Praseodymium neodymium fluoride purity detection method and device

Abstract

The invention discloses a purity detection method and device for praseodymium neodymium fluoride, and relates to the technical field of physical and chemical analysis, and the method comprises the following steps: carrying out industrial traceability on a praseodymium neodymium fluoride sample to obtain sample characteristic information; k impurity detection technologies are matched according to the information, the detection process is optimized, and M detection task sequences and sample distribution requirements are generated. And detecting the sample according to the distribution requirement to obtain K original detection results. Then, impurity component analysis is carried out on the result, and through cross validation, the purity detection result of the sample is finally obtained in a reverse mode. The technical problems that in an existing praseodymium neodymium fluoride purity detection method, the sample consumption is large, and the credibility of the detection result is low are solved, and the technical effects that the sample consumption needed by praseodymium neodymium fluoride purity detection is reduced, and the credibility of the detection result is improved are achieved.

Description

Technical Field

[0001] The present application relates to the field of physical and chemical analysis technology, and in particular to a purity detection method and device for praseodymium neodymium fluoride. Background Art

[0002] As an important rare earth material, praseodymium-neodymium fluoride is widely used in high-performance lasers, optical devices and other high-tech fields, and its purity and uniformity are extremely high. At present, the purity detection of praseodymium-neodymium fluoride mainly relies on traditional chemical analysis methods and physical testing techniques. These methods usually require a large number of samples for multiple tests, resulting in large sample consumption. In addition, the existing detection methods have certain limitations in the accuracy and reliability of the results, and it is difficult to meet the strict requirements of high-purity praseodymium-neodymium fluoride in high-precision applications. With the development of science and technology and the increasing demand for high-performance materials, there is an urgent need for a purity detection method with high efficiency, low sample consumption and high reliability to improve the quality control level of praseodymium-neodymium fluoride and meet the needs of modern industry for high-purity materials.

[0003] In the current related technologies, there are technical problems in the purity detection method of praseodymium neodymium fluoride, such as large sample consumption and low reliability of the detection results. Summary of the invention

[0004] The present application solves the technical problems of large sample consumption and low reliability of detection results in existing praseodymium neodymium fluoride purity detection methods by providing a purity detection method and device for praseodymium neodymium fluoride.

[0005] The present application provides a purity detection method for praseodymium neodymium fluoride, comprising:

[0006] Conduct industrial traceability on the sample to be tested to obtain sample characteristic information, wherein the sample to be tested is praseodymium neodymium fluoride; match detection technologies according to the sample characteristic information to obtain K impurity detection technologies; optimize the detection process for the K impurity detection technologies to obtain M impurity detection task sequences and M sample allocation requirements, wherein M is a positive integer less than K; after allocating samples for the sample to be tested according to the M sample allocation requirements, schedule the K impurity detection technologies to perform sample testing with the M impurity detection task sequences as constraints to obtain K original test results; perform impurity component analysis on the K original test results to obtain K initial impurity components; cross-validate the K initial impurity components, and reversely output the sample purity test result of the sample to be tested based on the verification result.

[0007] The present application provides a purity detection device for praseodymium-neodymium fluoride, comprising:

[0008] A sample characteristic information acquisition module, the sample characteristic information acquisition module is used to perform industrial traceability on the sample to be detected and obtain sample characteristic information, wherein the sample to be detected is praseodymium neodymium fluoride; a detection technology matching module, the detection technology matching module is used to match the detection technology according to the sample characteristic information and obtain K impurity detection technologies; a detection process optimization module, the detection process optimization module is used to optimize the detection process for the K impurity detection technologies and obtain M impurity detection task sequences and M sample allocation requirements, wherein M is a positive integer less than K; an original detection result acquisition module, the The original test result acquisition module is used to allocate the samples to be tested according to the M sample allocation requirements, and then schedule the K impurity detection technologies to perform sample detection with the M impurity detection task sequences as constraints to obtain K original test results; the impurity component analysis module is used to perform impurity component analysis on the K original test results to obtain K initial impurity components; the cross-validation module is used to perform cross-validation on the K initial impurity components, and reversely output the sample purity test result of the sample to be tested based on the validation result.

[0009] It is intended that the purity detection method and device for praseodymium neodymium fluoride proposed in this application first conduct industrial traceability of praseodymium neodymium fluoride samples to obtain sample characteristic information. K impurity detection technologies are matched according to the information, and the detection process is optimized to generate M detection task sequences and sample allocation requirements. The samples are tested according to the allocation requirements to obtain K original test results. Then, the results are analyzed for impurity components, and through cross-validation, the purity test results of the samples are finally inferred, achieving the technical effect of reducing the sample consumption required for the purity detection of praseodymium neodymium fluoride and improving the credibility of the test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0011] Figure 1 A schematic flow chart of a purity detection method for praseodymium neodymium fluoride provided in an embodiment of the present application;

[0012] Figure 2 A schematic structural diagram of a purity detection device for praseodymium neodymium fluoride provided in an embodiment of the present application;

[0013] Explanation of the reference numerals: sample characteristic information acquisition module 10 , detection technology matching module 20 , detection process optimization module 30 , original detection result acquisition module 40 , impurity component analysis module 50 , cross-validation module 60 . DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0017] The present application provides a method for detecting the purity of praseodymium neodymium fluoride, such as Figure 1 As shown, the method includes:

[0018] Step S100, industrial tracing of the sample to be tested to obtain sample characteristic information, wherein the sample to be tested is praseodymium neodymium fluoride. Specifically, when industrial tracing of the praseodymium neodymium fluoride sample to be tested is first traced back from the raw material supply link to determine the mining site of the rare earth ore, and obtain information such as the chemical composition of the ore and the mining and processing history by communicating with the supplier and consulting the information, and estimate the type and content range of impurities; then deeply analyze the production process, including mineral processing, fluorination reaction, crystallization, separation and purification, etc., to clarify the types, existence forms and distribution states of impurities that may be introduced in each link; analyze the production environment and equipment factors, examine the workshop environmental conditions and equipment materials, usage conditions, cleaning and maintenance records, and judge the potential impact of the environment and equipment on product purity; finally, investigate the post-processing and storage of the product, understand the grinding, screening, packaging and other links and storage containers, environmental conditions and other information, determine whether there are new impurities introduced or the original impurity content changes, so as to obtain comprehensive and accurate sample characteristic information, lay the foundation for subsequent detection processes, and improve the reliability, accuracy, efficiency and economic benefits of detection.

[0019] Step S200, matching detection technologies according to the sample characteristic information to obtain K impurity detection technologies. Specifically, when matching detection technologies according to the sample characteristic information of praseodymium neodymium fluoride to obtain K impurity detection technologies, firstly clarify the types of impurity elements, compound forms and physical and chemical properties that may exist based on the sample characteristic information, and preliminarily screen out corresponding technologies such as atomic absorption spectroscopy, inductively coupled plasma emission spectroscopy, gas chromatography, and high performance liquid chromatography for metal impurities such as iron and silicon and organic impurities. Combined with the material characteristics of the sample such as crystal structure, density, solubility, thermal stability, and optical and electrical properties, the adaptability of the preliminary screening technology is evaluated, and absorption spectrum and emission spectrum analysis technologies that are related to the optical characteristics of the sample and do not destroy the crystal structure are given priority, and thermal analysis technologies that are not suitable for high temperature sensitive samples are excluded, thereby narrowing the scope of technology selection. Finally, the balance between detection accuracy and efficiency is analyzed. For low-content key impurities, high-precision trace analysis technologies such as mass spectrometry are selected. For high-content common impurities, efficient technologies such as X-ray fluorescence spectroscopy are selected. K most suitable impurity detection technologies are comprehensively determined to provide support for subsequent impurity detection processes, meet quality control requirements, and improve the cost-effectiveness and practicality of detection.

