Load matching optimization method, device and medium for emulsion pump motor

Abstract

The application provides a load matching optimization method, device and medium for an emulsifying pump motor, relates to the technical field of motor load driving, and comprises the following steps: receiving a to-be-emulsified task and first motor control parameters; fitting a load imbalance stage and an imbalance vector under the influence of material rheological characteristics in an emulsification reaction process; generating M target load matching optimization stages and M matching optimization parameters; and performing parameter updating of the M target load matching optimization stages on the first motor control parameters by using the M matching optimization parameters. Through the application, the technical problem that the emulsification stability is affected due to the fact that, in the prior art, there is no overall fitting and modeling mechanism based on the emulsification task and the material properties, so that the load change cannot be accurately predicted and the parameter updating mechanism is discontinuous or has a sudden change in adjustment range can be solved, and the technical effects of improving the dynamic response capability of the emulsifying pump motor to complex loads, reducing energy consumption, improving emulsification stability and production efficiency are achieved.

Description

Technical Field

[0001] This application relates to the field of motor load drive technology, and in particular to a load matching optimization method, device and medium for emulsifying pump motors. Background Technology

[0002] Emulsifying pumps are used to mix two or more immiscible liquids to form a stable and homogeneous emulsion. During emulsification, the rheological properties of the materials (such as viscosity and shear rate) constantly change with factors such as stirring, temperature, and proportioning, causing the load on the emulsifying pump motor to exhibit nonlinear and multi-stage fluctuations. Traditional emulsifying pump motor control mostly relies on fixed control parameters or empirical settings, such as constant frequency or adjusting the motor's frequency, current, voltage, and other operating parameters through simple PID feedback. This control method lacks real-time identification and response to actual load changes, especially in applications involving high-viscosity materials or dynamic proportioning changes. This can easily lead to load imbalance, increased energy consumption, and even problems such as frequent motor start-stop, excessive temperature rise, and uneven emulsion particle size.

[0003] In recent years, although some systems have introduced frequency conversion control, fuzzy control or IoT-based data acquisition and feedback mechanisms, they still face the following technical bottlenecks: lack of an overall fitting and modeling mechanism based on emulsification tasks and material properties, inability to accurately predict load changes, and problems with discontinuous or abrupt adjustment ranges in the adjustment parameter update mechanism, which affect emulsification stability. Summary of the Invention

[0004] The purpose of this application is to provide a load matching optimization method, equipment, and medium for emulsifying pump motors, in order to solve the technical problems in the prior art that, due to the lack of an overall fitting and modeling mechanism based on emulsification tasks and material properties, it is impossible to accurately predict load changes, and the adjustment parameter update mechanism has discontinuities or abrupt changes in adjustment range, which affect the emulsification stability.

[0005] In view of the above problems, this application provides a method, equipment and medium for load matching optimization of emulsification pump motors.

[0006] In a first aspect, this application provides a load matching optimization method for an emulsifying pump motor, comprising: connecting a control terminal of an emulsifying pump device, receiving an emulsification task and first motor control parameters for controlling the motor within the emulsifying pump device after control analysis; fitting the emulsification reaction using the emulsification task and the first motor control parameters, fitting the load imbalance stage and imbalance vector under the influence of material rheological properties during the emulsification reaction process; combining the imbalance vector to perform multi-stage fluctuation consistency merging for load balancing matching of multiple stages in the load imbalance stage, generating M target load matching optimization stages and M matching optimization parameters; and updating the parameters of the M target load matching optimization stages using the M matching optimization parameters in the first motor control parameters.

[0007] In a second aspect, this application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the load matching optimization method for an emulsion pump motor as described in any one of the first aspects above.

