An optimized control method for processing of an environmentally friendly and efficient foam extinguishing agent
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA FIRE RESCUE ACAD
- Filing Date
- 2025-08-20
- Publication Date
- 2026-06-05
Smart Images
Figure CN121165637B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of fire extinguishing agent performance testing and control technology, and in particular to a method for optimizing and controlling the processing of an environmentally friendly and efficient foam fire extinguishing agent. Background Technology
[0002] In the field of foam fire extinguishing agent production, precise control of the processing is crucial to product performance. Current technologies often employ traditional segmented parameter settings and manual inspections for processing control. While these methods have proven effective in standardized, fixed-formulation production, their limitations are becoming increasingly apparent with the growing demand for environmentally friendly and efficient foam fire extinguishing agents. Because environmentally friendly and efficient foam fire extinguishing agents have complex formulations and highly interconnected processing stages, traditional control methods cannot comprehensively optimize parameters across the entire process. This can easily lead to significant processing losses, unstable product performance, and incomplete processing data, failing to meet the requirements for precise control of the production process and ensuring the environmentally friendly and efficient characteristics of the product. Summary of the Invention
[0003] This application solves the technical problems of traditional foam fire extinguishing agents having formulation designs that deviate from user needs, making it difficult to ensure the controllability of the entire process and resulting in high efficiency losses during processing.
[0004] To address the aforementioned technical problems, this application proposes a method for optimizing and controlling the processing of an environmentally friendly and efficient foam fire extinguishing agent. The method includes: analyzing user needs based on a set of fire extinguishing agent performance indicators to construct fire extinguishing agent performance constraints; designing a formulation based on the fire extinguishing agent performance constraints to determine a target fire extinguishing agent formulation; performing full-process processing control analysis based on the target fire extinguishing agent formulation to establish a first processing control pool for the fire extinguishing agent; introducing a fire extinguishing agent processing loss analysis channel to perform multi-dimensional processing loss analysis on the first processing control pool for the fire extinguishing agent, obtaining a first processing loss analysis map; introducing a predetermined scaling factor, and combining the fire extinguishing agent processing loss analysis channel and the first processing loss analysis map to perform differential variation optimization on the first processing control pool for the fire extinguishing agent, obtaining an optimal processing control scheme; and performing fire extinguishing agent processing monitoring feedback correction based on the target fire extinguishing agent formulation and the optimal processing control scheme.
[0005] This application proposes one or more technical solutions, which have at least the following technical effects:
[0006] This application analyzes user needs and constructs performance constraints based on the performance index set of fire extinguishing agents, and designs a target formulation accordingly. It analyzes the entire process control to establish a first pool, introduces a processing loss analysis channel to obtain a loss spectrum, and combines a predetermined scaling factor to perform differential variation optimization to obtain an optimal processing control scheme. Then, based on the target formulation and the optimization scheme, processing monitoring feedback correction is performed, thereby achieving optimized control of the environmentally friendly and efficient foam fire extinguishing agent processing process. This results in less processing loss, more stable and reliable product performance, and achieves the technical effect of ensuring the foam fire extinguishing agent formulation design meets user needs, guaranteeing full process controllability during processing, reducing efficiency losses during processing, and enabling the foam fire extinguishing agent to possess both environmentally friendly and efficient characteristics with stable performance. Attached Figure Description
[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0008] Figure 1 This is a schematic flowchart of a process optimization and control method for an environmentally friendly and efficient foam fire extinguishing agent provided in an embodiment of this application.
[0009] Figure 2 This is a flowchart illustrating the process control optimization method for obtaining the optimal solution in the processing of an environmentally friendly and efficient foam fire extinguishing agent, as provided in this application embodiment. Detailed Implementation
[0010] This application provides a method for optimizing and controlling the processing of an environmentally friendly and efficient foam fire extinguishing agent, which addresses the technical problems of traditional foam fire extinguishing agents having formulation designs that deviate from user needs, making it difficult to ensure the controllability of the entire processing process, and resulting in high efficiency losses during processing.
[0011] 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 a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0012] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.
[0013] like Figure 1 As shown, a method for optimizing and controlling the processing of an environmentally friendly and efficient foam fire extinguishing agent is disclosed, wherein the method includes:
[0014] Step A100: Analyze user requirements based on the extinguishing agent performance index set and construct extinguishing agent performance constraints.
[0015] Specifically, user requirements are collected based on the extinguishing agent performance index set to obtain a performance requirement dataset. This performance requirement dataset is then cleaned to generate extinguishing agent performance constraints. The specific steps are detailed in A110-A120.
[0016] Step A200: Design the formulation based on the performance constraints of the extinguishing agent to determine the target extinguishing agent formulation.
[0017] Optionally, the performance history of the fire extinguishing agent formula library is traced based on the fire extinguishing agent performance index set to obtain the performance sequence of each formula. According to the deviation detection of the fire extinguishing agent performance constraints, multiple performance deviation sequences are obtained. The fire extinguishing agent formula library is adjusted accordingly to obtain the adjusted formula library. Then, the target fire extinguishing agent formula is generated by optimization according to the fire extinguishing agent performance constraints. The specific steps are explained in detail in A210-A240.
[0018] Step A300: Based on the target fire extinguishing agent formula, perform full-process processing control analysis and establish the first pool for fire extinguishing agent processing control.
[0019] In one embodiment of this application, a corresponding control group is obtained based on the target extinguishing agent formulation analysis, mixing and homogenization, additive addition, and curing node control. The first extinguishing agent processing control pool is generated by combining these control groups. The specific steps are described in detail in A310-A340.
[0020] Step A400: Introduce the fire extinguishing agent processing loss analysis channel to perform multi-dimensional processing loss analysis on the first pool of the fire extinguishing agent processing control, and obtain the first processing loss analysis map.
[0021] Specifically, the first processing control scheme is extracted from the first pool of extinguishing agent processing control and three types of data are obtained through simulation. The extinguishing agent processing loss analysis channel containing three loss evaluation models is activated. The corresponding loss coefficients are obtained by inputting data, generating the first processing loss analysis sequence and adding it to the first processing loss analysis map. The specific steps are explained in detail in A410-A450.
[0022] Step A500: Introduce a predetermined scaling factor, and combine the fire extinguishing agent processing loss analysis channel and the first processing loss analysis spectrum to perform differential variation optimization on the first fire extinguishing agent processing control pool to obtain a processing control optimization scheme.
[0023] Optionally, based on the first spectrum of processing loss analysis, the first pool of extinguishing agent processing control is screened to obtain the second pool of extinguishing agent processing control, a full-process processing loss analysis model is generated and the benchmark processing control scheme is determined. The third pool of extinguishing agent processing control is established by combining the variation expansion of the predetermined scaling factor. Finally, the processing loss of the whole process is minimized to generate the optimal processing control scheme. The specific steps are explained in detail in A510-A550.
[0024] Step A600: Based on the target extinguishing agent formulation, perform extinguishing agent processing monitoring feedback correction according to the processing control optimization scheme.
