Control system and method based on spiral screening and mixing device

Abstract

The invention discloses a control system and method based on a spiral screening and mixing device, and relates to the technical field of material mixing. The control system and method based on the spiral screening and mixing device comprises the following steps that S1, mixing full-process data and historical mixing data are preprocessed; s2, performing suppression type product fluctuation analysis, judging a mixed state, and linking layered dynamic adjustment, an abnormal region intervention process and a comprehensive compensation intervention process; s3, spatial distribution deviation analysis and deviation superposition are carried out, layered dynamic adjustment and an abnormal region intervention process are executed, and a comprehensive compensation intervention process is entered; s4, abnormal deviation aggregation and dynamic mapping analysis are carried out, a comprehensive compensation intervention process is executed based on a dynamic mapping analysis result, a stirring regulation and control instruction is converted, and stirring compensation measures are taken according to the regulation and control instruction. The problem that when an existing spiral screening and mixing device treats materials with different particle sizes, layering or segregation is prone to occurring in a mixture due to the mobility and the grading effect, and the component uniformity is affected is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of material mixing, in particular to a spiral screen mixing device control system and method. BACKGROUND

[0002] Spiral screen mixing devices have been widely used in the fields of chemical industry, building materials, food, medicine, etc., and play a key role in the efficient mixing, uniform mixing and continuous dispersion of multi-component solids and solid-liquid systems. With the increasing complexity of industrial processes, modern mixing production links have higher requirements for the uniformity, layer control and process stability of materials. Existing spiral screen mixing devices generally use multi-stage blade structures to achieve mixing and conveying of materials with different particle sizes, material densities and flowability through mechanical disturbance. At the same time, with the development of process automation and digitization, more and more mixing equipment integrates basic process parameter acquisition and monitoring modules, achieving online detection and preliminary adjustment of main process parameters such as stirring speed, energy consumption and temperature, and providing a basic support for industrialization, scaling and intelligent operation of the mixing process.

[0003] For example, the utility model patent with publication number CN218962509U discloses a spiral discharging structure for flour mixing and sieving device, which includes a stirring box, stirring blades, a stirring shaft, a discharging hole, a discharging pipe and a discharging screw assembly. The structure is provided with stirring blades inside the stirring box, which are driven by the stirring shaft to realize the mixing of materials. The bottom of the stirring box is connected with the discharging pipe through the discharging hole, and the bottom is provided with a discharging screw assembly to assist the discharge of materials. The discharging screw assembly includes a screw, a motor base, a screw motor and a support rod. The screw motor drives the screw to rotate, realizing the efficient discharge of the mixed materials. The device also realizes structural stability through the support rod, and is provided with a vibration motor on the upper end of the screw, which cooperates with the conical cover structure to further improve the discharging effect of the materials by vibration. In addition, the device is provided with a box shell and a triangular cross-section support rod on the outer wall of the stirring box, improving the running stability and safety of the whole machine.

[0004] For example, the utility model patent with publication No. CN212101086U discloses a spiral screen mixing integrated machine, which comprises a feeding section, a base, a discharging section, a power device and a mixing cylinder rotatably arranged on the base. The device drives the mixing cylinder to rotate forward and backward by the power device, realizing the screening and mixing of the material in the cylinder. The mixing cylinder is composed of a feeding cone end, a mixing cylinder body and a discharging cone end in sequence. The feeding cone end is connected with the feeding section on the base, and the discharging cone end is connected with the discharging section, ensuring smooth feeding and discharging of the material. A plurality of straight blades are arranged radially inside the feeding cone end, cooperating with the screen mesh arranged between the feeding cone end and the mixing cylinder body to pre-filter the material. The mixing cylinder body is fixedly provided with spiral blades to effectively mix the material. The discharging cone end is provided with a discharging end spiral blade for conveying the mixed material to the discharging section. The device further comprises a fixed flange and a pressing flange to realize reliable installation of the screen mesh and the blades. Some structures are provided with observation windows for real-time monitoring of the material state.

[0005] The existing spiral screen mixing device mainly focuses on the structure of stirring, screening and material discharging design. Generally, the basic mechanism of motor, spiral blade, screen mesh and discharge screw rod is used to realize mechanical mixing and screening of the material. However, this kind of structure mainly takes mechanical movement as the core, lacks fine monitoring and real-time parameter adjustment of the mixing process, and it is difficult to realize comprehensive perception and intelligent control of multi-dimensional process indicators such as mixing uniformity, spatial stratification and energy consumption state. The existing device generally lacks online detection and fusion analysis of parameters, and cannot establish a dynamic adjustment and abnormal compensation mechanism. It is difficult to meet the actual needs of precise mixing, process traceability and high-quality output under the requirements of complex materials, dynamic batches and high consistency.

[0006] Therefore, in view of the above problems, there is an urgent need for a spiral screen mixing device control system and method. SUMMARY

[0007] Technical problems solved

[0008] In view of the deficiencies of the prior art, the present application provides a spiral screen mixing device control system and method, which solves the problem that the existing spiral screen mixing device is prone to stratification or segregation inside the mixture due to flowability and grading effect when processing materials of different particle sizes, affecting the uniformity of components.

[0009] Technical scheme

[0010] In order to achieve the above object, the present application is realized by the following technical scheme: based on the spiral screen mixing device control system and method, comprising the following steps: S1, collecting mixed full-process data, obtaining historical mixed data, preprocessing mixed full-process data and historical mixed data; S2, performing suppression type product fluctuation analysis on mixed full-process data and historical mixed data, determining the mixing state according to the suppression type product fluctuation analysis result, and connecting the hierarchical dynamic adjustment and abnormal area intervention process and the comprehensive compensation intervention process; S3, performing spatial distribution deviation analysis and dispersion superposition on mixed full-process data, executing hierarchical dynamic adjustment and abnormal area intervention process according to the spatial distribution deviation analysis and dispersion superposition result, and entering the comprehensive compensation intervention process; S4, performing abnormal deviation aggregation and dynamic mapping analysis on mixed full-process data, executing comprehensive compensation intervention process based on the dynamic mapping analysis result, converting positive and negative rotation rhythm optimization, variable speed stirring, pause stirring and impact stirring control instructions, and performing stirring compensation measures according to the control instructions.

[0011] Further, the specific process of collecting mixed full-process data and obtaining historical mixed data is: collecting mixed full-process data, obtaining historical mixed data, and establishing a historical mixed database; the mixed full-process data includes: mixed particle number, real-time power, particle size distribution data, material density, cavity moisture content, material viscosity, shear stress, yield stress, terminal product particle size distribution, terminal particle size distribution uniformity, terminal quality pass rate, terminal cavity moisture content, rheological data, abnormal duration, mixing time, original spectrum signal data and reflection spectrum data; the historical mixed data includes: historical temperature data, historical abnormal duration, historical mixing time, historical recovery time, historical particle size distribution data, historical rheological data, historical cavity moisture content, historical terminal quality pass rate and historical material density.

[0012] Further, the specific process of preprocessing mixed full-process data and historical mixed data is: performing clustering analysis on mixed full-process data by multi-source clustering algorithm, identifying abnormal clustering and classifying working condition types of multiple batches and multiple stages; respectively extracting main trends of real-time power, rheological data, particle size distribution data and material density by modal decomposition algorithm and sliding window local weighted regression, restoring the real change trajectory in the mixing process and stripping off high-frequency noise and short-time disturbance; correcting batch-to-batch distribution consistency of historical mixed data by distribution alignment algorithm, eliminating the offset caused by equipment state, raw material batch and formula switching; performing scale specification and abnormal period elimination on abnormal duration and mixing time by time series normalization and dynamic interval standardization algorithm; performing scale unification and interval mapping on mixed full-process data and historical mixed data by distribution standardization and linear normalization algorithm, performing standardization and normalization processing.