[0008] Thirdly, this application also provides a computer-readable storage medium storing a computer program that, when executed, implements the steps of the load matching optimization method for an emulsifying pump motor described in any of the first aspects above.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] A control terminal connected to the emulsifying pump equipment receives the emulsification task and, after control analysis, the first motor control parameters used to control the motor within the emulsifying pump equipment. The emulsification reaction is fitted using the emulsification task and the first motor control parameters, fitting the load imbalance stages and imbalance vectors under the influence of material rheological characteristics during the emulsification process. The imbalance vector is combined to perform multi-stage fluctuation merging for load balancing matching in multiple stages of the load imbalance stage, generating M target load matching optimization stages and M matching optimization parameters. The parameters of the M target load matching optimization stages are updated using the M matching optimization parameters within the first motor control parameters. Based on the emulsification task and material rheological characteristics, the motor load change process is modeled and analyzed to accurately identify load imbalance stages, quantify their imbalance characteristics, and achieve precise matching of dynamic motor control parameters through multi-stage parameter optimization. This results in improved dynamic response capability of the emulsifying pump motor to complex loads, reduced energy consumption, and enhanced emulsification stability and production efficiency.

[0011] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating the load matching optimization method for emulsification pump motors used in this application.

[0014] Figure 2 This is a schematic diagram of the process for generating M target load matching optimization stages and M matching optimization parameters in the load matching optimization method for emulsifying pump motors used in this application.

[0015] Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application.

[0016] Explanation of reference numerals in the attached drawings: Bus 300, Receiver 301, Processor 302, Transmitter 303, Memory 304, Bus Interface 305. Detailed Implementation

[0017] This application provides a load matching optimization method, equipment, and medium for emulsifying pump motors, solving the technical problems in existing technologies where the lack of an overall fitting and modeling mechanism based on emulsification tasks and material properties leads to inaccurate prediction of load changes, discontinuous or abrupt adjustment of parameter update mechanisms, and impacts emulsification stability. Based on the emulsification task and material rheological characteristics, the application models and analyzes the motor load change process, accurately identifies load imbalance stages, quantifies their imbalance characteristics, and achieves precise matching of dynamic motor control parameters through multi-stage parameter optimization. This results in improved dynamic response capability of emulsifying pump motors to complex loads, reduced energy consumption, and enhanced emulsification stability and production efficiency.

[0018] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0019] Example 1, please refer to the appendix. Figure 1 This application provides a load matching optimization method for an emulsifying pump motor, which specifically includes the following steps:

[0020] Step S100: Connect to the control terminal of the emulsification pump equipment, receive the emulsification task to be performed and the first motor control parameters used to control the motor in the emulsification pump equipment after control analysis.

[0021] Specifically, the control terminal is typically an industrial control system with real-time data acquisition, task analysis, and parameter adjustment capabilities, such as a PLC-based host computer. The core responsibility of the control terminal is to receive and analyze the emulsification task, i.e., the set of process requirement parameters for the current emulsification batch, including target emulsification time, material type, viscosity range, and shear rate requirements. The control terminal will then activate a preset control analysis module. Based on the task requirements and historical operating models, this module calculates the first motor control parameters to guide the operation of the emulsification pump motor. These parameters typically include, but are not limited to, the motor's frequency (Hz), voltage (V), and current (A), determining the emulsification pump's speed, torque, and instantaneous load response capability. For example, when emulsifying high-viscosity oil phase materials, the motor frequency might be set to 45Hz, and the corresponding current output adjusted to 6.2A to ensure sufficient shearing while avoiding motor overload.

[0022] Step S200: Fit the emulsification reaction using the task to be emulsified and the control parameters of the first motor, and fit the load imbalance stage and imbalance vector under the influence of material rheological properties during the emulsification reaction process.