[0020] In a possible implementation, detection technology matching is performed according to the sample characteristic information to obtain K impurity detection technologies, and step S200 further includes step S210, interactively obtaining the process flow settings of the sample to be detected, and performing industrial traceability according to the process flow settings to obtain a historical production characteristic set. Specifically, by interacting with the database system, process engineers, and related production records of the production department, detailed process flow setting information of the sample to be detected is obtained, including the specific parameters and operating steps of each link from the procurement, pretreatment, and synthesis reaction conditions of raw materials (such as temperature, pressure, reaction time, reactant ratio, etc.) to subsequent purification, crystallization, processing and molding, and packaging and storage. According to the process flow settings, a comprehensive and in-depth industrial traceability investigation is carried out. Trace the origin and quality characteristics of raw materials, analyze the impurities that may be introduced at different production stages, such as other mineral impurities that may be mixed in during the raw material mining process, residual reactants or by-product impurities generated due to incomplete reactions in the synthesis reaction, and impurities introduced due to contact with equipment, containers or the environment during processing and storage, etc., integrate and summarize various types of information in the historical production process to obtain a comprehensive and detailed set of historical production characteristics.

[0021] Step S220, the historical production characteristic set is aggregated in the impurity dimension to obtain the sample impurity composition, wherein the sample impurity composition includes M types of impurity. Specifically, for the collected historical production characteristic set, information mining and aggregation analysis are performed from the impurity dimension. The different types of impurities that may be generated or introduced in each link of the production process are studied, and classified and summarized according to their chemical composition, element type, and compound properties, and the M types of impurities present in the sample are determined. For example, by analyzing the composition of raw materials and chemical reactions in the production process, the existing metal impurities (such as rare earth element impurities, transition metal impurities, etc.), organic impurities (such as organic solvent residues used in the production process, equipment lubricant pollution, etc.) and inorganic impurities (such as sulfates, chlorides and other by-products generated by the reaction) are identified. The source, content range and existence form of each impurity type in the sample are recorded and sorted in detail to form clear and definite sample impurity composition information.

[0022] Step S230, aggregate the information of material characteristics of the historical production characteristic set, obtain the material characteristics of the sample, and output the impurity composition of the sample and the material characteristics of the sample as the sample characteristic information. Specifically, aggregate the information of the historical production characteristic set from the perspective of material characteristics. Analyze the physicochemical properties of the praseodymium-neodymium fluoride sample, such as optical properties (including properties such as light absorption, emission, refraction, and indicators such as optical uniformity), magnetic and electrical properties (such as parameters such as magnetic susceptibility and resistivity), and other related properties such as crystal structure, density, solubility, etc. Through the analysis of material characteristics, clarify the unique properties of the sample and the potential relationship between these properties and the presence of impurities, for example, some impurities may affect the optical transmittance of the sample or change its magnetic and electrical properties. Integrate the determined sample impurity composition information and the sample material characteristic information obtained by analysis, and output it as complete sample characteristic information. The information will serve as an important basis for subsequent detection technology matching and screening, guide the selection of the impurity detection technology that is most suitable for the sample, and improve the accuracy and effectiveness of the detection.

[0023] Step S240, according to the M types of impurities, network detection technology is called to obtain M groups of alternative detection technologies. Specifically, according to the determined M types of impurities, the detection technology database and the network search function are used to call the detection technology resources that match them to form M groups of alternative detection technologies. For example, for metal impurity types, in view of the characteristics and detection requirements of metal elements, high-precision metal element analysis technologies such as inductively coupled plasma mass spectrometry (ICP-MS) and energy dispersive X-ray spectroscopy (EDS) are called, and the technology can accurately detect the trace or even trace amount of metal impurity content in the sample; for organic impurities, considering the structure and properties of organic compounds, infrared spectroscopy (IR), nuclear magnetic resonance (NMR) and other technologies suitable for organic component analysis are selected, and the characteristic absorption peaks or resonance signals of the molecular structure of organic impurities are detected to achieve qualitative and quantitative analysis of organic impurities; for inorganic impurities, according to their chemical properties and common analysis methods, chemical titration, X-ray diffraction (XRD) and other technologies are selected to determine the type and content of inorganic impurities through chemical reaction measurement or crystal structure analysis. Through precise technology calls based on impurity types, we ensure that suitable detection methods are prepared for each possible impurity, providing a rich technical selection space for subsequent detection technology screening and optimization, and improving the targetedness and reliability of the detection plan.

[0024] Step S250, perform detection response scheduling according to the material characteristics of the sample to obtain a response detection technology set. Specifically, based on the material characteristics of the sample obtained by the previous analysis, the detection response scheduling evaluation is performed on various existing detection technologies. Considering the optical characteristics of the sample, for praseodymium-neodymium fluoride samples with high requirements for optical purity in fields such as laser applications, priority is given to detection technologies that can effectively detect changes in optical properties and have no damage or minimal damage to the optical properties of the sample, such as ultraviolet-visible spectroscopy (UV-Vis) and infrared spectroscopy (IR) and other methods, by analyzing the changes in the light absorption and emission characteristics of the sample within a specific wavelength range, to indirectly detect the impact of impurities on the optical properties, while ensuring that the detection process will not destroy the optical uniformity and crystal structure integrity of the sample; for samples with certain magnetic and electrical properties, combined with the correlation between the characteristics and the presence of impurities, select technologies such as XRD, magnetic susceptibility measurement and resistivity testing, and through the detection and analysis of the crystal structure, magnetic and electrical parameters of the sample, determine whether there are magnetic or conductive impurities that affect its performance, and classify and screen according to the response of the detection technology to the material characteristics of the sample to form a response detection technology set. This technology set includes the most suitable combination of detection technologies based on the material characteristics of the sample. It can more accurately detect impurities related to sample performance, improve the correlation and reliability between the test results and the actual application performance of the sample, and provide more valuable information for evaluating the quality and applicability of the sample.

[0025] Step S260, by aggregating the M groups of alternative detection technologies and the response detection technology set, the K impurity detection technologies are obtained, wherein the K impurity detection technologies have K groups of detection task orientation identifiers. Specifically, the M groups of alternative detection technologies obtained by impurity type screening and the response detection technology set obtained by screening based on sample material characteristics are aggregated and optimized. Comprehensively analyze the advantages, scope of application, detection accuracy, detection speed, destructiveness to samples, and cost-effectiveness of each detection technology, and select the K impurity detection technologies that are most suitable for the praseodymium-neodymium fluoride sample from the technology set. In order to facilitate subsequent detection process management and result analysis, each selected impurity detection technology is given a K group of detection task orientation identifiers, and it is clear that a technology is used to detect several impurities and its role and task focus in the entire detection system. For example, a certain spectral analysis technology may be identified as being used to detect the content of several specific metal impurities and some organic impurities, and its detection results will provide specific data support for subsequent impurity component analysis and purity assessment. Through the aggregation of detection technologies and identification setting, a complete and efficient impurity detection technology system has been constructed to ensure that various impurity components in praseodymium-neodymium fluoride samples can be detected comprehensively, accurately and quickly, effectively solving the technical needs of sample purity detection, while taking into account the economy and practicality of the detection process, providing strong technical support for improving product quality control level and production efficiency.

