Catalyst determination method and device and machine readable storage medium

Abstract

The invention discloses a catalyst determination method and device and a machine readable storage medium, and relates to the technical field of weather modification. The method comprises the steps that index values of test indexes of a plurality of different types of catalyst samples are obtained respectively, and the index values are the index values of the catalyst samples tested in a preset strong freezing rain simulation environment; the freezing rain key condition is reproduced through the simulation platform, and the deviation between a traditional laboratory and a real scene is overcome. Determining the weight of each test index based on all index values and a weight distribution algorithm; aiming at each type of catalyst samples, based on a multi-criterion decision algorithm and the index value and the weight of each test index, determining an evaluation score of the catalyst samples; and determining the catalyst sample with the highest evaluation score as a target catalyst. By scientifically and comprehensively evaluating the performance of different types of catalysts, a scientific basis is provided for selection and application of the catalysts, and the freezing rain disaster prevention and control cost is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of artificial weather modification, and in particular to a catalyst determination method, device, and machine-readable storage medium. Background Art

[0002] In the field of meteorological disaster prevention, severe freezing rain, as an extremely destructive weather phenomenon, poses a serious threat to many industries such as transportation, power supply, and communication facilities. When severe freezing rain occurs, supercooled water droplets freeze instantly when they come into contact with the ground or the surface of an object, forming a smooth and hard ice shell. This can easily lead to a series of problems such as slippery and icy roads, ice-covered and dancing power transmission lines, and unbalanced loads on communication towers. These problems can then trigger secondary disasters such as traffic accidents, power outages, and communication paralysis, resulting in huge economic losses and social impacts. To cope with severe freezing rain disasters, artificial intervention technology has become an important research and practical direction. Among them, the use of catalysts to change the microphysical mechanisms in the formation process of freezing rain, thereby inhibiting or reducing the intensity of freezing rain, has been a research topic that has attracted much attention in recent years.

[0003] Currently, a variety of catalysts have been tried for artificial intervention in severe freezing rain, primarily including hygroscopic particles such as calcium chloride, magnesium chloride, and other salts, as well as ice-nucleating substances such as silver iodide. However, in actual artificial intervention in severe freezing rain, the effectiveness of existing catalysts has been less than ideal, with numerous issues requiring urgent resolution, severely restricting their effective application in disaster prevention and mitigation. Summary of the Invention

[0004] In view of the above-mentioned deficiencies in the prior art, the purpose of the embodiments of the present application is to provide a catalyst determination method, device and machine-readable storage medium.

[0005] In order to achieve the above-mentioned object, the first aspect of the present application provides a catalyst determination method, comprising:

[0006] Obtaining index values ​​of test indicators of a plurality of different types of catalyst samples respectively, wherein the index values ​​are index values ​​of the catalyst samples tested under a preset severe freezing rain simulation environment;

[0007] Determine the weight of each test indicator based on all indicator values ​​and weight allocation algorithm;

[0008] For each type of catalyst sample, the evaluation score of the catalyst sample is determined based on the multi-criteria decision-making algorithm and the index value and weight of each test index;

[0009] The catalyst sample with the highest evaluation score is determined as the target catalyst.

[0010] In the embodiment of the present application, the weight allocation algorithm includes the entropy weight method and the hierarchical analysis method, and the weight of each test indicator is determined based on all indicator values ​​and the weight allocation algorithm, including:

[0011] Determine the initial weight of each test indicator based on the entropy weight method and all indicator values;

[0012] Based on the hierarchical analysis method and preset requirements, the initial weights are adjusted to obtain the weights of each test indicator.

[0013] In the embodiment of the present application, for each type of catalyst sample, the evaluation score of the catalyst sample is determined based on the multi-criteria decision-making algorithm and the index value and weight of each test index, including:

[0014] For each test indicator, obtain the optimal value and the worst value of the test indicator from all indicator values;

[0015] For each type of catalyst sample, the index value of each test index is normalized;

[0016] Determine the evaluation value of each test indicator based on the normalized indicator value and weight;

[0017] determining a first distance for each catalyst sample based on the evaluation value and the optimal value of each test indicator;

[0018] determining a second distance for each catalyst based on the evaluation value and the worst value of each test indicator;

[0019] determining a relative proximity of each catalyst sample based on the first distance and the second distance;

[0020] Based on the relative proximity, an evaluation score is determined for each catalyst sample.

[0021] In the embodiment of the present application, the index values ​​of the test indicators of multiple different types of catalyst samples are obtained respectively, including:

[0022] Determining target environmental control parameters based on preset test requirements, wherein the target environmental control parameters include at least one of temperature, humidity, wind speed, supercooled water content, and vertical temperature gradient;

[0023] Construct a preset severe freezing rain simulation environment based on target environmental control parameters;

[0024] The index values ​​of the test indicators of multiple different types of catalyst samples tested in a preset severe freezing rain simulation environment are obtained respectively.

