Automatic control method and system for a pressure cooking apparatus

Abstract

This application relates to the technical field of industrial automation control, and more particularly to an automatic control method and system for a pressure cooking equipment. The method includes: acquiring operational condition characterization data of the target work area and quantifying the dynamic complexity of the current operational condition to match a dynamic sensing allocation scheme; collecting dynamic environmental sensing data based on the scheme and evaluating the sensing response quality; in response to the sensing response quality meeting preset conditions, standardizing and fusing the data based on the spatiotemporal coordination deviation between sensing terminals to generate structured fused data; generating an initial control strategy based on the structured fused data, and dynamically calibrating control parameters and equipment operating paths according to execution feedback. This application improves the equipment control accuracy and operational stability.

Description

Technical Field

[0001] This application relates to the technical field of industrial automation control, and in particular to an automatic control method and system for a pressure cooking equipment. Background Technology

[0002] In complex industrial processing scenarios such as pressure cooking, the production process usually involves multiple overlapping processes, material flow, and equipment collaborative operation.

[0003] In existing technologies, automatic control of such equipment often employs fixed sensing and acquisition strategies and control parameters. Regardless of whether the current operating conditions are simple or complex, sensor data is collected at a preset, uniform frequency, and standardized control logic is executed. However, in actual production, the cooking process is highly dynamic and uncertain. When faced with complex conditions such as frequent process conflicts, high material handling intensity, or long handover delays, a fixed mode of sensing resource allocation can easily lead to the loss of critical data or insufficient sampling, resulting in inaccurate control strategies. Conversely, under simple operating conditions, excessively high acquisition frequencies can waste computing resources and communication bandwidth. Furthermore, multi-source sensing terminals are prone to spatiotemporal asynchrony and data noise during long-term operation. Without an adaptive data fusion mechanism based on operating condition characteristics, directly using raw data for control decisions can easily lead to equipment operation path conflicts or process scheduling disorder, resulting in a decrease in overall operational efficiency. Therefore, there is an urgent need for an adaptive control method for pressure cooking equipment that can adaptively adjust sensing resources and control strategies according to the complexity of the operating conditions. Summary of the Invention

[0004] To address at least one of the aforementioned technical problems, this application provides an automatic control method and system for a pressure cooking equipment.

[0005] In a first aspect, this application provides an automatic control method for a pressure cooking equipment, employing the following technical solution: Acquire the operating condition characterization data of the pressure cooking equipment within the target operating area, quantify the dynamic complexity of the current operating condition based on the operating condition characterization data, and match the corresponding dynamic sensing allocation scheme based on the dynamic complexity. Based on the dynamic sensing allocation scheme, the sensing terminal is controlled to collect dynamic environmental sensing data of the target work area, and the sensing response quality of the sensing terminal is evaluated. In response to the perception response quality meeting the preset quality conditions, the dynamic environment perception data is standardized and fused based on the spatiotemporal coordination deviation between the perception terminals to generate structured fused data. An initial control strategy is generated based on the structured fusion data, and the control parameters and equipment operation path are dynamically calibrated based on the execution feedback of the initial control strategy.

[0006] In one possible implementation, the operating condition characterization data includes the frequency of process crossover conflicts, the frequency of material handling per unit time, and the duration of process handover delay. The step of quantifying the dynamic complexity of the current working condition based on the working condition characterization data, and matching the corresponding dynamic sensing allocation scheme based on the dynamic complexity, includes: Based on the aforementioned working condition characterization data, calculate the collaborative adaptation coefficient, the work intensity coefficient, and the process stability coefficient; A dynamic working condition index is obtained by fusion calculation based on the collaborative adaptation coefficient, the work intensity coefficient, and the process stability coefficient to quantify the dynamic complexity. Based on the comparison results between the dynamic working condition index and the preset working condition classification threshold, the dynamic sensing allocation scheme is matched from the basic sensing allocation scheme, the advanced sensing allocation scheme and the enhanced sensing allocation scheme. The dynamic sensing allocation scheme includes at least one of the following: the sampling frequency configuration of the sensing terminal, the number of activated nodes configuration, and the data transmission bandwidth allocation.

[0007] In one possible implementation, evaluating the sensing response quality of the sensing terminal includes: Within a preset monitoring period, the delayed time-series data of the sensing terminal's sensing response to the working condition characterization data is acquired, and the sensitivity influence factor is determined based on the time-series difference results of the delayed time-series data. After the sensing terminal responds, the jitter time sequence data of the working condition characterization data during the transmission process is obtained, and the real-time impact factor is determined based on the frequency domain feature extraction result of the jitter time sequence data; Based on the sensitivity influence factor and the real-time influence factor, normalization and geometric mean calculation are performed to obtain the comprehensive influence value; In response to the overall impact value exceeding a preset impact threshold, a working condition adaptation anomaly verification prompt is generated. If the overall impact value is not greater than the preset impact level threshold, it is determined that the perceived response quality meets the preset quality condition.

[0008] In one possible implementation, the standardization and fusion of the dynamic environment perception data based on the spatiotemporal coordination deviation between the sensing terminals to generate structured fused data includes: Calculate the device coordination index when different sensing terminals collect data from the target work area; In response to the device collaboration index being greater than a preset device collaboration threshold, data standardization control is performed on the dynamic environment perception data to generate the structured fusion data; In response to the fact that the equipment coordination index is not greater than the preset equipment coordination threshold, and an abnormal feedback of the environmental parameters of the target work area is received, the corresponding dimension data is re-collected after environmental parameter control is performed; In response to the device collaboration index not being greater than the preset device collaboration threshold and no abnormal feedback of the environmental parameters being received, the corresponding dimension data is re-collected. The equipment coordination index is obtained by integrating the workstation occupancy matching degree and the accuracy of material anomaly early warning, and combined with the dynamic coordination correction coefficient of working conditions.

[0009] In one possible implementation, performing data standardization control on the dynamic environment-aware data includes: When the acquisition timing of the dynamic environment sensing data collected by different sensing terminals is consistent, the collaborative deviation value between the corresponding timing feature representations of adjacent sensing terminals is calculated. In response to the fact that the coordination deviation value is greater than a preset allowable coordination deviation, the dynamic environment perception data is corrected based on the cumulative integral value of the deviation value within a preset statistical period. When the acquisition timing of the dynamic environment sensing data collected by different sensing terminals is inconsistent, a timing calibration priority score is obtained by mapping the timing consistency deviation value with the cooking condition type. In response to the presence of single-dimensional data that meets preset priority conditions in the dynamic environment perception data, a real-time stream processing mode is used to perform time stamp synchronization calibration and data timing deviation correction. In response to the presence of non-single-dimensional data that meets the preset priority conditions in the dynamic environment perception data, batch processing mode is adopted to perform batch time-series calibration and heterogeneous data fusion.

