, flow control signals, valve feedback control signals, and steam control signals to obtain a first fine-grained correlation similarity set. Specifically, the fine-grained similarity analysis no longer calculates a single similarity by taking the entire signal set at two time points as a whole, but independently calculates the numerical correlation of the first multi-source dynamic sterilization process signal set and the second multi-source dynamic sterilization process signal set in each control variable dimension. This correlation can be quantified by calculating the cosine similarity, Pearson correlation coefficient, or Gaussian kernel function based on the difference value of the two values in the dimension. Finally, a set containing four similarity values, i.e., the first fine-grained correlation similarity set, is obtained, which describes whether the change pattern of each control variable is consistent at two adjacent time points.

[0050] Step S3322, the first fine-grained correlation similarity set is subjected to adjacency matrix processing to obtain a first adjacency matrix. Specifically, the discrete similarity information is structured, i.e., the four control variables are regarded as four nodes in a fully connected graph, and the correlation similarity between the four variables calculated in step S3321 is filled into the diagonal line of a 4*4 matrix. The matrix is the first adjacency matrix, which quantitatively describes the mutual correlation strength between the dynamic behaviors of the control variables in the transition from the first time point to the second time point.

[0051] Step S3323, the second multi-source dynamic sterilization process signal set is convoluted and integrated by using the first adjacency matrix to obtain a first time sequence memory. Specifically, the first adjacency matrix obtained in step S3322 is used as a graph structure in a graph convolution network, and the four signal values in the second multi-source dynamic sterilization process signal set are regarded as the initial features of the four nodes in the graph. A graph convolution operation is performed, which is essentially to make the information of each node interact and aggregate with the information of the associated nodes according to the correlation strength defined by the first adjacency matrix. Through this weighted aggregation, the information in the second multi-source dynamic sterilization process signal set is filtered or integrated according to its inherent correlation pattern, and a new feature representation considering the mutual influence between variables is output, which is the first time sequence memory. The first time sequence memory is no longer the original data at the second time point, but an enhanced representation containing the state transition pattern information from the first time point to the second time point.

[0052] In a possible implementation, the first set of fine-grained correlation similarities is subjected to an adjacency matrix processing, obtaining a first adjacency matrix, and step S3322 further includes step S33221, the first set of fine-grained correlation similarities is subjected to a standardization processing, obtaining a first set of fine-grained correlation similarity standard values. Specifically, the four similarity values in the first set of fine-grained correlation similarities are processed by a standardization algorithm. For example, Min-Max standardization or Z-Score standardization can be used. Through the standardization processing, it is ensured that all similarity values are in a unified numerical range, the dimensional influence is eliminated, and the numerical stability is improved.

[0053] Step S33222, the first set of fine-grained correlation similarity standard values is added to an initially empty matrix to construct a first adjacency matrix. Specifically, a 4x4 matrix with all elements being zero is initialized, and the values in the first set of fine-grained correlation similarity standard values after the standardization are placed on the main diagonal of the matrix to represent the correlation of each variable with itself. This filled matrix is the first adjacency matrix used for graph convolution operation.

[0054] Step S400, based on the multi-source dynamic sterilization working condition signal state feature, the switching action mapping analysis of the digital potentiometer array is performed, and is distributed to the digital potentiometer array for dynamic sterilization optimization control.

[0055] Specifically, the multi-source dynamic sterilization working condition signal state feature accurately describing the current system running state obtained by analysis is taken as input, a preset control decision mechanism is used to analyze the accurate resistance adjustment instructions, i.e. switching actions, required for the four digital potentiometers respectively, and then the instructions are sent to the corresponding digital potentiometers for execution, so as to optimize the control parameters of the sterilization process, improve the production efficiency, stabilize the product quality and reduce the energy consumption under the premise of ensuring the sterilization effect.