[0013] Further, the specific process of the suppression type product fluctuation analysis on the mixed full-process data and the historical mixed data is: obtaining the mixed particle number, real-time power, mixed time length, particle size distribution data, material density, historical mixed data, cavity moisture content, material viscosity, shear stress, yield stress, particle size distribution data, terminal product particle size distribution, terminal particle size distribution uniformity, terminal quality qualification rate and terminal cavity moisture content; performing weighted accumulation and noise removal algorithm on the mixed particle number to obtain the effective participating mixed particle number; performing power and time integration algorithm on the real-time power and the mixed time length to obtain the mechanical driving energy; performing standard deviation and mean value calculation on the particle size distribution data to obtain the particle size distribution dispersion; performing Pearson correlation coefficient algorithm on the material density and the historical mixed data to obtain the density temperature coordination value; performing Gini coefficient algorithm on the cavity moisture content to obtain the moisture content spatial deviation value; performing ratio algorithm and weighted average algorithm on the material viscosity, shear stress and yield stress to obtain the flowability interlayer damping ratio; taking the product of the effective participating mixed particle number and the energy required per unit volume of mixing as the base item of the efficiency; multiplying the particle size uniformity weight and the particle size distribution dispersion, adding the absolute value product of the density temperature coordination weight and the density temperature coordination value, adding the moisture content weight and the moisture content spatial deviation value product, adding the flowability weight and the flowability interlayer damping ratio product and adding the adjustment constant one to obtain a total adjustment item; dividing the base item by the total adjustment item to obtain the mixed state evaluation value.

[0014] Further, the specific process of the suppression type product fluctuation analysis on the mixed full-process data and the historical mixed data is: obtaining the mixed particle number, real-time power, mixed time length, particle size distribution data, material density, historical mixed data, cavity moisture content, material viscosity, shear stress, yield stress, particle size distribution data, terminal product particle size distribution, terminal particle size distribution uniformity, terminal quality qualification rate and terminal cavity moisture content; performing weighted accumulation and noise removal algorithm on the mixed particle number to obtain the effective participating mixed particle number; performing power and time integration algorithm on the real-time power and the mixed time length to obtain the mechanical driving energy; performing standard deviation and mean value calculation on the particle size distribution data to obtain the particle size distribution dispersion; performing Pearson correlation coefficient algorithm on the material density and the historical mixed data to obtain the density temperature coordination value; performing Gini coefficient algorithm on the cavity moisture content to obtain the moisture content spatial deviation value; performing ratio algorithm and weighted average algorithm on the material viscosity, shear stress and yield stress to obtain the flowability interlayer damping ratio; taking the product of the effective participating mixed particle number and the energy required per unit volume of mixing as the base item of the efficiency; multiplying the particle size uniformity weight and the particle size distribution dispersion, adding the absolute value product of the density temperature coordination weight and the density temperature coordination value, adding the moisture content weight and the moisture content spatial deviation value product, adding the flowability weight and the flowability interlayer damping ratio product and adding the adjustment constant one to obtain a total adjustment item; dividing the base item by the total adjustment item to obtain the mixed state evaluation value.

[0015] Further, the specific process of spatial distribution deviation analysis and dispersion superposition of mixed full-process data is as follows: the mixed cavity is equally divided into n partitions according to height, multi-point online sensors are arranged in each partition, mixed full-process data of each partition is collected in real time, spatial mapping and data fusion algorithms are used to classify the data of each collection point to the corresponding partition; deviation analysis layer quantization value, particle size distribution data, viscosity of the material, shear stress, yield stress, original spectrum signal data, reflection spectrum data, terminal particle size distribution uniformity and terminal quality pass rate are obtained; the standard deviation and mean value of the particle size distribution data of the kth partition are calculated to obtain the particle size distribution dispersion of the kth partition; the viscosity, shear stress and yield stress of the material in the kth partition are subjected to ratio algorithm and weighted average algorithm to obtain the interlayer damping ratio of the flowability of the kth partition; principal component analysis algorithm combined with multiple linear regression algorithm is performed on the original spectrum signal data to obtain the component concentration of the kth partition; spectral integral gray algorithm is performed on the reflection spectrum data to obtain the optical index value of the kth partition; the average component concentration of all partitions is obtained by adding and averaging the component concentrations of all partitions; the average value of the optical index of all partitions is obtained by adding and averaging the optical index values of all partitions; the particle size distribution dispersion of the kth partition is multiplied by the interlayer damping ratio of the flowability of the kth partition, and then multiplied by the square of the difference between the component concentration of the kth partition and the average component concentration of all partitions to obtain the component concentration deviation term; the optical index weight is multiplied by the square of the difference between the optical index value of the kth partition and the average value of the optical index of all partitions to obtain the optical index weighted deviation term; the component concentration deviation term and the optical index weighted deviation term are added to obtain the partition evaluation term of the kth partition, the values of all partition evaluation terms are recorded as partition values, and the sum of all partition values is calculated, and finally the square root is obtained to obtain the deviation analysis partition quantization value.

[0016] Further, the specific process of performing hierarchical dynamic adjustment and abnormal area intervention process according to the spatial distribution deviation analysis and the deviation superposition result and entering the comprehensive compensation intervention process is: obtaining the deviation analysis partition quantization value, performing hierarchical dynamic adjustment and abnormal area intervention process based on the deviation analysis partition quantization value, comparing the deviation analysis partition quantization value with the partition quantization threshold value in real time, and comparing the abnormal partition threshold value and the partition value: when the deviation analysis partition quantization value is less than or equal to the partition quantization threshold value, the hierarchical and energy consumption of each area of the mixing cavity are relatively balanced, and there is no widespread abnormal partition as a whole; the existing stirring parameters and working mode are maintained, and the change of the deviation analysis partition quantization value is continuously monitored; when the deviation analysis partition quantization value is greater than the partition quantization threshold value, for the abnormal partition whose partition value is greater than the abnormal partition threshold value, the spiral rotating speed is separately increased, the local overturning frequency is accelerated, the stirring intensity is increased, the feeding rate is reduced, and the mixing time is prolonged, and a separate parameter adjustment operation is started until the end of the prolonged mixing time, which is a separate running cycle, the mixing full-process data is reacquired at the end of the separate running cycle, the deviation analysis partition quantization value is recalculated, and when the deviation analysis partition quantization values calculated in two separate running cycles are still greater than the partition quantization threshold value, the comprehensive compensation intervention process is entered.

[0017] Further, the specific process of performing hierarchical dynamic adjustment and abnormal area intervention process according to the spatial distribution deviation analysis and the deviation superposition result and entering the comprehensive compensation intervention process is: obtaining the deviation analysis partition quantization value, performing hierarchical dynamic adjustment and abnormal area intervention process based on the deviation analysis partition quantization value, comparing the deviation analysis partition quantization value with the partition quantization threshold value in real time, and comparing the abnormal partition threshold value and the partition value: when the deviation analysis partition quantization value is less than or equal to the partition quantization threshold value, the hierarchical and energy consumption of each area of the mixing cavity are relatively balanced, and there is no widespread abnormal partition as a whole; the existing stirring parameters and working mode are maintained, and the change of the deviation analysis partition quantization value is continuously monitored; when the deviation analysis partition quantization value is greater than the partition quantization threshold value, for the abnormal partition whose partition value is greater than the abnormal partition threshold value, the spiral rotating speed is separately increased, the local overturning frequency is accelerated, the stirring intensity is increased, the feeding rate is reduced, and the mixing time is prolonged, and a separate parameter adjustment operation is started until the end of the prolonged mixing time, which is a separate running cycle, the mixing full-process data is reacquired at the end of the separate running cycle, the deviation analysis partition quantization value is recalculated, and when the deviation analysis partition quantization values calculated in two separate running cycles are still greater than the partition quantization threshold value, the comprehensive compensation intervention process is entered.

[0018] Further, based on the dynamic mapping analysis result, a comprehensive compensation intervention process is performed to convert positive and reverse rotation rhythm optimization, variable speed stirring, intermittent stirring and impact stirring control instructions, and the specific process of stirring compensation measures according to the control instructions is: obtaining a comprehensive compensation judgment value, based on the comprehensive compensation judgment value, performing a comprehensive compensation intervention process, and through response value mapping and linkage control technology, the comprehensive compensation judgment value is converted into positive and reverse rotation rhythm optimization, variable speed stirring, intermittent stirring and high speed impact control instructions; based on the positive and reverse rotation rhythm optimization control instruction, the positive and reverse rotation period and switching frequency of the spiral blade are adjusted; when the comprehensive compensation judgment value increases, the switching period is disturbed and random frequency switching is performed to avoid periodic motion mode; based on the variable speed stirring control instruction, periodic variable speed stirring of the spiral screen is performed, and high and low speed alternating stirring is performed; based on the intermittent stirring and impact stirring control instruction, when the comprehensive compensation judgment value increases, the intermittent stirring and impact stirring are switched, the stirring is stopped to allow the material to naturally settle and preliminarily stratify, and then the impact stirring is started; the mixing state evaluation value is continuously monitored until the mixing state evaluation value is greater than or equal to the normalized mixing secondary efficiency value.