[0010] In one possible implementation, generating the initial control strategy based on the structured fused data includes: Based on the structured fusion data, the material layout density and material processing rate within the target operating area are obtained; The target value of material layout density is determined based on material processing priority and equipment space utilization limit, and the target value of material processing rate is determined based on historical material processing peak and equipment operating capacity limit. The material layout density, material processing rate and spatial diffusion coefficient are input into the spatial distribution gradient model to obtain the layout density adjustment gradient, so as to dynamically correct the material layout and workstation allocation strategy. Based on the target value of the material handling rate as a constraint, a load balancing objective function is constructed. By solving the load balancing objective function, the optimal handling rate adjustment amount is obtained to dynamically correct the scheduling rules of the material conveying and cooking processes. In response to the fact that the dynamically corrected material layout density and the material processing rate are both within the corresponding allowable range, the initial control strategy is generated based on the dynamically corrected parameters.

[0011] In one possible implementation, the dynamic calibration of control parameters and device operating path based on the execution feedback of the initial control strategy includes: In response to the execution feedback of the initial control strategy not meeting the preset compliance conditions, the idle workstation ratio and order urgency weight of each work area in the target work area are obtained and fused to obtain the balance coefficient. The first priority area and the second priority area are divided based on the balance coefficient, and the process execution order is determined based on the product of the idle workstation ratio and the order urgency weight. The spatial three-dimensional coordinates of the first priority area and the dynamic occupancy status parameters of the workstation are used as independent variables to construct a spatial coordinate mapping model, and the coordinate mapping partial derivatives in the path planning are solved to correct the equipment running path. After the equipment operation path and process execution sequence are adjusted, the execution feedback is reacquired, and the effectiveness of the adjustment is judged based on the reacquired execution feedback.

[0012] Secondly, this application provides an automatic control system for a pressure cooking device, which adopts the following technical solution: An automatic control system for a pressure cooking apparatus includes: The working condition perception and allocation module is used to acquire the working condition characterization data of the pressure cooking equipment in the target working area, quantify the dynamic complexity of the current working condition based on the working condition characterization data, and match the corresponding dynamic perception and allocation scheme based on the dynamic complexity. The data standardization and fusion module is used to control the sensing terminal to collect dynamic environmental sensing data of the target work area based on the dynamic sensing allocation scheme, and to evaluate the sensing response quality of the sensing terminal. The data fusion module is used to standardize and fuse the dynamic environment perception data based on the spatiotemporal coordination deviation between the perception terminals in response to the perception response quality meeting the preset quality conditions, so as to generate structured fused data. The control strategy calibration module is used to generate an initial control strategy based on the structured fusion data, and to dynamically calibrate the control parameters and the device operation path based on the execution feedback of the initial control strategy.

[0013] Thirdly, this application provides an electronic device that adopts the following technical solution: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute an automatic control method for a pressure cooking apparatus as described in any of the first aspects.

[0014] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform an automatic control method for a pressure cooking apparatus as described in any of the first aspects.

[0015] In summary, this application includes at least one of the following beneficial technical effects: By quantifying the dynamic complexity of the target work area and matching it with a dynamic sensing allocation scheme, this method solves the problems of data loss in complex working conditions or resource waste in simple working conditions caused by traditional fixed sensing strategies, achieving adaptive matching between sensing resources and working condition requirements. By evaluating the quality of sensing response and standardizing and fusing multi-source data based on spatiotemporal coordination deviation, it eliminates data distortion caused by performance degradation of the sensing link or spatiotemporal asynchrony of multiple terminals, providing a highly reliable data foundation for control decisions. By constructing a spatial distribution gradient model and a load balancing objective function to generate an initial control strategy, and dynamically reconstructing the process sequence and equipment path based on the balance coefficient and spatial coordinate mapping model when the execution feedback is unsatisfactory, it forms a full-link adaptive closed loop from sensing to control to path planning, significantly improving the control accuracy, resource utilization efficiency, and system self-healing capability of pressure cooking equipment under dynamic and complex working conditions. This is an automatic control method and system for pressure cooking equipment. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an automatic control method for a pressure cooking device provided in an embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the structure of an automatic control system for a pressure cooking device provided in an embodiment of this application.

[0018] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] The following is in conjunction with the appendix Figure 1-3 This application will be described in further detail.

[0020] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of this application.

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0023] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0024] This application provides an automatic control method for a pressure cooking device, executed by an electronic device. This electronic device can be a standalone physical electronic device, a cluster of multiple physical electronic devices, a distributed system, or a cloud electronic device providing cloud computing services. This application does not impose limitations on the method described herein. Figure 1 As shown, the method includes: Step S10: Obtain the operating condition characterization data of the pressure cooking equipment in the target operating area, quantify the dynamic complexity of the current operating condition based on the operating condition characterization data, and match the corresponding dynamic sensing allocation scheme based on the dynamic complexity.

[0025] Specifically, the target operating area refers to the physical space where the pressure cooking equipment performs processing tasks, such as the interior of the cooking vessel, material conveying channels, or inlet / outlet buffer zones. Operating condition characterization data is a set of parameters that reflects the dynamic changes in the production operation status within this area. It includes not only environmental parameters such as temperature and pressure, but more importantly, dynamic indicators reflecting the operating rhythm and load status, such as the cross-conflict between processes, the material throughput intensity per unit time, and the temporal stability of process handover links. This embodiment emphasizes quantifying the dynamic complexity before allocating sensing resources because the complexity of the operating conditions during pressure cooking is not constant but fluctuates in real time with order scheduling, material characteristics, and equipment status. When the operating conditions are simple and stable, excessively high sensing frequency will result in ineffective consumption of communication bandwidth and computing resources; while when the operating conditions are complex and changeable, maintaining low-frequency sensing will easily lead to the omission of critical transient anomaly information. Therefore, by transforming abstract operating conditions into quantifiable dynamic complexity indicators, and matching the sampling frequency, number of activated nodes, or data transmission bandwidth of sensing terminals as needed, the optimal balance between sensing efficiency and resource consumption can be achieved from the source, laying a data foundation for subsequent precise control.

[0026] Step S11: Based on the dynamic sensing allocation scheme, control the sensing terminal to collect dynamic environmental sensing data of the target work area and evaluate the sensing response quality of the sensing terminal.