[0025] Optionally, based on the target extinguishing agent formulation and the optimization scheme for processing control, the key nodes and core indicators that need to be monitored during the processing should be identified first. Key nodes cover the entire process, including mixing and homogenization, additive addition, and curing. Core indicators include process parameters determined in the optimization scheme for processing control, such as stirring intensity, timing of addition, and temperature range, as well as intermediate product performance parameters corresponding to the performance of the target extinguishing agent formulation, such as homogeneity, additive utilization rate, and degree of curing. At the same time, they are related to the fire extinguishing performance and environmental performance requirements that the final product must meet, thereby establishing a complete monitoring system.
[0026] During actual processing, real-time data collection is conducted according to the established monitoring system. By deploying corresponding monitoring equipment at key nodes, the actual values of process parameters are continuously tracked. Simultaneously, intermediate products are periodically sampled and tested to obtain their physicochemical properties and preliminary functional performance data. The collected data must be transmitted to the analysis system in real time to ensure timely capture of subtle changes during processing.
[0027] Then, the collected real-time data is compared and analyzed with the standard parameters and performance thresholds of the target extinguishing agent formulation in the processing control optimization scheme to identify any deviations. If the process parameters of a certain step exceed the range set by the processing control optimization scheme, or if the performance of intermediate products deviates from the requirements of the target extinguishing agent formulation, the causes of the deviation need to be further analyzed. These deviations may involve multiple aspects such as fluctuations in equipment operating status, minor changes in raw material characteristics, and interference from environmental factors, providing a clear direction for subsequent corrections.
[0028] Finally, based on the deviation analysis results, a feedback correction mechanism is activated. For deviations caused by different reasons, the process parameters of the corresponding stages are dynamically corrected according to the parameter adjustment range given in the processing control optimization scheme. For example, if insufficient homogenization occurs in the mixing and homogenization stage due to equipment speed fluctuations, the speed can be appropriately adjusted within the speed range specified in the processing control optimization scheme; if deviations in the additive addition stage occur due to metering errors, the metering equipment can be calibrated and the additive dosage can be supplemented or adjusted. After correction, the adjusted parameters and product performance are continuously monitored, forming a closed-loop control of monitoring-analysis-correction-remonitoring, until the processing process stabilizes within the range set by the processing control optimization scheme, and the performance of intermediate and final products meets the requirements of the target extinguishing agent formulation.
[0029] By establishing a full-process monitoring system and coordinating real-time data analysis and dynamic feedback correction, we ensure that the processing strictly follows the target formula and optimization scheme, ultimately achieving the stability and reliability of foam fire extinguishing agent product performance.
[0030] Furthermore, step A100 in the method provided in this application embodiment includes:
[0031] A110: Collect user requirements based on the set of fire extinguishing agent performance indicators to obtain a performance requirement dataset.
[0032] A120: Clean the performance requirement dataset to generate the fire extinguishing agent performance constraints.
[0033] Specifically, the first step is to collect user requirements based on the fire extinguishing agent performance index set. Combining the fire extinguishing performance index set with the environmental performance index set, the collection process must cover specific performance requirements. For example, users may specify fire extinguishing performance requirements such as fire extinguishing level IA, fire extinguishing time ≤ 2.0 min, and fire resistance time ≥ 10.0 min, as well as environmental performance requirements such as PFOS content ≤ 10 mg / kg and zebrafish mortality rate ≤ 50%. This information is then aggregated through the system's collection process to obtain a performance requirement dataset.
[0034] After obtaining the performance requirements dataset, it needs to be cleaned. The cleaning process removes duplicate information, such as repeated requirements for the same performance metric from the same user. It also corrects outlier data, such as unreasonable specifications proposed by users that do not conform to the current standard GB 15308-2006 for foam fire extinguishing agents, for example, setting a corrosion rate requirement of >15.0 mg / (d·dm³). 2In cases where the requirements are not met, modifications need to be made in accordance with this standard. Through the above cleaning process, it is ensured that all requirements in the performance requirement dataset not only conform to the actual user scenario but also comply with relevant technical specifications. Ultimately, performance constraints for fire extinguishing agents are generated, such as specifying that fire extinguishing performance must meet the requirements of extinguishing time ≤ 2.0 min, fire resistance time ≥ 10.0 min, and environmental performance must meet the requirements of PFOS ≤ 10 mg / kg.
[0035] By collecting and cleaning the performance requirement dataset based on the extinguishing agent performance index set, precise extinguishing agent performance constraints are generated, providing a clear and reliable basis for subsequent constraint-based formulation design.
[0036] Furthermore, step A110 in the method provided in this application embodiment includes:
[0037] A111: The set of performance indicators for fire extinguishing agents includes a set of fire extinguishing performance indicators and a set of environmental performance indicators.
[0038] Optionally, the performance evaluation of foam fire extinguishing agents often suffers from problems such as a single indicator or an unbalanced focus. Some evaluations only focus on fire extinguishing effect while ignoring environmental impact, while others mention environmental protection but lack clear indicator definitions. This makes it difficult for foam fire extinguishing agents to achieve both high fire extinguishing efficiency and environmental friendliness in practical applications. To solve the above problems, the performance indicator set of fire extinguishing agents is first divided into a fire extinguishing performance indicator set and an environmental performance indicator set, making the performance evaluation system more comprehensive.
[0039] The set of fire extinguishing performance indicators must cover key parameters that directly reflect fire extinguishing effectiveness, including fire extinguishing rating, such as IA and ARIA; fire extinguishing time ≤ 2.0 min; fire resistance time, IA rating ≥ 10.0 min, ARIA rating ≥ 15.0 min; foaming ratio deviation from characteristic value not greater than 1.0 or 20% of characteristic value; 25% liquid separation time deviation from characteristic value not greater than 20%, etc. These indicators directly determine the actual effect of the fire extinguishing agent in a fire scenario and are the core basis for ensuring fire extinguishing efficiency.
[0040] The environmental performance index set focuses on the impact on the environment and organisms, including PFOS content ≤10mg / kg, zebrafish toxicity test results mortality rate ≤50%, and requirements that foam liquid and foam solution have no environmental pollution and no obvious biological toxicity during production and application. These indicators ensure that the fire extinguishing agent does not have a negative impact on the ecological environment and biosafety while playing a fire extinguishing role. The parameter data of the above fire extinguishing performance index set and environmental performance index set can be adjusted by those skilled in the art according to the actual processing process and user needs.
[0041] By clearly defining the fire extinguishing agent performance index set as including both fire extinguishing performance index set and environmental performance index set, a clear index framework is provided for subsequent user demand analysis and performance constraint construction, ensuring a comprehensive evaluation and control of fire extinguishing agent performance.
[0042] Furthermore, step A200 in the method provided in this application embodiment includes:
[0043] A210: Based on the aforementioned set of fire extinguishing agent performance indicators, the performance history of the fire extinguishing agent formulation library is traced to obtain the performance sequence of each formulation.
[0044] A220: Based on the performance constraints of the fire extinguishing agent, deviation detection is performed on the performance sequences of each formulation to obtain multiple performance deviation sequences.