[0019] The second aspect of the present application provides a spiral screen mixing device control system based on the spiral screen mixing device control system, which comprises a collection preprocessing module for collecting mixing full-process data, obtaining historical mixing data, and preprocessing the mixing full-process data and the historical mixing data; a multi-dimensional particle size real-time sensing module for performing inhibitory product fluctuation analysis on the mixing full-process data and the historical mixing data, determining the mixing state according to the inhibitory product fluctuation analysis result, and linking the stratified dynamic adjustment and abnormal area intervention process and the comprehensive compensation intervention process; a partition dynamic adjustment module for performing spatial distribution deviation analysis and dispersion superposition on the mixing full-process data, executing stratified dynamic adjustment and abnormal area intervention process according to the spatial distribution deviation analysis and dispersion superposition result, and entering the comprehensive compensation intervention process; a comprehensive inhibitory compensation module for performing abnormal deviation aggregation and dynamic mapping analysis on the mixing full-process data, executing the comprehensive compensation intervention process based on the dynamic mapping analysis result, converting positive and reverse rotation rhythm optimization, variable speed stirring, intermittent stirring and impact stirring control instructions, and performing stirring compensation measures according to the control instructions.

[0020] Advantages

[0021] The present application has the following advantages:

[0022] (1) The present application realizes the real-time regulation of stratification and uniformity abnormalities by collecting the particle size distribution, component concentration, rheological state and optical index in the mixing cavity in real time through multi-point online sensors, and effectively solves the problems of unquantifiable mixing uniformity and untimely abnormal detection in the prior art.

[0023] (2) The present application realizes dynamic stirring parameter self-adaptive optimization by mixing the performance value and the multi-level criterion of stratification evaluation value, and linkage of the process parameters of spiral rotation speed, turning intensity, feeding rate and mixing time, and then realizes intelligent closed-loop adjustment of the mixing process parameters, effectively solving the defects of relying on manual experience adjustment and reaction lag in the prior art.

[0024] (3) The present application proposes a comprehensive compensation judgment value for the case that continuous abnormality and spatial stratification cannot be eliminated by conventional adjustment, and linkage of periodic variable speed stirring, positive and negative rotation rhythm disturbance, stop and impact stirring compensation strategy, and then realizes full-process intervention of complex abnormality, effectively solving the limitations of extreme and continuous abnormality compensation means in the prior art.

[0025] (4) The present application can quantitatively evaluate the stratification and segregation degree of each partition in real time by introducing a spatial deviation evaluation method based on component concentration and optical index, and then realizes dynamic elimination of stratification and segregation, effectively overcoming the defects of control lag and stratification residue of the existing mixing device.

[0026] Of course, it is not necessary for any product implementing the present application to achieve all the advantages described above at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 The flow chart of the control method of the present application based on the spiral screen mixing device;

[0028] Figure 2 The structure diagram of the control system of the present application based on the spiral screen mixing device;

[0029] Figure 3 The comparison column chart of the mixing partition characteristic parameters of the present application; DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0031] Please refer to Figures 1-3The embodiment of the present application provides a technical scheme: a spiral screen mixing device control system and method, comprising the following steps: S1, collecting mixed full-process data, obtaining historical mixing data, and preprocessing the mixed full-process data and the historical mixing data; S2, performing suppression type product fluctuation analysis on the mixed full-process data and the historical mixing data, determining the mixing state according to the suppression type product fluctuation analysis result, and connecting the layered dynamic adjustment and the abnormal area intervention process and the comprehensive compensation intervention process; S3, performing spatial distribution deviation analysis and deviation superposition on the mixed full-process data, executing the layered dynamic adjustment and the abnormal area intervention process according to the spatial distribution deviation analysis and deviation superposition result, and entering the comprehensive compensation intervention process; S4, performing abnormal deviation aggregation and dynamic mapping analysis on the mixed full-process data, executing the comprehensive compensation intervention process based on the dynamic mapping analysis result, converting positive and negative rotation rhythm optimization, variable speed stirring, pause stirring and impact stirring control instructions, and performing stirring compensation measures according to the control instructions.

[0032] Specifically, the specific process of collecting mixed full-process data and obtaining historical mixing data is: collecting mixed full-process data, obtaining historical mixing data, and establishing a historical mixing database.

[0033] The mixed full-process data includes: real-time detection of the number of mixed particles by a laser particle size analyzer and a particle counter; real-time power, particle size distribution data, and material density; cavity moisture content of each partition of the cavity collected by a multi-point online moisture sensor; viscosity, shear stress and yield stress of the material, reflecting the flow and mixing performance of the material; terminal product particle size distribution, terminal particle size distribution uniformity, terminal quality pass rate, terminal cavity moisture content, rheological data, abnormal duration, mixing time, and original spectrum signal data and reflection spectrum data obtained by a near-infrared analyzer and a fiber-optic spectrometer, which are used for multi-component content, concentration and spatial distribution analysis.

[0034] The historical mixing data includes: historical temperature data, historical abnormal duration, historical mixing time and historical recovery time, which provide basic data for process optimization and abnormal early warning; historical particle size distribution data and historical rheological data, which are used for data mining, material characteristic comparison and formula tracing analysis; historical cavity moisture content, historical terminal quality pass rate and historical material density, which provide data basis for batch physical property database improvement and formula consistency checking.

[0035] In the present embodiment, through multiple types of advanced sensing and analysis means, the collection and archiving of mixed full-process and historical process data are realized. The core physical, chemical and quality parameters in the mixing process are comprehensively covered, and the historical batch data are integrated for management, thereby providing a solid data foundation and intelligent support for subsequent mixing uniformity evaluation, process optimization, abnormal early warning and formula consistency checking.

[0036] Specifically, the specific process of preprocessing the mixed full-process data and the historical mixed data is as follows: through a multi-source clustering algorithm, the mixed full-process data is subjected to clustering analysis, abnormal clustering identification and working condition type classification of multiple batches and multiple stages, so as to improve the intelligent identification capability of complex mixed states and abnormal working conditions; through a modal decomposition algorithm and a sliding window local weighted regression, the real-time power, the rheological data, the particle size distribution data and the material density are subjected to main trend extraction respectively, the real change trajectory in the mixing process is restored, and the high-frequency noise and short-time disturbance are stripped, thereby laying a foundation for subsequent process dynamic monitoring and precise regulation; through a distribution alignment algorithm, the historical mixed data is subjected to batch-to-batch distribution consistency correction, so as to eliminate the deviation caused by equipment state, raw material batch and formula switching, and ensure the comparability and analysis effectiveness of different batches of data; through a time series normalization and dynamic interval standardization algorithm, the abnormal duration and the mixing time are subjected to scale specification and abnormal period elimination, so as to strengthen the dynamic identification and processing capability of abnormal working conditions, and prevent the interference of extreme values on overall process evaluation; through a distribution standardization and linear normalization algorithm, the mixed full-process data and the historical mixed data are subjected to scale unification and interval mapping, and are subjected to standardization and normalization processing, so as to ensure that the data of different sources and different batches can be efficiently integrated and cooperatively applied under a unified platform.

[0037] In the embodiment, through the synergistic integration of various data preprocessing methods, the overall quality, standardization and consistency of the mixed process full-process data and the historical data are improved. The fusion and unification of data of different batches, different stages and different sources are effectively realized, which provides strong support for accurate identification, trend tracking and traceability analysis of complex mixed states and abnormal working conditions. The preprocessed data not only more truly reflects the physical change law of the mixing process, but also greatly improves the usability and reliability of various process characteristics in subsequent analysis and regulation. This data preprocessing process lays a solid data foundation for intelligent monitoring, dynamic analysis and precise regulation of the mixing process, and promotes intelligent control and high-quality operation.