[0056] In a possible implementation, the switching action mapping analysis of the digital potentiometer array is performed based on the multi-source dynamic sterilization process signal state features, and is distributed to the digital potentiometer array for dynamic sterilization optimization control, and step S400 further includes step S410, a switching action mapping analyzer is pre-constructed, and the multi-source dynamic sterilization process signal state features are transmitted to the switching action mapping analyzer for analysis to obtain a digital potentiometer switching action array. Specifically, a switching action mapping analyzer is pre-designed and constructed, and the switching action mapping analyzer is essentially a control strategy function, which can be implemented based on multiple technologies. Specifically, a fuzzy rule base can be used to store a series of expert experience rules of “IF (state feature) THEN (executed action)”. For example, “IF the state is ‘slow temperature rise’ THEN increase the temperature loop digital potentiometer resistance value X gear and increase the steam loop digital potentiometer resistance value Y gear”. A query table can also be used, that is, a pre-set table listing the optimal digital potentiometer resistance value configuration corresponding to various typical state features. A machine learning model, such as a neural network model trained by a large amount of historical data, can also be used. The model can learn the nonlinear mapping relationship from complex state features to optimal control actions, thereby achieving precise control.

[0057] During runtime, the multi-source dynamic sterilization process signal state features analyzed in real time are input into the switching action mapping analyzer, the analyzer calculates or queries, and outputs a set of instructions containing four elements, that is, a digital potentiometer switching action array, which specifies the target resistance values or gears that the first to fourth digital potentiometers need to adjust to.

[0058] Step S420, the digital potentiometer switching action array is distributed to the digital potentiometer array for dynamic sterilization optimization control. Specifically, the central controller sends the instructions in the digital potentiometer switching action array generated in step S410 to each digital potentiometer in the digital potentiometer array in the form of a digital signal through the SPI communication bus, sequentially or in parallel. After receiving the corresponding instructions, each digital potentiometer drives the internal electronic switch to change the resistance tap position and adjust the resistance value to the target value required by the instructions. This change in resistance value directly fine-tunes the voltage or current of the signal link where it is located, and ultimately conducts to the actuator, such as a heater or a regulating valve, to achieve dynamic and fine optimization control of the sterilization process. This perception-decision-execution cycle continues to be performed, forming an intelligent control system that can adapt to changes in working conditions.

[0059] This application employs a technique that connects four types of control signal links in the target sterilized milk production line to a digital potentiometer array via an SPI control interface. It then traverses these four types of links to monitor the timing of dynamic sterilization signals, determines a sequence of multi-source dynamic sterilization signals, memorizes and integrates this sequence, identifies signal state characteristics, analyzes and distributes the switching actions of the digital potentiometer array based on these characteristics, and performs dynamic sterilization optimization control. This technique solves the technical problems of insufficient sterilization control accuracy and dynamic response capability in existing dynamic sterilization control of sterilized milk, achieving the technical effect of improving sterilization control accuracy and dynamic response capability.

[0060] In the above text, refer to Figure 1 This paper describes in detail a dynamic sterilization optimization control method for sterilized milk based on efficient production, according to embodiments of the present invention. Next, reference will be made to... Figure 2 This invention describes a dynamic sterilization optimization control device for sterilized milk based on efficient production, according to an embodiment of the present invention.

[0061] The dynamic sterilization optimization control device for sterilized milk based on high-efficiency production, according to embodiments of the present invention, addresses the technical problems of insufficient sterilization control accuracy and dynamic response capability in existing dynamic sterilization control methods for sterilized milk, thereby improving the technical effect of enhancing sterilization control accuracy and dynamic response capability. The dynamic sterilization optimization control device for sterilized milk based on high-efficiency production includes: a digital potentiometer array acquisition module 10, a dynamic sterilization condition signal timing monitoring module 20, a timing memory integration module 30, and a dynamic sterilization optimization control module 40.

[0062] The digital potentiometer array acquisition module 10 is used to connect the temperature control signal link, flow control signal link, valve feedback control signal link, and steam control signal link of the target sterilized milk production line in series with digital potentiometers through an SPI control interface to obtain a digital potentiometer array; the dynamic sterilization condition signal timing monitoring module 20 is used to traverse the temperature control signal link, flow control signal link, valve feedback control signal link, and steam control signal link to perform dynamic sterilization condition signal timing monitoring and determine the multi-source dynamic sterilization condition signal set sequence; the timing memory integration module 30 is used to perform timing memory integration on the multi-source dynamic sterilization condition signal set sequence to determine the multi-source dynamic sterilization condition signal state characteristics; the dynamic sterilization optimization control module 40 is used to perform switching action mapping and parsing of the digital potentiometer array based on the multi-source dynamic sterilization condition signal state characteristics and distribute it to the digital potentiometer array for dynamic sterilization optimization control.