[0027] In this embodiment, after determining the sensing strategy adapted to the current operating conditions, the system drives the corresponding sensing terminals to perform data acquisition tasks according to the configured scheme. The dynamic environmental sensing data here covers multi-dimensional information such as temperature field distribution, pressure fluctuations, material location, and morphology. However, simply acquiring data is insufficient to support highly reliable control decisions, because the performance of the sensing terminals themselves may degrade or be interfered with under complex industrial electromagnetic environments and high temperature and humidity conditions. Therefore, this step introduces a sensing response quality assessment mechanism, which acts as a "gatekeeper" for data entering subsequent processing stages. This assessment does not focus on judging the authenticity of the data content itself, but rather on examining the health and timeliness of the sensing link, such as whether the sensor's response delay to changes in operating conditions is within acceptable limits, and whether jitter during data transmission affects real-time performance. Only when the response quality of the sensing system is verified as reliable does the acquired data have value in participating in control calculations, thereby effectively preventing the generation of erroneous control commands due to sensing link failures or performance degradation.

[0028] Step S12: In response to the perception response quality meeting the preset quality conditions, the dynamic environment perception data is standardized and fused based on the spatiotemporal coordination deviation between perception terminals to generate structured fused data.

[0029] Specifically, pressure cooking equipment typically deploys multiple heterogeneous sensing terminals. These terminals objectively differ in physical installation location, clock synchronization accuracy, and data transmission paths, leading to potential discrepancies in timestamp alignment and spatial coordinate mapping in data collected at the same time. Directly superimposing or averaging the raw data amplifies this spatiotemporal inconsistency, distorting the perception of the actual operating conditions. This step, assuming the sensing quality meets standards, specifically addresses the spatiotemporal coordination deviations between multi-source data through standardization processing. This process aims to eliminate physical non-ideal factors, transforming scattered, asynchronous, and heterogeneous raw sensing data into structured fused data that is strictly aligned in the spatiotemporal dimensions and semantically unified. It should be understood that this standardized fusion is not merely simple data cleaning, but rather an adaptive processing strategy based on the equipment's collaborative state. It dynamically selects the fusion algorithm according to the degree and nature of the deviation, ensuring that the output structured data can truly and accurately represent the comprehensive state of the target operating area, providing high-quality information input for the generation of control strategies.

[0030] Step S13: Generate an initial control strategy based on structured fusion data, and dynamically calibrate control parameters and equipment operation path based on the execution feedback of the initial control strategy.

[0031] In this embodiment, using the structured fusion data generated above, the system first calculates an initial control strategy adapted to the current operating conditions. This strategy includes basic control parameters such as cooking temperature curves, pressure holding time, material conveying speed, and workstation scheduling sequence. However, the initial strategy is essentially a theoretically optimal solution generated based on historical data and the current snapshot. In actual execution, it is often affected by unmodeled factors such as material batch differences, equipment mechanical wear, or sudden environmental disturbances, resulting in deviations. Therefore, this application does not stop at strategy generation but further constructs a dynamic calibration closed loop based on execution feedback. By monitoring the actual effects after strategy execution in real time (such as actual heating rate, material cooking uniformity, equipment load balance, etc.) and comparing them with the expected targets, the system can identify the degree of mismatch between the current control parameters and the actual operating conditions. Based on this, the system not only fine-tunes the control parameters but also dynamically reconstructs the equipment's operating path and process execution sequence according to the feedback results, enabling the entire control system to have the ability to continuously learn and self-evolve. This complete closed loop of "monitoring-decision-execution-feedback-calibration" ensures that the pressure cooking equipment can always maintain a highly efficient and stable operating state when facing a highly uncertain production environment.

[0032] In this embodiment, to achieve refined scheduling of sensing resources, it is necessary to accurately quantify the dynamic complexity of the operating conditions. Specifically, the operating condition characterization data includes the frequency of process cross-conflict, the frequency of material processing per unit time, and the delay duration of process handover. These three indicators characterize the complex characteristics of the pressure cooking equipment's operating environment from three dimensions: spatial coordination, temporal load, and temporal stability. Among them, the frequency of process cross-conflict reflects the number of times different work processes compete for or interfere with each other in physical space or logical resources within the same statistical period. The higher the frequency, the greater the uncertainty of on-site scheduling, and the higher the requirements for the real-time performance and coverage of sensing data. The frequency of material processing per unit time directly characterizes the throughput intensity of the production line. High-frequency material flow is often accompanied by a faster rate of state change, requiring higher-density sampling to capture transient features. The delay duration of process handover reflects the smoothness of the connection between upstream and downstream links. If the delay duration fluctuates drastically or exceeds expectations, it usually indicates a potential bottleneck or anomaly, requiring the system to maintain higher vigilance. By taking into account these three types of heterogeneous operational characterization data, this embodiment avoids the one-sidedness of relying solely on a single environmental variable to judge the operating condition, and provides comprehensive state input for subsequent adaptive control.

[0033] After acquiring the aforementioned operational condition characterization data, the system further calculates the collaboration adaptation coefficient, work intensity coefficient, and process stability coefficient based on this data. The essence of this step is to convert the original physical counts into standardized dimensionless evaluation indicators to facilitate cross-dimensional fusion calculations. Specifically, the collaboration adaptation coefficient can be determined by the ratio of the frequency of process cross-conflicts to the total number of collaborative operations within a cycle, and is corrected by combining the response synchronization rate between the pressure cooking equipment and the sensing terminal. The lower the coefficient, the more chaotic the current collaborative operation, and the more resources the sensing system needs to allocate to monitor conflict points. The work intensity coefficient is obtained based on the ratio of the actual material processing frequency per unit time to the maximum processing frequency of the equipment, and can be adjusted by adding a batch complexity coefficient to measure the proportion of the current load relative to the equipment's limit capacity. The process stability coefficient is determined based on the statistical characteristics of process handover delay time (such as variance or standard deviation) and the proportion of times exceeding the allowable delay time, reflecting the predictability of the work rhythm. Subsequently, the collaboration adaptation coefficient, work intensity coefficient, and process stability coefficient are fused and calculated to obtain a dynamic operational condition index used to quantify dynamic complexity. For example, the three coefficients can be combined into a single comprehensive index using a weighted summation or geometric mean. This dynamic operating condition index, as a scalar value, intuitively maps the overall complexity level of the target work area at the current moment, enabling the system to make decisions based on a unified scale.