[0023] Specifically, to further achieve intelligent optimization of emulsifying pump motor operation, it is necessary to use the received emulsification task and the first motor control parameters as input to perform fitting modeling of the entire emulsification reaction process. In this process, a virtual twin model of the emulsifying pump is first constructed. This is an existing digital simulation technology that can perform high-precision prediction and simulation of the emulsification process based on input parameters and historical process data, even without actual physical equipment operation. The virtual twin not only reproduces the pump structure and motor response characteristics but also focuses on mapping the rheological response of the material during actual flow, shearing, and dispersion processes. The rheological properties of the material, i.e., its flow behavior under external forces, including apparent viscosity, shear thinning behavior, and elastoviscosity, are important factors determining load changes. For example, when emulsifying a system containing a polymeric thickener, its shear viscosity curve exhibits non-Newtonian characteristics, meaning that the motor load performance will change abruptly at different speed stages, forming the so-called load imbalance stage. By fitting the reaction process, these stages can be identified and their characteristic changes quantified.

[0024] The imbalance vector describes the deviation between the actual output capacity of the motor and the required load of the material. Specifically, the imbalance vector can be represented as a time series vector, where each dimension corresponds to the difference between the motor current (or power) output and the optimal load at a given time point. For example, in an experiment, when processing a composite emulsion at a frequency of 48Hz, the current was detected to fluctuate from 6.2A to 7.5A within 5 to 7 minutes, while the ideal current during this period should be 6.4A. The average deviation of the imbalance vector range was 1.1A, indicating that the load was too high during this period, and further optimization of parameter control is needed.

[0025] This simulation process not only accurately identifies multiple load imbalance points during the emulsification process, but also lays the foundation for subsequent multi-stage optimization control, significantly improving emulsification efficiency and motor lifespan.

[0026] Step S300: Combine the imbalance vector to perform multi-stage fluctuation consistency merging of multiple stages in the load imbalance stage for load balancing matching, and generate M target load matching optimization stages and M matching optimization parameters.

[0027] Specifically, after identifying multiple load imbalance stages during the emulsification process, these stages need further refinement to achieve a more stable and energy-efficient motor control strategy. This process relies on in-depth analysis of the imbalance vector to achieve load balancing matching across multiple imbalance stages and to merge stages with similar fluctuation characteristics, forming a series of implementable optimized control strategies. This process is called multi-stage fluctuation consistency merging.

[0028] First, feature extraction is performed on parameters such as fluctuation amplitude, duration, and load direction (overload or underload) for each load imbalance stage based on the imbalance vector. For example, if multiple imbalance stages exhibit high consistency in these dimensions, such as amplitude changes all controlled within ±0.2A, consistent adjustment directions, and similar durations, these stages are determined to have fluctuation consistency and can be merged to simplify the control logic. The merged stages are divided into M target load matching optimization stages, each stage representing a time window in which a motor control strategy needs to be adjusted, where M is an integer greater than 1. Correspondingly, a matching optimization parameter is generated for each stage. These parameters are determined based on factors such as the average load deviation trend of the merged stages, the required compensation amount, and equipment safety margin, covering frequency regulation rate (e.g., from 50Hz to 47.5Hz), current limit adjustment (e.g., from the upper limit to 7.0A to 6.5A), and voltage fine-tuning.

[0029] Taking a practical application as an example, in a task processing high internal phase emulsions, three consecutive load imbalance stages were detected, occurring at 180-270, 270-280 seconds, and 280-310 seconds, with imbalance vector amplitudes of +0.9A, +0.8A, and +1.1A, respectively. After fluctuation consistency merging analysis, these three stages were combined into a single target optimization stage. This multi-stage optimization mechanism based on imbalance vectors not only avoids the fragmented correction problem in traditional control strategies but also constructs a more continuous and stable control scheme by integrating fluctuation trends. It is particularly suitable for production environments with complex materials or frequent batch changes, significantly improving the emulsification system's adaptability to nonlinear loads.

[0030] Step S400: Update the parameters of the first motor control parameters in the M target load matching optimization stage using the M matching optimization parameters.

