Solid waste-based zinc chloride preparation process control method and system

Abstract

The invention discloses a solid waste-based zinc chloride preparation process control method and system, and relates to the technical field of zinc chloride preparation, and the method comprises the following steps: obtaining a unified time base line, establishing a bubble phase reference field, collecting a sound-induced echo sequence, a micro-pressure waveform sequence and a light scattering sequence of a reaction liquid under the bubble phase reference field, and calculating the solid waste-based zinc chloride preparation process. Fusing to generate a bubble density space-time matrix; and performing reversible time-frequency rearrangement based on the bubble density space-time matrix, decomposing to obtain different frequency band distributions, extracting a narrow-band resident bubble group, calculating critical volume distribution and collapse precursor indexes, and constructing a dynamic risk fingerprint database. According to the method, a unified time base line and a bubble phase reference field are constructed, the bubble state is perceived in real time, collapse forebodes are recognized, a dynamic risk fingerprint database is established, directional intervention and reverse energy channel collapse nucleus extinguishing are achieved in combination with an energy aggregation node, finally closed-loop regulation is constructed through calibration write-back, the safety and stability of zinc chloride preparation are improved, and the method is suitable for large-scale popularization and application. And efficient utilization and clean production of solid waste are promoted.

Description

Technical Field

[0001] This invention relates to the field of zinc chloride preparation technology, and more specifically to a method and system for controlling the zinc chloride preparation process based on solid waste. Background Technology

[0002] "Zinc Chloride Preparation Process Control Based on Solid Waste" refers to the dynamic monitoring and adjustment of key process parameters such as temperature, pH, reaction time, feed ratio, stirring rate, and atmospheric conditions during the production of zinc chloride using solid waste (such as zinc-containing smelting slag, metal scrap, and dust byproducts) as raw materials. This ensures stable and controllable reaction processes and optimizes product quality. Its core meaning lies in two aspects: firstly, realizing the resource utilization of solid waste and reducing environmental pollution; and secondly, using process control technology to avoid decreased reaction efficiency or byproduct generation due to complex and fluctuating raw material composition, thereby ensuring the purity, yield, and stability of the zinc chloride product. This method is not only a chemical process control technique but also a combination of waste recycling and clean production.

[0003] The existing technology has the following shortcomings: In the existing zinc chloride preparation process, gaseous byproducts are inevitably generated in the reaction system, typically remaining in the liquid as bubbles. As the chlorination reaction continues, these bubbles may gradually accumulate in localized areas, forming dense bubble clusters. When the volume distribution of these bubble clusters exceeds a critical state, instantaneous collapse may occur, releasing localized energy and generating shock waves. These shock waves can easily cause abnormal disturbances or even splashing of the reaction liquid, leading to material loss and environmental risks. Simultaneously, the direct impact of the shock waves on the inner walls of the equipment can damage localized anti-corrosion or passivation layers, exposing the metal substrate to highly corrosive media, accelerating equipment corrosion, and shortening its service life.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for controlling the zinc chloride preparation process based on solid waste, so as to solve the problems in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for controlling the zinc chloride preparation process based on solid waste, comprising the following steps: A unified time baseline was obtained and a bubble phase reference field was established. Acoustic echo sequence, micro-pressure waveform sequence and light scattering sequence of the reaction liquid were collected under the bubble phase reference field and fused to generate a bubble density spatiotemporal matrix. Based on the spatiotemporal matrix of bubble density, reversible time-frequency rearrangement is performed to decompose different frequency band distributions, extract narrowband resident bubble groups, calculate critical volume distribution and collapse precursor index, and construct dynamic risk fingerprint database. By applying an ultrashort pulse acoustic field based on a dynamic risk fingerprint database, the synchronous response differences of the reaction liquid to micro-amplitude phase traction are obtained, and energy convergence nodes are identified. Based on the energy convergence node, a gas-liquid coupled energy migration map is constructed, the node response is replayed to a unified time baseline, and the shock wave intensity and arrival time are predicted by the causal consistency criterion to generate a preset suppression window. A multi-channel anti-phase compensation trajectory is constructed based on a preset suppression window. A flow field vortex injection and micro-nucleus propagation strategy are introduced into the compensation trajectory to weaken the connectivity of the bubble group, reduce the critical volume distribution, and form a corrected dynamic feedback state. Based on the modified dynamic feedback state-triggered time-reversal phase gating, the programmable acousto-optic metasurface is driven to release the phase conjugate micro-envelope, establishing a reverse energy channel inside the reaction liquid that matches the dynamic feedback state, extinguishing the residual collapsed core, and writing back the calibration factor to the unified time baseline, thus completing the closed-loop dynamic regulation.

[0007] Preferably, the steps for generating the bubble density spatiotemporal matrix are as follows: By setting a unified time baseline, time synchronization calibration is performed on all data channels involved in acoustic signal acquisition, pressure signal acquisition, and optical signal acquisition, and a unified timestamp system is established. A bubble phase reference field is established under a unified time baseline, and three types of physical signals are acquired and the spatial state of the bubble is calibrated by using an acoustic echo array, a micro-pressure sensor array, and a laser scattering receiver. The collected acoustic echo sequence, micro-pressure waveform sequence and light scattering sequence are spatially mapped in three dimensions and superimposed in time dimension according to a unified time baseline to generate a spatiotemporal matrix of bubble density. The spatiotemporal matrix of bubble density is used as the dynamic record basis for the evolution of bubble behavior, which supports the subsequent prediction of bubble collapse and the implementation of energy intervention strategies.

[0008] The preferred method for constructing a dynamic risk fingerprint database is as follows: Based on the density change sequence of each voxel in the bubble density spatiotemporal matrix on the time axis, a reversible time-frequency rearrangement operation is performed to obtain the frequency-time distribution relationship and identify the energy concentration frequency band. The frequency concentration region is mapped back to the bubble density spatiotemporal matrix, the location of the resident bubble group is extracted, and the volume growth rate, density gradient directionality coefficient and spatial connectivity are calculated to construct the collapse precursor index. A dynamic risk fingerprint database is established based on the feature parameters of the extracted bubble clusters, and the status is continuously updated and trends are predicted according to a unified time baseline, so as to realize the spatial marking and risk assessment of potential collapse areas.

[0009] Preferably, the energy convergence node identification process is as follows: An ultrashort pulse acoustic field is applied to the three-dimensional coordinates of the resident bubble cluster region marked as high-risk in the dynamic risk fingerprint database to form a micro-amplitude phase traction response. Acoustic and pressure response data of the reaction liquid were collected during the acoustic field excitation process, and three-dimensional acoustic response distribution map and pressure response map were constructed. By jointly analyzing acoustic and pressure response spectra, spatial regions with phase delay, pressure jump, and spectral shift characteristics were screened, and multiple excitation results were superimposed to confirm hotspot regions as energy convergence nodes. After verifying the continuity and consistency of phase delay, pressure change and spectral response of the energy convergence node during continuous excitation and confirming its potential to trigger collapse, the coordinates and response characteristics of the energy convergence node are written into the dynamic risk fingerprint database to update the risk level.