[0026] Step S300, the detection process of the K impurity detection technologies is optimized to obtain M impurity detection task sequences and M sample allocation requirements, where M is a positive integer less than K. Specifically, key data such as detection time consumption, sample damage degree, detection loss and parallel execution capability of the K impurity detection technologies are collected and analyzed, and then the K impurity detection technologies are grouped according to the sample damage degree according to the preset damage similarity scale to form a damage association detection set, and at the same time, secondary grouping is performed according to the intersection relationship of the parallel execution capability data to obtain a parallel association detection set, and a reasonable detection comprehensive time consumption is preset as a constraint, and an optimization algorithm is used to enumerate and evaluate all possible permutations and combinations of detection technologies to generate a large number of detection task allocation sets, and by analyzing the performance of each allocation set in terms of sample damage, detection loss, resource utilization and overall efficiency and optimizing iteratively for multiple times, M impurity detection task sequences and M sample allocation requirements are determined to achieve the shortest detection time, the minimum sample consumption and the best resource utilization, so as to ensure that the detection work is completed efficiently and accurately, meet actual needs, and improve economic benefits and technical feasibility.

[0027] In a possible implementation, the detection process of the K impurity detection technologies is optimized to obtain M impurity detection task sequences and M sample allocation requirements, where M is a positive integer less than K. Step S300 further includes step S310, interactively obtaining K detection-related information of the K impurity detection technologies, where the detection-related information includes the degree of sample damage, detection loss, detection time consumption, and parallel execution capability. Specifically, by interacting with the control system, technical manual, and past detection experience data of the detection equipment, comprehensive and detailed K detection-related information about the K impurity detection technologies is collected. The information covers the degree of sample damage, that is, the degree of change in the physical structure, chemical composition or properties of the praseodymium neodymium fluoride sample caused by each detection technology during implementation. For example, some detection methods may require grinding, dissolving, high-temperature burning and other operations on the sample, which may cause the integrity of the sample to be damaged to varying degrees; detection loss, including data on the mass of samples consumed during the detection process, the volume of chemical reagents used, the energy consumption of instruments and equipment, and other possible material losses; detection time, clarifying the average time required for each detection technology to complete a target impurity detection, this time is affected by many factors such as the principle of the detection method, the operating speed of the instrument, the complexity of the operation steps, etc.; parallel execution capability, determining whether each detection technology can operate on the same sample at the same time as other detection technologies in the same time period without interfering with each other. For example, some spectral analysis techniques and physical property testing techniques can be carried out simultaneously on the same detection equipment by switching different detection modules or in a shared detection environment without affecting each other's detection results.

[0028] Step S320, extracting the K sample damage degrees from the K detection association information, and performing primary grouping of the K impurity detection technologies according to the preset damage similarity scale and the K sample damage degrees, to obtain a damage association detection set. Specifically, accurately extract the K sample damage degree data from the collected K detection association information. According to the pre-set damage similarity scale, which is determined based on the requirements for sample integrity protection and the accuracy of subsequent test results, the K impurity detection technologies are divided into different groups to form a damage association detection set. For example, if the damage degree can be divided into three levels: mild, moderate and severe, then the detection technologies with similar damage degrees to the samples are grouped together. For a group with a lighter damage degree, some non-destructive detection technologies or detection methods that only have a slight impact on the sample surface, such as optical microscope observation, some spectral analysis technologies, etc., are included; while a group with a heavier damage degree involves detection technologies that require deep processing of the sample, such as chemical digestion, high-temperature melting, and other methods. Through this grouping method, when planning the subsequent testing process, priority can be given to testing technologies that cause less damage to samples, thereby reducing sample loss and property changes caused by the testing process, ensuring the reliability of test results and the repeatability of samples. Grouping based on the degree of sample damage is particularly important for samples that are limited in number, precious, or have high requirements for their original state.

[0029] Step S330, extract K parallel execution capabilities from the K detection association information, and perform secondary grouping on the K impurity detection technologies according to the intersection relationship of the K parallel execution capabilities to obtain a parallel association detection set. Specifically, extract K parallel execution capability data from the K detection association information, and perform secondary grouping on the K impurity detection technologies according to the intersection relationship between the data to obtain a parallel association detection set. Specifically, analyze which other detection technologies each detection technology can be simultaneously detected with without interfering with each other, and combine detection technologies with strong parallel execution capabilities. For example, if technology A and technology B can simultaneously detect samples on the same detection device through reasonable time allocation and resource sharing (such as sharing light sources, detectors and other equipment components, or under stable temperature, humidity and other environmental conditions), then group them together. The grouping method can make full use of detection equipment and time resources, realize the parallel operation of multiple detection tasks, effectively shorten the overall detection time, and improve the detection efficiency. Especially in the face of a large number of samples that need to be detected or the detection timeliness requirements are high, by reasonably arranging parallel detection tasks, the advancement speed of the detection process can be significantly accelerated to meet the needs of actual production and quality control.

[0030] Step S340, preset the comprehensive detection time, and use the comprehensive detection time as a constraint, enumerate the K impurity detection technologies according to the K detection time, and obtain multiple detection task allocation sets. Specifically, a reasonable comprehensive detection time is preset, which is determined after comprehensive consideration of various factors such as the actual production schedule, the urgency of the sample, the available time of the detection resources, and the customer's expected delivery time for the test results. Taking this comprehensive detection time as a constraint, combined with the detection time of each of the K impurity detection technologies, all possible permutations and combinations of detection technologies are enumerated and evaluated using advanced combinatorial optimization algorithms (such as genetic algorithms, simulated annealing algorithms, particle swarm optimization algorithms, etc.). In the enumeration process, a large number of different detection task allocation sets are generated, each of which represents a possible detection process arrangement, which clarifies the execution order of various detection technologies and the allocation method of samples. For example, a detection task allocation set may stipulate that technology C is first used to perform preliminary testing on the sample, then technology D and technology E are used simultaneously (because they belong to the same parallel associated detection set) to perform parallel testing on the sample, and finally technology F is used for final confirmation testing, and the sample amount used in each detection step and the sample allocation plan are determined.

[0031] Step S350, using the multiple detection task allocation sets as solution spaces to optimize the detection process for the K impurity detection technologies, and obtaining the M impurity detection task sequences and M sample allocation requirements. Specifically, for each generated detection task allocation set, further analyze its performance in terms of sample damage degree, detection loss, resource utilization efficiency, and overall detection efficiency, and continuously optimize the detection process combination through multiple iterative calculations and comparisons to find the optimal or near-optimal solution, and finally determine M impurity detection task sequences and M corresponding sample allocation requirements. These determined task sequences and allocation requirements, on the basis of meeting the requirements of detection accuracy and reliability, achieve the minimization of detection time, minimization of sample consumption, and optimal utilization of detection resources, and provide a scientific and reasonable detection process planning for efficiently and accurately completing the impurity detection of praseodymium-neodymium fluoride samples, ensuring that high-quality detection results can be obtained under limited time and resource conditions, meeting the actual needs of product quality control and R&D production, and improving the economic benefits and technical feasibility of the entire detection process.

[0032] In a possible implementation, the multiple detection task allocation sets are used as solution spaces to optimize the detection process of the K impurity detection technologies, and the M impurity detection task sequences and M sample allocation requirements are obtained. Step S350 further includes step S351, extracting H sample detection task combinations from the first detection task allocation set. Specifically, the first detection task allocation set contains a specific combination of multiple detection technologies that have been preliminarily screened and combined in the early stage. The combination is intended to complete the collection of purity detection data of praseodymium-neodymium fluoride samples within the preset detection comprehensive time, and covers different combinations of the K impurity detection technologies previously determined. From this first detection task allocation set, H sample detection task combinations are carefully sorted out and extracted. Each combination represents a unique detection technology implementation sequence and sample processing method, and is the basic unit for subsequent further optimization and analysis. Different combinations may show differences in detection efficiency, sample loss, and result accuracy. Through in-depth research on the combination, the detection process that best suits actual needs can be found.