[0063] The detailed description of the specific configuration of the digital potentiometer array acquisition module 10 is explained as follows: as described above, the temperature control signal link, the flow control signal link, the valve feedback control signal link and the steam control signal link of the target sterilization milk production line are respectively connected in series with the digital potentiometer through the SPI control interface to obtain the digital potentiometer array, and the digital potentiometer array acquisition module 10 can further include: a first digital potentiometer series connection unit for connecting the first digital potentiometer in series between the thermocouple signal and the amplifier feedback loop in the temperature control signal link through the first SPI control interface; a second digital potentiometer series connection unit for connecting the second digital potentiometer in series between the RC filter node and the PLC analog input in the flow control signal link through the second SPI control interface; a third digital potentiometer series connection unit for connecting the third digital potentiometer in series between the output of the valve position transmitter and the PLC analog input in the valve feedback control signal link through the third SPI control interface; a fourth digital potentiometer series connection unit for connecting the fourth digital potentiometer in series between the AO output of the PLC and the I / P control input in the steam control signal link through the fourth SPI control interface; and a digital potentiometer array generation unit for collecting the first digital potentiometer, the second digital potentiometer, the third digital potentiometer and the fourth digital potentiometer to obtain the digital potentiometer array.

[0064] The detailed description of the specific configuration of the timing memory integration module 30 is explained as follows: as described above, the timing memory integration module 30 can further include: an adjacent signal pre-memory association verification unit for performing adjacent signal pre-memory association verification on the multi-source dynamic sterilization working condition signal set sequence to obtain a memory association verification result sequence; a sequence cutting unit for cutting the multi-source dynamic sterilization working condition signal set corresponding to the memory association verification result of the memory association verification result sequence that fails verification as a cutting node, cutting the multi-source dynamic sterilization working condition signal set sequence to obtain Q multi-source dynamic sterilization working condition signal set subsequences, wherein Q is a positive integer; a timing memory integration unit for traversing the Q multi-source dynamic sterilization working condition signal set subsequences to perform timing memory integration and determine Q integrated multi-source dynamic sterilization working condition signals; and a signal state analysis unit for performing signal state analysis on the Q integrated multi-source dynamic sterilization working condition signals to determine the multi-source dynamic sterilization working condition signal state characteristics.

[0065] The adjacent signal presequence memory correlation verification unit can further include: a multi-source dynamic sterilization condition signal set extraction subunit configured to extract a first multi-source dynamic sterilization condition signal set and a second multi-source dynamic sterilization condition signal set from the multi-source dynamic sterilization condition signal set sequence; an adjacent signal inner product mapping subunit configured to perform adjacent signal inner product mapping on the first multi-source dynamic sterilization condition signal set and the second multi-source dynamic sterilization condition signal set, to obtain a first adjacent signal inner product mapping result; a verification subunit configured to, when the first adjacent signal inner product mapping result is less than a preset threshold, determine that a first memory correlation verification result corresponding to the second multi-source dynamic sterilization condition signal set is a verification failure, and when the first adjacent signal inner product mapping result is greater than or equal to the preset threshold, determine that the first memory correlation verification result corresponding to the second multi-source dynamic sterilization condition signal set is a verification success; and a memory correlation verification result sequence acquisition subunit configured to perform adjacent signal inner product mapping on all adjacent multi-source dynamic sterilization condition signal sets in the multi-source dynamic sterilization condition signal set sequence, and compare the result with the preset threshold to obtain the memory correlation verification result sequence.