[0038] Specifically, the specific process of suppressing the mixed full-process data and the historical mixed data for product fluctuation analysis is as follows: the mixed particle number, the real-time power, the mixing time, the particle size distribution data, the material density, the historical mixed data, the cavity moisture content, the viscosity of the material, the shear stress, the yield stress, the particle size distribution data, the terminal product particle size distribution, the terminal particle size distribution uniformity, the terminal quality qualification rate and the terminal cavity moisture content are obtained, thereby laying a foundation for multi-dimensional analysis of the mixing process.

[0039] The number of particles participating in the mixing is weighted and accumulated, and the noise elimination algorithm obtains the number of particles effectively participating in the mixing, which accurately reflects the actual mixing condition of the material; the real-time power and the mixing time are integrated to obtain the mechanical driving energy, which reflects the energy consumption level; the standard deviation and the mean value of the particle size distribution data are calculated to obtain the particle size distribution dispersion, which is an important indicator for evaluating the uniformity of particles; the Pearson correlation coefficient algorithm is performed on the material density and the historical mixing data to obtain the density-temperature coordination value, which reveals the influence of the change of material properties on the uniformity of mixing; the Gini coefficient algorithm is performed on the cavity moisture content to obtain the moisture content spatial deviation value, which characterizes the uniform distribution of moisture content in different regions; the viscosity, shear stress and yield stress of the material are subjected to ratio algorithm and weighted average algorithm to obtain the flowability interlayer damping ratio, which reflects the difference of flow resistance of each layer of the material.

[0040] The product of the number of particles effectively participating in the mixing and the energy required per unit volume for mixing is taken as the basic term of efficiency; the product of the particle size uniformity weight and the particle size distribution dispersion is added to the product of the absolute value of the density-temperature coordination weight and the density-temperature coordination value, and the product of the moisture content weight and the moisture content spatial deviation value is added, and the product of the flowability weight and the flowability interlayer damping ratio is added, and then a regulation constant one is added to obtain a total regulation term; the basic term is divided by the total regulation term to obtain the mixing state evaluation value, which realizes the precise quantification of the health state of the mixing process and the scientific decision support for process control.

[0041] The specific formula of the mixing state evaluation value is:

[0042]

[0043] In the formula, S represents the mixing state evaluation value, which is a comprehensive evaluation index for measuring the uniformity and stability of the mixing process; N represents the number of particles effectively participating in the mixing, which reflects the number and distribution of particles actually participating in the mixing; E mix represents the mechanical driving energy, which evaluates the mechanical energy consumed in the whole mixing process; CV represents the particle size distribution dispersion, which quantitatively describes the uniformity level of the particle size distribution of the material; κ ρ,T represents the density-temperature coordination value, which reflects the coordinated change relationship between the density and the temperature in the mixture; Φ W represents the moisture content spatial deviation value, which characterizes the uniform distribution of moisture content in each region of the mixing cavity; R Frepresents the ratio of interlayer damping of fluidity, reflecting the difference in flow characteristics of the mixture in the direction of stratification; a1 represents the particle size uniformity weight, which is obtained by quality index correlation analysis algorithm based on particle size distribution data, terminal product particle size distribution, terminal uniformity and terminal pass rate, and the value range is between 0 and 1; a2 represents the density temperature synergy weight, which is obtained by multivariate correlation analysis based on density and historical temperature data, and the value range is between 0 and 1; a3 represents the moisture content weight, which is obtained by sensitivity analysis and principal component regression algorithm based on the obtained cavity moisture content, terminal moisture content, terminal uniformity and terminal pass rate, and the value range is between 0 and 1; a4 represents the fluidity weight, which is obtained by process sensitivity analysis algorithm based on the viscosity, shear stress and yield stress of the material, combined with the terminal uniformity and terminal pass rate, and the value range is between 0 and 1.

[0044] In the embodiment, by multi-dimensional data fusion and modeling of mixed key parameters, the particle activity, energy consumption level, particle uniformity, density temperature synergy change, moisture content distribution and flow characteristics of the mixed process can be comprehensively quantified, and a mixed state evaluation value with scientific discrimination ability is formed. Not only the real level of mixing uniformity and process stability is fully reflected, but also the intelligent identification ability of process process abnormality, fluctuation and bottleneck is significantly improved. Compared with the traditional single index and experience judgment method, the method realizes the precise quantification and trend dynamic control of complex mixing state through the organic integration of parameter weight and data algorithm, provides a solid quantitative basis and technical support for the optimization adjustment, quality early warning and intelligent decision of mixing process, and promotes the continuous upgrading of intelligence, refinement and high reliability.

[0045] Specifically, the mixing state is determined according to the suppression type product wave fluctuation analysis result, and the specific process of linking the stratification dynamic adjustment and abnormal area intervention process and the comprehensive compensation intervention process is as follows: the mixing state evaluation value and the mixing state evaluation threshold value are compared in real time, and the mixing state evaluation threshold value includes a first evaluation threshold value and a second evaluation threshold value:

[0046] When the mixing state evaluation value is greater than or equal to the first evaluation threshold value, it is determined that the current mixing uniformity is good, the stratification and segregation are in a controllable range, and the energy consumption level is reasonable. At this time, the existing stirring parameters and mixing state are maintained, the equipment running state and key process parameters are continuously monitored online to ensure the stability of the mixing process, and no additional adjustment is needed.

[0047] When the mixing state evaluation value is greater than or equal to the second evaluation threshold value and less than the first evaluation threshold value, it is determined that the mixing uniformity has decreased, and stratification, segregation and energy consumption abnormal risk have occurred in local area. The stratification dynamic adjustment and abnormal area intervention process are switched into to adjust the stirring parameters and local mixing strategy in time to prevent the abnormality from further spreading.

[0048] When the mixing state evaluation value is less than the secondary evaluation threshold, the mixing uniformity is seriously decreased, the stratification segregation is obvious, and the energy consumption is abnormally prominent; the rotation speed of the helical blade, the frequency of forward and reverse rotation, and the tossing intensity are increased, the feeding is paused, and the mixing time is prolonged to strengthen the mixing effect, quickly break the stratification segregation, and eliminate the abnormal energy consumption; from the start of parameter adjustment operation to the end of prolonging the mixing time, it is a mixing operation cycle, the mixing full-process data is collected again at the end of the mixing operation cycle, the mixing state evaluation value is calculated again, and the actual effect of the current strengthening adjustment measure is objectively evaluated; when the mixing state evaluation values calculated in two continuous mixing operation cycles are both less than the normalized mixing secondary efficiency threshold, the comprehensive compensation intervention process is entered, and the multi-dimensional compensation measures are further started to restore the mixing uniformity and process stability.

[0049] In the embodiment, intelligent dynamic adjustment of the mixing process is realized, the strategy can trigger stratified adjustment, abnormal intervention, and multi-dimensional comprehensive compensation measures for different degrees of mixing uniformity fluctuation and abnormal energy consumption, realize early discovery, accurate identification, and stratified linkage treatment of mixing abnormalities, effectively prevent local abnormalities from spreading to the whole, improve the stability and process disturbance resistance of the mixing process, and significantly enhance the abnormal recovery efficiency and product quality consistency. Overall, this process provides efficient data support and decision basis for continuous optimization and intelligent closed-loop control in complex mixing scenarios, and promotes the transformation of mixing to a higher level of self-adaptation and high-reliability operation.

[0050] Specifically, the specific process of spatial distribution deviation analysis and dispersion superposition of the mixing full-process data is: dividing the mixing cavity into n partitions at equal intervals according to height, arranging multiple online sensors in each partition to realize fine real-time monitoring of the spatial distribution of the mixing process, collecting the mixing full-process data of each partition in real time, using spatial mapping and data fusion algorithms to classify the data of each collection point to the corresponding partition, and realizing data standardization and intelligent integration at the partition level.