[0010] Preferably, the steps for generating the preset suppression window are as follows: Based on the spatial coordinates and response characteristics of energy convergence nodes, a gas-liquid coupled energy migration map is constructed to record the propagation path and attenuation characteristics of energy in each direction. The response behavior of each node in the energy migration map is replayed to a unified time baseline to construct a complete energy propagation time sequence structure. The main axis trend in the energy propagation path is identified by the causal consistency criterion, and the propagation intensity and expected arrival time of the shock wave are calculated. Set the response area and suppression time point on the shock wave prediction path to generate a preset suppression window with spatiotemporal positioning capability; By linking the preset suppression window with the energy propagation axis and prediction indicators, a multi-path dynamic intervention structure indexed by a unified time baseline is constructed to trigger active responses.

[0011] The preferred, modified dynamic feedback state generation steps are as follows: A multi-channel anti-phase compensation trajectory is constructed within a preset suppression window, and a phase-reversed ultrasonic signal is emitted by a focused ultrasonic transmitter to interfere with the sound field of the main propagation path. A jet device is set up in the anti-phase sound field intervention area to form a vortex shear flow field, which destroys the spatial connectivity structure of the bubble group through shearing. Micro-gas nuclei are released within the vortex disturbance region, and the coupling strength of the bubbles is weakened by interface disturbance and phase disturbance, ultimately forming a structurally dispersed and dynamically stable feedback state.

[0012] Preferably, the steps for driving the programmable acousto-optic metasurface to release the phase conjugate microenvelope based on the modified dynamic feedback state-triggered time-reversal phase gating, constructing a matched reverse energy channel in the reaction solution, extinguishing the residual collapsed core, and writing the calibration factor back to the unified time baseline are as follows: Residual collapsed kernels are identified based on the modified dynamic feedback state, and time-reversal phase gating is triggered. Phase-gated drive programmable acousto-optic metasurfaces are used to release phase-conjugate micro-envelopes to form inverse wavefronts; A reverse energy channel matching the dynamic feedback state is constructed along the phase conjugate wavefront path, and energy is focused onto the collapsed nucleus. The calibration factor is extracted based on the response error in the reverse energy channel and written into the unified time baseline to complete the closed-loop dynamic control.

[0013] A control system for zinc chloride preparation based on solid waste is characterized by comprising a unified baseline construction and sensing module, a bubble risk identification module, an acoustic field response detection module, a shock wave prediction module, an active intervention compensation module, and a closed-loop control execution module. The unified baseline construction and sensing module acquires a unified temporal baseline and establishes a bubble phase reference field. Under the bubble phase reference field, it collects the acoustic echo sequence, micro-pressure waveform sequence and light scattering sequence of the reaction liquid and fuses them to generate a bubble density spatiotemporal matrix. The bubble risk identification module performs reversible time-frequency rearrangement based on the bubble density spatiotemporal matrix, decomposes it to obtain different frequency band distributions, extracts narrowband resident bubble groups, calculates critical volume distribution and collapse precursor index, and constructs a dynamic risk fingerprint database. The acoustic field response detection module applies an ultrashort pulse acoustic field based on a dynamic risk fingerprint database to obtain the synchronous response differences of the reaction liquid to micro-amplitude phase traction and identify energy convergence nodes. The shock wave prediction module constructs a gas-liquid coupled energy migration map based on energy convergence nodes, replays the node response to a unified time baseline, predicts the shock wave intensity and arrival time through causal consistency criteria, and generates a preset suppression window. The active intervention compensation module constructs a multi-channel anti-phase compensation trajectory based on a preset suppression window. It introduces flow field vortex injection and superimposes micro-nucleus propagation strategy in the compensation trajectory to weaken the connectivity of the bubble group, reduce the critical volume distribution, and form a corrected dynamic feedback state. The closed-loop control execution module, based on the modified dynamic feedback state trigger time-reversal phase gating, drives the programmable acousto-optic metasurface to release the phase conjugate micro-envelope, establishes a reverse energy channel matching the dynamic feedback state inside the reaction liquid, extinguishes the residual collapsed core, and writes back the calibration factor to the unified time baseline, thus completing the closed-loop dynamic control.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention establishes a unified time baseline and bubble phase reference field to achieve multi-dimensional real-time perception of the bubble state in the reaction system. Then, based on time-frequency rearrangement and collapse precursor identification, a dynamic risk fingerprint database is constructed. Combined with precise positioning of energy convergence nodes and the construction of reverse energy channels, this not only provides early warning and targeted intervention before collapse occurs but also proactively eliminates risk points during the collapse remnant stage. Finally, a real-time calibration and write-back mechanism incorporates each response process into a unified temporal evolution logic, forming a full-cycle intelligent control closed loop. This method is non-destructive, programmable, and self-learning, significantly improving the safety, stability, and equipment lifespan of the zinc chloride preparation process, while also promoting the efficient utilization of solid waste and the integrated application of clean production technologies. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 This is a flowchart of the zinc chloride preparation process control method based on solid waste according to the present invention.

[0017] Figure 2 This is a schematic diagram of the control system for zinc chloride preparation process based on solid waste according to the present invention. Detailed Implementation

[0018] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0019] This invention provides, for example Figure 1 The method for controlling the zinc chloride preparation process based on solid waste, as shown, includes the following steps: A unified time baseline was obtained and a bubble phase reference field was established. Acoustic echo sequence, micro-pressure waveform sequence and light scattering sequence of the reaction liquid were collected under the bubble phase reference field and fused to generate a bubble density spatiotemporal matrix. To achieve precise dynamic monitoring of the bubble behavior generated by chemical chlorination during the reaction of solid waste, and to ensure the stability and safety of the zinc chloride preparation process, a technical solution is proposed, which involves constructing a unified time baseline, establishing a bubble phase reference field, collecting multi-dimensional signals, and fusing them to generate a spatiotemporal matrix of bubble density. The specific steps include: By establishing a unified time baseline, time synchronization calibration is performed on all data channels involved in acoustic, pressure, and optical acquisition. In implementation, a highly stable time base reference source is first constructed using a GPS time synchronization device and a temperature-controlled crystal oscillator (TCXO). This reference source is connected via a wired interface to the main control boards of the ultrasonic signal acquisition equipment, the micro-pressure sensor controller, and the laser light scattering acquisition equipment, and automatically issues a unified timestamp upon startup. To ensure time consistency during long-term continuous responses, a real-time error correction mechanism based on a digital phase-locked loop is used to dynamically adjust the clock drift within each acquisition device, while periodic time deviation detection is performed using a high-frequency calibration signal. Under this unified baseline, each data acquisition event carries a high-precision timestamp, achieving millisecond-level synchronization of acoustic, pressure, and optical signals, providing a unified time-domain framework for subsequent data fusion.