[0066] The time sequence memory integration unit can further include: a first multi-source dynamic sterilization condition signal set subsequence extraction subunit configured to extract a first multi-source dynamic sterilization condition signal set subsequence from the Q multi-source dynamic sterilization condition signal set subsequences; a first time sequence memory acquisition subunit configured to perform time sequence memory integration on a first multi-source dynamic sterilization condition signal set and a second multi-source dynamic sterilization condition signal set in the first multi-source dynamic sterilization condition signal set subsequence, to obtain a first time sequence memory; a first integrated multi-source dynamic sterilization condition signal acquisition subunit configured to perform time sequence memory integration on a third multi-source dynamic sterilization condition signal set in the first multi-source dynamic sterilization condition signal set subsequence by using the first time sequence memory, and perform time sequence memory integration on a next multi-source dynamic sterilization condition signal set in the first multi-source dynamic sterilization condition signal set subsequence according to a result of each time sequence memory integration, until a last multi-source dynamic sterilization condition signal set in the first multi-source dynamic sterilization condition signal set subsequence is reached, to obtain a first integrated multi-source dynamic sterilization condition signal; and a Q integrated multi-source dynamic sterilization condition signal acquisition subunit configured to add the first integrated multi-source dynamic sterilization condition signal to the Q integrated multi-source dynamic sterilization condition signals.

[0067] Wherein, the first multi-source dynamic sterilization condition signal set and the second multi-source dynamic sterilization condition signal set are time sequence memory integrated to obtain a first time sequence memory, and the first time sequence memory acquisition subunit can further include: a fine-grained correlation similarity calculation component for calculating the fine-grained correlation similarity of the first multi-source dynamic sterilization condition signal set and the second multi-source dynamic sterilization condition signal set from four dimensions of temperature control signals, flow control signals, valve feedback control signals and steam control signals to obtain a first fine-grained correlation similarity set; an adjacency matrix processing component for performing adjacency matrix processing on the first fine-grained correlation similarity set to obtain a first adjacency matrix; and a convolution integration component for performing convolution integration on the second multi-source dynamic sterilization condition signal set by using the first adjacency matrix to obtain the first time sequence memory.

[0068] Wherein, the first multi-source dynamic sterilization condition signal set and the second multi-source dynamic sterilization condition signal set are time sequence memory integrated to obtain a first time sequence memory, and the first time sequence memory acquisition subunit can further include: a fine-grained correlation similarity calculation component for calculating the fine-grained correlation similarity of the first multi-source dynamic sterilization condition signal set and the second multi-source dynamic sterilization condition signal set from four dimensions of temperature control signals, flow control signals, valve feedback control signals and steam control signals to obtain a first fine-grained correlation similarity set; an adjacency matrix processing component for performing adjacency matrix processing on the first fine-grained correlation similarity set to obtain a first adjacency matrix; and a convolution integration component for performing convolution integration on the second multi-source dynamic sterilization condition signal set by using the first adjacency matrix to obtain the first time sequence memory.

[0069] The dynamic sterilization optimization control module 40 is described in detail as follows: as described above, the switching action mapping analysis based on the multi-source dynamic sterilization condition signal state characteristics is mapped and distributed to the digital potentiometer array for dynamic sterilization optimization control, and the dynamic sterilization optimization control module 40 can further include: a switching action mapping unit for pre-building a switching action mapping analyzer and transmitting the multi-source dynamic sterilization condition signal state characteristics to the switching action mapping analyzer for analysis to obtain a digital potentiometer switching action array; and a switching action array distribution unit for distributing the digital potentiometer switching action array to the digital potentiometer array for dynamic sterilization optimization control.

[0070] Wherein, the digital potentiometer array acquisition module 10 can further include: the resolution of the digital potentiometer is greater than or equal to 8 bits, and the end-to-end resistance is 10kΩ-50kΩ.

[0071] The dynamic sterilization optimization control device for sterilized milk based on efficient production provided by the embodiments of the present application can execute the dynamic sterilization optimization control method for sterilized milk based on efficient production provided by any embodiment of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0072] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server, the various units and modules are only divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific name of each functional unit is only for easy mutual differentiation, and does not limit the protection scope of the present application.

[0073] The above is only the preferred embodiment of the present application, not any form of limitation on the present application, although the present application has been disclosed as above with the preferred embodiment, however, not to limit the present application, any person skilled in the art, without departing from the scope of the technical scheme of the present application, can make some changes or modifications to the above disclosed technical content for equivalent embodiments of equivalent changes, but as long as it does not deviate from the technical scheme content of the present application, any modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application, still belongs to the scope of the technical scheme of the present application.