[0025] In the embodiment of the present application, the target environment control parameters are determined based on the preset test requirements, including:

[0026] Determine multiple sets of initial environmental control parameters based on preset test requirements;

[0027] Construct the corresponding initial severe freezing rain simulation environment based on each initial environmental control parameter;

[0028] Obtain the nucleation rate under each initial severe freezing rain simulation environment;

[0029] Based on all nucleation rates and the corresponding initial environmental control parameters, the optimal environmental control parameters are determined using a variance analysis algorithm and / or a multiple regression model as target environmental control parameters.

[0030] In an embodiment of the present application, the catalyst determination method further includes:

[0031] The target environmental control parameters are dynamically adjusted based on the feedback control algorithm so that the preset environmental indicators under the preset severe freezing rain simulation environment meet the preset test requirements, wherein the preset environmental indicators include ice crystal concentration and / or supercooled water consumption rate.

[0032] In the embodiment of the present application, the test indicators include nucleation rate, nucleation rate, catalyst residue and catalyst economic cost, and the index values ​​of the test indicators of multiple different types of catalyst samples are obtained respectively, including:

[0033] Obtain the number of ice crystals generated per unit time as an indicator of nucleation rate;

[0034] Obtaining a first difference as an index value of the nucleation rate, wherein the first difference is the difference between the number of ice crystals generated per unit mass of the catalyst sample and the number of ice crystals generated in a blank catalyst experiment;

[0035] Obtaining a first ratio as an indicator of the amount of catalyst residue, wherein the first ratio is a ratio of the mass of the unreacted catalyst sample after the test to the mass of the catalyst sample before the test;

[0036] The purchase cost of the catalyst sample is obtained as an indicator value of the economic cost of the catalyst.

[0037] In the embodiment of the present application, the multiple different types of catalyst samples include multiple catalyst samples with different catalyst formulations and / or multiple catalyst samples with different catalyst dosages.

[0038] A second aspect of the present application provides a catalyst determination device, comprising:

[0039] a memory configured to store instructions;

[0040] The processor is configured to call instructions from the memory and implement the catalyst determination method as described in the above embodiment when executing the instructions.

[0041] A third aspect of the present application provides a machine-readable storage medium having stored thereon instructions for causing a machine to execute the catalyst determination method as described in the above embodiment.

[0042] Through the above technical solution, the index values ​​of the test indicators of multiple different types of catalyst samples are obtained respectively, wherein the index values ​​are the index values ​​of the catalyst samples tested under a preset strong freezing rain simulation environment; the key conditions of freezing rain are reproduced through the simulation platform to overcome the deviation between traditional laboratories and real scenes. The weight of each test indicator is determined based on all index values ​​and the weight distribution algorithm; for each type of catalyst sample, the evaluation score of the catalyst sample is determined based on the multi-criteria decision-making algorithm and the index values ​​and weights of each test indicator; the catalyst sample with the highest evaluation score is determined as the target catalyst. Through a standardized test environment, objective weight distribution and a comprehensive evaluation algorithm, the performance of different types of catalysts can be evaluated more scientifically and comprehensively, providing a scientific basis for the selection and application of catalysts and reducing the cost of preventing and controlling freezing rain disasters.

[0043] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present application but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0045] Figure 1 The following schematically shows a flow chart of a catalyst determination method according to an embodiment of the present application;

[0046] Figure 2 The schematic diagram shows the structure of a catalyst determination device according to an embodiment of the present application. DETAILED DESCRIPTION

[0047] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0048] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of laws and regulations. In the embodiments of this application, certain software, components, models, and other existing solutions in the industry may be mentioned. These should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use such solutions.

[0049] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0050] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0051] Figure 1 The following schematically shows a flow chart of a catalyst determination method according to an embodiment of the present application. Figure 1 As shown, the embodiment of the present application provides a method for determining a catalyst, which may include the following steps:

[0052] Step 100, respectively obtaining index values ​​of test indicators of a plurality of different types of catalyst samples, wherein the index values ​​are index values ​​of the catalyst samples tested under a preset severe freezing rain simulation environment;

[0053] It should be noted that severe freezing rain, as an extreme phase-disaster weather phenomenon, releases latent heat and complex thermodynamic effects during the supercooled water droplet-ice crystal phase transition process, posing a major threat to transportation, power transmission and distribution, communication infrastructure, and agricultural and forestry production. In order to weaken the chain-destructive effect of such disasters, ice nucleation catalysts represented by silver iodide and dry ice have been widely used in artificial intervention practices. However, limited by the spatiotemporal heterogeneity of multi-scale cloud physical processes in severe freezing rain environments, key indicators such as the nucleation efficiency, diffusion stability, and economy of existing catalysts are difficult to achieve collaborative optimization, resulting in frequent problems such as insufficient ice crystallization rate and lagging supercooled water phase control in actual operation scenarios. In response to the above-mentioned technical bottlenecks, this embodiment proposes a catalyst optimization method based on multi-dimensional performance evaluation. By constructing a dynamic coupling model of freezing rain microphysical processes and catalyst characteristics, the targeted design of catalytic formulations and accurate prediction of intervention effectiveness are achieved.