[0020] After establishing a unified time baseline, the bubble phase reference field was constructed to describe the full-cycle evolution of bubbles during different time periods, including generation, growth, movement, and collapse. In this process, multiple ultrasonic transmitting and receiving arrays were installed along the reactor wall in a fan-shaped arrangement. Each array included a 5MHz focused ultrasonic transducer with a 2MHz bandwidth as the transmitter and three receivers distributed at different azimuth angles. By periodically transmitting square-wave modulated excitation signals, acoustic echoes at different depths and orientations within the reaction liquid were obtained. The echo intensity, spectral characteristics, and propagation delay were analyzed to obtain preliminary spatial characteristics of the bubble distribution. Simultaneously, a miniature piezoelectric pressure sensor array was installed at a location completely overlapping with the acoustic scanning area. Each sensor had a sensitivity of 0.1Pa, a spacing of 2cm, and a sampling frequency of 500Hz. These sensors were used to record local micro-pressure disturbances in the liquid caused by bubble generation, coalescence, and collapse. At each micro-pressure event, the peak value, duration, and time derivative were recorded to aid in determining the bubble interactions. The optical component employs a 532nm continuous-wave laser with an output power of 20mW, illuminating the reaction region from multiple vertical directions. Photodetectors are positioned in front, to the sides, and behind the reaction region to capture changes in the scattered light signal caused by the bubble interface, thus obtaining spatial light scattering distribution maps of different areas. By performing time matching, amplitude normalization, and frequency analysis on the acoustic, micro-pressure, and light scattering data, and after calibrating the three-dimensional position coordinates, a bubble phase reference field with high temporal consistency and high spatial resolution is constructed. This reference field calibrates the bubble's behavior in all dimensions, reflecting the bubble's position, density, volume change trends, and interface perturbation characteristics at specific moments.

[0021] After the bubble phase reference field was constructed, high-frequency data acquisition was initiated based on a unified time baseline and timestamp system to capture the dynamic evolution of bubbles in the reaction liquid. Acoustic echo acquisition employed a 16-channel ultrasonic receiver array positioned at the top and sidewalls of the reactor. Each receiver received reflected signals at a repetition frequency of 40 kHz. Combining amplitude-time delay characteristics, the reflection feature changes before and after each bubble formation and collapse were extracted. For micro-pressure acquisition, each piezoelectric sensor continuously acquired pressure fluctuation curves in the reaction liquid, identifying micro-shock waves caused by rapid bubble contraction and recording the amplitude and occurrence time of each fluctuation event in a time-series format. Light scattering data acquisition used multi-angle synchronous beam scanning technology. Each photodetector read light intensity data at a frequency of 200 Hz and calculated the rate of change of light scattering intensity per unit time to determine whether bubble aggregation occurred in the liquid phase. All three types of signals were accompanied by a unified timestamp and buffered within a time period using a sliding window of one second, preparing a continuous data stream for subsequent partitioning fusion and dynamic modeling. All data acquisition actions are strictly performed according to a unified baseline to avoid data misalignment due to device response delays and ensure the consistency of the entire acquisition process in both space and time.

[0022] The acquired acoustic echo sequence, micropressure waveform sequence, and light scattering sequence were aligned with a unified time baseline and fused in a three-dimensional partitioned manner within the reaction liquid space to construct a bubble density spatiotemporal matrix. In practice, the reaction region was first divided into cubic voxel units with 1 cm sides, and a three-dimensional buffer was established within each voxel to store the three types of physical signals at that spatial location over consecutive time periods. During data fusion, the probability of bubble presence at each voxel was calculated based on the acoustic echo signal intensity and echo delay. Then, the pulse density and average pressure gradient of the micropressure fluctuations were superimposed to determine the bubble activity frequency in that region. Finally, the bubble volume distribution and surface disturbance range were estimated based on changes in light scattering intensity and spot area. After normalization, these three parameters were weighted and superimposed to generate a single density factor value, which was then filled into the time series matrix of the corresponding voxel, forming a matrix element of the bubble density spatiotemporal matrix. The three-dimensional matrix composed of all voxels in the entire reaction space forms a complete cross-section in each time slice. Multiple time slices are stacked sequentially to form a complete four-dimensional data structure, which can be updated and replayed in real time, comprehensively reflecting the generation, migration, aggregation, and dissipation of bubbles during the reaction process. This matrix not only has high-resolution dynamic recording capabilities, but also provides accurate and reliable basic data support for subsequent bubble collapse identification, energy aggregation localization, and intervention mechanism design.

[0023] Based on the spatiotemporal matrix of bubble density, reversible time-frequency rearrangement is performed to decompose different frequency band distributions, extract narrowband resident bubble groups, calculate critical volume distribution and collapse precursor index, and construct dynamic risk fingerprint database. To identify and warn of the collapse risk of bubble clusters generated in the zinc chloride preparation process due to the participation of solid waste in the reaction solution, a full-process analysis of frequency domain deconstruction, spatial extraction, and dynamic modeling was further carried out based on the constructed bubble density spatiotemporal matrix. The specific steps include: To reveal the frequency characteristics of bubble aggregation and residence, and their energy distribution over time, a reversible time-frequency rearrangement operation was performed on the density change sequence of each three-dimensional voxel unit in the bubble density spatiotemporal matrix along the time axis. Specifically, the operation involved: first, sampling the bubble density sequence of each voxel unit at a frequency of 100 Hz for a continuous 10-second sampling period to obtain the raw data curve of density change over time. Then, a sliding windowing process was used, constructing a 2-second observation window every 500 milliseconds. A windowed Fourier transform was performed on each data segment, and the time-domain data was mapped to the frequency domain based on the concentration of the frequency power spectral density, forming a frequency-time distribution map with the current timestamp. To ensure a balance between spectral resolution and temporal positioning capability, multiple overlapping time windows were introduced, with the window function overlap ratio set to 75%, ensuring that the frequency domain information within each time segment continuously covered the entire observation interval. Subsequently, energy concentration regions were marked in the spectrum, particularly the frequency bands forming stable peak regions between 10 Hz and 30 Hz, which were considered as signal components potentially corresponding to bubble residence phenomena. Regions in this frequency band where the frequency peak remains stable across multiple consecutive time windows will be initially identified as localized high-density clusters of bubble swarms. After frequency domain data processing, the original spatiotemporal matrix information is bound to the spectral features, and the mapping relationship is preserved to support subsequent spatial backtracking of the residing region and comparison of multi-source signals.