[0031] Specifically, after generating the M target load matching optimization stages and their corresponding M matching optimization parameters, the next step is the parameter update stage. The core task of this stage is to dynamically integrate these optimized parameters into the original first motor control parameters to form a new control strategy with load adaptive capabilities, thereby optimizing the drive of the emulsifying pump motor throughout the emulsification process. This update process is time-oriented, using the first motor control parameters as the basic reference trajectory and treating the M matching optimization parameters as phased incremental adjustment patches. Each matching optimization parameter is bound to a specific load matching stage, which typically consists of its start time, end time, and intensity (e.g., adjustment range). To avoid abrupt jumps during adjustment, a gradient transition strategy is used to smooth the update behavior. This strategy ensures that the control parameter changes exhibit a continuous, differentiable curve before and after each load matching stage, rather than a step change. For example, the frequency can be gradually and smoothly reduced from 50Hz to 47Hz between t=240s and t=250s to avoid impacting the motor's inertia and thus prevent adverse effects such as torque jitter and current spikes.

[0032] Furthermore, step S200 of this application includes:

[0033] Step S210: Construct a virtual twin emulsifying pump for the emulsifying pump, and perform twin fitting on the task to be emulsified using the first motor control parameters to determine the fitting mapping between the material rheological characteristics and the load; Step S220: Construct an ideal load response curve for the material rheological characteristics; Step S230: Compare the fitting mapping with the ideal load response curve to determine the load imbalance stage and the imbalance vector.

[0034] Furthermore, step S230 of this application includes:

[0035] The imbalance vector refers to the deviation between the motor output capacity and the load demand based on the material rheological characteristics, obtained by comparing the fitted mapping with the ideal load response curve.

[0036] Specifically, to achieve a deep understanding and proactive optimization of the emulsifying pump motor's operating status, a virtual twin emulsifying pump with real-time simulation and prediction capabilities must first be constructed. This is achieved by collecting historical operating data and using an existing digital twin platform for modeling. The virtual twin emulsifying pump digitally and holographically replicates the operating characteristics of the physical emulsifying pump, including pump structure, pipeline resistance, motor dynamic response, and the microscopic action mechanism of the shear unit. Based on this, key physical property parameters of the emulsification task are input, such as the initial viscosity, density, and phase distribution ratio of the material, and combined with the first motor control parameters (e.g., frequency 50Hz, current 6.8A, voltage 380V). The virtual twin model then performs a twin fitting of the entire emulsification process. Twin fitting refers to using the virtual twin model to deduce the interaction between the motor response and the material, generating a mapping relationship between the motor control variables and the material rheological response—a fitting mapping. This mapping clearly presents the load fluctuations caused by changes in the material's rheological properties at different time points or shear rates. For example, when the shear rate is increased to 15000 rpm, the rheological response exhibits a sudden decrease in viscosity, which corresponds to a rapid decrease in motor torque. This generates a mapping node such as (t=240s, Δ torque=-1.2Nm) for subsequent imbalance judgment.

[0037] Simultaneously, an ideal load response curve is constructed based on the material's rheological properties. This curve represents the theoretical output curve that the motor should exhibit under optimal shear efficiency and stable load conditions. The ideal curve is established based on historical best operating data. For example, under ideal conditions, a certain oil-water composite system should exhibit a slow current increase to 6.5A within 7 minutes and then stably maintain a fluctuation of ±0.1A.

[0038] By comparing the above fitted mapping with the ideal load response curve, the deviation region, i.e. the load imbalance stage, is identified, which usually corresponds to the motor output overshoot, underload, or sudden change region. These deviation points are quantified in vector form, forming an imbalance vector, where each component represents the load deviation at a specific time point.

[0039] Specifically, the imbalance vector is obtained by comparing the fitted mapping generated by the virtual twin model with the ideal load response curve constructed based on the material rheological properties point by point. It is used to quantify the deviation between the motor output capacity and the actual rheological load demand, including deviations in motor frequency, current, and voltage. Taking motor current as a representative output variable, in a certain experiment, when processing an emulsion containing a high molecular weight polymer, the historical ideal operating conditions predicted that it should maintain 6.3A at 480 seconds, while the actual output predicted by the fitted mapping was 7.0A, indicating that the motor was under excessive rheological load, and the imbalance vector at this node was +0.7A. If this deviation continues to accumulate, it will not only affect the emulsification efficiency but may also lead to unstable motor operation or even trigger the overload protection mechanism.