[0054] It should be noted that in this example, by pre-setting a standardized severe freezing rain simulation environment and conducting experiments on multiple catalysts in this pre-set severe freezing rain simulation environment, key indicator data was obtained to ensure that different catalysts were evaluated under the same conditions, ensuring the comparability of the results. Furthermore, by collecting actual data, a basis was provided for subsequent analysis and decision-making. The index values ​​of these test indicators reflect the performance of the catalysts in the simulated actual use environment.

[0055] Specifically, in one embodiment, the test indicators include nucleation rate, nucleation rate, catalyst residue, and catalyst economic cost, and the index values ​​of the test indicators of multiple different types of catalyst samples are obtained respectively, including:

[0056] Obtain the number of ice crystals generated per unit time as an indicator of nucleation rate;

[0057] Obtaining a first difference as an index value of the nucleation rate, wherein the first difference is the difference between the number of ice crystals generated per unit mass of the catalyst sample and the number of ice crystals generated in a blank catalyst experiment;

[0058] Obtaining a first ratio as an indicator of the amount of catalyst residue, wherein the first ratio is a ratio of the mass of the unreacted catalyst sample after the test to the mass of the catalyst sample before the test;

[0059] The purchase cost of the catalyst sample is obtained as an indicator value of the economic cost of the catalyst.

[0060] It should be noted that the test indicators include nucleation rate, nucleation rate, catalyst residue and catalyst economic cost. The above test indicators can comprehensively and specifically cover the multi-dimensional key performance and cost factors of the catalyst in the application scenario of severe freezing rain simulation environment. The nucleation rate and nucleation rate are directly related to the ability of the catalyst to promote the formation of ice crystals, and are the core indicators for measuring its catalytic activity; the catalyst residue reflects the remaining situation of the catalyst after the reaction, which can reflect the utilization rate of the catalyst and the possible impact on the subsequent process; the economic cost is an important factor that must be considered in practical applications and is related to the overall benefit.

[0061] Specifically, the nucleation rate reflects the kinetic efficiency of a catalyst in triggering the ice phase transition, directly impacting the timeliness of freezing rain interventions. The nucleation rate, measured as the number of ice crystals generated per unit time, intuitively and quantitatively reflects the rate at which a catalyst promotes ice crystal formation, enabling clear comparison of catalytic efficiencies across different catalyst types over the same timeframe. In practical applications, ice nuclei can be monitored in real time using high-speed video or a particle counter. The nucleation rate represents the absolute activity of a catalyst. By calculating the difference between the number of ice crystals generated per unit mass of a catalyst sample and the number of ice crystals generated in a blank catalyst experiment, it cleverly eliminates the influence of non-catalytic factors, such as environmental factors, on ice crystal formation, focusing precisely on the catalyst's inherent role in promoting ice crystal formation, providing a more scientific measure of a catalyst's catalytic performance. Catalyst residue can assess catalyst utilization and environmental risks. High residues can lead to secondary pollution or resource waste. Using the ratio of the mass of the unreacted catalyst sample after testing to the mass of the catalyst sample before testing as an indicator, it accurately reflects the extent of catalyst consumption during the reaction, helps assess catalyst stability and reaction efficiency, and is crucial for optimizing reaction conditions and improving catalyst utilization. The economic cost of the catalyst directly obtains the purchase cost of the catalyst sample as an indicator value, which simply and directly reflects the cost investment of the catalyst at the economic level. It is convenient to comprehensively consider the relationship between the performance and cost of the catalyst in the subsequent evaluation process and screen out catalysts with high cost performance.

[0062] In this example, test indicators are standardized through differential and ratio methods to eliminate systematic errors and enhance data comparability. Comprehensive coverage, from catalytic activity to environmental footprint, aligns with the principles of green chemistry. Furthermore, economic cost indicators are considered, directly linking them to practical deployment feasibility, thus avoiding a disconnect between laboratory performance and industrialization.

[0063] In one embodiment, the plurality of catalyst samples of different types include a plurality of catalyst samples with different catalyst formulations and / or a plurality of catalyst samples with different catalyst dosages.

[0064] In this embodiment, different catalyst formulations mean that the chemical composition and structure of the catalyst are different, which will directly affect its catalytic performance; while different catalyst dosages reflect the impact of changes in the amount of catalyst used in actual operation on the reaction effect. By covering these two situations, it is possible to more comprehensively evaluate the performance of the catalyst under various possible conditions, providing a rich sample base for finding the optimal catalyst. By covering multiple catalyst samples of different types, including catalyst samples with multiple catalyst formulations and / or catalyst samples with multiple catalyst dosages, the complexity and diversity of catalysts in actual applications are fully considered. The diverse sample setting helps to more accurately identify the catalyst most suitable for the severe freezing rain simulation environment. Catalyst samples with different formulations and dosages will exhibit different characteristics in various test indicators. By comprehensively testing and analyzing these samples, it is possible to gain a deep understanding of the relationship between catalyst performance and formulation and dosage, thereby screening out the catalyst with the best overall performance in terms of nucleation rate, nucleation rate, catalyst residue, and economic cost from a large number of samples as the target catalyst, improving the accuracy and reliability of the target catalyst determination. The catalyst formulation can be a single-function catalyst or a multifunctional combined catalyst obtained by combining multiple single-function catalysts. For example, the catalyst formula may include a cold cloud catalyst (AgI, silver iodide), a warm cloud catalyst (KCl, potassium chloride), a refrigerant (LN2, liquid nitrogen), etc. The catalyst formula may be any one of the cold cloud catalyst, the warm cloud catalyst and the refrigerant as a single-function catalyst, or two or more of the cold cloud catalyst, the warm cloud catalyst and the refrigerant may be used as a multifunctional combined catalyst, for example, the catalyst formula is AgI+KCl+LN2.