[0051] Obtaining the deviation analysis stratification quantitative value, the particle size distribution data, the viscosity of the material, the shear stress, the yield stress, the original spectrum signal data, the reflection spectrum data, the terminal particle size distribution uniformity, and the terminal quality qualification rate;

[0052] The standard deviation and mean value of the particle size distribution data of the kth partition are calculated to obtain the particle size distribution dispersion of the kth partition, to ensure the consistency and accuracy of the analysis caliber; the viscosity, shear stress and yield stress of the kth partition material are subjected to ratio algorithm and weighted average algorithm to obtain the flow interlayer damping ratio of the kth partition, to facilitate data fusion processing under the conditions of multiple components and multiple physical properties; the original spectral signal data are subjected to principal component analysis algorithm combined with multivariate linear regression algorithm, to realize the numerical output of the concentration of the target component in the kth partition; the spectral integral gray algorithm is performed on the reflection spectrum data to obtain the optical index value of the kth partition, to provide a stable data basis for subsequent quantitative analysis and mathematical modeling; the average component concentration of all partitions is obtained by adding and averaging the component concentrations of all partitions; the average value of the optical index of all partitions is obtained by adding and averaging the optical index values of all partitions, to ensure the formation of a unified reference baseline between different spatial positions and different collection points.

[0053] The particle size distribution dispersion of the kth partition and the flow interlayer damping ratio of the kth partition are multiplied, and then multiplied by the square of the difference between the component concentration of the kth partition and the average component concentration of all partitions, to obtain the component concentration deviation term; the optical index weight is multiplied by the square of the difference between the optical index value of the kth partition and the average value of the optical index of all partitions, to obtain the optical index weighted deviation term; the component concentration deviation term and the optical index weighted deviation term are added to obtain the partition evaluation term of the kth partition, the values of all partition evaluation terms are recorded as partition values, and then all partition values are summed and finally squared to obtain the deviation analysis partition quantization value, to realize the accurate quantitative evaluation of the uniformity and stratification state of the mixed space distribution.

[0054] The calculation formula of the deviation analysis partition quantization value is:

[0055]

[0056] In the formula, ΔP represents the deviation analysis partition quantization value, which is a quantitative index of the deviation of the mixed space after the calculation results of all partitions are integrated; k represents the spatial partition number of the mixing cavity, which is used to identify each independent partition divided according to equal height; n represents the number of spatial partitions of the mixing cavity, that is, the total number of partitions into which the entire mixing cavity is subdivided; CV k represents the particle size distribution dispersion of the kth partition, which is used to represent the uniformity of the particle distribution in the partition; RF k represents the flow interlayer damping ratio of the kth partition, which reflects the flow characteristics of the material in the partition in the stratification direction; C k represents the component concentration of the kth partition, which is a quantitative index of the spatial distribution component characteristic; C a represents the average component concentration of all partitions, which is used as a global comparison benchmark for the concentrations of all partitions; L k represents the optical index value of the kth partition, which reflects the spatial characteristics of the optical properties of the material in the partition; La η represents the optical index weight, which is obtained by a correlation analysis algorithm for the optical index value of all partitions, terminal uniformity, and terminal qualification rate, and ranges from 0 to 1, reflecting the importance and influence of the optical feature in the partition deviation evaluation.

[0057] In this embodiment, Table 1 is a mixed partition characteristic parameter data table, which records the key indicators of each partition of the mixed cavity in the spatial deviation analysis in detail. Each row in the table corresponds to a spatial partition, and the particle size distribution dispersion of the kth partition, the flowability interlayer damping ratio of the kth partition, the component concentration of the kth partition, and the optical index value of the kth partition are given, which can reflect the spatial characteristics of each partition in terms of particle size distribution uniformity, flow performance, component distribution, and optical properties, and provide basic data support for subsequent spatial distribution deviation analysis partition quantization value calculation and spatial anomaly identification. Among them, the particle size distribution dispersion of partition 1 is 0.8, the flowability interlayer damping ratio is 1.1, the component concentration is 0.72, and the optical index value is 0.92; the particle size distribution dispersion of partition 2 is 0.6, the flowability interlayer damping ratio is 1.4, the component concentration is 0.85, and the optical index value is 0.93; the particle size distribution dispersion of partition 3 is 1.0, the flowability interlayer damping ratio is 1.2, the component concentration is 0.80, and the optical index value is 1.04; the particle size distribution dispersion of partition 4 is 0.9, the flowability interlayer damping ratio is 1.3, the component concentration is 0.78, and the optical index value is 1.00. In order to facilitate the unified calculation of the spatial distribution deviation analysis partition quantization value, the average value of the component concentration is fixed to 0.79, the average value of the optical index is fixed to 0.97, and the optical index weight η is fixed to 1.0, which ensures the baseline consistency of the deviation analysis calculation and the comparability of the results.

[0058] Table 1 Mixed partition characteristic parameter data table

[0059]

[0060] As Figure 3The mixed partition characteristic parameter contrast column chart is shown. It can be seen from Table 1 that different spatial partitions have significant differences in key parameters such as particle size distribution dispersion, interlayer damping ratio of fluidity, component concentration and optical index value. Specifically, the interlayer damping ratio of fluidity of the second partition is the highest, which is 1.4, reflecting that the flow resistance of this region is relatively large and the particle mixing is relatively insufficient; the particle size distribution dispersion of the third partition is the largest, reaching 1.0, indicating that the uniformity of the particles in this partition is the weakest and needs to be focused on the optimization of mixing uniformity; the component concentration of the second partition is also the highest, which is 0.85, showing that the material components in this region are relatively concentrated. Overall, the parameter change trend of each partition is intuitively reflected in the column chart, which can provide accurate data basis for subsequent spatial distribution deviation analysis, hierarchical dynamic adjustment and mixing uniformity evaluation, and can also be used as an important basis for partition optimization control and process parameter adjustment.

[0061] In the embodiment, through the partition quantitative analysis, the multi-source structural data in the mixing cavity space is effectively integrated and mathematically fused, and the spatial uniformity and hierarchical state of the mixing process are accurately measured. The particle distribution, flow characteristics, component concentration and optical property multi-dimensional parameters of each partition are comprehensively evaluated in the same evaluation system, which can sensitively reveal the change trend of local abnormalities, spatial deviation and overall mixing consistency. The introduction of the deviation analysis partition quantitative value enables the quantitative discrimination of the spatial non-uniformity and hierarchical segregation phenomenon of the mixing process, provides a solid data basis and scientific criterion for process optimization, partition control and early warning of abnormalities, and significantly improves the spatial resolution and intelligent control level of the mixing process.

[0062] Specifically, the specific process of performing hierarchical dynamic adjustment and abnormal zone intervention process according to the spatial distribution deviation analysis and deviation superposition result and entering the comprehensive compensation intervention process is as follows: obtaining the deviation analysis partition quantitative value, performing hierarchical dynamic adjustment and abnormal zone intervention process based on the deviation analysis partition quantitative value, comparing the deviation analysis partition quantitative value with the partition quantitative threshold value in real time, and comparing the abnormal partition threshold value and the partition value, to realize hierarchical response and accurate positioning:

[0063] When the deviation analysis partition quantitative value is less than or equal to the partition quantitative threshold value, it indicates that the hierarchical state and energy consumption of each zone of the mixing cavity are balanced as a whole, and no obvious abnormality occurs in each partition, and the process runs in the controlled interval; the existing stirring parameters and working mode are maintained, and the change of the deviation analysis partition quantitative value is continuously monitored to ensure the long-term stability of the mixing process.

[0064] When the deviation analysis partition quantification value is greater than the partition quantification threshold value, for the abnormal partition with a partition value greater than the abnormal partition threshold value, the rotational speed of the screw is separately increased, the local tumbling frequency is accelerated, the stirring intensity is increased, the feeding rate is reduced, and the mixing time is prolonged, targeted intensified mixing and control intervention are implemented on the abnormal partition, from the start of separate parameter adjustment operation to the end of prolonged mixing time as a single operation cycle, the mixed full-process data is re-acquired at the end of the single operation cycle, the deviation analysis partition quantification value is re-calculated, from the start of separate parameter adjustment operation to the end of prolonged mixing time as a single operation cycle, the mixed full-process data is re-acquired at the end of the single operation cycle, when the deviation analysis partition quantification values calculated in the two single operation cycles are still greater than the partition quantification threshold value, the comprehensive compensation intervention process is entered, and multi-dimensional control measures are further started to realize the re-equilibrium of the mixing space distribution and the recovery of the process state.