[0045] A230: Adjust the fire extinguishing agent formula library based on the multiple performance deviation sequences to obtain an adjusted fire extinguishing agent formula library.
[0046] A240: Based on the performance constraints of the extinguishing agent, optimize the formula of the extinguishing agent adjustment formula library to generate the target extinguishing agent formula.
[0047] In this embodiment of the application, the fire extinguishing agent formula library is a collection that stores various foam fire extinguishing agent formulas and their historical performance data, including the raw material composition, process parameters, and corresponding fire extinguishing performance data and environmental performance data of different formulas.
[0048] Specifically, the performance history of the fire extinguishing agent formulation library is first traced based on the fire extinguishing agent performance index set. The fire extinguishing agent performance index set covers both fire extinguishing performance index set and environmental performance index set. The specific parameters are as described in step A111 above. By tracing the actual performance data of each historical formulation in the fire extinguishing agent formulation library, the performance sequence of each formulation can be obtained. For example, the performance sequence of a certain historical formulation may include specific data such as fire extinguishing time of 2.3 min, PFOS of 12 mg / kg, and foaming ratio deviation of 25%.
[0049] The construction of the fire extinguishing agent formulation library is based on existing foam fire extinguishing agent formulations. First, the raw material composition and proportions of various basic formulations are collected, covering the specific proportions of raw materials such as compound hydrocarbon surfactants, film-forming surfactants without PFOS and PFOA, and environmentally friendly cooling and suppressing fire extinguishing agents, while also incorporating product type information. Based on this, performance data corresponding to each formulation is collected, including fire extinguishing performance (fire extinguishing level, fire extinguishing time, anti-burning time, foaming ratio, 25% separation time, etc.), environmental performance (PFOS content, zebrafish mortality rate), and physicochemical properties (freezing point, surface tension, interfacial tension, pH value, corrosion rate, etc., referring to testing and inspection indicators). Production records and performance test results of each formulation under different process parameters (such as reaction temperature, stirring speed, emulsification time) are also linked to form a structured set containing raw material information, proportioning parameters, performance indicators, and historical application data. This integrated set is then used to construct the fire extinguishing agent formulation library.
[0050] Next, deviation detection was performed on the performance sequences of each formulation based on the established performance constraints of the extinguishing agent. Assuming that the performance constraints clearly state that the extinguishing time must be ≤2.0 min, PFOS ≤10 mg / kg, and the foaming ratio deviation must be ≤20%, by comparing with the historical formulation performance sequences of the example above, it can be detected that the extinguishing time deviates by 0.3 min, the PFOS deviates by 2 mg / kg, and the foaming ratio deviates by 5%. These deviation data are integrated to form the performance deviation sequence of the formulation. By testing each of all historical formulations, multiple performance deviation sequences are obtained.
[0051] Then, the fire extinguishing agent formulation library is adjusted based on multiple performance deviation sequences to form an adjusted fire extinguishing agent formulation library. For example, for formulations with deviations in extinguishing time, the proportion of compound hydrocarbon surfactants can be adjusted to improve foaming efficiency; for formulations with excessive PFOS, they can be replaced with film-forming surfactants that do not contain PFOS; for formulations with excessive deviations in foaming ratio, the amount of co-solvent added can be optimized. Through these targeted adjustments, the performance of the formulations in the fire extinguishing agent formulation library is made closer to the specific requirements of the fire extinguishing agent performance constraints.
[0052] Finally, when optimizing the extinguishing agent formulation library based on the extinguishing agent performance constraints, the fire extinguishing performance indicators, environmental performance indicators, and physicochemical performance indicators covered by the extinguishing agent performance constraints specified in step A111 are obtained, such as freezing point, freeze-thaw resistance, specific flowability, pH value, surface tension, interfacial tension, diffusion coefficient, and corrosion rate. Subsequently, all formulations in the adjusted formulation library undergo the above-mentioned full-index performance testing to obtain the actual performance data of each formulation. These data are then compared one by one with the requirements of the extinguishing agent performance constraints to screen out formulations that meet all performance indicators. Based on this, a comprehensive performance evaluation is conducted on the qualified formulations. Through the synergistic analysis of multi-dimensional indicators, the formulation with the best comprehensive performance is determined as the target extinguishing agent formulation.
[0053] By tracing the performance history of the fire extinguishing agent formula library, detecting deviations, adjusting and optimizing it, target fire extinguishing agent formulas that meet the performance constraints of fire extinguishing agents were generated, providing a precise formula basis for subsequent processing control.
[0054] Furthermore, step A300 in the method provided in this application embodiment includes:
[0055] A310: Based on the target extinguishing agent formulation, perform mixed homogeneous node control analysis to obtain a mixed homogeneous control group.
[0056] A320: Based on the target extinguishing agent formulation, analyze the auxiliary agent addition node control to obtain the auxiliary agent addition control group.
[0057] A330: Based on the target extinguishing agent formulation, perform curing node control analysis to obtain the curing control group.
[0058] A340: Based on the mixing and homogenization control group, the additive addition control group and the maturation control group, a full-process control combination is performed to generate the first pool for the processing control of the extinguishing agent.
[0059] In this embodiment, additives are auxiliary substances used to improve the performance of foam extinguishing agents, in addition to the main components. These include foam stabilizers, liquid-resistant additives, solubilizers, antifreeze agents, corrosion inhibitors, and preservatives. Curing is a crucial step in the processing of foam extinguishing agents. By controlling parameters such as temperature and time, the materials are further reacted or stabilized to improve product performance.
[0060] Specifically, firstly, a twin formula set is retrieved based on the target extinguishing agent formula. Based on the twin formula set, the history of mixed homogeneous node control is traced back to obtain a mixed homogeneous control sample set. The control indicators are classified to obtain multiple mixed homogeneous control regions. Multiple mixed homogeneous confidence regions are obtained by filtering according to a predetermined confidence level. Then, control parameters are combined based on these confidence regions to generate a mixed homogeneous control group. The specific steps are explained in detail in A311-A315.
[0061] After obtaining the mixed homogenization control group, it is necessary to further analyze the control parameters of the additive addition node and the maturation node in order to construct a complete process control system.
[0062] When performing auxiliary agent addition node control analysis based on the target extinguishing agent formulation, the process is similar to that of the mixing and homogenization node. First, the twin formulation set is retrieved, following the same procedure as step A311. Next, historical control data for auxiliary agent addition is traced back based on the twin formulation set to obtain an auxiliary agent addition control sample set, covering parameters such as auxiliary agent type (e.g., foam stabilizer, solubilizer), addition amount, addition timing, and stirring speed. Subsequently, multiple auxiliary agent addition control regions are obtained by classifying them according to control indicators. Then, based on a predetermined confidence level of 85%, control parameters with an occurrence frequency ≥85% are retained to form auxiliary agent addition confidence regions. Finally, an auxiliary agent addition control group is generated by combining control parameters.