[0024] Based on the low-frequency concentrated energy regions in the frequency-time spectrum obtained from the reversible time-frequency rearrangement, these regions are projected back into the three-dimensional spatial structure of the bubble density spatiotemporal matrix to extract the areas where bubble clusters with obvious dwelling characteristics reside. Specifically, using the voxel numbers corresponding to the frequency concentrated regions as clues, voxel-by-voxel locations are established in the bubble density spatiotemporal matrix, and a 5×5×5 cubic spatial search window is established around each region to analyze the density values ​​of neighboring voxels within the same time period. The density gradient descent method is used to calculate the density change rate between adjacent voxels, and voxel regions with continuous density distribution and consistent growth trends are merged accordingly, ultimately constructing a dwelling bubble cluster envelope with spatial continuity and closed boundaries. Subsequently, the total density integral value of all voxels within this envelope is calculated and multiplied by the volume of a single voxel to obtain the instantaneous volume value of the bubble cluster in the current time slice. To assess its collapse risk level, characteristic parameters from historical data prior to collapse events are introduced, setting three key indicators: volume growth rate, density gradient directionality coefficient, and connectivity criterion. The volume increment of the current bubble swarm is calculated by subtracting it from the previous time slice to determine the volume growth rate per unit time. If this value exceeds a set threshold of 0.15 cm³ / s, and the density distribution shows an inward clustering trend with spatial connectivity exceeding 95%, the bubble swarm is determined to be in the critical volume distribution range. Based on this, a collapse precursor index is introduced. By weighted fusion of three parameters—bubble density slope, volume growth rate, and three-dimensional connectivity—the trend value of collapse in the next cycle is calculated and classified into five risk levels, labeled R1 to R5, from extremely low risk to extremely high risk.

[0025] After completing the spatial identification of resident bubble clusters and the calculation of collapse precursor indices, a dynamic risk fingerprint database is constructed to identify the temporal continuity and spatial stability of potential collapse regions. The specific implementation process is as follows: For each identified resident bubble cluster, a parameter set containing more than ten dimensions is established, including timestamp number, three-dimensional spatial center coordinates, frequency bandwidth, peak frequency, maximum density, density growth rate, density gradient vector, volume change curve, connectivity change sequence, and precursor index level. All parameters are indexed based on a unified time baseline and stored in a real-time updatable data structure. This structure is updated every second, allowing for state tracking of each bubble cluster, determining whether its risk level has increased, whether it exhibits continuous evolution characteristics, and predicting the shortest response time for it to enter a high-risk state based on parameter fluctuation trends. When a fingerprint maintains a precursor index level of R5 for three consecutive time windows, and its density growth rate shows an increasing trend, while multiple overlapping regions of fingerprints with levels of R3 or higher appear in adjacent spaces, this region is identified as having a risk of multi-source coupling collapse. Its spatial hotspot coordinates are recorded, providing specific target input for subsequent acoustic intervention and energy release control strategies. This risk fingerprint database does not rely on a single threshold triggering mechanism but achieves deep perception and forward-looking prediction capabilities for risk identification through multi-parameter fusion of spatiotemporal features and dynamic evolution trend judgment. This significantly improves the safety and controllability of the reaction process and energy response efficiency. By applying an ultrashort pulse acoustic field based on a dynamic risk fingerprint database, the synchronous response differences of the reaction liquid to micro-amplitude phase traction are obtained, and energy convergence nodes are identified. To achieve accurate identification of high-risk areas for bubble collapse, an ultrashort pulse sound field is applied to the liquid reaction medium based on a dynamic risk fingerprint database. By acquiring the spatiotemporal response differences of the reaction liquid to micro-amplitude phase traction, energy convergence nodes that may accumulate acoustic energy and trigger collapse are identified. The specific steps include: Based on the three-dimensional coordinates of the high-risk bubble cluster region marked in the dynamic risk fingerprint database, the target and spatial location of the acoustic excitation were determined, and an ultrashort pulse acoustic field was applied at this location. The acoustic source used was a frequency-adjustable focusing ultrasonic transducer with a working center frequency set to 2 MHz and a pulse width limited to 1 microsecond. The transmission rhythm and period were controlled by a digital pulse modulator, with each transmission lasting 10 milliseconds, an interval of 100 milliseconds, and a total excitation duration of 5 seconds. To accurately cover the target bubble region, the acoustic waves were incident obliquely through the container wall, and the transmission angle was adjusted by calculating the ray path and acoustic refraction coefficient to ensure the focal point completely coincided with the center of the high-risk bubble cluster. The acoustic energy density was controlled below 20 kPa to ensure that it would not cause direct bubble collapse or violent disturbance of the reaction liquid, but only form a small-amplitude vibration field within a local area, thus facilitating the identification of the phase response characteristics of the reaction liquid under this excitation condition. All acoustic excitation events are strictly synchronized with a unified time baseline, and the excitation time points are recorded at the millisecond level using a high-precision clock for subsequent multimodal data fusion and response trend analysis.

[0026] During the synchronous execution of acoustic excitation, multiple high-sensitivity receivers are deployed opposite and to the sides of the sound source to acquire acoustic and pressure response data generated inside the reaction liquid after excitation. Specifically, this includes an ultrasonic receiver array distributed in a fan-shaped array and a miniature piezoelectric pressure sensor array distributed in a rectangular array. The ultrasonic receivers are set to a sampling frequency of 5 MHz and a resolution of 0.2 microseconds to acquire echo data after the sound waves propagate and scatter through the reaction liquid. When the sound waves pass through high-risk bubble areas, waveform distortion, amplitude attenuation, and phase shift occur due to reflection, focusing, and absorption at the bubble interface. Simultaneously, the pressure sensors are set to a sampling frequency of 2 kHz and a sensitivity better than 0.1 Pascals to specifically acquire local micro-pressure changes caused by acoustic field disturbances in the bubble group, paying particular attention to instantaneous pressure peaks that may occur at acoustic resonance or beam overlap locations. Data generated from each excitation is acquired within a 2-millisecond window, and the current timestamp, acquisition angle, response amplitude, and phase delay are marked. After time-series comparison and spatial interpolation, all acquired signals are processed to form a complete three-dimensional acoustic response distribution map, covering the high-risk area and its surroundings. By comparing the differences in response characteristics of different areas under the same excitation conditions, the coupling ability of local areas to acoustic energy can be further determined, serving as a basis for subsequent identification of energy accumulation areas.

[0027] After acquiring data from multiple excitation sequences, a joint analysis of acoustic and pressure response spectra was performed to identify the physical locations within the reaction liquid where acoustic energy convergence effects might occur. The analysis methods included: firstly, selecting voxel regions with significant phase delay, waveform stretching, or abnormal amplitude enhancement in the response spectra as preliminary candidate regions; then, screening for synchronously occurring pressure jumps in the corresponding region's micro-pressure signals, requiring jump amplitudes to be at least 1.5 times the background average pressure fluctuations and exhibiting a continuous or recurring trend; further, in frequency domain analysis, observing whether the echo spectrum of this region showed a significant frequency shift to the lower end, indicating that energy might be concentrated in a local area, forming a standing wave effect. All three characteristics must be simultaneously satisfied and repeated in at least three independent excitation cycles to confirm the voxel region as a hotspot node with an energy convergence effect. During the identification process, a phase superposition and three-dimensional fitting algorithm was used to intersect and fit the response hotspot regions under each excitation, constructing a spatial coordinate set of energy convergence with clear physical boundaries and strong response continuity. The spatial center coordinates, volume range, maximum phase delay value, maximum pressure abrupt change value, and spectral offset direction of each energy gathering node will be used as its feature labels to form a standardized expression format for subsequent energy intervention and control target inputs.