Claims

1. A dynamic sterilization optimization control method for pasteurized milk based on efficient production, characterized in that, The method comprises: Respectively connecting the temperature control signal link, the flow control signal link, the valve feedback control signal link and the steam control signal link of the target sterilization milk production line in series with the digital potentiometer through the SPI control interface, to obtain a digital potentiometer array; Traversing the temperature control signal link, the flow control signal link, the valve feedback control signal link and the steam control signal link to perform dynamic sterilization working condition signal timing monitoring, to determine a multi-source dynamic sterilization working condition signal set sequence; Performing timing memory integration on the multi-source dynamic sterilization working condition signal set sequence to determine a multi-source dynamic sterilization working condition signal state feature; Based on the multi-source dynamic sterilization working condition signal state feature, performing switching action mapping analysis of the digital potentiometer array and distributing to the digital potentiometer array for dynamic sterilization optimization control; Wherein, the timing memory integration on the multi-source dynamic sterilization working condition signal set sequence to determine a multi-source dynamic sterilization working condition signal state feature comprises: Performing adjacent signal pre-sequence memory association verification on the multi-source dynamic sterilization working condition signal set sequence to obtain a memory association verification result sequence; Taking the multi-source dynamic sterilization working condition signal set corresponding to the memory association verification result that fails in the memory association verification result sequence as a segmentation node, segmenting the multi-source dynamic sterilization working condition signal set sequence to obtain Q multi-source dynamic sterilization working condition signal set subsequences, wherein Q is a positive integer; Traversing the Q multi-source dynamic sterilization working condition signal set subsequences to perform timing memory integration to determine Q integrated multi-source dynamic sterilization working conditions; Performing signal state analysis on the Q integrated multi-source dynamic sterilization working conditions to determine the multi-source dynamic sterilization working condition signal state feature.

2. The high efficiency production based dynamic sterilization optimization control method for sterilized milk as claimed in claim 1, wherein, Respectively connecting the temperature control signal link, the flow control signal link, the valve feedback control signal link and the steam control signal link of the target sterilization milk production line in series with the digital potentiometer through the SPI control interface, to obtain a digital potentiometer array, comprising: Connecting the first digital potentiometer in series between the thermocouple signal and the amplifier feedback loop in the temperature control signal link through the first SPI control interface; Connecting the second digital potentiometer in series between the RC filter node and the PLC analog input in the flow control signal link through the second SPI control interface; Connecting the third digital potentiometer in series between the output of the valve position transmitter and the PLC analog input in the valve feedback control signal link through the third SPI control interface; Connecting the fourth digital potentiometer in series between the AO output of the PLC and the I / P control input in the steam control signal link through the fourth SPI control interface; Collecting the first digital potentiometer, the second digital potentiometer, the third digital potentiometer and the fourth digital potentiometer to obtain a digital potentiometer array.

3. The high efficiency production based dynamic sterilization optimization control method for sterilized milk as claimed in claim 1, wherein, Performing adjacent signal pre-sequence memory association verification on the multi-source dynamic sterilization working condition signal set sequence to obtain a memory association verification result sequence, comprising: Extracting the first multi-source dynamic sterilization working condition signal set and the second multi-source dynamic sterilization working condition signal set in the multi-source dynamic sterilization working condition signal set sequence; The first adjacent signal inner product mapping result of the first multi-source dynamic sterilization working condition signal set and the second multi-source dynamic sterilization working condition signal set is obtained; When the first adjacent signal inner product mapping result is less than a preset threshold, the first memory correlation verification result corresponding to the second multi-source dynamic sterilization working condition signal set is verification failure; When the first adjacent signal inner product mapping result is greater than or equal to the preset threshold, the first memory correlation verification result corresponding to the second multi-source dynamic sterilization working condition signal set is verification success; The adjacent multi-source dynamic sterilization working condition signal sets in the multi-source dynamic sterilization working condition signal set sequence are subjected to adjacent signal inner product mapping, and the results are compared with a preset threshold to obtain the memory correlation verification result sequence.