[0063] The same process is followed when performing maturation node control analysis. Historical maturation data from the twin recipe set is retrieved to obtain a maturation control sample set, including parameters such as maturation temperature, time, and ambient humidity. Multiple maturation control regions are obtained by classifying them according to control indicators. After filtering with a predetermined confidence level of 80%, control parameters with an occurrence frequency ≥ 80% are retained to form maturation confidence regions. Then, control parameter combinations are performed to generate a maturation control group.
[0064] Finally, when combining the entire process control based on the mixing and homogenization control group, the additive addition control group, and the maturation control group, the control parameters in each control group are cross-matched. For example, mixing and homogenization: 300 r / min, 15 min; additive addition: 2.5% foam stabilizer, 30-minute timing; maturation: 40℃, 150 minutes, etc., thereby forming multiple processing control schemes covering the entire process, namely the first pool of extinguishing agent processing control.
[0065] By analyzing the control parameters of the additive addition and maturation nodes and combining them with the mixing and homogenization control group, a processing control scheme pool containing collaborative parameters for the entire process was generated, providing a comprehensive control basis for the optimization of subsequent processing.
[0066] Furthermore, step A310 in the method provided in this application embodiment includes:
[0067] A311: Perform a twin formula search based on the target fire extinguishing agent formula to obtain a twin formula set.
[0068] A312: Based on the twin formula set, perform historical backtracking of mixed homogeneous node control to obtain a mixed homogeneous control sample set.
[0069] A313: Based on the mixed homogeneous control sample set, classify the control indicators to obtain multiple mixed homogeneous control regions.
[0070] A314: Filter the plurality of mixed homogeneous control regions according to a predetermined confidence level to obtain a plurality of mixed homogeneous confidence regions.
[0071] A315: The mixed homogeneous control group is generated by combining control parameters based on the multiple mixed homogeneous confidence regions.
[0072] In this embodiment, the twin formulation is a foam extinguishing agent formulation similar to the target extinguishing agent formulation in terms of type, raw material composition, and performance characteristics. Homogenization is a crucial step in the foam extinguishing agent processing; by stirring and other treatments, the materials are mixed evenly to achieve a homogeneous state.
[0073] Specifically, the first step is to perform a twin formulation search based on the target fire extinguishing agent formulation to obtain a twin formulation set. The similarity of these formulations in terms of raw material composition and processing characteristics can provide a reliable reference benchmark for mixing homogeneity control. The similarity between the twin formulation and the target fire extinguishing agent formulation needs to be defined through multi-dimensional data. Specifically, the product types must be completely identical; in the main raw material composition, the matching degree of key components such as surfactants and additives must be ≥95%, and the proportion deviation of core raw materials such as compound hydrocarbon surfactants and PFOS-free film-forming agents must be controlled within ±10%. For example, if a certain surfactant accounts for 2.5% in the target formulation, the twin formulation must have that component in the range of 2.25%-2.75%; in terms of performance indicators, the deviation of key fire extinguishing performance parameters such as extinguishing time, anti-burning time, and foaming ratio from the target formulation must be ≤15%, and the deviation of environmental indicators such as PFOS content must be ≤5mg / kg, and all must meet the corresponding technical requirements in GB 15308-2006. This ensures that the twin formulation is highly similar to the target formulation in terms of raw material characteristics and processing compatibility, providing effective historical data reference for mixing and homogenization control.
[0074] Next, based on the twin formulation set, the historical control data of the mixing and homogenizing nodes is backtracked to obtain a mixing and homogenizing control sample set. The control parameters of the mixing and homogenizing nodes usually include stirring speed, mixing time, reaction temperature, etc. Historical backtracking involves extracting the historical operation data of these twin formulations in the mixing and homogenizing stage. For example, the mixing and homogenizing parameters of one twin formulation are stirring speed of 300 r / min, mixing time of 15 min, and temperature of 50℃, while those of another formulation are 280 r / min, 14 min, and 48℃. By summarizing multiple such data, a mixing and homogenizing control sample set containing multiple sets of parameters is formed.
[0075] Then, the control indices are classified according to the mixing and homogenization control sample set to obtain multiple mixing and homogenization control zones. The parameters in the mixing and homogenization control sample set are classified according to the control indices: stirring speed, mixing time, and reaction temperature. Each control index forms an independent control zone. For example, the stirring speed control zone covers various speed values from 260 to 320 r / min, the mixing time control zone covers time values from 12 to 18 min, and the reaction temperature control zone covers temperature values from 45 to 55℃. Each control zone is a set of different parameters under the same index.
[0076] Subsequently, multiple mixing and homogenization control zones are filtered based on a predetermined confidence level of 80%, resulting in multiple mixing and homogenization confidence zones. The confidence level of each control parameter is its frequency of occurrence within the corresponding control zone. For example, the stirring speed of 300 r / min has a frequency of 85% in the speed control zone, meaning it occurs 85 times out of 100 historical data points, while 260 r / min has a frequency of 75%. Parameters with a frequency ≥80% are retained during filtering. For example, the final speed confidence zone might be 290-310 r / min, the mixing time confidence zone might be 14-16 min, and the reaction temperature confidence zone might be 48-52℃, with the corresponding control parameter frequencies all ≥80%.
[0077] Finally, control parameters are combined based on multiple mixed homogeneous confidence regions to generate a mixed homogeneous control group. The control parameters of these multiple mixed homogeneous confidence regions are cross-combined to form several feasible control schemes, such as 290 r / min, 14 min, 48℃; 300 r / min, 15 min, 50℃; 310 r / min, 16 min, 52℃, etc. These combinations together constitute the mixed homogeneous control group.
[0078] By using twin recipe retrieval, historical data backtracking, index classification, confidence filtering, and parameter combination, a mixing and homogenizing control group adapted to the target recipe was generated, providing diverse and reliable parameter schemes for the precise control of the mixing and homogenizing nodes.
[0079] Furthermore, step A400 in the method provided in this application embodiment includes:
[0080] A410: Extract the first processing control scheme from the first pool of the extinguishing agent processing control, and perform a full-process simulation based on the first processing control scheme to obtain the first simulation data of mixing and homogenization, the first simulation data of additive addition, and the first simulation data of maturation.
[0081] A420: Activate the fire extinguishing agent processing loss analysis channel, which includes a mixing homogeneity loss evaluation model, an additive addition loss evaluation model, and a maturation loss evaluation model.
[0082] A430: Input the first simulated data of the mixed homogeneous mass into the mixed homogeneous mass loss evaluation model to obtain the first mixed homogeneous mass loss coefficient.
[0083] A440: Input the first simulation data of the addition of the adjuvant into the adjuvant addition loss evaluation model to obtain the first adjuvant addition loss coefficient.
[0084] A450: Input the first simulation data of maturation into the maturation loss evaluation model to obtain the first maturation loss coefficient. Combine the first homogenization loss coefficient and the first additive addition loss coefficient to generate the first processing loss analysis sequence, and add the first processing loss analysis sequence to the first processing loss analysis map.