[0028] After obtaining the coordinates and response characteristics of the energy convergence node, it is necessary to verify the node's temporal response stability and spatial energy coupling persistence to ensure that it is not caused by accidental physical disturbances. The verification process includes: during a 5-second continuous excitation period, selecting a sound field excitation event once per second, and extracting the response characteristic change curves of the same node; if the node exhibits persistent phase delay, increased stability pressure, and repetitive spectral shift in at least three excitations, and the trends of these characteristics are correlated (e.g., the phase delay value continuously increases, and the spectral shift range gradually expands), then the node is judged to have an energy convergence trend and the potential to trigger bubble collapse or shock wave release. Finally, the verified energy convergence node is designated as a high-priority control target, and its three-dimensional coordinates, response indicators, historical change trends, excitation response consistency, and other parameters are written into the risk control data table and integrated with the dynamic risk fingerprint database to update the risk level of the location.

[0029] Based on the energy convergence node, a gas-liquid coupled energy migration map is constructed, the node response is replayed to a unified time baseline, and the shock wave intensity and arrival time are predicted by the causal consistency criterion to generate a preset suppression window. To proactively identify and intervene in the propagation of shock waves potentially caused by bubble collapse, it is necessary to construct a gas-liquid coupled energy migration map based on energy convergence nodes, replay the response behavior to a unified time baseline, predict the intensity and arrival time of the shock wave using dynamic causal criteria, and ultimately generate a pre-set suppression window with spatiotemporal correspondence. The specific steps include: Based on the spatial coordinates, phase response characteristics, spectral shift parameters, and micro-pressure jump values ​​of the identified energy convergence nodes, each node is considered a local energy excitation source, and a gas-liquid coupled energy migration map is constructed in three-dimensional space. During the operation, each energy convergence node is used as a spherical propagation center. According to the sound velocity, density, viscosity, and local bubble distribution characteristics of the liquid medium, the propagation speed, attenuation rate, and propagation path of energy in each direction are calculated. Multiple concentric shells are divided around the node with equidistant radii. Each shell serves as a first-order spatial unit for energy propagation, recording the propagation time delay, energy density attenuation value, frequency composition change, and pressure coupling degree at the corresponding location. If there are spatial overlaps in the propagation paths of multiple nodes, their energy components are superimposed using a phase vector superposition method to obtain the local energy concentration trend. This map fully describes the process of energy expansion from the convergence node to the surrounding liquid phase space, forming a replayable and predictable dynamic migration structure.

[0030] After the energy migration map is constructed, the response data of all energy convergence nodes and their propagation paths are replayed to a unified time baseline to achieve full-time domain reconstruction of energy events. Specifically, the excitation time point and response signal of each node are rearranged according to a unified timestamp, the node excitation event is set as the zero-point reference, and the time delay of its propagation to other shells is converted into relative propagation time. Along each propagation path, the energy density value, frequency components, phase change, and micro-pressure perturbation curve at the corresponding time point are recorded and arranged in a time series to form an energy propagation sequence set. After all propagation paths are aligned in the time dimension, a multi-node, multi-directional energy evolution sequence map under a unified time base is formed. This step ensures the dynamics and sequence logic of the energy response process, solves the misjudgment problems caused by isolated data and chaotic response timing in traditional methods, and lays the foundation for subsequent causal chain analysis and shock wave trend prediction.

[0031] Based on the energy response sequence at the time baseline, a causal consistency chain between energy events is constructed to identify shock wave propagation trends. First, the order and correspondence of energy responses in each propagation path are analyzed to determine if causal consistency conditions are met. If the energy excitation time of node A is earlier than the response time of node B, and the energy propagation path direction is consistent with the direction of the node connection, and the response signals exhibit similar phase, frequency convergence, and waveform morphology, then a causal coupling relationship is determined between them. All nodes satisfying causal consistency are gradually connected to construct the energy propagation axis, identifying whether there is a trend of concentrated energy enhancement in a certain direction. Within the axis path, the propagation velocity, energy density increase curve, and spectral convergence are further analyzed to calculate the shock wave intensity gradient in that direction and determine whether it will form a wavefront energy burst in the future. If features such as spectral compression to low frequencies, phase synchronization superposition, and local density exceeding the threshold are observed, a shock wave event is predicted to occur in that direction at a specified time point.

[0032] After predicting the intensity and propagation path of the shock wave, a preset suppression window is generated based on the prediction results, clarifying the intervention timing and spatial range. In operation, the suppression time point is first set to 0.5 seconds before the predicted arrival time of the shock wave. Spatially, a dynamic response area with a radius of 5 cm is constructed centered on the predicted focal point of the wavefront propagation path. This response area is set as a three-dimensional spatial sphere that slides over time, its trajectory consistent with the predicted shock wave path. Within this response area, the activation conditions for active measures such as acoustic field reverse intervention, microbubble perturbation injection, or phase perturbation shielding are set, and their triggering time, response strategy, and intervention duration are all clearly defined. During the actual response, the actual response signal within the area is compared with the prediction model in real time. If warning signals such as abnormal rise in micropressure, acoustic echo shift, or sharp increase in light scattering occur, the activation time and spatial radius of the suppression window are dynamically adjusted to ensure early intervention, precise coverage, and full effectiveness.

[0033] After generating the preset suppression window, this window is jointly modeled with energy convergence nodes, propagation paths, and shock wave prediction models to construct a spatiotemporally linked response safety control structure. This structure uses a unified time baseline as an index and each node as the root source, connecting its propagation path, shock wave prediction indicators, and suppression window response conditions to form a multi-level intervention logic relationship. When the response characteristics of multiple nodes along a propagation path are detected to be synchronously enhanced or a trend of historical high-risk trajectory recurrence is observed, the suppression window is automatically activated and the corresponding intervention strategy is initiated. This control structure responds in real time, updates dynamically, and supports parallel judgment across multiple paths and regions, significantly improving the proactive control capability of the response system against sudden energy anomalies.

[0034] A multi-channel anti-phase compensation trajectory is constructed based on a preset suppression window. A flow field vortex injection and micro-nucleus propagation strategy are introduced into the compensation trajectory to weaken the connectivity of the bubble group, reduce the critical volume distribution, and form a corrected dynamic feedback state. To further reduce the structural connectivity and critical volume distribution trend of high-risk bubble swarms, a spatially oriented multi-channel anti-phase compensation trajectory needs to be established within the pre-generated suppression window. Local vortex disturbances and micro-nucleus propagation operations are then sequentially introduced into this trajectory to weaken bubble coupling behavior at multiple scales and dimensions, ultimately forming a dynamic feedback state with a stable dispersed structure. This method specifically includes the following steps: Within the preset suppression window established by the predicted shock wave path and its area of ​​influence, a set of multi-channel anti-phase compensation trajectories is constructed to achieve phase cancellation of the resonant energy coupling process that may be triggered by the bubble swarm. The operation steps are as follows: Based on the acoustic wave propagation path in the energy convergence node area and its directional distribution in three-dimensional space, six sets of focused ultrasonic transmitters are deployed at equal distances on both sides of the path. Each set consists of four independent channels, forming a twelve-directional distributed anti-phase intervention structure. The frequency of the ultrasonic signal applied to each transmission channel is set at 1.95 MHz, the pulse width is 2 microseconds, and the excitation time is initiated 0.4 seconds before the predicted arrival time of the shock wave. The signals emitted by all channels are out of phase with the original phase of the main propagation path, with a phase difference of 180 degrees. Based on the propagation velocity estimation results in the energy migration spectrum, the time delay of each channel is corrected to ensure that the intervention acoustic wave accurately coincides with the original energy wavefront in the spatial propagation path. To prevent the formation of new acoustic resonance points, the sound pressure amplitude of each channel is controlled between 10 and 12 kPa, and the uniformity of the intervention coverage area is maintained by adjusting the emitted energy in real time. This anti-phase acoustic field intervention does not change the overall flow field of the reaction liquid, but it can generate a phase disturbance band in the bubble group coupling region, which significantly weakens the superposition effect of energy along the same direction and reduces the sound pressure peak and wavefront steepness in the original propagation path.