[0065] In the embodiment, through real-time monitoring and hierarchical response, local abnormal areas in the mixing cavity can be dynamically identified and accurately located, early discovery and directional control of stratification, segregation and abnormal energy consumption problems can be realized. Single intensified mixing and multi-parameter intervention are implemented on the abnormal partition, and an independent operation cycle and effect feedback mechanism are set for each intervention, which effectively improves the pertinence and scientificity of abnormal treatment. Combined with periodic full-process data acquisition and deviation index re-evaluation, the control effect can be continuously tracked to avoid repeated abnormalities and local hidden danger diffusion. For persistent stubborn abnormalities, the comprehensive compensation intervention process is switched to, and multi-dimensional control means are linked to ensure efficient recovery of the uniformity of the mixing space distribution and the process state. Overall, this process significantly enhances the adaptability, control accuracy and operation resilience of complex spatial abnormalities, providing a strong guarantee and data support for high-quality and intelligent mixing processes.

[0066] Specifically, the specific process of abnormal deviation aggregation and dynamic mapping analysis of the mixed full-process data is as follows: obtaining the mixing state evaluation value, the abnormal duration, the mixing time, the particle size distribution data, the rheological data, the cavity moisture content, the terminal quality qualification rate and the material density, to provide comprehensive data support for comprehensive compensation decision.

[0067] The mixing state evaluation value is obtained by the sliding window method to obtain the historical maximum mixing state evaluation value, forming the best working condition benchmark in time sequence; the spatial abnormal distribution degree is obtained by the partition abnormal proportion algorithm on the particle size distribution data, the rheological data and the cavity moisture content, fully reflecting the abnormal distribution of each region of the mixing cavity and its severity; the terminal quality deviation degree is obtained by the deviation absolute value and mean square error algorithm on the particle size distribution data, the cavity moisture content, the terminal quality qualification rate and the material density, to provide a criterion basis for process optimization and product consistency control.

[0068] The ratio of the mixed state evaluation value to the historical maximum mixed state evaluation value is denormalized, multiplied by the ratio of the abnormal duration to the mixing duration, plus the abnormal persistence influence coefficient multiplied by the ratio of the abnormal duration to the mixing duration, plus the spatial abnormal weight coefficient multiplied by the spatial abnormal distribution degree, plus the terminal quality deviation adjustment coefficient multiplied by the terminal quality deviation degree, plus one, to obtain a comprehensive compensation judgment value, which is used as a core criterion and trigger signal for entering the multi-dimensional comprehensive compensation stage.

[0069] The calculation formula of the comprehensive compensation judgment value is as follows:

[0070]

[0071] In the formula, C s represents the comprehensive compensation judgment value, dynamically reflects the overall health level and abnormal accumulation degree of the mixing process, and S represents the mixed state evaluation value, which is a key input variable of the comprehensive compensation intervention process; S max represents the historical maximum mixed state evaluation value, which is used as a reference benchmark for optimal process operation; T a represents the abnormal duration, which is used to represent the cumulative duration of the abnormal state in the mixing process and reflects the persistence and stubbornness of the abnormality; T mix represents the mixing duration, which is convenient for normalized comparison with the abnormal duration; D s represents the spatial abnormal distribution degree, which characterizes the distribution range and influence intensity of the abnormality in the mixing cavity space; Q d represents the terminal quality deviation degree, which quantifies the overall deviation degree between the key quality indicators of the current terminal product and the ideal and target state; β1 represents the abnormal persistence influence coefficient, which is obtained by abnormal persistence ratio analysis algorithm on the historical abnormal duration, historical mixing duration, and historical recovery time, and has a value range of 0 to 1, and is used to measure the weight of abnormal persistence on the compensation judgment value; β2 represents the spatial abnormal weight coefficient, which is obtained by spatial abnormal distribution statistical analysis algorithm on the historical particle size distribution data, historical rheological data, and historical cavity moisture content, and has a value range of 0 to 1, and reflects the contribution of spatial distribution abnormality to the overall compensation criterion; β3 represents the terminal quality deviation adjustment coefficient, which is obtained by terminal quality deviation degree correlation analysis algorithm on the historical particle size distribution data, historical cavity moisture content, historical terminal qualification rate, and historical material density, and has a value range of 0 to 1, and is used to adjust the influence weight of the terminal quality deviation degree on the comprehensive compensation judgment value.

[0072] In this embodiment, by performing abnormal deviation aggregation and dynamic mapping analysis on mixed full-process data, the real-time running state, abnormal persistence, spatial distribution abnormality and terminal quality deviation multi-dimensional information of the mixed process can be efficiently integrated, and the dynamic quantification of abnormal discrimination and compensation decision under complex conditions can be realized. This mechanism improves the comprehensiveness of abnormal identification and the scientificity of compensation response, can realize real-time perception and quantification of multi-source abnormalities, and timely trigger multi-dimensional compensation intervention measures, thereby effectively preventing abnormal continuous diffusion or affecting terminal product quality, enhancing the adaptive response ability to complex process abnormalities and the overall operation resilience, and providing a solid data foundation and decision support for high-quality regulation and intelligent closed-loop optimization of the mixed process.

[0073] Specifically, based on the dynamic mapping analysis result, a comprehensive compensation intervention process is executed to transform the forward and reverse rhythm optimization, variable speed stirring, intermittent stirring and impact stirring control instructions. The specific process of stirring compensation measures according to the control instructions is as follows: obtaining a comprehensive compensation judgment value, executing a comprehensive compensation intervention process based on the comprehensive compensation judgment value, transforming the comprehensive compensation judgment value into forward and reverse rhythm optimization, variable speed stirring, intermittent stirring and high-speed impact control instructions through response value mapping and linkage control technology, and realizing intelligent adaptation and linkage control of compensation instructions.

[0074] Based on the forward and reverse rhythm optimization control instruction, the forward and reverse cycle and switching frequency of the spiral blade are adjusted. When the comprehensive compensation judgment value increases, the switching cycle is disturbed and the random frequency switching is performed, which effectively avoids the repeated disturbance of periodic motion to local materials and the mixing dead angle phenomenon.

[0075] Based on the variable speed stirring control instruction, periodic variable speed stirring of the spiral screen is performed, and high and low speed alternating stirring is performed to enhance the energy injection and disturbance diversity in the material mixing process and improve the overall mixing efficiency.

[0076] Based on the intermittent stirring and impact stirring control instruction, when the comprehensive compensation judgment value increases, the intermittent stirring and impact stirring are switched, the stirring is stopped to make the materials naturally settle and preliminarily stratify, and then the impact stirring is started to realize the re-homogenization of difficult-to-mix materials.

[0077] The mixing state evaluation value is continuously monitored until the mixing state evaluation value is greater than or equal to the normalized mixing secondary efficiency value, ensuring the accuracy and effectiveness of the process compensation closed loop, and finally realizing the recovery of mixing uniformity and the improvement of running quality.

[0078] In this embodiment, intelligent linkage and dynamic adaptation of compensation control instructions can be realized, the comprehensive compensation judgment value is accurately mapped to multi-dimensional control measures of positive and reverse rotation rhythm optimization, variable speed stirring, intermittent stirring and high speed impact, real-time switching and optimization of different control strategies, effectively breaking the mixing dead angle, eliminating stratification segregation, and improving the sufficiency of material mixing and energy utilization efficiency. For complex and difficult mixing conditions, the stirring mode can be self-adaptively adjusted to ensure that the material realizes re-homogenization and rapid recovery of process state under the optimal disturbance and compensation rhythm. The mixing state evaluation value is continuously monitored throughout the process to realize closed-loop feedback of the control effect, ensure accurate and effective compensation intervention, and ultimately achieve significant improvement of uniformity and stable and controllable process quality, providing a solid technical support for high-quality and intelligent mixing process management.