[0033] Step S352, sort the detection tasks of the H sample detection task combinations according to the destruction association detection set and the parallel association detection set, and obtain H sample parallel execution sequences. Specifically, the detection tasks of the H sample detection task combinations are finely sorted according to the previously established destruction association detection set and parallel association detection set. The destruction association detection set classifies the K impurity detection technologies according to the degree of damage to the sample. In the sorting process, the detection technology with a low degree of damage is prioritized in the front, which can minimize the damage to the sample in the early stage of the detection and ensure the accuracy of subsequent detection and the integrity of the sample, because once the sample is excessively damaged, it will affect the implementation effect of other detection technologies or cause deviations in the detection results. At the same time, the parallel association detection set clarifies which detection technologies can be carried out simultaneously. For the detection tasks that can be executed in parallel, they will be arranged together, so that when executing a group of detection tasks, multiple technologies can act on the same sample simultaneously, making full use of detection resources and time, and maximizing detection efficiency. Through the double sorting principle based on two associated sets, each sample detection task combination is optimized and adjusted, and finally a parallel execution sequence of H samples is obtained. Under the premise of ensuring detection accuracy, the sequence improves the detection speed and resource utilization as much as possible, and reduces unnecessary time and sample consumption.

[0034] Step S353, perform material loss analysis on the H sample parallel execution sequences according to the K detection loss amounts, obtain H detection loss requirements, and obtain the first task loss requirement by adding the H detection loss requirements. Specifically, for the obtained H sample parallel execution sequences, in-depth material loss analysis is performed in combination with the data of the K detection loss amounts. For each sample parallel execution sequence, the loss of various materials involved in the entire detection process is calculated, including the consumption of the sample itself, the amount of chemical reagents used in the detection process, the energy consumption of the equipment, and other possible material losses, so as to obtain H detection loss requirements, each of which represents the total amount of resources required to complete the detection task under the corresponding sequence. Then, the H detection loss requirements are added and calculated to obtain the first task loss requirement, which reflects the overall resource level required to be consumed when performing detection according to different sample detection task combinations and execution sequences under the first detection task allocation set. It is one of the important indicators for evaluating the economy and sustainability of the detection scheme. By analyzing it, the degree of material demand for different detection processes can be intuitively understood, providing key data support for subsequent optimization decisions, which helps to select the detection scheme with the least resource consumption and reduce the detection cost and impact on the environment.

[0035] Step S354, calculate the time consumption of the H sample parallel execution sequence according to the K detection time consumptions, and obtain the execution time consumption of H tasks. Specifically, based on the data of the K detection time consumptions, accurately calculate the time consumption of the H sample parallel execution sequence. Analyze the execution order and parallel situation of each detection technology in the sequence, calculate the time required from the start of detection to the completion of all tasks, and thus obtain the execution time consumption of H tasks.

[0036] Step S355, extract the maximum value of the execution time of the H tasks to obtain the time requirement of the first task. Specifically, in the time-consuming data, the time requirement of the first task is determined by extracting the maximum value. Because in the actual detection process, the completion time of the entire detection process depends on the link that takes the longest time, that is, the bottleneck link. By paying attention to the maximum value, it can be clearly understood that under this detection task allocation set, no matter what sample detection task combination is used, the shortest time requirement that must be met is of great significance for the reasonable arrangement of detection progress, coordination of resource allocation, and connection with other production links. At the same time, it also provides a key time dimension indicator for subsequent comparison and optimization with other detection task allocation sets, which helps to screen out detection processes that meet the detection accuracy requirements and can be completed in the shortest time.

[0037] Step S356, preset a weight configuration combination, use the weight configuration combination to conduct a comprehensive evaluation of the first task loss requirement and the first task time requirement, and output the first detection fitness. Specifically, a weight configuration combination is pre-set, and the combination is determined according to the actual needs and key focus factors of the detection project. For example, if the current detection task pays more attention to the detection speed, then in the weight configuration, the weight of the detection time may be relatively high; if the protection of the sample and resource conservation are more critical, then the weight of the detection loss requirement will increase accordingly. Using the weight configuration combination, the first task loss requirement and the first task time requirement are comprehensively evaluated, and the two indicators are unified into a comprehensive value, i.e., the first detection fitness, through mathematical methods such as weighted summation. The detection fitness reflects the comprehensive performance of different sample detection task combinations in terms of detection efficiency, resource consumption and time cost under the first detection task allocation set. The higher the value, the more the combination meets the overall detection goals and requirements, the more adaptable and superior it is, and provides a quantitative evaluation standard for subsequent screening and decision-making in many detection task allocation sets, making the optimization process of the detection process more scientific, objective and efficient.

[0038] Step S357, and so on, perform detection adaptability analysis on the multiple detection task allocation sets to obtain multiple detection fitnesses. Specifically, according to the above steps, the same detection adaptability analysis is performed on the multiple detection task allocation sets in sequence, that is, a series of operations such as extracting sample detection task combinations, sorting based on association sets, calculating losses and time requirements, and comprehensive evaluation are repeated to calculate a corresponding detection fitness for each detection task allocation set.

[0039] Step S358, serialize the multiple detection fitnesses to locate the M impurity detection task sequences and the M sample allocation requirements. Specifically, the multiple detection fitnesses are serialized in a certain order, such as arranging from high to low or from low to high. By analyzing and comparing the serialized detection fitnesses, the differences in the pros and cons of the various detection task allocation sets can be clearly seen, thereby accurately locating the M impurity detection task sequences with the best comprehensive performance and the corresponding M sample allocation requirements. These determined task sequences and allocation schemes optimize the detection process based on multiple key factors such as the degree of detection damage, the parallelism of detection technology, sample loss, and the total detection time. The most accurate and reliable detection results can be obtained with minimal sample consumption and time cost, providing a set of efficient and feasible operating solutions for the purity detection of praseodymium neodymium fluoride samples.

[0040] In a possible implementation, the H sample detection task combinations are sorted according to the damage association detection set and the parallel association detection set to obtain H sample parallel execution sequences, and step S352 further includes step S3521, which initializes the detection sorting of the H sample detection task combinations according to the damage degree of the K samples to obtain H sample detection execution sequences. Specifically, the data of the damage degree of the K samples are analyzed, and these data describe in detail the degree of damage caused to the praseodymium-neodymium fluoride sample by each impurity detection technology. For the H sample detection task combinations, the detection sorting initialization operation is started based on the principle that the smaller the damage degree, the earlier the detection is performed. For example, if one of the sample detection task combinations contains three detection technologies: the sample damage degree of technology A is low, the damage degree of technology B is moderate, and the damage degree of technology C is high, then in the initial sorting of this combination, technology A is ranked first, technology B is ranked second, and technology C is ranked last. By performing such sorting operations on the H sample detection task combinations one by one, according to the sample damage degree of different detection technologies, their execution order is reasonably arranged, thereby obtaining H sample detection execution sequences. The purpose of this is to minimize damage to samples in the early stages of the testing process. Once the samples are significantly damaged in the early stages, it may affect the accuracy and reliability of subsequent testing technologies, and may even cause some tests to fail to proceed normally. Therefore, giving priority to less destructive testing technologies can protect the integrity and original characteristics of the samples to the greatest extent.