[0065] Step 200, determining the weight of each test indicator based on all indicator values ​​and a weight distribution algorithm;

[0066] It should be noted that based on all collected indicator values, a weighting algorithm is used to determine the weights of each test indicator. The weights reflect the relative importance of different indicators in the overall evaluation. This step ensures that the subsequent evaluation process comprehensively considers all aspects of performance, rather than focusing solely on a single indicator. Weighting algorithms can include objective and subjective weighting methods. Objective weighting methods include entropy weighting and coefficient of variation methods, while subjective weighting methods include expert scoring and the analytic hierarchy process. Weights for test indicators can be assigned based on actual application needs.

[0067] Step 300: For each type of catalyst sample, determine the evaluation score of the catalyst sample based on the multi-criteria decision-making algorithm and the index value and weight of each test index;

[0068] Step 400: Determine the catalyst sample with the highest evaluation score as the target catalyst.

[0069] It should be noted that for each catalyst sample, a multi-criteria decision-making algorithm is used to calculate an evaluation score, combining the values ​​and weights of each test criterion. This algorithm comprehensively considers multiple indicators to provide a comprehensive evaluation of the catalyst. By comparing the evaluation scores of each catalyst sample, the catalyst with the highest score is selected as the target catalyst. This step concludes the entire process, selecting the catalyst with the best performance under the given conditions through comprehensive evaluation and comparison.

[0070] In this embodiment, the index values ​​of the test indicators of multiple different types of catalyst samples are obtained respectively, wherein the index values ​​are the index values ​​of the catalyst samples tested under a preset strong freezing rain simulation environment; the key conditions of freezing rain are reproduced through the simulation platform to overcome the deviation between traditional laboratories and real scenes. The weight of each test indicator is determined based on all index values ​​and the weight distribution algorithm; for each type of catalyst sample, the evaluation score of the catalyst sample is determined based on the multi-criteria decision-making algorithm and the index values ​​and weights of each test indicator; the catalyst sample with the highest evaluation score is determined as the target catalyst. Through standardized test environment, objective weight distribution and comprehensive evaluation algorithm, the performance of different types of catalysts can be evaluated more scientifically and comprehensively, providing a scientific basis for the selection and application of catalysts and reducing the cost of preventing and controlling freezing rain disasters.

[0071] In one embodiment, the weight allocation algorithm includes an entropy weight method and a hierarchical analysis method, and the weights of the various test indicators are determined based on all indicator values ​​and the weight allocation algorithm, including:

[0072] Determine the initial weight of each test indicator based on the entropy weight method and all indicator values;

[0073] Based on the hierarchical analysis method and preset requirements, the initial weights are adjusted to obtain the weights of each test indicator.

[0074] In the present embodiment, it should be noted that the entropy weight method is a method for determining weights based on the amount of information contained in the data itself. After obtaining the test index values ​​of multiple different types of catalyst samples, there are differences between the index values, and the entropy weight method can calculate the weights based on the degree of dispersion of these index values. The greater the degree of dispersion of the index value, the richer the amount of information provided by the index, and the greater the role played in the evaluation process, and the entropy weight method will give it a larger initial weight. This approach is completely based on the data itself, avoids the interference of subjective factors, and can objectively reflect the importance differences of each test index at the data level. The initial weight obtained by the entropy weight method is based on the objective analysis results of the data, which provides a relatively objective and scientific benchmark for subsequent weight adjustment. Subsequent adjustments are made on this basis, combined with actual needs for optimization, so that the weight distribution takes into account the inherent laws of the data and meets specific application scenarios.

[0075] Furthermore, the AHP breaks down complex problems into multiple levels and factors, constructs a judgment matrix, and determines weights by comparing each factor pairwise based on the decision maker's subjective judgment. In practical applications, catalyst evaluation relies not only on the objective characteristics reflected by the data but also requires consideration of certain pre-determined requirements, such as specific application scenarios requiring higher performance in certain aspects of the catalyst, or the emphasis of companies or research institutions on cost or environmental performance. The AHP can incorporate these subjective factors and pre-determined requirements into the weight allocation process, adjusting the initial weights obtained by the entropy weight method to better align the weight allocation with actual needs.

[0076] In this embodiment, the entropy weight method and the analytic hierarchy process are combined, which not only utilizes the objectivity of the entropy weight method based on data, but also gives play to the flexibility of the analytic hierarchy process in considering subjective needs and practical applications. Through this combination of subjective and objective methods, the objective weights can be reasonably adjusted according to various complex needs in practical applications, so that the final weights can better serve the screening of target catalysts. This lays the foundation for the subsequent calculation of the evaluation scores of catalyst samples based on the multi-criteria decision-making algorithm, thereby improving the accuracy and reliability of determining the target catalyst.