[0079] As shown in Figure 2 The second aspect of the present application provides a spiral screen mixing device control system based on the spiral screen mixing device, which comprises a collection preprocessing module, a multi-dimensional particle size real-time sensing module, a partition dynamic adjustment module and a comprehensive suppression compensation module. The collection preprocessing module is used for collecting mixing whole-process data, obtaining historical mixing data, and preprocessing the mixing whole-process data and the historical mixing data. The multi-dimensional particle size real-time sensing module is used for performing suppression type product fluctuation analysis on the mixing whole-process data and the historical mixing data, determining the mixing state according to the suppression type product fluctuation analysis result, and linking the stratification dynamic adjustment and the abnormal area intervention process and the comprehensive compensation intervention process. The partition dynamic adjustment module is used for performing spatial distribution deviation analysis and dispersion superposition on the mixing whole-process data, executing stratification dynamic adjustment and abnormal area intervention process according to the spatial distribution deviation analysis and dispersion superposition result, and entering the comprehensive compensation intervention process. The comprehensive suppression compensation module is used for performing abnormal deviation aggregation and dynamic mapping analysis on the mixing whole-process data, executing the comprehensive compensation intervention process based on the dynamic mapping analysis result, converting the positive and reverse rotation rhythm optimization, variable speed stirring, intermittent stirring and impact stirring control instructions, and performing stirring compensation measures according to the control instructions.

[0080] In this embodiment, efficient collection, dynamic analysis and intelligent closed-loop control of mixing whole-process data can be realized. The mixing state and spatial abnormal distribution can be accurately distinguished, real-time linkage stratification adjustment and abnormal area intervention measures can be realized, and under complex working conditions, the multi-dimensional compensation strategy can be self-adaptively switched to realize multi-parameter self-adaptive optimization of the stirring process. The overall scheme greatly improves the mixing uniformity and spatial consistency, effectively eliminates stratification, segregation and energy consumption abnormal stubborn process bottlenecks, enhances the process toughness and intelligent response capability, and provides a solid data foundation and technical support for high-quality and intelligent mixing production.

[0081] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other presenters can develop. It is also possible, however, that only a single element can be present. It is further noted that such a term as "comprising" is intended to mean that the embodiments include the recited elements, but not excluding other elements. "Consisting essentially of when used herein in relation to a composition, means that the composition includes the recited elements, and can include additional elements, so long as the additional elements do not materially alter the basic and novel properties of the claimed composition. "Consisting of" when used herein in relation to a composition means that the composition includes the recited elements and nothing more.

[0082] The preferred embodiments of the application disclosed above are only to help explain the principles of the present application. The preferred embodiments do not limit the present application to only the specific embodiments described. It is apparent that many modifications and variations of this application are possible in light of this disclosure. The preferred embodiments are chosen and described in order to best explain the principles of the application and the practical application, to thereby enable others skilled in the art to best utilize the application. The present application is limited only by the claims and their full scope and equivalents.

Claims

1. A control method for a spiral screen mixing device, characterized in that The method comprises the following steps: S1, collecting mixed full-process data, obtaining historical mixed data, and preprocessing the mixed full-process data and the historical mixed data; S2, performing suppressive product fluctuation analysis on the mixed full-process data and the historical mixed data, determining the mixing state according to the suppressive product fluctuation analysis result, and linking the layered dynamic adjustment and the abnormal area intervention process and the comprehensive compensation intervention process; S3, performing spatial distribution deviation analysis and deviation superposition on the mixed full-process data, executing the layered dynamic adjustment and the abnormal area intervention process according to the spatial distribution deviation analysis and deviation superposition result, and entering the comprehensive compensation intervention process; S4, performing abnormal deviation aggregation and dynamic mapping analysis on the mixed full-process data, executing the comprehensive compensation intervention process based on the dynamic mapping analysis result, converting positive and negative rotation rhythm optimization, variable speed stirring, pause stirring and impact stirring control instructions, and performing stirring compensation measures according to the control instructions.

2. The control method for a spiral screen mixing device based system according to claim 1, wherein: The specific process of collecting mixed full-process data and obtaining historical mixed data is as follows: Collecting mixed full-process data, obtaining historical mixed data, and establishing a historical mixed database; The mixed full-process data includes: mixed particle number, real-time power, particle size distribution data, material density, cavity moisture content, material viscosity, shear stress, yield stress, terminal product particle size distribution, terminal particle size distribution uniformity, terminal quality pass rate, terminal cavity moisture content, rheological data, abnormal duration, mixing time, original spectrum signal data and reflection spectrum data; The historical mixed data includes: historical temperature data, historical abnormal duration, historical mixing time, historical recovery time, historical particle size distribution data, historical rheological data, historical cavity moisture content, historical terminal quality pass rate and historical material density.

3. The control method for a spiral screen mixing device based system as claimed in claim 1, wherein: The specific process of preprocessing the mixed full-process data and the historical mixed data is as follows: Performing clustering analysis on the mixed full-process data by a multi-source clustering algorithm, identifying abnormal clustering and classifying working conditions of multiple batches and multiple stages, extracting main trends of real-time power, rheological data, particle size distribution data and material density by a modal decomposition algorithm and a sliding window local weighted regression, respectively, restoring the real change trajectory in the mixing process and stripping off high-frequency noise and short-time disturbance, correcting batch-to-batch distribution consistency of the historical mixed data by a distribution alignment algorithm, eliminating the offset caused by equipment state, raw material batch and formula switching, performing scale specification and abnormal period elimination on abnormal duration and mixing time by time series normalization and dynamic interval standardization algorithm, and performing scale unification and interval mapping on the mixed full-process data and the historical mixed data by distribution standardization and linear normalization algorithm, and performing standardization and normalization processing.

4. The control method for a spiral screen mixing device based system as claimed in claim 1, wherein: The specific process of performing suppressive product fluctuation analysis on the mixed full-process data and the historical mixed data is as follows: Obtaining mixed particle number, real-time power, mixing time, particle size distribution data, material density, historical mixed data, cavity moisture content, material viscosity, shear stress, yield stress, particle size distribution data, terminal product particle size distribution, terminal particle size distribution uniformity, terminal quality pass rate and terminal cavity moisture content; The number of particles participating in the mixing is weighted and accumulated, and a noise removal algorithm is used to obtain the number of particles effectively participating in the mixing; the real-time power and the mixing duration are integrated to obtain the mechanical driving energy; the standard deviation and the mean value of the particle size distribution data are calculated to obtain the particle size distribution dispersion; the Pearson correlation coefficient algorithm is used on the material density and historical mixing data to obtain the density-temperature coordination value; the Gini coefficient algorithm is used on the cavity moisture content to obtain the moisture content spatial deviation value; the viscosity, shear stress and yield stress of the material are used in the ratio algorithm and the weighted average algorithm to obtain the interlayer damping ratio of the flowability; The product of the number of particles effectively participating in the mixing and the energy required per unit volume of mixing is taken as the basic term of the efficiency; the product of the particle size uniformity weight and the particle size distribution dispersion is added to the product of the absolute value of the density-temperature coordination weight and the density-temperature coordination value, and the product of the moisture content weight and the moisture content spatial deviation value is added to the product of the flowability weight and the interlayer damping ratio of the flowability, and then an adjustment constant one is added to obtain a total adjustment term, and the mixing state evaluation value is obtained by dividing the basic term by the total adjustment term.

5. The control method for a spiral-sieve-mixing device based system according to claim 1, wherein: The specific process of determining the mixing state according to the results of the suppression type product fluctuation analysis, and simultaneously linking the hierarchical dynamic adjustment and abnormal zone intervention process and the comprehensive compensation intervention process is as follows: The mixing state evaluation value is compared with the mixing state evaluation threshold value in real time, and the mixing state evaluation threshold value includes a first evaluation threshold value and a second evaluation threshold value: When the mixing state evaluation value is greater than or equal to the first evaluation threshold value, it is determined that the current mixing is uniform, the layering and segregation are controllable, and the energy consumption is reasonable; the current stirring parameters and mixing state are maintained, and the equipment operating state is continuously monitored online, without the need for adjustment; When the mixing state evaluation value is greater than or equal to the second evaluation threshold value and less than the first evaluation threshold value, it is determined that the current mixing uniformity decreases, local layering and segregation and energy consumption anomalies begin to appear, and the hierarchical dynamic adjustment and abnormal zone intervention process is entered; When the mixing state evaluation value is less than the second evaluation threshold value, the mixing uniformity decreases seriously, the layering and segregation are obvious, and the energy consumption anomaly is prominent; The rotational speed of the helical blade, the forward and reverse rotation frequency and the throwing intensity are increased, the feeding is paused, and the mixing duration is extended; from the start of the parameter adjustment operation to the end of the extension of the mixing duration is a mixing operation cycle, the mixing full-process data is reacquired at the end of the mixing operation cycle, and the mixing state evaluation value is recalculated; when the mixing state evaluation values calculated in two consecutive mixing operation cycles are both less than the normalized mixing second efficiency threshold value, the comprehensive compensation intervention process is entered.