[0040] Through the above twin modeling and comparative analysis, the ability to identify nonlinear load changes in complex emulsification processes can be significantly improved, providing a precise reference for the phased control and fluctuation suppression of motors, and also laying a data foundation for process optimization and energy efficiency improvement.

[0041] Further details are attached. Figure 2 As shown, step S300 of this application includes:

[0042] Step S310: Perform load balancing matching on the imbalance vector to determine multiple balance correction parameters for multiple stages in the load imbalance stage; Step S320: Update and fit the first motor control parameters with the multiple balance correction parameters to determine the adjustment nodes and corresponding adjustment amplitudes of each motor control parameter; Step S330: Based on the adjustment amplitude, merge the nodes with consecutive adjacent nodes whose amplitude difference is less than a preset threshold to generate a merging result; Step S340: Compare the merging result with the first motor control parameters to determine the final motor control parameter adjustment nodes and adjustment values, and generate the M target load matching optimization stages and the M matching optimization parameters.

[0043] Specifically, after identifying and extracting the imbalance vector during the load imbalance phase of the emulsification process, the next step is to optimize the control strategy for each phase using a refined load balancing matching method to achieve dynamic load balance and energy efficiency control of the motor. This process comprises four key steps, from extracting correction parameters to generating final optimization parameters, constructing a closed-loop, progressively converging control adjustment mechanism.

[0044] First, based on the imbalance vector, a load balancing matching network is invoked to perform matching calculations for each imbalance stage. This network integrates multiple historical operating samples and balance response patterns, enabling adaptive compensation for different types of deviations. For example, if the imbalance vector remains positive in a certain stage, the corresponding correction parameters output through matching are: frequency adjusted to 46.8Hz, current upper limit set to 6.4A, and voltage slightly reduced by 2V. These outputs are the balance correction parameters for that stage, and their type is consistent with the first motor control parameters, mainly including control factors such as frequency (Hz), voltage (V), and current (A). This yields multiple balance correction parameters for multiple stages.

[0045] The process involves updating and fitting multiple balance correction parameters from various stages with the original first motor control parameters. This means that, while maintaining the emulsification process objectives, the motor response behavior is re-estimated, and a new motor control curve is calibrated. During this process, a series of adjustment nodes are generated, each identifying the adjustment point of the control variable in the time dimension and its corresponding adjustment magnitude. For example, at node t=300s, the frequency is reduced from 50Hz to 47.2Hz, with an adjustment magnitude of -2.8Hz, forming a control update point.

[0046] Next, cluster analysis is performed on the amplitude changes of these adjustment nodes to identify node groups that have minimal numerical differences, consistent directions, and continuous time. If the amplitude difference between any two adjacent nodes in a group is less than a preset threshold (e.g., 0.3Hz or 0.2A), it is considered as fluctuation redundancy, i.e., redundant adjustment. Such nodes will be merged into a unified adjustment stage to reduce control frequency and improve control stability. For example, if node groups {t=300s, Δf=-2.8Hz}, {t=310s, Δf=-2.6Hz}, and {t=325s, Δf=-2.7Hz} meet the aggregation condition, they will be merged into a single interval [300s, 325s], and uniformly adjusted to Δf=-2.8Hz.

[0047] Finally, all the merged adjustment nodes are compared point by point with the initial first motor control parameters to form the final motor control parameter adjustment nodes and the adjustment value of each node. This set of final results constitutes the M target load matching optimization stages and the corresponding M matching optimization parameters, which are used to guide the dynamic control of the motor during the subsequent emulsification task.