[0077] In one embodiment, for each type of catalyst sample, an evaluation score of the catalyst sample is determined based on a multi-criteria decision-making algorithm and the index values ​​and weights of each test index, including:

[0078] For each test indicator, obtain the optimal value and the worst value of the test indicator from all indicator values;

[0079] For each type of catalyst sample, the index value of each test index is normalized;

[0080] Determine the evaluation value of each test indicator based on the normalized indicator value and weight;

[0081] determining a first distance for each catalyst sample based on the evaluation value and the optimal value of each test indicator;

[0082] determining a second distance for each catalyst based on the evaluation value and the worst value of each test indicator;

[0083] determining a relative proximity of each catalyst sample based on the first distance and the second distance;

[0084] Based on the relative proximity, an evaluation score is determined for each catalyst sample.

[0085] In the present embodiment, it should be noted that the optimal value and the worst value of each index are found from the test index values ​​of all catalyst samples, which provides a clear and unified reference benchmark for subsequent normalization and distance calculation. The optimal value represents the best level that the index can reach in all samples, and the worst value reflects the most unfavorable situation that the index may encounter. Through these two extreme values, the index values ​​of each catalyst sample can be placed in a relatively unified evaluation framework, which is convenient for subsequent comparison and analysis. Different test indices may have different evaluation directions. For example, the larger the value, the better the index such as nucleation speed and nucleation rate. The optimal value is the maximum value, and the worst value is the minimum value. While there may be an expected reasonable range for indices such as catalyst residue and economic cost, a relative optimal value is usually pre-set in multi-criteria decision making, such as the residue is as low as possible, the cost is as low as possible, and a worst case scenario is pre-set. Clarifying the optimal value and the worst value helps to accurately judge the performance of each sample in each index.

[0086] It should be noted that because the units and numerical ranges of different test indicators can vary significantly, directly comparing these indicator values ​​can lead to illogical results. Normalization converts the values ​​of each indicator to a unified numerical range, eliminating the impact of differences in dimensions and numerical ranges. This makes different indicators comparable and lays the foundation for subsequent calculations of evaluation values ​​and distances. Normalized indicator values ​​better highlight the relative position and degree of difference of each sample relative to the optimal and worst values ​​for each indicator, helping to more accurately assess the sample's performance on that indicator.

[0087] It should be noted that the evaluation value for each test metric is calculated based on the normalized metric values ​​and pre-determined weights. The weights reflect the relative importance of each metric in the overall evaluation. Combining the normalized metric values ​​with the weights comprehensively considers the impact of different metrics on catalyst performance, allowing the evaluation value to more comprehensively reflect the sample's overall performance across all metrics. By calculating the evaluation value, the contribution of each metric to catalyst performance is quantified, providing key data support for the subsequent calculation of distance and evaluation scores.

[0088] The first distance, calculated based on the evaluation and optimal values ​​of each test metric, reflects each catalyst sample's distance from its optimal state when all metrics are present. The second distance, calculated based on the evaluation and worst-case values, represents the sample's distance from its worst-case state when all metrics are present. Both distances are Euclidean distances, measuring the sample's position in the evaluation space from different perspectives and providing a basis for determining relative proximity. By calculating these two distances, we can clearly see the degree of deviation of each sample from its optimal and worst-case states, allowing us to further analyze the sample's performance in the overall evaluation.

[0089] The relative proximity is calculated based on the first distance and the second distance, taking into account the relationship between the sample and the optimal state and the worst state. The greater the relative proximity, the closer the sample is to the optimal state, and the better the overall performance in various indicators; the smaller the relative proximity, the closer the sample is to the worst state, and the worse the overall performance. The above method can more scientifically evaluate the relative advantages and disadvantages of each type of catalyst sample in the entire evaluation process. The evaluation score of each catalyst sample is determined based on the relative proximity, and the relative proximity is converted into an intuitive numerical value, which is convenient for sorting and comparing different catalyst samples. The higher the evaluation score, the better the overall performance of the catalyst sample in all test indicators, and the more likely it is to become the target catalyst. Through the above method, the performance of each catalyst sample can be clearly quantified, providing decision makers with a clear basis for selection.

[0090] This example comprehensively considers multiple test indicators and their weightings, uniformly processing and analyzing indicator values ​​across different dimensions and evaluation dimensions to ultimately derive an evaluation score that accurately reflects the overall performance of the catalyst samples. This comprehensive, objective, and scientific assessment of the strengths and weaknesses of each catalyst sample provides a reliable method and basis for selecting the target catalyst most suitable for a specific application scenario.

[0091] In one embodiment, the index values ​​of the test indicators of a plurality of different types of catalyst samples are obtained respectively, including:

[0092] Determining target environmental control parameters based on preset test requirements, wherein the target environmental control parameters include at least one of temperature, humidity, wind speed, supercooled water content, and vertical temperature gradient;

[0093] Construct a preset severe freezing rain simulation environment based on target environmental control parameters;

[0094] The index values ​​of the test indicators of multiple different types of catalyst samples tested in a preset severe freezing rain simulation environment are obtained respectively.