[0034] Based on the comparison between the dynamic operating condition index and the preset operating condition classification thresholds, a dynamic sensing allocation scheme is matched from the basic sensing allocation scheme, the advanced sensing allocation scheme, and the enhanced sensing allocation scheme. Specifically, the preset operating condition classification thresholds include a basic operating condition threshold and an enhanced operating condition threshold. When the dynamic operating condition index is not greater than the basic operating condition threshold, it is determined that the current operation is stable, and the basic sensing allocation scheme is matched; when the dynamic operating condition index is between the basic operating condition threshold and the enhanced operating condition threshold, it is determined to be a normal dynamic operating condition, and the advanced sensing allocation scheme is matched; when the dynamic operating condition index is greater than the enhanced operating condition threshold, it is determined to be a dense dynamic operating condition, and the enhanced sensing allocation scheme is matched.

[0035] The dynamic sensing allocation scheme includes at least one of the following: configuration of the sampling frequency of the sensing terminal, configuration of the number of activated nodes, and allocation of data transmission bandwidth. For example, under the basic sensing allocation scheme, the sampling frequency of the temperature sensor can be set to 1Hz, activating only the monitoring function of key nodes, and the data transmission bandwidth is limited to within 1Mbps to minimize power consumption and communication load. Under the enhanced sensing allocation scheme, the sampling frequency can be increased to 10Hz or even higher, activating all redundant backup nodes to achieve full coverage monitoring, and the transmission bandwidth is extended to over 10Mbps to ensure that no critical transient information is lost under high-conflict, high-load conditions. It should be understood that, in addition to the three typical schemes mentioned above, more gradient allocation schemes can be set according to actual needs, or a continuous function mapping method can be used to achieve stepless parameter adjustment. As long as the core logic is based on dynamically adjusting the sensing resource configuration according to the complexity of the operating conditions, it falls within the scope of protection of this application.

[0036] Furthermore, the sensing response quality of the sensing terminal is evaluated, including: acquiring the delayed time-series data of the sensing terminal's sensing response to the operating condition characterization data within a preset monitoring period, and determining the sensitivity impact factor based on the time-series difference results of the delayed time-series data; acquiring the jitter time-series data of the operating condition characterization data during transmission after the sensing terminal's response, and determining the real-time impact factor based on the frequency domain feature extraction results of the jitter time-series data; performing normalization and geometric mean calculations based on the sensitivity impact factor and the real-time impact factor to obtain a comprehensive impact value; generating an operating condition adaptation anomaly verification prompt when the comprehensive impact value is greater than a preset impact level threshold; and determining that the sensing response quality meets the preset quality conditions when the comprehensive impact value is not greater than the preset impact level threshold.

[0037] Specifically, the delayed time-series data records the time interval sequence from the change in operating conditions to the corresponding data output by the sensing terminal. Performing time-series differential operations on this data yields the rate of change of delay, which is then used to quantify the sensitivity impact factor by calculating the time-domain standard deviation. This factor reflects the sensing system's ability to track fluctuations in operating conditions; if the sensitivity is too low, it indicates that the current sampling frequency or processing power is insufficient to cope with the current rate of change in operating conditions. Simultaneously, jitter time-series data of the operating condition characterization data during transmission after the sensing terminal's response is acquired, and the real-time impact factor is determined based on the frequency domain feature extraction results of the jitter time-series data.

[0038] Jitter time-series data describes the dispersion of data packet arrival times. By extracting the energy value corresponding to the main peak frequency through frequency domain analysis methods such as Fast Fourier Transform, periodic transmission anomalies caused by network congestion or electromagnetic interference can be identified. The higher the real-time impact factor, the more unstable the data transmission link and the worse the data timeliness.

[0039] In this embodiment, the advantage of using a geometric mean instead of an arithmetic mean is that it is more sensitive to extreme values. If either sensitivity or real-time performance deteriorates significantly, the overall value will increase significantly, thus preventing the "weakest link effect" from being masked. Subsequently, the system compares the overall value of the perceived response impact with a preset threshold for the degree of perceived response impact. If the overall value of the perceived response impact exceeds the preset threshold, an anomaly verification prompt is generated. This prompt is not merely an alarm signal, but also a proactive defensive control command. For example, when the overall value is detected to exceed the limit, the system can automatically trigger an anomaly verification process, including but not limited to: temporarily increasing the sampling frequency to confirm whether it is a transient interference, switching to a backup communication link, or sending a degraded operation request to the next higher-level control system. Conversely, if the overall value of the perceived response impact is not greater than the threshold for the degree of perceived response impact, the perceived response quality is determined to meet preset quality conditions. This means that the current dynamic sensing allocation scheme matches the complexity of the operating conditions, the collected data has high reliability, and can be confidently used for subsequent data fusion and control strategy generation. Through this closed-loop evaluation mechanism, this embodiment ensures that "adaptation" is not just a one-way policy issuance, but also includes a two-way adjustment process of effect verification and self-correction, effectively preventing the risk of control failure caused by perceived policy mismatch.

[0040] Furthermore, as one implementation method, dynamic environmental sensing data is standardized and fused based on the spatiotemporal coordination deviation between sensing terminals to generate structured fused data. This includes: calculating the equipment coordination index when different sensing terminals collect data from the target work area; in response to the equipment coordination index being greater than a preset equipment coordination threshold, performing data standardization control on the dynamic environmental sensing data to generate structured fused data; in response to the equipment coordination index not being greater than the preset equipment coordination threshold and receiving abnormal feedback on environmental parameters of the target work area, triggering re-collection of data in the corresponding dimension after performing environmental parameter control; in response to the equipment coordination index not being greater than the preset equipment coordination threshold and not receiving abnormal feedback on environmental parameters, triggering re-collection of data in the corresponding dimension; wherein, the equipment coordination index is obtained by fusing workstation occupancy matching degree and material abnormality warning accuracy, and combined with dynamic coordination correction coefficients for working conditions.

[0041] In this embodiment, the equipment coordination index is a physical quantity that comprehensively characterizes the degree of coordination and adaptation of the sensing system in terms of both spatial coverage and status monitoring. Specifically, the workstation occupancy matching degree reflects the accuracy of the sensing terminal's recognition of the physical space status. This is obtained, for example, by comparing the real-time collected workstation occupancy status with the reference status in the preset planning scheme and statistically analyzing the proportion of consistent frequencies between the two within a preset period. The material anomaly warning accuracy reflects the reliability of the sensing terminal's judgment of changes in the status of the work object. This is obtained, for example, by calculating the proportion of overlap between the captured abnormal signal and the standard abnormal signal in the historical feature database to the total number of warnings. By fusing these two factors and combining them with the dynamic coordination correction coefficient for the working condition, an equipment coordination index that truly reflects the reliability of the current sensing data can be obtained. This design avoids the one-sidedness of relying solely on the confidence level of a single sensor to evaluate data quality, ensuring that subsequent fusion processing is based on reliable sensing.