[0041] Step S3522, using the destruction association detection set to traverse the H sample detection execution sequences to perform destruction parallel task sorting, and obtain H sample destruction execution sequences. Specifically, after obtaining the H sample detection execution sequences, the destruction association detection set is introduced to further optimize the sorting. The destruction association detection set is a set that groups K impurity detection technologies according to the similarity of the sample destruction degree. Use this set to traverse the H sample detection execution sequences, find detection tasks that can be executed in parallel and have similar destruction degrees in each sequence, and reorder them. For example, if in a sample detection execution sequence, technology D and technology E are originally arranged in sequence according to the degree of destruction, but after analysis, it is found that they belong to the same group in the destruction association detection set, that is, their sample destruction degrees are similar and can be executed in parallel, then they are adjusted to a parallel order so that they detect the samples in the same time period. Through such traversal and adjustment, for each sample detection execution sequence, the execution order of detection tasks with similar destruction degrees can be arranged more reasonably, thereby obtaining H sample destruction execution sequences. This optimized sorting method not only takes into account the degree of damage of a single detection technology, but also makes full use of the parallelism between detection technologies and the correlation between the degrees of damage, further improving the detection efficiency while reducing the risk of excessive sample damage caused by unreasonable detection order.

[0042] Step S3523, use the parallel association detection set to traverse the H sample destruction execution sequences to perform detection parallel task decomposition, and obtain H sample parallel execution sequences. Specifically, use the parallel association detection set to perform detection parallel task decomposition operations on the H sample destruction execution sequences to obtain H sample parallel execution sequences. The parallel association detection set clarifies which impurity detection technologies can be detected simultaneously without interfering with each other, and has a strong parallel execution capability. Traverse each sample destruction execution sequence, find out the combination of detection tasks that can be executed in parallel according to the parallel association detection set, and decompose them into parallel tasks that can be performed simultaneously. For example, in a sample destruction execution sequence, technology F and technology G are determined as detection technologies that can be executed in parallel, then the two tasks that were originally executed sequentially are decomposed into parallel tasks that are performed simultaneously, so that they can perform detection operations on the same sample at the same time point, making full use of detection resources and time, and improving detection efficiency. By performing such parallel task decomposition on H sample destruction execution sequences one by one, H sample parallel execution sequences are finally obtained. These sequences realize the optimal configuration of the detection process based on considering the degree of sample destruction and the parallelism of detection technology, and can complete the comprehensive detection of samples in the shortest time, while minimizing sample loss and detection costs. This provides a set of efficient, reliable and economical detection solutions for impurity detection of praseodymium-neodymium fluoride samples, meets the needs of rapid and accurate detection of product quality in actual production processes, and has important practical application value and technical guidance significance.

[0043] Step S400, after the samples to be tested are distributed according to the M sample distribution requirements, the K impurity detection technologies are scheduled to perform sample testing based on the M impurity detection task sequences as constraints to obtain K original test results. Specifically, based on the M sample distribution requirements, high-precision segmentation equipment, precise weighing tools and professional pretreatment methods are used to accurately distribute the praseodymium neodymium fluoride samples to be tested, ensuring that each sample meets the requirements of the corresponding detection technology for quantity, state and characteristics, laying the foundation for subsequent testing. Based on the M impurity detection task sequences as constraints, K impurity detection technologies are strictly scheduled in sequence to carry out testing work, and the testing environment, equipment parameters, reagent purity dosage, sample placement and processing and other links are accurately controlled. The operator closely monitors and records the data and phenomena in detail to ensure smooth testing. After rigorous testing, each technology produces a test result, and a total of K original test results are obtained. They are then reviewed and sorted, anomalies are checked, the root causes of the problems are traced and corrected, to ensure that the data is true and reliable, and to lay a solid foundation for subsequent impurity analysis, cross-validation and purity calculation, so as to achieve high-precision detection of the purity of praseodymium neodymium fluoride samples to meet actual needs.

[0044] Step S500, perform impurity component analysis on the K original test results to obtain K initial impurity components. Specifically, collect K original test results generated by K impurity detection technologies, integrate and preprocess them, unify the data format, unit and precision, correct abnormal values, and ensure that the data is accurate and consistent. Then, according to the characteristics of each detection technology, such as spectrum, chromatography, mass spectrometry, etc., preliminarily analyze the original test results, determine the possible impurity elements, compounds and their approximate content range, and construct the impurity component profile. Then, use the data fusion algorithm to perform correlation analysis on the preliminary analysis results of different detection technologies, integrate the advantages of various technologies, improve the impurity component information, and refine the profile. Finally, compare and match with various impurity standard databases, determine the type and content of impurities by quantitative analysis methods, and obtain K initial impurity components, which are used as the key basis for subsequent cross-validation and purity calculation, meet the quality control requirements of praseodymium neodymium fluoride samples, and have important application value.

[0045] In a possible implementation, the K original test results are analyzed for impurity components to obtain K initial impurity components, and step S500 further includes step S510, calling sample test data according to the K group test task guide identifier to obtain K group sample test result sets and K group sample impurity component sets. Specifically, the K group test task guide identifiers are interpreted, and the identifiers clearly define the scope of impurity detection tasks for each impurity detection technology, that is, one technology is used to detect several impurities. According to the identifier, the sample test data related to each test task is accurately screened and called from a large amount of previously stored test data. For example, if a group of test task guide identifiers indicates that the technology is mainly used to detect several specific metal impurities, then all data obtained by using the technology to detect samples containing metal impurities are extracted from the database, including changes in light signal intensity, electrical signal response, changes in the mass of the substance, and other physical or chemical parameter changes related to the test. Through targeted data calling methods, an accurate and targeted data foundation is established for subsequent analysis, and finally K group sample test result sets and K group sample impurity component sets are obtained. Each set of sample test results contains various original test data generated by the corresponding test technology, while the sample impurity component set is the possible impurity component information related to the test technology determined in the previous preliminary analysis. The data will serve as the key material for building the impurity component analysis model to ensure that the model can accurately reflect the impurity conditions and test characteristics involved in each test technology.

[0046] Step S520, the K groups of sample test result sets and the K groups of sample impurity component sets are used as training data to construct K groups of impurity component analysis branches. Specifically, the obtained K groups of sample test result sets and the K groups of sample impurity component sets are used as training data, and machine learning and data analysis algorithms are used to construct K groups of impurity component analysis branches. For each set of data, a suitable algorithm is selected, such as linear regression, decision tree, support vector machine (SVM) or neural network, etc., and model training is performed according to the intrinsic relationship between the data features in the sample test result set and the known impurity components in the sample impurity component set. Taking spectral analysis technology as an example, if it is found in the sample test result set that the light absorption intensity change within a certain wavelength range is correlated with the content of a specific impurity, then when constructing the corresponding impurity component analysis branch, the light absorption intensity data is used as the input feature, and the corresponding impurity content is used as the output target. Through the training of a large amount of sample data, the model can learn the complex mapping relationship between this input and output, so that the impurity components and contents can be accurately predicted according to the new spectral detection data. By performing such a model training process on K groups of data respectively, K groups of impurity component analysis branches for different detection technologies and impurity types are obtained, and each branch has the ability to perform in-depth analysis on specific detection data to determine the impurity components.

[0047] Step S530, the K groups of impurity component analysis branches are connected in parallel within the group, and after obtaining K impurity component analysis models, the construction of the impurity component analyzer is completed by connecting the K impurity component analysis models in parallel. Specifically, after obtaining the K groups of impurity component analysis branches, each branch in each group is connected in parallel. Because the analysis branches in each group are constructed based on different sample data and detection technologies, and are intended to analyze and determine the impurity components within a specific range, their analysis capabilities can be integrated through parallel connection to improve the accuracy and reliability of the analysis. For example, in a group of analysis branches for metal impurity detection, there are analysis branches based on characteristic spectra of different metal elements. After being connected in parallel, when new detection data is input, the branches can analyze the data at the same time and combine their results, so as to more comprehensively and accurately determine the types and contents of metal impurities in the sample. After the parallel operation within the group, K impurity component analysis models are obtained, and each model represents the comprehensive analysis capability of a certain class or several classes of impurities. The K impurity component analysis models are connected in parallel again to form a complete impurity component analyzer.