[0085] In one embodiment, a first processing control scheme is first extracted from the first processing control pool of the extinguishing agent. The first processing control pool of the extinguishing agent is generated by combining the mixing and homogenization control group, the additive addition control group and the maturation control group for the whole process control. It includes multiple processing control schemes covering the whole process. The first processing control scheme is the first or representative processing control scheme. It includes the specific stirring speed and mixing time in the mixing and homogenization stage, the amount and timing of foam stabilizer addition in the additive addition stage, and the temperature and time in the maturation stage, etc., as well as other whole process control parameters.
[0086] Next, a full-process simulation was conducted based on the first processing control scheme. The full-process simulation was carried out based on the parameters of each stage of the first processing control scheme: For the mixing and homogenization stage, the simulation showed that the stirring device was running at a set speed and time, and the data such as the homogeneity of the material, the uniformity of mixing, and energy consumption were monitored to generate the first simulation data of mixing and homogenization; For the additive addition stage, the simulation showed that the foam stabilizer and other additives were added according to the set amount and timing, and the data such as the degree of integration between the additives and the material, the residual amount, and the utilization rate were monitored to generate the first simulation data of additive addition; For the maturation stage, the maturation process of the material was simulated at a set temperature and time, and the data such as the performance changes of the material (such as foaming performance and stability) and time deviation were monitored to generate the first simulation data of maturation, thus completing the full-process simulation.
[0087] Then, the fire extinguishing agent processing loss analysis channel is activated, where the mixing homogenization loss evaluation model, additive addition loss evaluation model, and aging loss evaluation model are all machine learning models. The mixing homogenization loss evaluation model is trained using historical mixing homogenization data. Inputs include stirring parameters and homogenization degree, and the output reflects loss coefficients reflecting raw material waste and excessive energy consumption. The specific construction and training process is as follows:
[0088] The construction and training process of the mixing and homogenization loss evaluation model is based on historical mixing and homogenization data. First, historical backtracking is controlled through the mixing and homogenization nodes of the twin recipe set to collect a large amount of historical data, including stirring parameters such as stirring speed, mixing time, and reaction temperature, as well as corresponding data on homogenization degree, actual raw material consumption, theoretical consumption, and energy consumption per unit time. The collected historical mixing and homogenization data is preprocessed to remove outliers, such as extreme energy consumption data caused by equipment failure, and input parameters are standardized, i.e., stirring speed, time, etc., are converted into standardized values in the 0-1 range. Simultaneously, based on the raw material waste rate = (actual consumption - theoretical consumption) / theoretical consumption and the energy consumption exceedance rate = (actual energy consumption - standard energy consumption) / standard energy consumption, and combining the weights of their impact on processing losses (e.g., raw material waste weight 0.6, energy consumption exceedance weight 0.4), a comprehensive loss coefficient is calculated as the model label. For example, when the raw material waste rate is 20% and the energy consumption exceedance rate is 10%, the comprehensive loss coefficient = 20% × 0.6 + 10% × 0.4 = 16%.
[0089] A random forest regression model was chosen as the basic architecture, suitable for regression tasks with multiple feature inputs. The preprocessed input parameters and loss coefficient labels were combined to form a dataset, which was divided into training and validation sets in a 7:3 ratio. The model was trained using the training set, and hyperparameters were iteratively adjusted using the validation set. The number of decision trees was set to 100-200, and the maximum tree depth to 8-12. The mean squared error was used as the optimization objective to minimize the deviation between the model's predicted loss coefficients and the actual loss coefficients. After training, the model performance was evaluated using a test set. If the mean squared error in the prediction error was ≤5%, the model was considered usable. If the error was large, additional edge case data, such as mixed data under extremely low / extremely high speeds, was added for retraining until the model could accurately output loss coefficients reflecting raw material waste and excessive energy consumption, providing a reliable basis for evaluating mixed homogeneous losses.
[0090] Similar to the construction and training process of the above-mentioned mixed homogenization loss evaluation model, the adjuvant addition loss evaluation model is trained based on historical data of adjuvant addition amount, timing, residual loss, and insufficient effect to quantify adjuvant utilization efficiency loss; the curing loss evaluation model is trained by combining historical data of temperature, time, and performance fluctuations to evaluate performance loss caused by insufficient or excessive curing.
[0091] Next, the first simulation data of mixing and homogenization is input into the mixing and homogenization loss evaluation model to obtain the first mixing and homogenization loss coefficient; the first simulation data of adjuvant addition is input into the adjuvant addition loss evaluation model to obtain the first adjuvant addition loss coefficient; the first simulation data of maturation is input into the maturation loss evaluation model to obtain the first maturation loss coefficient. It is assumed that the above three coefficients are 0.12, 0.08 and 0.10, respectively, representing a loss ratio of 12%, 8% and 10%.
[0092] The three loss coefficients mentioned above are combined to generate the first analytical sequence of processing loss (0.12, 0.08, 0.10), which is then added to the first analytical spectrum of processing loss. Since the first processing control pool of the extinguishing agent contains multiple processing control schemes, the above simulation and calculation steps are repeated for each processing control scheme to generate its own analytical sequence of processing loss, which together constitute the first analytical spectrum of processing loss.
[0093] By simulating the entire process, activating the machine learning model to calculate the loss coefficient, and generating a graph containing the loss sequences of each scheme, multi-dimensional loss quantification of the first pool of processing control was achieved, providing a precise loss assessment basis for the optimization of subsequent processing processes.
[0094] Furthermore, such as Figure 2 As shown, step A500 in the method provided in this application embodiment includes:
[0095] A510: Based on the first analysis map of the processing loss, the first extinguishing agent processing control pool is optimized and screened according to the processing loss constraints to obtain the second extinguishing agent processing control pool.
[0096] A520: Based on the multi-dimensional loss evaluation index of the fire extinguishing agent processing loss analysis channel, weights are allocated to generate a full-process processing loss analysis model.
[0097] A530: Based on the full-process processing loss analysis model, perform current optimization on the second pool of the extinguishing agent processing control to determine the baseline processing control scheme.
[0098] A540: Based on the fire extinguishing agent processing loss analysis channel and the full-process processing loss analysis model, the fire extinguishing agent processing control second pool is differentially mutated and expanded according to the predetermined scaling factor and the benchmark processing control scheme to establish the fire extinguishing agent processing control third pool.
[0099] A550: Based on the full-process processing loss analysis model, the third pool for extinguishing agent processing control is optimized to minimize the full-process processing loss, and the optimal processing control scheme is generated.
[0100] In this embodiment of the application, the multidimensional loss evaluation index includes mixing and homogenization loss, additive addition loss, and maturation loss index.
[0101] Optionally, based on the first processing loss analysis map, the first extinguishing agent processing control pool is first optimized and screened according to the processing loss constraints to obtain the second extinguishing agent processing control pool. The processing loss constraints include a mixing homogeneity loss threshold of 0.15, an additive addition loss threshold of 0.10, and a curing loss threshold of 0.12. The specific acquisition process is explained in detail in step A511. During screening, it must be ensured that the mixing homogeneity loss coefficient, additive addition loss coefficient, and curing loss coefficient of the processing control scheme are all satisfied simultaneously. For example, if the loss sequence of a certain scheme in the first processing loss analysis map is (0.13, 0.08, 0.11), all three loss coefficients are lower than the corresponding loss thresholds, and it can be included in the second extinguishing agent processing control pool; if the loss sequence of a certain processing control scheme is (0.16, 0.09, 0.10), it is excluded because the mixing homogeneity loss coefficient exceeds the threshold of 0.15. Finally, all processing control schemes that meet the processing loss constraints are selected to form the second extinguishing agent processing control pool.