6. The control method for a spiral-sieve-mixing device based system according to claim 1, wherein: The specific process of spatial distribution deviation analysis and dispersion superposition on the mixing full-process data is as follows: The mixing cavity is equally divided into n sub-zones according to the height, and multiple-point online sensors are arranged in each sub-zone to acquire the mixing full-process data of each sub-zone in real time; the spatial mapping and data fusion algorithm is used to classify the data of each acquisition point to the corresponding sub-zone; The deviation analysis layering quantization value, the particle size distribution data, the viscosity, the shear stress and the yield stress of the material, the original spectrum signal data, the reflected spectrum data, the terminal particle size distribution uniformity and the terminal quality qualification rate are obtained. The standard deviation and mean value of the particle size distribution data of the kth partition are calculated to obtain the particle size distribution dispersion of the kth partition; the viscosity, shear stress and yield stress of the material in the kth partition are subjected to ratio algorithm and weighted average algorithm to obtain the interlayer damping ratio of the flowability of the kth partition; The principal component analysis algorithm and the multivariate linear regression algorithm are combined to obtain the component concentration of the kth partition from the original spectral signal data; The spectral integral gray algorithm is performed on the reflectance spectral data to obtain the optical index value of the kth partition; The component concentrations of all partitions are added and averaged to obtain the average component concentration of all partitions; The optical index values of all partitions are added and averaged to obtain the average optical index value of all partitions; The particle size distribution dispersion of the kth partition is multiplied by the interlayer damping ratio of the flowability of the kth partition, and then multiplied by the square of the difference between the component concentration of the kth partition and the average component concentration of all partitions to obtain the component concentration deviation term; the optical index weight is multiplied by the square of the difference between the optical index value of the kth partition and the average optical index value of all partitions to obtain the optical index weighted deviation term; the component concentration deviation term and the optical index weighted deviation term are added to obtain the partition evaluation term of the kth partition, the values of all partition evaluation terms are recorded as partition values, and the sum of all partition values is calculated to obtain the deviation analysis partition quantization value.

7. The control method for a spiral-sieve-mixing device based system according to claim 1, wherein: The specific process of performing hierarchical dynamic adjustment and abnormal zone intervention process and entering comprehensive compensation intervention process according to the spatial distribution deviation analysis and dispersion superposition results is as follows: The deviation analysis partition quantization value is obtained, and the hierarchical dynamic adjustment and abnormal zone intervention process is performed based on the deviation analysis partition quantization value; the deviation analysis partition quantization value and the partition quantization threshold value are compared in real time, and the abnormal partition threshold value and the partition value are compared: When the deviation analysis partition quantization value is less than or equal to the partition quantization threshold value, the hierarchical distribution and energy consumption of each zone of the mixing chamber are relatively balanced, and there is no widespread abnormal partition; the existing stirring parameters and working mode are maintained, and the change of the deviation analysis partition quantization value is continuously monitored; When the deviation analysis partition quantization value is greater than the partition quantization threshold value, for the abnormal partition whose partition value is greater than the abnormal partition threshold value, the screw rotation speed is increased, the local turnover frequency is accelerated, the stirring intensity is increased, the feeding rate is reduced, and the mixing time is prolonged; one separate operation cycle starts from the separate parameter adjustment operation and ends until the extension of the mixing time; the mixing process data is reacquired after the separate operation cycle ends, the deviation analysis partition quantization value is recalculated, and when the deviation analysis partition quantization values calculated in two separate operation cycles are still greater than the partition quantization threshold value, the comprehensive compensation intervention process is entered.

8. The control method for a spiral-sieve-mixing-device-based system according to claim 1, characterized by: The specific process of performing abnormal deviation aggregation and dynamic mapping analysis on the mixing process data is as follows: The mixing state evaluation value, abnormal duration, mixing time, particle size distribution data, rheological data, cavity moisture content, terminal quality pass rate and material density are obtained; The history maximum mixing state evaluation value is obtained by using a sliding window method on the mixing state evaluation value; the spatial abnormal distribution degree is obtained by using a partition abnormal proportion algorithm on the particle size distribution data, rheological data and cavity moisture content; the terminal quality deviation degree is obtained by using a deviation absolute value and mean square error algorithm on the particle size distribution data, cavity moisture content, terminal quality qualified rate and material density; The ratio of the mixing state evaluation value to the history maximum mixing state evaluation value is anti-normalized, multiplied by the ratio of the abnormal duration to the mixing duration, added to the ratio of the abnormal duration to the mixing duration multiplied by the abnormal persistence influence coefficient, added to the spatial abnormal weight coefficient multiplied by the spatial abnormal distribution degree, added to the terminal quality deviation adjustment coefficient multiplied by the terminal quality deviation degree, and added to one, to obtain a comprehensive compensation judgment value.

9. The control method for a spiral-sieve-mixing-device-based system according to claim 1, characterized by: The comprehensive compensation intervention process is executed based on the dynamic mapping analysis result, and transformation of the forward and reverse rotation rhythm optimization, variable speed stirring, pause stirring and impact stirring control instructions is performed. The specific process of the stirring compensation measures according to the control instructions is as follows: The comprehensive compensation judgment value is obtained, and the comprehensive compensation intervention process is executed based on the comprehensive compensation judgment value. Through response value mapping and linkage control technology, the comprehensive compensation judgment value is transformed into forward and reverse rotation rhythm optimization, variable speed stirring, intermittent stirring and high speed impact control instructions. Based on the forward and reverse rotation rhythm optimization control instruction, the forward and reverse rotation period and switching frequency of the spiral blade are adjusted. When the comprehensive compensation judgment value increases, the switching period is disturbed and the random frequency switching is performed to avoid the periodic motion mode. Based on the variable speed stirring control instruction, the periodic variable speed stirring of the spiral screen is performed, and the high and low speed alternating stirring is performed. Based on the pause stirring and impact stirring control instruction, when the comprehensive compensation judgment value increases, the pause stirring and impact stirring are switched, the stirring is stopped to make the material naturally settle and preliminarily stratify, and then the impact stirring is started. The mixing state evaluation value is continuously monitored until the mixing state evaluation value is greater than or equal to the normalized mixing secondary efficiency value.

10. A control system for a spiral screen mixing apparatus, the control system comprising: It includes: The acquisition preprocessing module is used for acquiring mixed full process data, obtaining historical mixing data, and preprocessing the mixed full process data and the historical mixing data; The multi-dimensional particle size real-time sensing module is used for inhibitory product fluctuation analysis on the mixed full process data and the historical mixing data, determining the mixing state according to the inhibitory product fluctuation analysis result, and linking the stratified dynamic adjustment and abnormal area intervention process and the comprehensive compensation intervention process; The partition dynamic adjustment module is used for spatial distribution deviation analysis and dispersion superposition on the mixed full process data, and the stratified dynamic adjustment and abnormal area intervention process is executed according to the spatial distribution deviation analysis and dispersion superposition result, and the comprehensive compensation intervention process is entered; The comprehensive inhibition compensation module is used for abnormal deviation aggregation and dynamic mapping analysis on the mixed full process data, and the comprehensive compensation intervention process is executed based on the dynamic mapping analysis result, and transformation of the forward and reverse rotation rhythm optimization, variable speed stirring, pause stirring and impact stirring control instructions is performed, and the stirring compensation measures are performed according to the control instructions.

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