[0095] In this embodiment, it should be noted that the preset test requirements are derived from the simulation requirements of the actual application environment of the catalyst. In the field related to severe freezing rain, environmental factors such as temperature, humidity, wind speed, supercooled water content, and vertical temperature gradient will have a significant impact on the freezing rain formation process and the effect of the catalyst in it. Among them, temperature directly affects the generation and growth rate of ice crystals. The efficiency of the catalyst in promoting ice crystal nucleation may vary greatly in different temperature ranges; humidity determines the water vapor content in the air, which in turn affects the water vapor supply required for ice crystal formation; wind speed may change the diffusion and distribution of the catalyst in the environment, as well as the movement trajectory of droplets during the formation of freezing rain; supercooled water content is one of the key factors in the formation of freezing rain, which determines the amount of liquid water resources in the environment that can be used to form ice crystals; vertical temperature gradient may affect the structure and stability of the freezing rain cloud layer, thereby indirectly affecting the effect of the catalyst. Therefore, determining these parameters as target environmental control parameters based on the preset test requirements can ensure that the simulation environment is closer to reality and make the test results more practical.

[0096] It is understandable that in one embodiment, the temperature, humidity, wind speed, supercooled water content and vertical temperature gradient can be considered simultaneously to perform corresponding parameter settings; or at least one of the temperature, humidity, wind speed, supercooled water content and vertical temperature gradient can be selected as the target environment control parameter, reflecting the targeted screening of key influencing factors. These parameters have a relatively direct and significant impact on the formation of freezing rain and the action of catalysts. By controlling these parameters in accordance with actual needs, a representative severe freezing rain environment can be more effectively simulated, while avoiding the increase in test complexity and cost due to considering too many irrelevant or less influential parameters. The preset test requirements can be the corresponding test requirements proposed with reference to severe freezing rain disaster events that occurred in previous years, combined with the application area and application environment.

[0097] It should be noted that the core purpose of constructing a preset severe freezing rain simulation environment is to create a controllable and repeatable test condition. In the actual natural environment, the occurrence of severe freezing rain is affected by the combined effects of a variety of complex factors, making it difficult to conduct precise control and repeat experiments. By constructing a simulation environment based on the target environment control parameters, key parameters such as temperature, humidity, and wind speed can be accurately adjusted and stably maintained to ensure that each test is carried out under the same conditions, thereby eliminating the interference of natural environmental fluctuations on the test results and improving the accuracy and reliability of the test data. Furthermore, the index values ​​of the test indicators of multiple different types of catalyst samples tested in a preset severe freezing rain simulation environment are obtained, such as nucleation rate, nucleation rate, catalyst residue, and catalyst economic cost, etc., to convert the originally abstract catalyst performance into specific quantitative data. By comparing the values ​​of different samples in various indicators, the performance differences between different types of catalyst samples can be clearly seen, providing direct data support for screening out the catalyst that is most suitable for the target environment.

[0098] In one embodiment, the construction of a preset severe freezing rain simulation environment can be controlled based on an experimental platform. Specifically, the experimental platform can include a cloud and fog climate chamber that integrates a temperature gradient control module, a humidity control module, a wind speed adjustment device, and a supercooled water atomization system for configuring environmental control parameters. The experimental platform can also include an artificial ice nucleus generator that uses aerosol injection technology to support the precise injection of multiple catalysts. It is also equipped with an ice crystal and environmental observation system, including a high-speed camera, a meteorological particle spectrometer, a cloud droplet spectrometer, a fog droplet spectrometer, an ice nucleus particle counter, and a temperature and humidity sensor to capture the ice nucleation and ice crystal growth process in real time.

[0099] In this example, by testing multiple different catalyst samples in a pre-set severe freezing rain simulation environment, we can systematically evaluate the catalyst's performance under specific severe freezing rain conditions. By varying the target environmental control parameters, we can also study the catalyst's adaptability and performance changes under different environmental conditions, providing a scientific basis for catalyst research and development, optimization, and application.

[0100] Specifically, in one embodiment, determining target environment control parameters based on preset test requirements includes:

[0101] Determine multiple sets of initial environmental control parameters based on preset test requirements;

[0102] Construct the corresponding initial severe freezing rain simulation environment based on each initial environmental control parameter;

[0103] Obtain the nucleation rate under each initial severe freezing rain simulation environment;

[0104] Based on all nucleation rates and the corresponding initial environmental control parameters, the optimal environmental control parameters are determined using a variance analysis algorithm and / or a multiple regression model as target environmental control parameters.

[0105] In this embodiment, it should be noted that the preset test requirements usually include a general description of the catalyst application scenario, based on which multiple sets of initial environmental control parameters are determined, which need to cover the range of environmental parameters that may have a significant impact on the performance of the catalyst. For example, in the temperature parameter setting, if severe freezing rain may occur in the temperature range of 5°C to -15°C in actual application, the initial parameter setting will include different temperature points within this range, such as -5°C, -10°C, -15°C, etc., and the range can even be appropriately expanded to explore boundary effects to ensure that temperature conditions that may have a significant impact on the catalyst nucleation rate are not missed. For situations involving multiple environmental control parameters, such as temperature, humidity, wind speed and other parameters, the interaction between the parameters should be comprehensively considered. For example, humidity and temperature are interrelated during the formation of freezing rain, and lower temperatures in high humidity environments may be more conducive to ice crystal formation. Therefore, when determining the initial environmental control parameter combination, methods such as orthogonal experimental design can be used to reasonably combine the parameters at different levels, covering the parameter space as comprehensively as possible to discover the influence of each parameter and its combination on the nucleation rate.