[0102] Subsequently, a full-process processing loss analysis model was generated by weighting the multi-dimensional loss evaluation indicators based on the fire extinguishing agent processing loss analysis channel. The multi-dimensional loss evaluation indicators are mixing and homogenization loss, additive addition loss, and curing loss. The weighting allocation needs to consider the degree of influence of each stage on the overall process loss. For example, the mixing and homogenization stage has a significant impact on product homogeneity and raw material utilization, so its weight is set to 0.4; the additive addition stage affects additive utilization and cost, so its weight is set to 0.3; and the curing stage affects product stability, so its weight is set to 0.3. Therefore, the generated full-process processing loss analysis model is: Full-process processing loss = 0.4 × mixing and homogenization loss coefficient + 0.3 × additive addition loss coefficient + 0.3 × curing loss coefficient.
[0103] Then, based on the analytical model of the entire process processing loss, the second pool of the extinguishing agent processing control is optimized to determine the benchmark processing control scheme. For each processing control scheme in the second pool of the extinguishing agent processing control, the entire process processing loss is calculated by substituting it into the model. For example, the loss sequence of Scheme 1 is (0.12, 0.07, 0.10), and the entire process loss = 0.4×0.12 + 0.3×0.07 + 0.3×0.10 = 0.048 + 0.021 + 0.03 = 0.099; the loss sequence of Scheme 2 is (0.13, 0.09, 0.11), and the entire process loss = 0.4×0.13 + 0.3×0.09 + 0.3×0.11 = 0.052 + 0.027 + 0.033 = 0.112. By comparison, the processing control scheme with the smallest entire process processing loss is selected as the benchmark processing control scheme. In the above example, Scheme 1 can be selected as the benchmark processing control scheme.
[0104] Subsequently, based on the baseline processing control scheme, the differences in the second pool of extinguishing agent processing control are detected to obtain the control difference vectors of each scheme. After adjustment by a predetermined scaling factor, multiple differential adjustment vector sets are generated. Based on this, the second pool of extinguishing agent processing control is mutated to obtain the first mutated pool of processing control. After analyzing it to obtain the second analytical spectrum of processing loss, the second mutated pool of processing control is obtained by screening according to the processing loss constraint conditions. Then, the second mutated pool of processing control is expanded with the second pool of extinguishing agent processing control to generate the third pool of extinguishing agent processing control. The specific steps are explained in detail in A541-A546.
[0105] Next, based on the full-process processing loss analysis model, the third pool of extinguishing agent processing control is optimized to minimize the full-process processing loss. This requires traversing all processing control schemes in the third pool of extinguishing agent processing control and substituting them into the model one by one to calculate the full-process processing loss. For example, in the third pool, the mixing and homogenization loss coefficient of Scheme 1 is 0.12, the additive addition loss coefficient is 0.08, and the maturation loss coefficient is 0.09, so the total process loss is 0.4×0.12+0.3×0.08+0.3×0.09=0.048+0.024+0.027=0.099; the loss coefficients of Scheme 2 are 0.10, 0.07, and 0.08, so the total process loss is 0.4×0.10+0.3×0.07+0.3×0.08=0.04+0.021+0.024=0.085; and the loss coefficients of Scheme 3 are 0.13, 0.09, and 0.10, so the total process loss is 0.4×0.13+0.3×0.09+0.3×0.10=0.052+0.027+0.03=0.109.
[0106] The total process loss of all schemes is calculated and compared, and the scheme with the minimum loss is selected. For example, among the three schemes mentioned above, the total process loss of Scheme 2 is 0.085, which is the minimum value. Moreover, the loss coefficients of each stage are all lower than the loss thresholds of 0.15 for mixing and homogenization, 0.10 for additive addition, and 0.12 for maturation in the processing loss constraints. Therefore, Scheme 2 is determined as the optimal processing control scheme. The parameters of this processing control scheme can minimize processing loss while ensuring product performance.
[0107] By applying the whole-process processing loss analysis model, the processing control schemes in the third pool of the fire extinguishing agent processing control were calculated and compared to select the scheme with the minimum whole-process processing loss, and the optimal processing control scheme was generated, providing the optimal control basis for the efficient and low-loss processing of foam fire extinguishing agents.
[0108] Furthermore, step A510 in the method provided in this application embodiment includes:
[0109] A511: The processing loss constraints include the mixing and homogenization loss threshold, the additive addition loss threshold, and the aging loss threshold.
[0110] Optionally, the mixing and homogenization loss threshold, the additive addition loss threshold, and the aging loss threshold in the processing loss constraints are quantitative standards set to effectively screen out processing control schemes with controllable losses, and can define the acceptable range of losses in each processing stage.
[0111] The mixing and homogenization loss threshold is set for losses in the mixing and homogenization process, which mainly manifest as raw material waste and excessive energy consumption. Based on the correlation analysis between mixing and homogenization losses and product homogeneity in historical processing data, when the loss coefficient exceeds a certain value, the raw material utilization rate drops below 85%, and the product homogeneity does not meet the requirements of GB 15308-2006 for no visible stratification and heterogeneity. Therefore, the mixing and homogenization loss threshold is set to 0.15, i.e., the loss ratio is ≤15%, to ensure that the losses in this process are within a controllable range.
[0112] The additive addition loss threshold is used to constrain losses in the additive addition process, which mainly stems from low additive utilization and excessive residue. Considering the cost of additives and their impact on the product's foaming performance, when the additive addition loss coefficient exceeds 0.10, the residual additive will cause the foaming ratio deviation to exceed 20% of the characteristic value, failing to meet performance requirements. Therefore, the additive addition loss threshold is set to 0.10, i.e., a loss ratio ≤ 10%, to ensure effective additive utilization without affecting product performance.
[0113] The maturation loss threshold addresses losses during the maturation process, primarily manifested as decreased product stability due to insufficient or excessive maturation. Analysis of the relationship between the maturation loss coefficient and the 25% separation time reveals that when the loss coefficient exceeds 0.12, the deviation between the 25% separation time and the characteristic value exceeds 20%. Therefore, the maturation loss threshold is set at 0.12, meaning a loss percentage ≤ 12%, ensuring sufficient product maturation and stable performance.
[0114] In practical applications, these loss thresholds are used to screen processing control schemes: if the mixing and homogenization loss coefficient of a certain scheme is 0.13, the additive addition loss coefficient is 0.09, and the aging loss coefficient is 0.11, all of which are lower than the corresponding thresholds, then the scheme meets the constraints; if the mixing and homogenization loss coefficient of a certain scheme is 0.16, which exceeds the threshold, then it is excluded, thereby effectively screening out processing control schemes where the losses in each stage are controllable.