[0042] Conversely, if the response equipment coordination index is not greater than the preset equipment coordination threshold, it indicates that the sensed data has a significant deviation or distortion risk, and the system needs to further determine the cause of the distortion to take targeted measures. If abnormal feedback of environmental parameters in the target operating area is received at this time, such as a sudden drop in pressure or excessive temperature in the cooking kettle, which may directly affect the normal operation of the sensor, it indicates that the data distortion stems from the deterioration of the physical environment. In this case, environmental parameter control should be executed first (such as triggering emergency pressure relief or temperature control adjustment), and the corresponding dimension data should be re-acquired after the environment returns to stability. This effectively prevents resource waste and control delays caused by ineffective re-acquisition in harsh environments.

[0043] Furthermore, data standardization control is performed on the dynamic environmental sensing data, including: when the acquisition time sequence of the dynamic environmental sensing data collected by different sensing terminals is consistent, calculating the coordination deviation value between the corresponding time sequence feature representations of adjacent sensing terminals; in response to the coordination deviation value being greater than the preset allowable coordination deviation, correcting the dynamic environmental sensing data based on the cumulative integral value of the deviation value within the preset statistical period; when the acquisition time sequence of the dynamic environmental sensing data collected by different sensing terminals is inconsistent, mapping the time sequence consistency deviation value to the cooking condition type to obtain the time sequence calibration priority score; in response to the existence of single-dimensional data in the dynamic environmental sensing data that meets the preset priority conditions, using a real-time stream processing mode to perform time stamp-level synchronous calibration and data time sequence deviation correction; in response to the existence of non-single-dimensional data in the dynamic environmental sensing data that meets the preset priority conditions, using a batch processing mode to perform batch time sequence calibration and heterogeneous data fusion.

[0044] In this embodiment, timing consistency means that the sampling times of each terminal are strictly aligned or the deviation is within the allowable tolerance range. If the cooperative deviation value is greater than the preset allowable cooperative deviation, the system does not immediately perform high-intensity filtering, but further calculates the cumulative integral of the deviation value within a preset statistical period. The cumulative integral value of the deviation quantifies the continuous impact of the deviation on the stability of the workflow from an energy perspective: if the integral value is small, it indicates that the deviation is mostly random transient noise, and a smoothing correction algorithm (such as moving average) is selected for processing to retain the transient details of the data to the greatest extent while removing noise; if the integral value is large, it indicates that there is systematic drift or trend error, and a noise reduction correction algorithm (such as Kalman filtering) is selected for processing to effectively suppress the cumulative error from misleading the control model. This algorithm adaptive selection strategy based on integral values ​​solves the problem that traditional fixed filtering parameters are difficult to balance steady-state accuracy and dynamic response speed.

[0045] When the timing of dynamic environmental sensing data collected by different sensing terminals is inconsistent, such as due to communication delays or clock asynchrony causing out-of-order data packets or misaligned timestamps, the system adopts a priority-based differentiated synchronization strategy. Specifically, the timing consistency deviation value characterizes the degree of dispersion of data of the same dimension on the time axis, while the cooking condition type (e.g., basic, regular, intensive) determines the system's tolerance for real-time performance. For example, in intensive dynamic conditions, even small timing deviations may be assigned higher priority scores. Responding to the presence of single-dimensional data in the dynamic environmental sensing data that meets preset priority conditions, such as key safety monitoring parameters, a real-time stream processing mode is used for step-by-step timestamp synchronization calibration and data timing deviation correction. The real-time stream processing mode emphasizes low latency and immediacy, ensuring that key data can be quickly aligned and participate in real-time control by progressively reducing the synchronization adjustment step size and increasing the timing correction coefficient. Responding to the presence of non-single-dimensional data in the dynamic environmental sensing data that meets preset priority conditions, such as multimodal data like temperature fields and material images used for comprehensive analysis, a batch processing mode is used for batch-based timing calibration and heterogeneous data fusion. Batch processing mode focuses on the integrity of data correlation. By dynamically adjusting the batch calibration window and data integration weight, it achieves accurate alignment of multi-source heterogeneous data within a slightly longer time window. It should be understood that the above-mentioned smoothing correction, noise reduction correction, real-time stream processing, and batch processing are only illustrative examples. In practical applications, other equivalent data fusion and synchronization algorithms can be adopted according to specific hardware computing power and process requirements. As long as they follow the core concept of adaptively selecting processing strategies based on spatiotemporal deviation characteristics, they should be covered within the scope of protection of this application.

[0046] Furthermore, an initial control strategy is generated based on structured fusion data, including: obtaining the material layout density and material processing rate within the target operating area based on structured fusion data; determining the target value of the material layout density based on material processing priority and equipment space utilization limit, and determining the target value of the material processing rate based on historical material processing peak and equipment operating capacity limit; inputting the currently obtained material layout density, material processing rate, and spatial diffusion coefficient into the spatial distribution gradient model to obtain the layout density adjustment gradient, so as to dynamically correct the material layout and workstation allocation strategy; constructing a load balancing objective function based on the material processing rate target value as a constraint, and dynamically correcting the material conveying and cooking process scheduling rules by solving the optimal processing rate adjustment amount through the load balancing objective function; and generating an initial control strategy based on the dynamically corrected parameters, provided that the dynamically corrected material layout density and material processing rate are both within the corresponding allowable range.

[0047] In this embodiment, material distribution density refers not only to the quantity of material per unit area, but also to the spatial uniformity of material distribution within the cooking vessel or conveying channel and its impact on heat transfer efficiency; the material handling rate reflects the actual throughput capacity of the current production line. These two parameters are the key bridge connecting perceived data and control decisions, directly determining the baseline state for subsequent strategy generation.

[0048] In the embodiments of this application, the target value is not fixed but dynamically adapted to production needs. For example, when a high-priority order is detected, the system will correspondingly increase the material processing priority weight, thereby increasing the target value of the arrangement density to shorten the batch cycle. At the same time, combined with the current health status of the equipment and historical peak data, a processing rate target value is set that is neither overloaded nor underutilized. This dual-target setting mechanism ensures that the control strategy, while pursuing efficiency, always remains within the envelope of safe equipment operation.

[0049] The spatial distribution gradient model is essentially a physical field model describing the rate of change of material state with spatial location. The spatial diffusion coefficient characterizes the transfer properties of heat or pressure within the cooking environment. The density adjustment gradient calculated by this model indicates the direction and degree to which the current material distribution deviates from the ideal uniform state. Based on this, the system generates specific workstation reassignment instructions or material placement adjustment suggestions, such as migrating materials from densely populated areas to sparsely populated areas, or adjusting the opening sequence of the inlet and outlet, thereby physically eliminating the risk of localized overheating or undercooking and improving product consistency.