[0035] Within the anti-phase compensation sound field coverage area, fluid vortex disturbances are actively introduced to disrupt the aggregation and connectivity of the bubble swarm in the structural space. Specifically, eight sets of radial jet devices are installed on the inner walls of the container near both sides of the anti-phase sound source array. Each set consists of a conical nozzle and an electrically controlled pulse driver. The nozzle diameter is 3 mm, and the injection angle maintains a 15-degree angle with the normal to the inner wall of the container to ensure that the jets form a vortex flow along the tangential direction. The injected liquid is a temperature-controlled injection liquid with the same composition as the reaction liquid, with an injection speed controlled at 35 cm / s and a spray duration of 6 seconds. After the jets converge in the central region of the compensation trajectory, a closed spiral shear band is formed, with its rotation direction opposite to the sound wave propagation direction and a shear rate of approximately 28 Hz. Under the action of this shear band, the bubble swarm encounters a local high-shear zone during its outward expansion. The outer bubbles are broken by the fluid disturbance, losing their dense connection with the central bubbles, increasing the distance between bubbles, and thus disrupting the volumetric continuity of the bubble swarm. Simultaneously, a negative pressure, low-velocity zone forms at the center of the vortex, causing the peripheral bubbles to migrate non-uniformly towards the center, resulting in a more diffuse distribution of the bubble swarm. This fluid disturbance not only blocks the spatial expansion trend of the bubble swarm but also effectively weakens the resonance propagation channels caused by close coupling between adjacent bubble interfaces.

[0036] Based on the formation of a coupled region of acoustic interference and flow field disturbance, the surface tension equilibrium of bubbles is further disrupted by controlling the release of micro-sized gas nuclei, thus breaking the bubble fusion chain reaction. The operation is as follows: Two linear micro-gas nucleus release channels, each 30 cm long and 12 cm apart, are set at the bottom of the container, arranged along the main propagation direction. Fluorocarbon-coated inert gas is injected into the reaction liquid through a gas nucleus generator, releasing 100,000 gas nuclei per second, with a particle size between 1.5 and 2.5 micrometers. After release, these gas nuclei are propelled by upward buoyancy, traversing the anti-phase compensation region and vortex disturbance zone, entering the bubble aggregation region, and contacting the original bubble structure. Because the gas nucleus particle size is much smaller than the main bubble diameter, the interfacial tension difference is significant. Upon contact, this induces localized interface collapse or rupture of the original bubbles, leading to the failure of the connection between adjacent bubbles and the breakage of the bubble fusion chain. Meanwhile, these micro-nuclei exhibit vibration frequencies different from the main bubble under acoustic field intervention, disrupting the rhythm of the originally synchronously vibrating bubble group and causing nonlinear cross-response, further suppressing the phase resonance behavior of the overall bubble system. The superposition of the dispersal strategy changes the collapse evolution path of the bubble group from synchronous concentration to discrete distribution, resulting in a decrease in bubble distribution density, a decrease in resonance response amplitude, and a decline in collapse risk level, forming a dynamic feedback state of dispersed structure and asynchronous response.

[0037] Based on the modified dynamic feedback state-triggered time-reversal phase gating, the programmable acousto-optic metasurface is driven to release the phase conjugate micro-envelope, establishing a reverse energy channel inside the reaction liquid that matches the dynamic feedback state, extinguishing the residual collapsed core, and writing back the calibration factor to the unified time baseline to complete the closed-loop dynamic regulation. To completely extinguish any remaining collapsed nuclei bubbles after dynamic intervention and prevent secondary collapse induced by local stress recovery in the later stages of the reaction, based on the corrected dynamic feedback state, time-reversal phase-gated triggering, conjugate wavefront driving, reverse energy channel construction, and calibration factor write-back operations are sequentially executed to ultimately achieve closed-loop dynamic control. Specifically, the following steps are included: After the dynamic feedback state is established, the location, response characteristics, and phase behavior of the residual collapsed nuclei in the reaction solution are acquired and used as the trigger condition for initiating time-inversion phase gating. Specifically, in the dynamic feedback state, a high-frequency pulse scan is performed on the entire reaction area using twelve arrays of phase-controlled ultrasonic sensors uniformly distributed around the reactor. The scan frequency is set to 500 times per second, with a center frequency of 3.0 MHz, and each scan lasts for 2 milliseconds. After scanning, the detected micro-echo signals are sorted and recombined according to the timestamps of a unified time baseline. High-frequency hysteresis echo points appearing within each spatial voxel are marked, and their phase perturbation amplitude is determined to exceed a preset threshold (usually set to 0.8π). If it exceeds this threshold, it is identified as a residual collapsed nucleus. After identification, the three-dimensional coordinates, echo signal morphology, frequency response bandwidth, and pressure fluctuation curves of all identified collapsed nuclei are input to the next phase gating trigger module, and an inversion phase reference map centered on the residual collapsed nuclei is constructed.

[0038] Using a phase reference map to drive time-reversal phase gating, a programmable acousto-optic metasurface array is activated to release a phase-conjugate wavefront, achieving reverse matching with the original energy path of the residual collapsed nucleus. During implementation, the central control unit first performs time-reversal processing on the reflected signal from the residual collapsed nucleus, ensuring its propagation path and waveform structure are completely reversed from the original path. Then, phase conjugation calculations are performed to obtain an inverse waveform with the opposite phase but the same amplitude as the original wavefront. The processing results are then input into the programmable acousto-optic metasurface array arranged around the reaction vessel. Each unit of the array is equipped with an independent phase modulator and acousto-optic conversion control element, which can adjust the sound pressure emission delay and waveform spectral components according to control commands. The signal frequency emitted by each unit is set to 1.95 MHz, and the phase offset angle is strictly controlled within ±0.05π error range, thus forming a highly directional, spectrally concentrated, and highly reproducible phase-conjugate micro-energy envelope. Spatially, this wavefront focusing path points to the predicted coordinate region of the residual collapsed nucleus and maintains the integrity of the phase coupling structure during spatial propagation, ensuring that all energy converges to the predetermined target point.