[0115] By setting loss thresholds for mixing and homogenization, additive addition, and maturation, the acceptable range of loss in each stage was clarified, enabling quantitative screening of processing control schemes and providing clear constraints for subsequent optimization of the processing process.
[0116] Furthermore, step A540 in the method provided in this application embodiment includes:
[0117] A541: Based on the benchmark processing control scheme, perform difference detection on the second pool of the extinguishing agent processing control to obtain the control difference vector of each scheme.
[0118] A542: Adjust the control differential vectors of each scheme based on the predetermined scaling factor to generate multiple differential adjustment vector sets.
[0119] A543: The second processing control pool of the extinguishing agent is mutated according to the multiple differential adjustment vector sets to obtain the first mutated processing control pool.
[0120] A544: Based on the processing loss analysis channel of the extinguishing agent, perform multi-dimensional processing loss analysis on the first variation pool of the processing control to obtain the second processing loss analysis map.
[0121] A545: Based on the second graph of the processing loss analysis, the first variation pool of the processing control is optimized and screened according to the processing loss constraints to obtain the second variation pool of the processing control.
[0122] A546: Expand the second extinguishing agent processing control pool according to the second variation pool of the processing control to generate the third extinguishing agent processing control pool.
[0123] In this embodiment, the predetermined scaling factor is used to adjust the coefficient of the differential control vector of each scheme when the differential variation expansion of the second pool of fire extinguishing agent processing control is carried out. Its function is to control the magnitude of parameter variation and avoid excessive deviation of parameters from the baseline processing control scheme, which would lead to performance instability.
[0124] Optionally, the difference detection of the second pool for extinguishing agent processing control is first performed based on the benchmark processing control scheme to obtain the control difference vector for each scheme. The benchmark processing control scheme is the processing control scheme with the minimum processing loss throughout the entire process in the second pool for extinguishing agent processing control. For example, its parameters are 300 r / min and 15 min for the mixing and homogenization stage, 2.5% and 30 min for the additive addition stage, and 40℃ and 150 min for the curing stage. Other schemes in the second pool for extinguishing agent processing control, such as scheme X, have parameters of 290 r / min and 14 min, 2.3% and 28 min, and 39℃ and 145 min. By calculating the parameter differences with the benchmark scheme, the control difference vector (-10 r / min, -1 min, -0.2%, -2 min, -1℃, -5 min) is obtained. The control difference vectors of all processing control schemes are obtained by analogy.
[0125] Next, the control differential vectors of each scheme are adjusted based on a predetermined scaling factor (e.g., 0.1) to generate multiple sets of differential adjustment vectors. The scaling factor is used to control the variation amplitude and avoid excessive deviation of parameters from the baseline scheme, which could lead to performance instability. For example, multiplying the control differential vectors by 0.1 yields adjustment vectors (-1r / min, -0.1min, -0.02%, -0.2min, -0.1℃, -0.5min). By combining adjustment values of different dimensions, multiple sets of differential adjustment vectors are generated.
[0126] Then, the second processing control pool for the extinguishing agent is mutated based on multiple sets of differential adjustment vectors to obtain the first variation pool for processing control. For each processing control scheme in the second processing control pool for the extinguishing agent, different differential adjustment vectors are applied to correct the parameters. For example, applying the differential adjustment vector (-1r / min, -0.1min, -0.02%, -0.2min, -0.1℃, -0.5min) to scheme X yields the mutated parameters of 289r / min, 13.9min, 2.28%, 27.8min, 38.9℃, and 144.5min. Through such mutations, a large number of new processing control schemes are generated, forming the first variation pool for processing control.
[0127] Subsequently, the fire extinguishing agent processing loss analysis channel calculates the loss coefficients of each variant processing control scheme through the mixing and homogenization, additive addition, and curing loss evaluation models. For example, the mixing and homogenization loss coefficient of a certain variant processing control scheme is 0.12, the additive addition loss coefficient is 0.08, and the curing loss coefficient is 0.09, forming a loss sequence (0.12, 0.08, 0.09). Multiple such loss sequences constitute the second spectrum of processing loss analysis.
[0128] Subsequently, based on the second processing loss analysis map, and according to the processing loss constraints of 0.15 for mixing and homogenization loss, 0.10 for additive addition loss, and 0.12 for maturation loss, the first processing control variation pool was optimized to obtain the second processing control variation pool. All schemes with loss coefficients lower than their corresponding thresholds were selected. For example, the scheme with the loss sequence (0.13, 0.09, 0.11) met the constraints, while (0.16, 0.08, 0.10) was excluded because the mixing and homogenization loss exceeded the limit, thus forming the final second processing control variation pool.
[0129] Finally, the second processing control pool for extinguishing agents is expanded based on the second variation pool for processing control. For example, the second processing control pool for extinguishing agents originally had 8 schemes, and the second variation pool for processing control added 5 high-quality variation processing control schemes that meet the processing loss constraints. After merging, a third processing control pool for extinguishing agents containing 13 processing control schemes is formed, which significantly expands the range of processing control schemes to be selected.
[0130] By combining differential mutation and loss screening, the pool of processing control schemes is expanded, providing a richer pool of candidate schemes for minimizing processing losses throughout the entire process, and improving the reliability and optimization space of the optimization results.
[0131] In summary, the process optimization and control method for an environmentally friendly and efficient foam fire extinguishing agent provided in this application has the following technical effects:
[0132] This application constructs performance constraints by analyzing user needs based on the performance index set of fire extinguishing agents, designs target fire extinguishing agent formulations through performance history tracing and deviation detection, establishes a processing control pool by analyzing the entire process control, introduces loss analysis channels and differential variation optimization to obtain optimal processing control schemes, and combines the target formulation with processing monitoring feedback correction to optimize the processing process of environmentally friendly and efficient foam fire extinguishing agents. This makes the control of the foam fire extinguishing agent processing process more precise and reliable, achieving the technical effect of foam fire extinguishing agent formulation design meeting user needs, ensuring full process controllability during processing, reducing efficiency losses during processing, and making the foam fire extinguishing agent both environmentally friendly and efficient with stable performance.
[0133] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
[0134] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.