[0050] The load balancing objective function comprehensively considers multi-dimensional resource constraints such as steam supply capacity, conveyor belt load, and cooling water flow rate. The optimal processing rate adjustment obtained by solving this function is not an abstract mathematical solution, but directly mapped to specific control parameters such as the frequency setpoint of the inverter, the valve opening percentage, or the waiting time between processes. This step ensures that the loads of each subsystem are in a dynamic balance while meeting the target production capacity, avoiding overall efficiency decline or energy waste caused by bottlenecks in a single link. Only when the parameters optimized by the model pass the safety boundary verification are they encapsulated into a deployable initial control strategy. This verification step is a crucial safeguard against the model calculation results deviating from engineering reality, ensuring the executability and safety of the strategy.

[0051] Furthermore, the dynamic calibration of control parameters and equipment operation paths based on the execution feedback of the initial control strategy includes: responding to the execution feedback of the initial control strategy not meeting the preset compliance conditions, obtaining the proportion of idle workstations and the order urgency weight of each work zone within the target work area, and performing fusion processing to obtain a balance coefficient; dividing the first priority area and the second priority area based on the balance coefficient, and determining the process execution order based on the product of the proportion of idle workstations and the order urgency weight; constructing a spatial coordinate mapping model using the spatial three-dimensional coordinates of the first priority area and the dynamic occupancy status parameters of the workstations as independent variables, and solving the coordinate mapping partial derivatives in the path planning to correct the equipment operation path; after the equipment operation path and process execution order are adjusted, re-acquiring the execution feedback, and judging the effectiveness of the adjustment based on the re-acquiring execution feedback.

[0052] In this embodiment, the preset compliance conditions may include indicators such as the percentage of rate balance compliance and the percentage of improved work efficiency. When feedback indicates that the strategy has not met the criteria, it means that there is a mismatch between the current resource allocation and task requirements. The balance coefficient, as a comprehensive evaluation indicator, quantifies the potential and slack of each partition when undertaking urgent tasks. For example, a partition with many idle workstations but low order urgency may have a lower balance coefficient than a partition with few idle workstations but processing urgent orders, thereby guiding resources to be tilted towards truly critical tasks.

[0053] The first priority area is typically a work zone with a high balance coefficient and rapid response capability. By multiplying idle resources by task urgency, the system can identify process nodes that are both capable and urgently needed, and place them at the front of the execution sequence. This dynamic sorting mechanism breaks the rigid pattern of traditional fixed process routes, enabling production scheduling to respond in real time to changes in the on-site situation.

[0054] In pressure cooking workshops, the operating paths of material handling equipment (such as AGVs and robotic arms) often conflict due to the temporary occupancy or release of workstations. A spatial coordinate mapping model establishes a nonlinear mapping relationship between physical spatial coordinates and the occupancy status of workstations. Solving for the partial derivatives of this model essentially assesses the path's sensitivity to changes in obstacle states. When a sudden change in the state of a workstation increases the risk of the original path, the partial derivative information can guide the system to quickly calculate a new collision-free trajectory or adjust the movement speed curve, achieving smooth obstacle avoidance and path reconstruction, rather than simply stopping and waiting.

[0055] In this embodiment, if the adjusted feedback still fails to meet the standards, the system will iteratively execute the above calibration process until all indicators return to the normal range or trigger the manual intervention threshold. It should be understood that the aforementioned spatial distribution gradient model, load balancing objective function, and spatial coordinate mapping model are merely exemplary mathematical tools for achieving adaptive control. In practical applications, machine learning models, heuristic search algorithms, or other equivalent control theory methods can also be used to achieve the same function, as long as they adhere to the core concept of dynamic calibration of control parameters and paths based on feedback, and should all be covered within the scope of protection of this application. Through this deep closed-loop calibration mechanism, this embodiment ensures that the pressure cooking equipment, when facing a highly uncertain production environment, not only generates a reasonable initial strategy but also possesses the ability to continuously self-evolve and resist interference, significantly improving the system's robustness and production efficiency.

[0056] The following describes an automatic control system for a pressure cooking apparatus provided in an embodiment of this application. The automatic control system described below corresponds to the automatic control method for the pressure cooking apparatus described above. Please refer to the following description. Figure 2 , Figure 2 This is a schematic diagram of the structure of an automatic control system 20 for a pressure cooking device provided in an embodiment of this application, including: The working condition perception and allocation module 21 is used to acquire the working condition characterization data of the pressure cooking equipment in the target working area, quantify the dynamic complexity of the current working condition based on the working condition characterization data, and match the corresponding dynamic perception and allocation scheme based on the dynamic complexity. The data standardization and fusion module 22 is used to control the sensing terminal to collect dynamic environmental sensing data of the target work area based on the dynamic sensing allocation scheme, and to evaluate the sensing response quality of the sensing terminal. Data fusion module 23 is used to standardize and fuse dynamic environmental perception data based on the spatiotemporal coordination deviation between perception terminals in response to the perception response quality meeting the preset quality conditions, and generate structured fused data. The control strategy calibration module 24 is used to generate an initial control strategy based on structured fusion data, and to dynamically calibrate control parameters and equipment operation paths based on the execution feedback of the initial control strategy.

[0057] One possible implementation in this application embodiment is that the working condition characterization data includes the frequency of process crossover conflicts, the frequency of material handling per unit time, and the delay time of process handover. When the working condition perception and allocation module 21 quantifies the dynamic complexity of the current working condition based on the working condition characterization data and matches the corresponding dynamic perception and allocation scheme based on the dynamic complexity, it is specifically used for: The collaborative adaptation coefficient, the work intensity coefficient, and the process stability coefficient are calculated based on the working condition characterization data. A dynamic working condition index is obtained by fusion calculation based on the collaborative adaptation coefficient, the work intensity coefficient, and the process stability coefficient to quantify the dynamic complexity. Based on the comparison results between the dynamic working condition index and the preset working condition classification threshold, the dynamic sensing allocation scheme is matched from the basic sensing allocation scheme, the advanced sensing allocation scheme and the enhanced sensing allocation scheme. The dynamic sensing allocation scheme includes at least one of the following: the sampling frequency configuration of the sensing terminal, the number of active nodes configuration, and the data transmission bandwidth allocation.