[0048] For example, a polysaccharide-oil phase emulsion system was processed, and five imbalance stages were detected, initially generating 18 adjustment nodes. After polymerization and comparison, four effective adjustment stages were finally identified, with each stage reducing the frequency by an average of approximately 2.4 Hz, significantly enhancing the emulsion system's adaptability to complex operating conditions and its control stability.

[0049] Furthermore, step S310 of this application includes:

[0050] Step S311: Construct a load balancing matching network, which is connected to the balance optimization library; Step S312: Input the imbalance vector into the load balancing matching network, which traverses and matches the corresponding correction parameters in the balance optimization library to generate multiple balance correction parameters for the multiple stages.

[0051] Furthermore, step S312 of this application includes:

[0052] The multiple balance correction parameters include the same parameter types as the first motor control parameters, specifically including motor frequency, voltage, and current.

[0053] Specifically, after extracting the load imbalance stage and imbalance vector, a load balancing matching network is constructed to intelligently analyze and match the imbalance data in order to achieve precise optimization and adjustment of motor control parameters. This network architecture not only improves the understanding of complex nonlinear load responses, but also generates highly adaptive balance correction schemes based on historical operating conditions and optimization strategy libraries.

[0054] The load balancing matching network is essentially an integrated matching discrimination and parameter backpropagation system, bidirectionally connected to a backend load balancing optimization library. This library pre-stores a large amount of load imbalance data under typical dairy industry conditions and their corresponding optimal correction parameter pairs, sourced from historical task data, simulation results, and manually calibrated empirical values. The network structure can employ a graph model-based state matcher or a reinforcement learning regression module. Its function is to perform high-dimensional feature comparison between the input imbalance vector and multiple samples in the library to find the closest parameter response path.

[0055] In practice, the extracted imbalance vectors are input into the matching network in stages. Each stage's vector contains multi-dimensional features such as the motor's output deviation trajectory, trend, and amplitude range within that time period. For example, in a certain stage, the imbalance vector might be described as a current deviation of 0.9~1.2A, accompanied by frequency fluctuations of ±0.5Hz. The network calculates the similarity between this vector and thousands of optimized samples in the library, prioritizing samples with similarity scores higher than a set threshold (e.g., 0.92), and then outputs matching suggestions based on their corresponding correction parameters.

[0056] Each set of output balance correction parameters is completely consistent with the type of the current first motor control parameters of the emulsifying pump, and typically includes three core elements: motor frequency (Hz), used to regulate motor speed; voltage (V), used to adjust driving force and response speed; and current (A), directly related to load capacity and overload protection. For example, in the above scenario of high current, the matching result may output a frequency reduction to 46.5Hz, a voltage adjustment to 215V, and a current limit reduction to 6.0A. This set of parameters will serve as a candidate scheme for subsequent motor adjustment.

[0057] In an emulsification test involving highly shear-sensitive materials (such as the chitosan-oil system), three typical imbalance stages were identified. Three sets of correction parameters were quickly generated using a load-balanced matching network, corresponding to frequency adjustments of -2.0Hz, -3.1Hz, and -2.4Hz, and current limit reductions of 0.5A, 0.8A, and 0.6A, respectively. After implementation, the control response became more stable.

[0058] Furthermore, step S330 of this application includes:

[0059] Step S331: Extract multiple consecutive adjacent nodes from each motor control parameter adjustment node; Step S332: Perform superposition calculation of adjustment start deviation and adjustment amplitude difference between any two nodes in the multiple consecutive adjacent nodes. If the superposition value between any two nodes is less than the preset threshold, merge the multiple consecutive adjacent nodes to generate a merged adjustment stage; Step S333: Add the merged adjustment stage to the merged result.

[0060] Furthermore, step S332 of this application includes:

[0061] During the merging process, the first node among multiple consecutive adjacent nodes is used as the starting point for adjustment, and the last node is used as the ending point for adjustment, thus generating the merging adjustment phase.