[0048] Step S540, the K original test results are loaded into the impurity component analyzer, and the K impurity component analysis models of the impurity component analyzer perform parallel analysis of impurity components, and output the K initial impurity components. Specifically, the K original test results obtained previously are loaded into the constructed impurity component analyzer, and the parallel analysis process of the analyzer is started. The K impurity component analysis models analyze the original test results corresponding to each other at the same time. Each model extracts characteristic information related to the impurity components from the original test results according to its pre-trained algorithm and parameters, and uses the information to predict and determine the types and contents of impurities that may exist in the sample. For example, for an original test result based on chromatography technology, the corresponding impurity component analysis model will analyze the retention time, peak area, peak shape and other characteristics of the chromatographic peak, and determine whether there are specific organic impurities in the sample and their content in combination with the relationship between impurities and chromatographic characteristics established in the training data. Through the parallel analysis method, the advantages of different detection technologies and analysis models are fully utilized, and the impurity situation in the sample is quickly and accurately evaluated comprehensively, and finally K initial impurity components are output.

[0049] Step S600, by cross-validating the K initial impurity components, and inversely outputting the sample purity test results of the sample to be tested based on the validation results. Specifically, for the K initial impurity components, the Euclidean distance, Pearson correlation coefficient and other algorithms are used to calculate the degree of difference from the dimensions of element content ratio, impurity distribution characteristics, etc., and a K×K similarity matrix is ​​constructed to intuitively display the relationship between the data of different detection groups, providing a basis for subsequent steps. Then, the credibility of each detection group is evaluated based on the similarity between each detection group and other groups in the matrix, and a threshold is set. The data above the threshold is high-credibility data, and the data below the threshold is low-credibility data. This process is completely based on the objective similarity of the data, and is not affected by the importance of impurities, ensuring that reliable data is screened out. Then, the low-credibility data is cross-validated and corrected using high-credibility data, the causes of the differences are analyzed, deviations caused by instrument errors, operational errors, etc. are corrected, abnormal data caused by sample contamination or unexpected reactions are identified and excluded, and the overall quality of the data is improved. Finally, assuming that the ideal purity of the sample is 100% (or based on actual conversion), the contents of the K initial impurity components after cross-validation and correction are added together, and the total amount of impurities is subtracted from 100%, and the sample purity test result is reversed. This result is based on rigorous mathematical logic and reliable data, can solve existing detection problems, meet the quality control needs of high-purity materials, provide guarantees for scientific research, production and testing, improve detection efficiency and quality, reduce cost risks, and has important application value and theoretical significance.

[0050] In a possible implementation, by cross-validating the K initial impurity components and outputting the sample purity test result of the sample to be tested based on the validation result, step S600 further includes step S610, by performing similarity measurement on the K initial impurity components to construct an impurity adjacency matrix. Specifically, similarity measurement work is carried out for the K initial impurity components to construct an impurity adjacency matrix. Similar to constructing a relationship matrix of a graph in graph theory, each initial impurity component is regarded as a node. The similarity is measured from multiple dimensions by considering the impurity characteristics, such as chemical composition, accurately analyzing the type, content and proportion of elements, and converting them into similarity values ​​using chemometric algorithms; at the chemical structure level, the chemical bond architecture, functional group layout and spatial conformation characteristics are deeply analyzed, and the degree of similarity is quantified by the molecular structure comparison algorithm; in the physical property dimension, the weight of the influence of attribute differences such as density, melting point, solubility, etc. on the similarity is comprehensively evaluated, and the similarity is calculated by multivariate statistical methods. The impurity adjacency matrix constructed in this way uses matrix elements to accurately characterize the close correlation between each impurity component, laying a solid foundation for subsequent analysis. It is like drawing a blueprint for the relationship network of impurity components. Under the framework of graph theory, it provides key architectural support for mining deep-level impurity information, and determines the direction for accurately determining the sample purity detection method, avoiding blind detection that leads to excessive sample loss.

[0051] Step S620, construct an impurity degree matrix based on the impurity adjacency matrix. Specifically, construct an impurity degree matrix based on the constructed impurity adjacency matrix. This link is just like mining the node influence level in the network structure of the graph, using graph theory core algorithms such as PageRank and HITS algorithm variants to deeply analyze the criticality of impurities in the entire system. If an impurity is strongly associated with many impurities in the adjacency matrix, serves as a key hub for connection, or is in a core structural position, its impurity value will be significantly improved, directly affecting the sample purity assessment results. The matrix provides an accurate basis for the subsequent scientific and reasonable configuration of detection confidence weights, guides detection resources to accurately focus on key impurity detection processes, effectively avoids consuming too many resources and samples due to the detection of minor impurities, effectively ensures the accurate and efficient advancement of purity detection, and improves the scientificity and economy of the detection strategy.

[0052] Step S630, based on the impurity type, the K initial impurity components are aggregated to obtain the real-time impurity content of M groups of the M impurity types. Specifically, the K initial impurity components are aggregated according to the impurity chemical category attributes, so as to obtain the real-time impurity content of M groups of M impurity types. Based on the unique chemical identification clustering in the node clustering of the graph, metal impurities are merged according to the periodic table family, oxidation state and other characteristics, and organic impurities are clustered and integrated according to the carbon skeleton structure, functional group category and reaction activity. When calculating each group of real-time content, the concentration distribution uniformity of each impurity in the initial detection result, the sensitivity adaptability of the detection technology and the representativeness of the sample volume are analyzed comprehensively. With the accurate weighted average or statistical distribution model, the real content of each group of impurities is accurately determined, and the accurate data cornerstone is presented for the subsequent accurate configuration of weights and rigorous calculation of sample purity. The scale and characteristics of the node groups of different categories in the graph are adjusted, and the subsequent complex graph structure in-depth analysis and function precision quantification are firmly supported to ensure that the impurity content statistics are accurate and reliable, and prevent the misleading detection method selection and purity evaluation due to inaccurate content, and avoid the loss of additional samples in the wrong detection process.

[0053] Step S640, based on the impurity matrix, the detection confidence weights of the M groups of real-time impurity content are configured to obtain the M groups of detection trust weights. Specifically, based on the constructed impurity matrix, the detection confidence weights are configured for the M groups of real-time impurity content. Based on the allocation of decision weights according to the criticality of factors in the risk decision network, the impurity type with high impurity is given a higher weight because it deeply dominates the purity fluctuation and quality pattern of the sample, and vice versa, the weight is moderately lowered. Multi-criteria decision analysis methods can be flexibly used, such as constructing a hierarchical hierarchical architecture, layering and analyzing key decision criteria such as impurity, stability of detection methods, and potential impact on product application performance, and comparing them two by two, and deriving accurate weights through rigorous calculation; or enabling a comprehensive evaluation system based on fuzzy logic, designing a membership function to reflect the mapping relationship between the gradual change of impurity characteristics and weights, and handling detection uncertainty. The weight allocation paradigm ensures that the subsequent weighted calculation link accurately weighs the contribution of each impurity information, avoids the purity assessment deviation and sample loss risk caused by unreliable impurity data, optimizes the detection resource allocation strategy, and improves the authority and credibility of the detection results.

[0054] Step S650, using the M groups of detection credible weights to map and weightedly calculate the real-time impurity content of the M groups, and obtain M credible impurity contents. Specifically, the determined M groups of detection credible weights are mapped and weightedly calculated on the real-time impurity content of the M groups, which is to accurately purify the impurity information and is conducive to approaching the real impurity level. Through weight multiplication and summation integration, the gradient of impurity importance difference and the reliability level of detection data are deeply analyzed. For example, in the detection of key electronic materials, even if the content of the high-weighted trace metal impurity group is low, it highlights the key influence in the comprehensive impurity content after weighted calculation; and even if the content of the low-weighted trace organic impurity group fluctuates, the overall impact is accurately controllable after weighting. This calculation result is based on the actual content level of sample impurities, which helps to provide a data basis for reverse inference of sample purity, ensures that the basis for reverse inference of purity calculation is as solid as a rock, firmly supports the subsequent accurate sample purity determination, prevents erroneous data from causing repeated detection processes and unreasonable loss of samples, and improves detection efficiency and quality.