[0058] In another possible implementation of this application embodiment, the data standardization and fusion module 22, when evaluating the sensing response quality of the sensing terminal, is specifically used for: Within a preset monitoring period, acquire the delayed time-series data of the sensing terminal's response to the working condition characterization data, and determine the sensitivity influence factor based on the time-series difference results of the delayed time-series data. The jitter time-series data of the working condition characterization data after the sensing terminal responds is obtained during the transmission process, and the real-time impact factor is determined based on the frequency domain feature extraction results of the jitter time-series data; The comprehensive impact value is obtained by normalizing and geometrically averaging the sensitivity impact factor and the real-time impact factor. In response to the overall impact value exceeding the preset impact threshold, a working condition adaptation anomaly verification prompt is generated. If the overall impact value is not greater than the preset impact level threshold, the perceived response quality is determined to meet the preset quality conditions.

[0059] In another possible implementation of this application embodiment, when the data fusion module 23 performs standardized fusion of dynamic environmental perception data based on the spatiotemporal coordination deviation between sensing terminals to generate structured fused data, it is specifically used for: Calculate the device coordination index when different sensing terminals collect data from the target work area; In response to a device collaboration index exceeding a preset device collaboration threshold, data standardization control is performed on dynamic environmental perception data to generate structured fusion data; In response to the fact that the equipment coordination index is not greater than the preset equipment coordination threshold and abnormal feedback of environmental parameters of the target work area is received, the corresponding dimension data is re-collected after the environmental parameter control is executed. If the device collaboration index is not greater than the preset device collaboration threshold and no abnormal feedback of environmental parameters is received, the corresponding dimension data will be re-collected. Among them, the equipment coordination index is obtained by integrating the workstation occupancy matching degree and the accuracy of material abnormality early warning, and combined with the dynamic coordination correction coefficient of working conditions.

[0060] In another possible implementation of this application embodiment, when the data fusion module 23 performs data standardization control on the dynamic environment perception data, it is specifically used for: When the acquisition time sequence of dynamic environmental perception data collected by different sensing terminals is consistent, calculate the collaborative deviation value between the corresponding time sequence feature representations of adjacent sensing terminals. In response to a coordination deviation value exceeding a preset allowable coordination deviation, the dynamic environment perception data is corrected based on the cumulative integral value of the coordination deviation value within a preset statistical period. When the timing of the dynamic environmental sensing data collected by different sensing terminals is inconsistent, a timing calibration priority score is obtained by mapping the timing consistency deviation value with the cooking condition type. In response to the presence of single-dimensional data that meets preset priority conditions in the dynamic environment perception data, a real-time stream processing mode is adopted to perform time stamp synchronization calibration and data timing deviation correction. In response to the presence of non-single-dimensional data that meet preset priority conditions in the dynamic environment perception data, a batch processing mode is adopted to perform batch-based time-series calibration and heterogeneous data fusion.

[0061] In another possible implementation of this application embodiment, the control strategy calibration module 24, when generating the initial control strategy based on structured fusion data, is specifically used for: Based on structured fusion data, the material layout density and material handling rate within the target operating area are obtained; The target value of material layout density is determined based on material processing priority and equipment space utilization limit, and the target value of material processing rate is determined based on historical material processing peak and equipment operating capacity limit. Input the currently acquired material layout density, material processing rate and spatial diffusion coefficient into the spatial distribution gradient model to obtain the layout density adjustment gradient, so as to dynamically correct the material layout and workstation allocation strategy. A load balancing objective function is constructed based on the target value of material handling rate as a constraint. The optimal handling rate adjustment is obtained by solving the load balancing objective function to dynamically correct the scheduling rules of material conveying and cooking processes. In response to the fact that the dynamically corrected material layout density and material handling rate are both within the corresponding allowable range, an initial control strategy is generated based on the dynamically corrected parameters.

[0062] In another possible implementation of this application embodiment, when the control strategy calibration module 24 dynamically calibrates the control parameters and the device operating path based on the execution feedback of the initial control strategy, it is specifically used for: In response to the feedback from the execution of the initial control strategy not meeting the preset compliance conditions, the idle workstation ratio and order urgency weight of each work zone in the target work area are obtained and fused to obtain the balance coefficient. The first priority area and the second priority area are divided based on the balance coefficient, and the process execution order is determined based on the product of the idle workstation ratio and the order urgency weight. A spatial coordinate mapping model is constructed using the three-dimensional spatial coordinates of the first priority area and the dynamic occupancy status parameters of the workstation as independent variables, and the partial derivatives of the coordinate mapping in the path planning are solved to correct the equipment running path. After the equipment operation path and process execution sequence are adjusted, the execution feedback is reacquired, and the effectiveness of the adjustment is judged based on the reacquired execution feedback.

[0063] This application provides an electronic device, such as... Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0064] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in connection with the embodiments of this application. Processor 301 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0065] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0066] The memory 303 may be a ROM (Read-Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or it may be an EEPROM (Electrically Erasable Programmable Read-Only Memory), a CD-ROM (Compact Disc Read-Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0067] The memory 303 is used to store application code that executes the scheme of the embodiments of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0068] Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0069] The following describes a computer-readable storage medium provided by an embodiment of this application. The computer-readable storage medium described below can be referred to in correspondence with the method described above.

[0070] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the automatic control method for the pressure cooking equipment described above.

[0071] Since the embodiments of the computer-readable storage medium portion correspond to the embodiments of the method portion, please refer to the description of the embodiments of the method portion for the embodiments of the computer-readable storage medium portion.

[0072] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0073] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An automatic control method for a pressure cooking device, characterized in that, The method includes: Acquire the operating condition characterization data of the pressure cooking equipment within the target operating area, quantify the dynamic complexity of the current operating condition based on the operating condition characterization data, and match the corresponding dynamic sensing allocation scheme based on the dynamic complexity. Based on the dynamic sensing allocation scheme, the sensing terminal is controlled to collect dynamic environmental sensing data of the target work area, and the sensing response quality of the sensing terminal is evaluated. In response to the perception response quality meeting the preset quality conditions, the dynamic environment perception data is standardized and fused based on the spatiotemporal coordination deviation between the perception terminals to generate structured fused data. An initial control strategy is generated based on the structured fusion data, and the control parameters and equipment operation path are dynamically calibrated based on the execution feedback of the initial control strategy.