4. The high efficiency production based dynamic sterilization optimization control method for sterilized milk as claimed in claim 3, wherein, The Q integrated multi-source dynamic sterilization working condition signals are determined by performing time sequence memory integration on the Q multi-source dynamic sterilization working condition signal set subsequences, including: A first multi-source dynamic sterilization working condition signal set subsequence is extracted from the Q multi-source dynamic sterilization working condition signal set subsequences; The first time sequence memory is obtained by performing time sequence memory integration on the first multi-source dynamic sterilization working condition signal set subsequence and the second multi-source dynamic sterilization working condition signal set. The third multi-source dynamic sterilization working condition signal set of the first multi-source dynamic sterilization working condition signal set subsequence is integrated by using the first time sequence memory, and the next multi-source dynamic sterilization working condition signal set in the first multi-source dynamic sterilization working condition signal set subsequence is integrated by using the time sequence memory integration result each time until the last multi-source dynamic sterilization working condition signal set in the first multi-source dynamic sterilization working condition signal set subsequence is reached, and the first integrated multi-source dynamic sterilization working condition signal is obtained. The first integrated multi-source dynamic sterilization working condition signal is added to the Q integrated multi-source dynamic sterilization working condition signals.

5. The high efficiency production based dynamic sterilization optimization control method for sterilized milk as claimed in claim 4, wherein, The first time sequence memory is obtained by performing time sequence memory integration on the first multi-source dynamic sterilization working condition signal set subsequence and the second multi-source dynamic sterilization working condition signal set, including: The first fine-grained correlation similarity set is obtained by calculating the fine-grained correlation similarity of the first multi-source dynamic sterilization working condition signal set and the second multi-source dynamic sterilization working condition signal set from four dimensions of temperature control signal, flow control signal, valve feedback control signal and steam control signal. The first adjacency matrix is obtained by performing adjacency matrix processing on the first fine-grained correlation similarity set. The first time sequence memory is obtained by performing convolution integration on the second multi-source dynamic sterilization working condition signal set by using the first adjacency matrix.

6. The high efficiency production based dynamic sterilization optimization control method for sterilized milk as claimed in claim 5 wherein, The first adjacency matrix is obtained by performing adjacency matrix processing on the first fine-grained correlation similarity set, including: The first fine-grained correlation similarity standard value set is obtained by performing standardization processing on the first fine-grained correlation similarity set. The first adjacency matrix is constructed by adding the first fine-grained correlation similarity standard value set into the initially empty matrix.

7. The high efficiency production based dynamic sterilization optimization control method for sterilized milk as claimed in claim 1, wherein, The switching action mapping analysis is performed based on the multi-source dynamic sterilization working condition signal state characteristics, and is distributed to the digital potentiometer array for dynamic sterilization optimization control, including: A switching action mapping analyzer is pre-constructed, and the multi-source dynamic sterilization working condition signal state characteristics are transmitted to the switching action mapping analyzer for analysis by the digital potentiometer array, to obtain a digital potentiometer switching action array; The digital potentiometer switching action array is distributed to the digital potentiometer array for dynamic sterilization optimization control.

8. The high efficiency production based dynamic sterilization optimization control method for sterilized milk as claimed in claim 1, wherein, The resolution of the digital potentiometer is greater than or equal to 8 bits, and the end-to-end resistance is 10kΩ-50kΩ.

9. A dynamic sterilization optimization control device for pasteurized milk based on efficient production, characterized by, The device is used to implement the dynamic sterilization optimization control method of the high-efficiency production-based sterilized milk based on the high-efficiency production-based sterilized milk dynamic sterilization optimization control method, and the device includes: A digital potentiometer array acquisition module is used to connect the temperature control signal link, the flow control signal link, the valve feedback control signal link, and the steam control signal link of the target sterilized milk production line through the SPI control interface and the digital potentiometer in series, to obtain a digital potentiometer array; A dynamic sterilization working condition signal time sequence monitoring module is used to traverse the temperature control signal link, the flow control signal link, the valve feedback control signal link, and the steam control signal link for dynamic sterilization working condition signal time sequence monitoring, to determine a multi-source dynamic sterilization working condition signal set sequence; A time sequence memory integration module is used to perform time sequence memory integration on the multi-source dynamic sterilization working condition signal set sequence, to determine multi-source dynamic sterilization working condition signal state characteristics; A dynamic sterilization optimization control module is used to perform switching action mapping analysis of the digital potentiometer array based on the multi-source dynamic sterilization working condition signal state characteristics, and to distribute the digital potentiometer array for dynamic sterilization optimization control.

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