[0055] Step S660, reversely output the sample purity test result based on the M credible impurity contents. Specifically, reversely output the sample purity test result based on the obtained M credible impurity contents. Taking the ideal purity of the sample as 100% as the benchmark, strictly deduct the sum of the M credible impurity contents to accurately determine the sample purity value. Effectively avoid the traditional detection method that reduces the credibility of the purity result due to inaccurate or one-sided impurity detection. By optimizing the impurity analysis strategy in each link of the detection process, the sample loss in the invalid detection path is reduced, thereby reducing sample consumption and improving detection accuracy.

[0056] In the above, refer to Figure 1 The purity detection method for praseodymium neodymium fluoride according to an embodiment of the present invention is described in detail. Figure 2 A purity detection device for praseodymium neodymium fluoride according to an embodiment of the present invention is described.

[0057] The purity detection device for praseodymium-neodymium fluoride according to the embodiment of the present invention is used to solve the technical problems of large sample consumption and low reliability of detection results in the existing praseodymium-neodymium fluoride purity detection method, thereby achieving the technical effect of reducing the sample consumption required for praseodymium-neodymium fluoride purity detection and improving the reliability of the detection results. The purity detection device for praseodymium-neodymium fluoride includes: a sample characteristic information acquisition module 10, a detection technology matching module 20, a detection process optimization module 30, an original detection result acquisition module 40, an impurity component analysis module 50, and a cross-validation module 60.

[0058] The sample characteristic information acquisition module 10 is used to perform industrial traceability on the sample to be detected and obtain the sample characteristic information, wherein the sample to be detected is praseodymium neodymium fluoride.

[0059] The detection technology matching module 20 is used to match detection technologies according to the sample characteristic information to obtain K impurity detection technologies.

[0060] The detection process optimization module 30 is used to optimize the detection process of the K impurity detection technologies to obtain M impurity detection task sequences and M sample allocation requirements, where M is a positive integer less than K.

[0061] The original detection result acquisition module 40 is used to allocate the samples to be detected according to the M sample allocation requirements, and then schedule the K impurity detection technologies to perform sample detection based on the M impurity detection task sequences as constraints to obtain K original detection results.

[0062] The impurity component analysis module 50 is used to perform impurity component analysis on the K original detection results to obtain K initial impurity components.

[0063] The cross-validation module 60 is used to perform cross-validation on the K initial impurity components, and reversely output the sample purity test result of the sample to be tested based on the validation result.

[0064] The specific configuration of the detection technology matching module 20 will be described in detail below. As described above, detection technology matching is performed according to the sample characteristic information to obtain K types of impurity detection technologies. The detection technology matching module 20 further includes: a process flow setting unit, the process flow setting unit is used to interactively obtain the process flow setting of the sample to be detected, and perform industrial traceability according to the process flow setting to obtain a historical production characteristic set; a sample impurity composition acquisition unit, the sample impurity composition acquisition unit is used to aggregate information of the impurity dimension of the historical production characteristic set to obtain the sample impurity composition, wherein the sample impurity composition includes M types of impurities; a sample material feature acquisition unit, the sample material feature acquisition unit is used to perform material feature acquisition on the historical production characteristic set to obtain sample material characteristics, and output the sample impurity composition and sample material characteristics as the sample characteristic information; an online detection technology calling unit, the online detection technology calling unit is used to call the online detection technology according to the M impurity types, and obtain M groups of alternative detection technologies; a detection response scheduling unit, the detection response scheduling unit is used to perform detection response scheduling according to the sample material characteristics, and obtain a response detection technology set; an impurity detection technology acquisition unit, the impurity detection technology acquisition unit is used to obtain the K impurity detection technologies by aggregating the M groups of alternative detection technologies and the response detection technology set, wherein the K impurity detection technologies have K groups of detection task orientation identifiers.

[0065] The specific configuration of the detection process optimization module 30 will be described in detail below. As described above, the detection process optimization is performed on the K impurity detection technologies to obtain M impurity detection task sequences and M sample allocation requirements, where M is a positive integer less than K. The detection process optimization module 30 further includes: a detection association information acquisition unit, which is used to interactively obtain K detection association information of the K impurity detection technologies, where the detection association information includes sample damage degree, detection loss, detection time consumption and parallel execution capability;

[0066] A sample damage degree extraction unit, the sample damage degree extraction unit is used to extract K sample damage degrees from the K detection association information, and to perform a primary grouping of the K impurity detection technologies according to a preset damage similarity scale and the K sample damage degrees to obtain a damage association detection set;

[0067] A parallel association detection set acquisition unit, the parallel association detection set acquisition unit is used to extract K parallel execution capabilities from the K detection association information, and perform secondary grouping on the K impurity detection technologies according to the intersection relationship of the K parallel execution capabilities to obtain a parallel association detection set;

[0068] A permutation and combination enumeration unit, the permutation and combination enumeration unit is used to preset a comprehensive detection time consumption, and use the comprehensive detection time consumption as a constraint, perform permutation and combination enumeration on the K impurity detection technologies according to the K detection time consumptions, and obtain multiple detection task allocation sets;

[0069] A detection process optimization unit is used to optimize the detection process of the K impurity detection technologies by using the multiple detection task allocation sets as solution spaces to obtain the M impurity detection task sequences and M sample allocation requirements.

[0070] Among them, the detection process optimization unit further includes: a sample detection task combination acquisition subunit, the sample detection task combination acquisition subunit is used to extract H sample detection task combinations from the first detection task allocation set; a detection task sorting subunit, the detection task sorting subunit is used to sort the detection tasks of the H sample detection task combinations according to the destruction association detection set and the parallel association detection set, and obtain H sample parallel execution sequences; a material loss analysis subunit, the material loss analysis subunit is used to perform material loss analysis on the H sample parallel execution sequences according to K detection loss amounts, obtain H detection loss requirements, and obtain the first task loss requirement by adding the H detection loss requirements; a time consumption calculation subunit, the time consumption calculation subunit is used to perform the H sample according to the K detection time consumption. The time consumption of this parallel execution sequence is calculated to obtain the execution time consumption of H tasks; a maximum value extraction subunit, the maximum value extraction subunit is used to extract the maximum value of the execution time consumption of the H tasks to obtain the time consumption requirement of the first task; a first detection fitness output subunit, the first detection fitness output subunit is used to preset a weight configuration combination, use the weight configuration combination to perform a comprehensive evaluation of the first task loss requirement and the first task time consumption requirement, and output the first detection fitness; a detection fitness analysis subunit, the detection fitness analysis subunit is used to perform detection fitness analysis on the multiple detection task allocation sets by analogy to obtain multiple detection fitnesses; a data positioning subunit, the data positioning subunit is used to serialize the multiple detection fitnesses to locate the M impurity detection task sequences and the M sample allocation requirements.

[0071] Among them, the detection task sorting subunit further includes: a detection sorting initialization micro-unit, which is used to initialize the detection sorting of the H sample detection task combinations according to the degree of destruction of the K samples, and obtain H sample detection execution sequences; a destruction parallel task sorting micro-unit, which is used to use the destruction associated detection set to traverse the H sample detection execution sequences to perform destruction parallel task sorting, and obtain H sample destruction execution sequences; a detection parallel task decomposition micro-unit, which is used to use the parallel associated detection set to traverse the H sample destruction execution sequences to perform detection parallel task decomposition, and obtain H sample parallel execution sequences.