2. The automatic control method for the pressure cooking equipment according to claim 1, characterized in that, The operating condition characterization data includes the frequency of process crossover conflicts, the frequency of material handling per unit time, and the delay time of process handover. The step of quantifying the dynamic complexity of the current working condition based on the working condition characterization data, and matching the corresponding dynamic sensing allocation scheme based on the dynamic complexity, includes: Based on the aforementioned working condition characterization data, calculate the collaborative adaptation coefficient, the work intensity coefficient, and the process stability coefficient; A dynamic working condition index is obtained by fusion calculation based on the collaborative adaptation coefficient, the work intensity coefficient, and the process stability coefficient to quantify the dynamic complexity. Based on the comparison results between the dynamic working condition index and the preset working condition classification threshold, the dynamic sensing allocation scheme is matched from the basic sensing allocation scheme, the advanced sensing allocation scheme and the enhanced sensing allocation scheme. The dynamic sensing allocation scheme includes at least one of the following: the sampling frequency configuration of the sensing terminal, the number of activated nodes configuration, and the data transmission bandwidth allocation.

3. The automatic control method for the pressure cooking equipment according to claim 2, characterized in that, The evaluation of the sensing response quality of the sensing terminal includes: Within a preset monitoring period, the delayed time-series data of the sensing terminal's sensing response to the working condition characterization data is acquired, and the sensitivity influence factor is determined based on the time-series difference results of the delayed time-series data. After the sensing terminal responds, the jitter time sequence data of the working condition characterization data during the transmission process is obtained, and the real-time impact factor is determined based on the frequency domain feature extraction result of the jitter time sequence data; Based on the sensitivity influence factor and the real-time influence factor, normalization and geometric mean calculation are performed to obtain the comprehensive influence value; In response to the overall impact value exceeding a preset impact threshold, a working condition adaptation anomaly verification prompt is generated. If the overall impact value is not greater than the preset impact level threshold, it is determined that the perceived response quality meets the preset quality condition.

4. The automatic control method for the pressure cooking equipment according to claim 1, characterized in that, The standardization and fusion of the dynamic environment perception data based on the spatiotemporal coordination deviation between the sensing terminals to generate structured fused data includes: Calculate the device coordination index when different sensing terminals collect data from the target work area; In response to the device collaboration index being greater than a preset device collaboration threshold, data standardization control is performed on the dynamic environment perception data to generate the structured fusion data; In response to the fact that the equipment coordination index is not greater than the preset equipment coordination threshold, and an abnormal feedback of the environmental parameters of the target work area is received, the corresponding dimension data is re-collected after environmental parameter control is performed; In response to the device collaboration index not being greater than the preset device collaboration threshold and no abnormal feedback of the environmental parameters being received, the corresponding dimension data is re-collected. The equipment coordination index is obtained by integrating the workstation occupancy matching degree and the accuracy of material anomaly early warning, and combined with the dynamic coordination correction coefficient of working conditions.

5. The automatic control method for the pressure cooking equipment according to claim 4, characterized in that, The process of performing data standardization control on the dynamic environment perception data includes: When the acquisition timing of the dynamic environment sensing data collected by different sensing terminals is consistent, the collaborative deviation value between the corresponding timing feature representations of adjacent sensing terminals is calculated. In response to the fact that the coordination deviation value is greater than a preset allowable coordination deviation, the dynamic environment perception data is corrected based on the cumulative integral value of the deviation value within a preset statistical period. When the acquisition timing of the dynamic environment sensing data collected by different sensing terminals is inconsistent, a timing calibration priority score is obtained by mapping the timing consistency deviation value with the cooking condition type. In response to the presence of single-dimensional data that meets preset priority conditions in the dynamic environment perception data, a real-time stream processing mode is used to perform time stamp synchronization calibration and data timing deviation correction. In response to the presence of non-single-dimensional data that meets the preset priority conditions in the dynamic environment perception data, batch processing mode is adopted to perform batch time-series calibration and heterogeneous data fusion.

6. The automatic control method for the pressure cooking equipment according to claim 1, characterized in that, The generation of the initial control strategy based on the structured fusion data includes: Based on the structured fusion data, the material layout density and material processing rate within the target operating area are obtained; The target value of material layout density is determined based on material processing priority and equipment space utilization limit, and the target value of material processing rate is determined based on historical material processing peak and equipment operating capacity limit. The material layout density, material processing rate and spatial diffusion coefficient are input into the spatial distribution gradient model to obtain the layout density adjustment gradient, so as to dynamically correct the material layout and workstation allocation strategy. Based on the target value of the material handling rate as a constraint, a load balancing objective function is constructed. By solving the load balancing objective function, the optimal handling rate adjustment amount is obtained to dynamically correct the scheduling rules of the material conveying and cooking processes. In response to the fact that the dynamically corrected material layout density and the material processing rate are both within the corresponding allowable range, the initial control strategy is generated based on the dynamically corrected parameters.

7. The automatic control method for the pressure cooking equipment according to claim 6, characterized in that, The execution feedback dynamic calibration of control parameters and equipment operating path based on the initial control strategy includes: In response to the execution feedback of the initial control strategy not meeting the preset compliance conditions, the idle workstation ratio and order urgency weight of each work area in the target work area are obtained and fused to obtain the balance coefficient. The first priority area and the second priority area are divided based on the balance coefficient, and the process execution order is determined based on the product of the idle workstation ratio and the order urgency weight. The spatial three-dimensional coordinates of the first priority area and the dynamic occupancy status parameters of the workstation are used as independent variables to construct a spatial coordinate mapping model, and the coordinate mapping partial derivatives in the path planning are solved to correct the equipment running path. After the equipment operation path and process execution sequence are adjusted, the execution feedback is reacquired, and the effectiveness of the adjustment is judged based on the reacquired execution feedback.

8. An automatic control system for a pressure cooking device, characterized in that, The system includes: The working condition perception and allocation module is used to acquire the working condition characterization data of the pressure cooking equipment in the target working area, quantify the dynamic complexity of the current working condition based on the working condition characterization data, and match the corresponding dynamic perception and allocation scheme based on the dynamic complexity. The data standardization and fusion module is used to control the sensing terminal to collect dynamic environmental sensing data of the target work area based on the dynamic sensing allocation scheme, and to evaluate the sensing response quality of the sensing terminal. The data fusion module is used to standardize and fuse the dynamic environment perception data based on the spatiotemporal coordination deviation between the perception terminals in response to the perception response quality meeting the preset quality conditions, so as to generate structured fused data. The control strategy calibration module is used to generate an initial control strategy based on the structured fusion data, and to dynamically calibrate the control parameters and the device operation path based on the execution feedback of the initial control strategy.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory; One or more applications, wherein the applications are stored in memory and configured to be executed by one or more processors, the applications being configured to: perform an automatic control method for a pressure cooking apparatus according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements an automatic control method for a pressure cooking device as described in any one of claims 1 to 7.

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