[0062] Specifically, in the process of motor load matching optimization, to avoid the problem of unstable response caused by frequent and redundant control adjustments, all identified motor control parameter adjustment nodes are intelligently aggregated to form a simpler and more stable adjustment strategy. Specifically, this method extracts multiple consecutive adjacent nodes from the generated motor control parameter adjustment nodes in chronological order as candidate aggregation objects, and further filters and merges them based on precision control logic.

[0063] Each adjustment node contains a defined adjustment starting point (i.e., the frequency and current before the adjustment action, e.g., originally 40Hz) and a corresponding adjustment amplitude (e.g., frequency reduction of 2.5Hz, current limit reduction of 0.4A). When processing these adjacent nodes, the adjustment starting point deviation and adjustment amplitude difference between any two nodes are calculated pairwise. For example, if the adjustment starting points of two adjacent nodes are 39Hz and 40Hz, with an adjustment starting point deviation of 1Hz, and amplitudes of -2.8Hz and -2.6Hz respectively, with an amplitude difference of 0.2Hz, these are summed to form the overall deviation value, i.e., 1 + 0.2 = 1.2Hz. Within a preset threshold range (e.g., 5.0 or less), if the summed value is lower than this threshold, the difference between the two nodes is considered sufficiently small to warrant merging. Finally, if the summed values ​​between any two nodes in a node sequence satisfy the merging condition, they are merged into a merged adjustment stage, serving as a new control execution unit.

[0064] The merging adjustment phase is not a simple arithmetic average, but rather combines the time window length and amplitude stability to select the median or weighted average as the adjustment command, ensuring a balance between response sensitivity and energy consumption. For example, the node sequences {300s, -2.8Hz}, {310s, -2.6Hz}, and {325s, -2.7Hz} are merged into a merging adjustment phase [300s, 325s], with a unified adjustment amplitude set to -2.7Hz, an adjustment start at 300s, and an end at 325s.

[0065] Ultimately, this merging and adjustment phase will be added to the merged results list as the basis for subsequent control parameter updates. This not only reduces unnecessary and frequent parameter updates but also significantly improves the stability and consistency of the adjustment.

[0066] Taking a typical experiment as an example, when processing an emulsion, 7 out of the initially identified 12 regulation nodes were aggregated into 3 regulation stages, reducing the frequency of control execution by 41.7%.

[0067] In summary, the load matching optimization method for emulsification pump motors provided in this application has the following technical effects:

[0068] A control terminal connected to the emulsifying pump equipment receives the emulsification task and, after control analysis, the first motor control parameters used to control the motor within the emulsifying pump equipment. The emulsification reaction is fitted using the emulsification task and the first motor control parameters, fitting the load imbalance stages and imbalance vectors under the influence of material rheological characteristics during the emulsification process. The imbalance vector is combined to perform multi-stage fluctuation merging for load balancing matching in multiple stages of the load imbalance stage, generating M target load matching optimization stages and M matching optimization parameters. The parameters of the M target load matching optimization stages are updated using the M matching optimization parameters within the first motor control parameters. Based on the emulsification task and material rheological characteristics, the motor load change process is modeled and analyzed to accurately identify load imbalance stages, quantify their imbalance characteristics, and achieve precise matching of dynamic motor control parameters through multi-stage parameter optimization. This results in improved dynamic response capability of the emulsifying pump motor to complex loads, reduced energy consumption, and enhanced emulsification stability and production efficiency.

[0069] Example 2: Based on the same inventive concept as the load matching optimization method for emulsifying pump motor in the foregoing embodiments, this application also provides an electronic device, including: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the load matching optimization method for emulsifying pump motor described in any one of the above Examples 1.

[0070] Appendix Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application. Figure 3 In this document, the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges, and bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0071] In Embodiment 3, based on the same inventive concept as the load matching optimization method for emulsifying pump motors in the foregoing embodiments, this application also provides a computer-readable storage medium storing a computer program, which, when executed, implements the steps of the load matching optimization method for emulsifying pump motors described in any one of Embodiment 1 above.