[0039] After the successful release of the phase-conjugate micro-envelope, the reverse energy channel construction operation is immediately initiated. The specific steps are as follows: relying on the propagation path of the conjugate wavefront emitted by the acousto-optic metasurface array in the reaction liquid, a "closed-loop" energy migration path under the original dynamic feedback state is performed. That is, a multi-band, progressive, spatially convergent energy focusing trajectory is constructed along the reverse path of the original energy channel in space. This trajectory is precisely aligned with the echo point of the last maximum acoustic pressure response of the collapsed nucleus on the time axis, and the energy focusing ring is advanced layer by layer on the spatial axis using this point as an anchor. The construction of each energy focusing ring requires consideration of the current temperature gradient, local viscosity distribution, and microparticle concentration of the reaction liquid, and dynamic adjustments are made to the wave velocity parameters and propagation delay. Before the energy reaches the core region of the collapsed nucleus, its phase structure automatically adjusts, ensuring that the final released compression wave forms a repulsive state with the natural oscillation frequency of the collapsed nucleus, causing the bubble surface to disintegrate or fragment in a non-explosive oscillation. The entire energy transfer process is controlled within 600 milliseconds. In the final stage, the frequency and energy density are automatically reduced to ensure that no new disturbances are caused. At the same time, the energy channel is softly shut down through a synchronization control device to prevent residual sound pressure rebound.

[0040] After the residual collapse core is extinguished by the reverse energy channel, the structural response data, phase shift residuals, energy coupling errors, and timing delay data generated throughout the process are extracted and quantified to form a dynamic calibration factor, which is then written into the unified time baseline index structure. Specifically, the method involves cross-comparing all sound pressure signals from the start to the end of the reverse channel with the response results of the target collapse core, marking the average error value between the actual response time and the predicted time; performing residual fitting on the phase cancellation effect to identify whether there is phase drift caused by uneven liquid density or local sound velocity anomalies; evaluating the energy release efficiency to determine if the actual energy convergence rate is below 95%, and if so, recording the waveform attenuation model parameters and position coordinates. All error parameters are merged into a multi-dimensional calibration vector, bound to the unified time baseline, and finally written back to the sound control scheduling table and the excitation feedback control table. Through this step, each sound field intervention process can reversely correct its own control accuracy and adaptively adjust the control strategy in the next round of intervention, thereby continuously enhancing the control accuracy, response sensitivity, and system stability in the closed-loop structure, completing full-cycle closed-loop dynamic control.

[0041] This invention establishes a unified time baseline and bubble phase reference field to achieve multi-dimensional real-time perception of the bubble state in the reaction system. Then, based on time-frequency rearrangement and collapse precursor identification, a dynamic risk fingerprint database is constructed. Combined with precise positioning of energy convergence nodes and the construction of reverse energy channels, this not only provides early warning and targeted intervention before collapse occurs but also proactively eliminates risk points during the collapse remnant stage. Finally, a real-time calibration and write-back mechanism incorporates each response process into a unified temporal evolution logic, forming a full-cycle intelligent control closed loop. This method is non-destructive, programmable, and self-learning, significantly improving the safety, stability, and equipment lifespan of the zinc chloride preparation process, while also promoting the efficient utilization of solid waste and the integrated application of clean production technologies.

[0042] This invention provides, for example Figure 2 The control system for zinc chloride preparation based on solid waste, as shown, includes a unified baseline construction and sensing module, a bubble risk identification module, an acoustic field response detection module, a shock wave prediction module, an active intervention compensation module, and a closed-loop control execution module. The unified baseline construction and sensing module acquires a unified temporal baseline and establishes a bubble phase reference field. Under the bubble phase reference field, it collects the acoustic echo sequence, micro-pressure waveform sequence and light scattering sequence of the reaction liquid and fuses them to generate a bubble density spatiotemporal matrix. The bubble risk identification module performs reversible time-frequency rearrangement based on the bubble density spatiotemporal matrix, decomposes it to obtain different frequency band distributions, extracts narrowband resident bubble groups, calculates critical volume distribution and collapse precursor index, and constructs a dynamic risk fingerprint database. The acoustic field response detection module applies an ultrashort pulse acoustic field based on a dynamic risk fingerprint database to obtain the synchronous response differences of the reaction liquid to micro-amplitude phase traction and identify energy convergence nodes. The shock wave prediction module constructs a gas-liquid coupled energy migration map based on energy convergence nodes, replays the node response to a unified time baseline, predicts the shock wave intensity and arrival time through causal consistency criteria, and generates a preset suppression window. The active intervention compensation module constructs a multi-channel anti-phase compensation trajectory based on a preset suppression window. It introduces flow field vortex injection and superimposes micro-nucleus propagation strategy in the compensation trajectory to weaken the connectivity of the bubble group, reduce the critical volume distribution, and form a corrected dynamic feedback state. The closed-loop control execution module, based on the modified dynamic feedback state trigger time-reversal phase gating, drives the programmable acousto-optic metasurface to release the phase conjugate micro-envelope, establishes a reverse energy channel matching the dynamic feedback state inside the reaction liquid, extinguishes the residual collapsed core, and writes back the calibration factor to the unified time baseline, thus completing the closed-loop dynamic control.

[0043] The zinc chloride preparation process control method based on solid waste provided in this embodiment of the invention is implemented through the above-mentioned zinc chloride preparation process control system based on solid waste. For details of the specific methods and processes of the zinc chloride preparation process control system based on solid waste, please refer to the embodiments of the zinc chloride preparation process control method based on solid waste, which will not be repeated here.

[0044] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for controlling the zinc chloride preparation process based on solid waste, characterized in that, Includes the following steps: A unified time baseline was obtained and a bubble phase reference field was established. Acoustic echo sequence, micro-pressure waveform sequence and light scattering sequence of the reaction liquid were collected under the bubble phase reference field and fused to generate a bubble density spatiotemporal matrix. Based on the spatiotemporal matrix of bubble density, reversible time-frequency rearrangement is performed to decompose different frequency band distributions, extract narrowband resident bubble groups, calculate critical volume distribution and collapse precursor index, and construct dynamic risk fingerprint database. By applying an ultrashort pulse acoustic field based on a dynamic risk fingerprint database, the synchronous response differences of the reaction liquid to micro-amplitude phase traction are obtained, and energy convergence nodes are identified. Based on the energy convergence node, a gas-liquid coupled energy migration map is constructed, the node response is replayed to a unified time baseline, and the shock wave intensity and arrival time are predicted by the causal consistency criterion to generate a preset suppression window. A multi-channel anti-phase compensation trajectory is constructed based on a preset suppression window. A flow field vortex injection and micro-nucleus propagation strategy are introduced into the compensation trajectory to weaken the connectivity of the bubble group, reduce the critical volume distribution, and form a corrected dynamic feedback state. Based on the modified dynamic feedback state-triggered time-reversal phase gating, the programmable acousto-optic metasurface is driven to release the phase conjugate micro-envelope, establishing a reverse energy channel inside the reaction liquid that matches the dynamic feedback state, extinguishing the residual collapsed core, and writing back the calibration factor to the unified time baseline.