[0106] In the present embodiment, the corresponding initial severe freezing rain simulation environment is constructed based on each initial environmental control parameter, and the core purpose is to simulate the severe freezing rain environment in actual application as realistically as possible under controllable conditions. By accurately adjusting parameters such as temperature, humidity, wind speed, supercooled water content and vertical temperature gradient, physical and chemical conditions similar to those of a natural freezing rain environment are simulated, providing a stable and repeatable experimental platform for catalyst performance testing. It should be noted that the nucleation rate is an important indicator for measuring the ability of a catalyst to promote ice crystal formation in a freezing rain environment, and directly reflects the catalytic activity of the catalyst under specific environmental conditions. Obtaining the nucleation rate under each initial severe freezing rain simulation environment can quantitatively describe the performance of the catalyst under different environmental parameter combinations.

[0107] It should be noted that variance analysis analyzes the significance of the influence of each environmental parameter on the nucleation rate by comparing the variance of the nucleation rate under different combinations of environmental parameters. For example, when studying the influence of temperature, humidity and wind speed on the nucleation rate, variance analysis can determine the contribution of each parameter alone and the interaction between parameters to the variance of the nucleation rate, thereby determining which parameters have a significant effect on the nucleation rate and which parameters have no significant effect. For parameters with no significant effect, their values ​​can be appropriately simplified or fixed in subsequent optimization to reduce experimental variables and improve experimental efficiency. Based on the results of variance analysis, key environmental parameters that have a significant effect on the nucleation rate can be screened out to provide direction for further optimization of environmental control parameters. The multivariate regression model establishes a quantitative model between environmental parameters and catalyst performance by fitting the mathematical relationship between environmental parameters and nucleation rate. The multivariate regression model can intuitively show the direction and degree of influence of each environmental parameter on the nucleation rate, thereby finding the optimal environmental control parameters.

[0108] In the present embodiment, by determining the target environment control parameters, the use of the catalyst in actual applications provides optimal environmental conditions guidance. Based on the target environment control parameters, corresponding catalyst application strategies can also be formulated. For example, in different regions or different seasons, natural environmental conditions may be different. By comparing with the target environment control parameters, the amount of catalyst to be put in, the way of putting in, etc. can be adjusted to adapt to actual environmental conditions, to achieve the maximum utilization of catalyst performance. In addition, the determination of the target environment control parameters provides clear direction for the research and development of catalyst. Research and development personnel can, according to these parameters, specifically design and optimize the formula and structure of catalyst so that it has better performance under the target environment conditions.

[0109] In one embodiment, the catalyst determination method further comprises:

[0110] The target environmental control parameters are dynamically adjusted based on the feedback control algorithm so that the preset environmental indicators under the preset severe freezing rain simulation environment meet the preset test requirements, wherein the preset environmental indicators include ice crystal concentration and / or supercooled water consumption rate.

[0111] In this embodiment, it should be noted that the preset environmental indicators include ice crystal concentration and / or supercooled water consumption rate. According to the preset test requirements, the target value and the allowed fluctuation range of the preset environmental indicators are determined. For example, the target value of ice crystal concentration may be set to 1000 / L, and the allowed fluctuation range is ±100 / L; the target value of supercooled water consumption rate may be set to 5g / (m 3 ·min), the tolerance range is ±0.5g / (m 3·min). These target values ​​and tolerance ranges provide a clear control basis for feedback control. It is understandable that when multiple preset environmental indicators exist at the same time, the control priority of each indicator can be determined based on actual needs. For example, if the impact of ice crystal concentration on the test results is more critical, priority is given to ensuring that the ice crystal concentration is within the target range, and the adjustment of the supercooled water consumption rate is carried out under the premise of meeting the ice crystal concentration requirements. High-precision sensors are deployed in the preset severe freezing rain simulation environment to monitor preset environmental indicators such as ice crystal concentration and supercooled water consumption rate in real time. For example, a laser particle size analyzer is used to measure ice crystal concentration, and the supercooled water consumption rate is monitored by weighing or spectral analysis.

[0112] Understandably, the feedback control algorithm monitors preset environmental indicators in real time within a pre-set severe freezing rain simulation environment, compares the actual measurements with the target values ​​specified in the test requirements, calculates the deviation, and dynamically adjusts the target environmental control parameters, such as temperature, humidity, and wind speed, accordingly. This real-time feedback mechanism ensures that the simulation environment remains ideally aligned with test requirements, preventing test conditions from deviating from expectations due to environmental parameter drift or external interference.

[0113] In this embodiment, by utilizing the adaptive characteristics of the feedback control algorithm, the control strategy can be automatically adjusted according to changes in environmental indicators, so that the simulation environment can flexibly respond to various uncertainties and improve the stability and reliability of the test.