[0072] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0073] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A load matching optimization method for an emulsifying pump motor, characterized in that, The method comprises the following steps: a control terminal connected to an emulsifying pump device receives a task to be emulsified and first motor control parameters for controlling motors in the emulsifying pump device after control analysis; fitting of an emulsification reaction is performed with the task to be emulsified and the first motor control parameters, fitting of a load imbalance stage and an imbalance vector under the influence of material rheological characteristics in the emulsification reaction process; multi-stage fluctuation consistent merging of load balancing matching of multiple stages in the load imbalance stage is performed in combination with the imbalance vector, generating M target load matching optimization stages and M matching optimization parameters; parameter updating of the M target load matching optimization stages is performed with the M matching optimization parameters in the first motor control parameters; wherein the multi-stage fluctuation consistent merging of load balancing matching of multiple stages in the load imbalance stage in combination with the imbalance vector comprises: load balancing matching of the imbalance vector is performed to determine multiple balancing correction parameters of the multiple stages in the load imbalance stage; updating fitting of the first motor control parameters is performed with the multiple balancing correction parameters to determine each motor control parameter adjustment node and a corresponding adjustment amplitude; based on the adjustment amplitude, nodes with a continuous adjacent node amplitude difference less than a preset threshold value in the each motor control parameter adjustment node are merged to generate a merging result; the merging result is compared with the first motor control parameters to determine a final motor control parameter adjustment node and an adjustment value, generating the M target load matching optimization stages and the M matching optimization parameters; wherein the merging result is generated based on the adjustment amplitude by merging nodes with a continuous adjacent node amplitude difference less than a preset threshold value in the each motor control parameter adjustment node, comprising: a continuous plurality of adjacent nodes in the each motor control parameter adjustment node are extracted; adjustment start point deviation and adjustment amplitude difference between any two nodes in the continuous plurality of adjacent nodes are calculated, and if the superposition value between any two nodes is less than the preset threshold value, the continuous plurality of adjacent nodes are merged to generate a merged adjustment stage; the merged adjustment stage is added to the merging result; wherein the load balancing matching of the imbalance vector is performed to determine the multiple balancing correction parameters of the multiple stages in the load imbalance stage, comprising: a load balancing matching network is constructed, which is connected to a balancing optimization library; the imbalance vector is input into the load balancing matching network, and the load balancing matching network traverses and matches corresponding correction parameters in the balancing optimization library to generate the multiple balancing correction parameters of the multiple stages; wherein the fitting of the emulsification reaction is performed with the task to be emulsified and the first motor control parameters, fitting of a load imbalance stage and an imbalance vector under the influence of material rheological characteristics in the emulsification reaction process, comprising: a virtual twin emulsifying pump of the emulsifying pump is constructed, and the task to be emulsified is fitted with the first motor control parameters to determine the fitting mapping of material rheological characteristics and load; an ideal load response curve about material rheological characteristics is constructed; The load imbalance phase and the imbalance vector are determined by comparing the fitted mapping with the ideal load response curve.

2. The load matching optimization method for an emulsifying pump motor of claim 1, wherein, The merged adjustment phase is generated by taking the first node in the continuous multiple adjacent nodes as the adjustment starting point and the end node as the adjustment ending point.

3. The load matching optimization method for an emulsifying pump motor of claim 1, wherein, The plurality of balance correction parameters include the same parameter types as the first motor control parameters, specifically including motor frequency, voltage, and current.

4. The load matching optimization method for an emulsifying pump motor of claim 1, wherein, The imbalance vector refers to the deviation of motor output capacity from material flow characteristic load demand based on the comparison of the fitted mapping with the ideal load response curve.

5. An electronic device, comprising: Comprise: At least one processor; A memory connected in communication with the at least one processor; Wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the load matching optimization method for the emulsifying pump motor in any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that, The computer program stored on the computer readable storage medium implements the steps of the load matching optimization method for the emulsifying pump motor in any one of claims 1 to 4 when executed.

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