2. The method for controlling the zinc chloride preparation process based on solid waste according to claim 1, characterized in that, The steps for generating the bubble density spacetime matrix are as follows: By setting a unified time baseline, time synchronization calibration is performed on all data channels involved in acoustic signal acquisition, pressure signal acquisition, and optical signal acquisition, and a unified timestamp system is established. A bubble phase reference field is established under a unified time baseline, and three types of physical signals are acquired and the spatial state of the bubble is calibrated by using an acoustic echo array, a micro-pressure sensor array, and a laser scattering receiver. The collected acoustic echo sequence, micro-pressure waveform sequence and light scattering sequence are spatially mapped in three dimensions and superimposed in time dimension according to a unified time baseline to generate a spatiotemporal matrix of bubble density. The spatiotemporal matrix of bubble density is used as the dynamic record basis for the evolution of bubble behavior, which supports the subsequent prediction of bubble collapse and the implementation of energy intervention strategies.

3. The method for controlling the zinc chloride preparation process based on solid waste according to claim 2, characterized in that, The process of building a dynamic risk fingerprint database is as follows: Based on the density change sequence of each voxel in the bubble density spatiotemporal matrix on the time axis, a reversible time-frequency rearrangement operation is performed to obtain the frequency-time distribution relationship and identify the energy concentration frequency band. The frequency concentration region is mapped back to the bubble density spatiotemporal matrix, the location of the resident bubble group is extracted, and the volume growth rate, density gradient directionality coefficient and spatial connectivity are calculated to construct the collapse precursor index. A dynamic risk fingerprint database is established based on the feature parameters of the extracted bubble clusters, and the status is continuously updated and trends are predicted according to a unified time baseline, so as to realize the spatial marking and risk assessment of potential collapse areas.

4. The method for controlling the zinc chloride preparation process based on solid waste according to claim 3, characterized in that, The energy convergence node identification process is as follows: An ultrashort pulse acoustic field is applied to the three-dimensional coordinates of the resident bubble cluster region marked as high-risk in the dynamic risk fingerprint database to form a micro-amplitude phase traction response. Acoustic and pressure response data of the reaction liquid were collected during the acoustic field excitation process, and three-dimensional acoustic response distribution map and pressure response map were constructed. By jointly analyzing acoustic and pressure response spectra, spatial regions with phase delay, pressure jump, and spectral shift characteristics were screened, and multiple excitation results were superimposed to confirm hotspot regions as energy convergence nodes. After verifying the continuity and consistency of phase delay, pressure change and spectral response of the energy convergence node during continuous excitation and confirming its potential to trigger collapse, the coordinates and response characteristics of the energy convergence node are written into the dynamic risk fingerprint database to update the risk level.

5. The method for controlling the zinc chloride preparation process based on solid waste according to claim 4, characterized in that, The steps for generating the preset suppression window are as follows: Based on the spatial coordinates and response characteristics of energy convergence nodes, a gas-liquid coupled energy migration map is constructed to record the propagation path and attenuation characteristics of energy in each direction. The response behavior of each node in the energy migration map is replayed to a unified time baseline to construct a complete energy propagation time sequence structure. The main axis trend in the energy propagation path is identified by the causal consistency criterion, and the propagation intensity and expected arrival time of the shock wave are calculated. Set the response area and suppression time point on the shock wave prediction path to generate a preset suppression window with spatiotemporal positioning capability; By linking the preset suppression window with the energy propagation axis and prediction indicators, a multi-path dynamic intervention structure indexed by a unified time baseline is constructed to trigger active responses.

6. The method for controlling the zinc chloride preparation process based on solid waste according to claim 5, characterized in that, The revised dynamic feedback state generation steps are as follows: A multi-channel anti-phase compensation trajectory is constructed within a preset suppression window, and a phase-reversed ultrasonic signal is emitted by a focused ultrasonic transmitter to interfere with the sound field of the main propagation path. A jet device is set up in the anti-phase sound field intervention area to form a vortex shear flow field, which destroys the spatial connectivity structure of the bubble group through shearing. Micro-gas nuclei are released within the vortex disturbance region, and the coupling strength of the bubbles is weakened by interface disturbance and phase disturbance, ultimately forming a structurally dispersed and dynamically stable feedback state.

7. The method for controlling the zinc chloride preparation process based on solid waste according to claim 6, characterized in that, Based on the modified dynamic feedback state-triggered time-reversal phase gating, the programmable acousto-optic metasurface is driven to release the phase-conjugated micro-envelope, constructing a matched reverse energy channel in the reaction solution to extinguish the residual collapsed core, and writing the calibration factor back to the unified time baseline. The steps are as follows: Residual collapsed kernels are identified based on the modified dynamic feedback state, and time-reversal phase gating is triggered. Phase-gated drive programmable acousto-optic metasurfaces are used to release phase-conjugate micro-envelopes to form inverse wavefronts; A reverse energy channel matching the dynamic feedback state is constructed along the phase conjugate wavefront path, and energy is focused onto the collapsed nucleus. The calibration factor is extracted based on the response error in the reverse energy channel and written into the unified time baseline to complete the closed-loop dynamic control.

8. A zinc chloride preparation process control system based on solid waste, used to implement the zinc chloride preparation process control method based on solid waste according to any one of claims 1-7, characterized in that, It includes a unified baseline construction and sensing module, a bubble risk identification module, a sound field response detection module, a shock wave prediction module, an active intervention compensation module, and a closed-loop control execution module: The unified baseline construction and sensing module acquires a unified temporal baseline and establishes a bubble phase reference field. Under the bubble phase reference field, it collects the acoustic echo sequence, micro-pressure waveform sequence and light scattering sequence of the reaction liquid and fuses them to generate a bubble density spatiotemporal matrix. The bubble risk identification module performs reversible time-frequency rearrangement based on the bubble density spatiotemporal matrix, decomposes it to obtain different frequency band distributions, extracts narrowband resident bubble groups, calculates critical volume distribution and collapse precursor index, and constructs a dynamic risk fingerprint database. The acoustic field response detection module applies an ultrashort pulse acoustic field based on a dynamic risk fingerprint database to obtain the synchronous response differences of the reaction liquid to micro-amplitude phase traction and identify energy convergence nodes. The shock wave prediction module constructs a gas-liquid coupled energy migration map based on energy convergence nodes, replays the node response to a unified time baseline, predicts the shock wave intensity and arrival time through causal consistency criteria, and generates a preset suppression window. The active intervention compensation module constructs a multi-channel anti-phase compensation trajectory based on a preset suppression window. It introduces flow field vortex injection and superimposes micro-nucleus propagation strategy in the compensation trajectory to weaken the connectivity of the bubble group, reduce the critical volume distribution, and form a corrected dynamic feedback state. The closed-loop control execution module, based on the modified dynamic feedback state trigger time-reversal phase gating, drives the programmable acousto-optic metasurface to release the phase conjugate micro-envelope, establishes a reverse energy channel inside the reaction liquid that matches the dynamic feedback state, extinguishes the residual collapsed core, and writes back the calibration factor to the unified time baseline.

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