[0114] Figure 2 The structure block diagram of a catalyst determination device according to an embodiment of the present application is schematically shown. Figure 2 As shown, an embodiment of the present application provides a catalyst determination device, which may include:

[0115] Memory 1001, configured to store instructions;

[0116] The processor 1002 is configured to call instructions from the memory 1001 and implement the above-mentioned catalyst determination method when executing the instructions.

[0117] An embodiment of the present application further provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the above-mentioned catalyst determination method.

[0118] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0119] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0120] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0122] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0123] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0124] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0125] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0126] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for determining a catalyst, characterized in that: include: Obtaining index values ​​of test indicators of a plurality of different types of catalyst samples respectively, wherein the index values ​​are index values ​​of the catalyst samples tested under a preset severe freezing rain simulation environment; Determining the weight of each of the test indicators based on all the indicator values ​​and a weight allocation algorithm; For each type of catalyst sample, determining an evaluation score of the catalyst sample based on a multi-criteria decision-making algorithm and the index value and weight of each test index; The catalyst sample with the highest evaluation score is determined as the target catalyst.

2. The catalyst determination method according to claim 1, characterized in that: The weight distribution algorithm includes an entropy weight method and a hierarchical analysis method. The weight of each test indicator is determined based on all the indicator values ​​and the weight distribution algorithm, including: Determining the initial weight of each of the test indicators based on the entropy weight method and all the indicator values; The initial weights are adjusted based on the hierarchical analysis method and preset requirements to obtain the weights of the various test indicators.

3. The catalyst determination method according to claim 1, characterized in that: For each type of catalyst sample, the evaluation score of the catalyst sample is determined based on a multi-criteria decision-making algorithm and the index value and weight of each test index, including: For each of the test indicators, obtaining the optimal value and the worst value of the test indicator from all indicator values; For each type of catalyst sample, the index value of each test index is normalized; Determining the evaluation value of each of the test indicators based on the normalized indicator value and the weight; determining a first distance for each of the catalyst samples based on the evaluation value of each of the test indicators and the optimal value; determining a second distance for each of the catalysts based on the evaluation value of each of the test indicators and the worst value; determining a relative proximity of each of the catalyst samples based on the first distance and the second distance; Based on the relative proximity, evaluation scores are determined for each of the catalyst samples.

4. The catalyst determination method according to claim 1, characterized in that: The step of respectively obtaining the index values ​​of the test indexes of the catalyst samples of different types includes: Determining target environmental control parameters based on preset test requirements, wherein the target environmental control parameters include at least one of temperature, humidity, wind speed, supercooled water content, and vertical temperature gradient; Constructing a preset severe freezing rain simulation environment based on the target environment control parameters; The index values ​​of the test indicators of a plurality of different types of catalyst samples tested under the preset severe freezing rain simulation environment are respectively obtained.

5. The catalyst determination method according to claim 4, characterized in that: Determining target environment control parameters based on preset test requirements includes: Determine multiple sets of initial environmental control parameters based on preset test requirements; Constructing corresponding initial severe freezing rain simulation environments based on each of the initial environmental control parameters; Obtaining the nucleation rate under each of the initial severe freezing rain simulation environments; Based on all nucleation rates and the corresponding initial environmental control parameters, the optimal environmental control parameters are determined using a variance analysis algorithm and / or a multiple regression model as target environmental control parameters.

6. The catalyst determination method according to claim 4, characterized in that: Also includes: The target environmental control parameters are dynamically adjusted based on a feedback control algorithm so that the preset environmental indicators under the preset severe freezing rain simulation environment meet the preset test requirements, wherein the preset environmental indicators include ice crystal concentration and / or supercooled water consumption rate.

7. The catalyst determination method according to claim 1, characterized in that: The test indicators include nucleation speed, nucleation rate, catalyst residue and catalyst economic cost. The index values ​​of the test indicators of multiple different types of catalyst samples are obtained respectively, including: Obtaining the number of ice crystals generated per unit time as an indicator value of the nucleation rate; Obtaining a first difference as an index value of the nucleation rate, wherein the first difference is a difference between the number of ice crystals generated per unit mass of the catalyst sample and the number of ice crystals generated in a blank catalyst experiment; Obtaining a first ratio as an indicator value of the catalyst residue, wherein the first ratio is a ratio of the mass of the unreacted catalyst sample after the test to the mass of the catalyst sample before the test; The purchase cost of the catalyst sample is obtained as an indicator value of the economic cost of the catalyst.

8. The catalyst determination method according to claim 1, characterized in that: The multiple different types of catalyst samples include multiple catalyst samples with different catalyst formulations and / or multiple catalyst samples with different catalyst dosages.

9. A catalyst determination device, characterized in that: include: a memory configured to store instructions; A processor is configured to call the instructions from the memory and implement the catalyst determination method according to any one of claims 1 to 8 when executing the instructions.

10. A machine-readable storage medium, characterized in that The machine-readable storage medium stores instructions for causing a machine to execute the catalyst determination method according to any one of claims 1 to 8.

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