Claims
1. A method for optimizing and controlling the processing of an environmentally friendly and efficient foam fire extinguishing agent, characterized in that, The method includes: Based on the set of fire extinguishing agent performance indicators, user requirements are analyzed, and fire extinguishing agent performance constraints are constructed. Based on the performance constraints of the extinguishing agent, the formulation is designed to determine the target extinguishing agent formulation; Based on the target fire extinguishing agent formulation, a full-process processing control analysis was conducted to establish the first pool for fire extinguishing agent processing control. A processing loss analysis channel for extinguishing agent is introduced to perform multi-dimensional processing loss analysis on the first pool of the extinguishing agent processing control, and a first processing loss analysis map is obtained. By introducing a predetermined scaling factor, and combining the fire extinguishing agent processing loss analysis channel and the first analysis spectrum of processing loss, differential variation optimization is performed on the first pool of fire extinguishing agent processing control to obtain a processing control optimization scheme. Based on the target extinguishing agent formulation, the extinguishing agent processing monitoring feedback correction is performed according to the processing control optimization scheme; This involves introducing a predetermined scaling factor, combining the extinguishing agent processing loss analysis channel and the first processing loss analysis spectrum to perform differential variation optimization on the first extinguishing agent processing control pool, thereby obtaining a processing control optimization scheme, including: Based on the first analysis map of the processing loss, the first extinguishing agent processing control pool is optimized and screened according to the processing loss constraints to obtain the second extinguishing agent processing control pool. Based on the multi-dimensional loss evaluation index of the fire extinguishing agent processing loss analysis channel, a weight allocation is performed to generate a full-process processing loss analysis model; Based on the full-process processing loss analysis model, the current optimization of the second pool for extinguishing agent processing control is performed to determine the baseline processing control scheme; Based on the fire extinguishing agent processing loss analysis channel and the full-process processing loss analysis model, the fire extinguishing agent processing control second pool is differentially mutated and expanded according to the predetermined scaling factor and the benchmark processing control scheme to establish the fire extinguishing agent processing control third pool; Based on the analytical model of the whole process processing loss, the third pool of the fire extinguishing agent processing control is optimized to minimize the whole process processing loss, and the optimization scheme of the processing control is generated. Specifically, based on the extinguishing agent processing loss analysis channel and the full-process processing loss analysis model, the second extinguishing agent processing control pool is differentially mutated and expanded according to the predetermined scaling factor and the benchmark processing control scheme to establish a third extinguishing agent processing control pool, including: Based on the benchmark processing control scheme, the second pool of the extinguishing agent processing control is subjected to difference detection to obtain the control difference vector of each scheme; Based on the predetermined scaling factor, the control differential vectors of each scheme are adjusted to generate multiple differential adjustment vector sets; The second processing control pool of the extinguishing agent is mutated according to the multiple differential adjustment vector sets to obtain the first mutated processing control pool; Based on the fire extinguishing agent processing loss analysis channel, a multi-dimensional processing loss analysis is performed on the first variation pool of processing control to obtain a second processing loss analysis map; Based on the second map of the processing loss analysis, the first variation pool of the processing control is optimized and screened according to the processing loss constraints to obtain the second variation pool of the processing control. The second processing control pool for the extinguishing agent is expanded according to the second variation pool for processing control, thereby generating the third processing control pool for the extinguishing agent.
2. The method for optimizing and controlling the processing of an environmentally friendly and efficient foam fire extinguishing agent as described in claim 1, characterized in that, Based on the performance constraints of the extinguishing agent, the formulation design is carried out to determine the target extinguishing agent formulation, including: Based on the set of fire extinguishing agent performance indicators, the performance history of the fire extinguishing agent formula library is traced to obtain the performance sequence of each formula. Based on the performance constraints of the fire extinguishing agent, deviation detection is performed on the performance sequences of each formulation to obtain multiple performance deviation sequences; Based on the multiple performance deviation sequences, the fire extinguishing agent formulation library is adjusted to obtain an adjusted fire extinguishing agent formulation library; Based on the performance constraints of the extinguishing agent, the formula optimization of the extinguishing agent adjustment formula library is performed to generate the target extinguishing agent formula.
3. The method for optimizing and controlling the processing of an environmentally friendly and efficient foam fire extinguishing agent as described in claim 1, characterized in that, Based on the target fire extinguishing agent formulation, a full-process processing control analysis is performed to establish the first pool for fire extinguishing agent processing control, including: Based on the target fire extinguishing agent formulation, the mixing and homogeneous node control is analyzed to obtain the mixing and homogeneous control group; Based on the target extinguishing agent formulation, analyze the auxiliary agent addition node control to obtain the auxiliary agent addition control group; Based on the target extinguishing agent formulation, the curing node control is analyzed to obtain the curing control group; The first pool for extinguishing agent processing control is generated by combining the mixing and homogenization control group, the additive addition control group, and the maturation control group for the entire process.
4. The method for optimizing and controlling the processing of an environmentally friendly and efficient foam fire extinguishing agent as described in claim 3, characterized in that, Based on the target extinguishing agent formulation, a homogeneous mixing node control analysis is performed to obtain a homogeneous mixing control group, including: Based on the target fire extinguishing agent formulation, a twin formulation search is performed to obtain a twin formulation set; Based on the twin formula set, the history of mixed homogeneous node control is traced back to obtain the mixed homogeneous control sample set; Based on the mixed homogeneous control sample set, control indicators are classified to obtain multiple mixed homogeneous control regions; The multiple mixed homogeneous control regions are filtered according to a predetermined confidence level to obtain multiple mixed homogeneous confidence regions; The mixed homogeneous control group is generated by combining control parameters based on the multiple mixed homogeneous confidence regions.
5. The method for optimizing and controlling the processing of an environmentally friendly and efficient foam fire extinguishing agent as described in claim 1, characterized in that, A processing loss analysis channel for extinguishing agents is introduced to perform multi-dimensional processing loss analysis on the first pool of the extinguishing agent processing control, obtaining a first processing loss analysis map, including: According to the first processing control scheme of the first pool for the extinguishing agent processing control, and according to the first processing control scheme, a full-process simulation is performed to obtain the first simulation data of mixing and homogenization, the first simulation data of additive addition and the first simulation data of maturation; Activate the fire extinguishing agent processing loss analysis channel, which includes a mixing homogeneity loss evaluation model, an additive addition loss evaluation model, and a maturation loss evaluation model; The first simulated data of the mixed homogeneous mass is input into the mixed homogeneous mass loss evaluation model to obtain the first mixed homogeneous mass loss coefficient; The first simulation data of the addition of the adjuvant is input into the adjuvant addition loss evaluation model to obtain the first adjuvant addition loss coefficient; The first simulation data of maturation is input into the maturation loss evaluation model to obtain the first maturation loss coefficient. The first mixing and homogenization loss coefficient and the first additive addition loss coefficient are combined to generate the first processing loss analysis sequence, and the first processing loss analysis sequence is added to the first processing loss analysis map.
6. The method for optimizing and controlling the processing of an environmentally friendly and efficient foam fire extinguishing agent as described in claim 1, characterized in that, Based on the set of fire extinguishing agent performance indicators, user requirements are analyzed, and fire extinguishing agent performance constraints are constructed, including: User requirements are collected based on the set of fire extinguishing agent performance indicators to obtain a performance requirement dataset. Clean the performance requirement dataset to generate the fire extinguishing agent performance constraints.
7. The method for optimizing and controlling the processing of an environmentally friendly and efficient foam fire extinguishing agent as described in claim 1, characterized in that, The set of performance indicators for fire extinguishing agents includes a set of fire extinguishing performance indicators and a set of environmental performance indicators.
8. The method for optimizing and controlling the processing of an environmentally friendly and efficient foam fire extinguishing agent as described in claim 1, characterized in that, The processing loss constraints include the mixing and homogenization loss threshold, the additive addition loss threshold, and the maturation loss threshold.