[0072] The specific configuration of the impurity component analysis module 50 will be described in detail below. As described above, the impurity component analysis is performed on the K original detection results to obtain K initial impurity components. The impurity component analysis module 50 further includes: a sample detection data calling unit, the sample detection data calling unit is used to call the sample detection data according to the K group detection task guidance identifier to obtain K groups of sample detection result sets and K groups of sample impurity component sets; an analysis branch construction unit, the analysis branch construction unit is used to construct the K groups of impurity component analysis branches using the K groups of sample detection result sets and the K groups of sample impurity component sets as training data; an analyzer construction unit, the analyzer construction unit is used to perform intra-group parallel connection on the K groups of impurity component analysis branches, and after obtaining K impurity component analysis models, the construction of the impurity component analyzer is completed by parallel connection of the K impurity component analysis models; a parallel analysis unit, the parallel analysis unit is used to load the K original detection results into the impurity component analyzer, perform parallel analysis of impurity components through the K impurity component analysis models of the impurity component analyzer, and output the K initial impurity components.

[0073] The specific configuration of the cross-validation module 60 will be described in detail below. As described above, by cross-validating the K initial impurity components and outputting the sample purity test result of the sample to be tested in reverse according to the validation result, the cross-validation module 60 further includes: an impurity adjacency matrix construction unit, the impurity adjacency matrix construction unit is used to construct an impurity adjacency matrix by measuring the similarity of the K initial impurity components; an impurity degree matrix construction unit, the impurity degree matrix construction unit is used to construct an impurity degree matrix based on the impurity adjacency matrix; a real-time impurity content acquisition unit, the real-time impurity content acquisition unit is used to aggregate the K initial impurity components based on the impurity type , obtaining M groups of real-time impurity contents of the M impurity types; a detection confidence weight configuration unit, the detection confidence weight configuration unit is used to configure the detection confidence weights of the M groups of real-time impurity contents based on the impurity matrix, and obtain M groups of detection credible weights; a mapping weight calculation unit, the mapping weight calculation unit is used to perform mapping weight calculation on the M groups of real-time impurity contents using the M groups of detection credible weights, and obtain M credible impurity contents; a sample purity detection result output unit, the sample purity detection result output unit is used to reversely output the sample purity detection result according to the M credible impurity contents.

[0074] The purity detection device for praseodymium neodymium fluoride provided in the embodiment of the present invention can execute the purity detection method for praseodymium neodymium fluoride provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0075] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0076] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for detecting the purity of praseodymium neodymium fluoride, characterized in that: The method comprises: Conducting industrial tracing of the sample to be tested to obtain sample characteristic information, wherein the sample to be tested is praseodymium neodymium fluoride; Matching detection technologies according to the sample characteristic information to obtain K impurity detection technologies; Optimizing the detection process of the K impurity detection technologies to obtain M impurity detection task sequences and M sample allocation requirements, where M is a positive integer less than K; After allocating the samples to be detected according to the M sample allocation requirements, scheduling the K impurity detection technologies to perform sample detection based on the M impurity detection task sequences as constraints to obtain K original detection results; Performing impurity component analysis on the K original detection results to obtain K initial impurity components; The K initial impurity components are cross-validated, and the sample purity test result of the sample to be tested is reversely outputted based on the validation result.

2. The purity detection method for praseodymium neodymium fluoride according to claim 1, characterized in that: According to the sample characteristic information, detection technology matching is performed to obtain K impurity detection technologies, and the method further includes: Interactively obtain the process flow setting of the sample to be tested, and perform industrial traceability according to the process flow setting to obtain a historical production characteristic set; Aggregating information of the impurity dimension on the historical production characteristic set to obtain a sample impurity composition, wherein the sample impurity composition includes M impurity types; Aggregating material property information of the historical production property set to obtain sample material properties, and outputting the sample impurity composition and sample material properties as the sample property information; Calling networked detection technologies according to the M impurity types to obtain M groups of candidate detection technologies; Perform detection response scheduling according to the material characteristics of the sample to obtain a response detection technology set; The K impurity detection technologies are obtained by aggregating the M groups of candidate detection technologies and the response detection technology set, wherein the K impurity detection technologies have K groups of detection task-oriented identifiers.

3. The purity detection method for praseodymium neodymium fluoride according to claim 2, characterized in that: Optimizing the detection process of the K impurity detection technologies to obtain M impurity detection task sequences and M sample allocation requirements, the method further includes: Interactively obtaining K detection-related information of the K impurity detection technologies, wherein the detection-related information includes sample damage degree, detection loss amount, detection time consumption and parallel execution capability; Extracting K sample damage degrees from the K detection association information, and performing primary grouping of the K impurity detection technologies according to a preset damage similarity scale and the K sample damage degrees to obtain a damage association detection set; Extracting K parallel execution capabilities from the K detection association information, and performing secondary grouping on the K impurity detection technologies according to the intersection relationship of the K parallel execution capabilities to obtain a parallel association detection set; Preset the comprehensive detection time consumption, and use the comprehensive detection time consumption as a constraint, and enumerate the K impurity detection technologies according to the K detection time consumptions to obtain multiple detection task allocation sets; The multiple detection task allocation sets are used as solution spaces to optimize the detection process of the K impurity detection technologies, and the M impurity detection task sequences and M sample allocation requirements are obtained.

4. The purity detection method for praseodymium neodymium fluoride according to claim 3, characterized in that: The method further comprises: optimizing the detection process of the K impurity detection technologies by using the multiple detection task allocation sets as solution spaces to obtain the M impurity detection task sequences and M sample allocation requirements. Extract H sample detection task combinations from the first detection task allocation set; Sorting the detection tasks of the H sample detection task combinations according to the destruction association detection set and the parallel association detection set to obtain H sample parallel execution sequences; Performing material loss analysis on the H sample parallel execution sequences according to the K detection loss amounts to obtain H detection loss requirements, and obtaining the first task loss requirement by adding the H detection loss requirements; Calculate the time consumption of the H sample parallel execution sequences according to the K detection time consumptions to obtain the execution time consumptions of the H tasks; Extract the maximum value of the execution time of the H tasks to obtain the time requirement of the first task; Preset a weight configuration combination, use the weight configuration combination to perform a comprehensive evaluation of the first task loss requirement and the first task time requirement, and output a first detection fitness; Similarly, performing detection adaptability analysis on the multiple detection task allocation sets to obtain multiple detection fitnesses; The plurality of detection fitnesses are sequenced to locate the M impurity detection task sequences and the M sample allocation requirements.

5. The purity detection method for praseodymium neodymium fluoride according to claim 4, characterized in that: The detection tasks of the H sample detection task combinations are sorted according to the destruction association detection set and the parallel association detection set to obtain H sample parallel execution sequences, and the method further includes: Initialize the detection sorting of the H sample detection task combinations according to the damage degrees of the K samples to obtain H sample detection execution sequences; The destruction association detection set is used to traverse the H sample detection execution sequences to sort the destruction parallel tasks, and H sample destruction execution sequences are obtained; The parallel associated detection set is used to traverse the H sample destruction execution sequences to perform detection parallel task decomposition, and H sample parallel execution sequences are obtained.

6. The purity detection method for praseodymium neodymium fluoride according to claim 2, characterized in that: By performing impurity component analysis on the K original detection results, K initial impurity components are obtained, and the method further includes: Call sample detection data according to the K group detection task-oriented identifiers to obtain K group sample detection result sets and K group sample impurity component sets; The K groups of sample detection result sets and the K groups of sample impurity component sets are used as training data to construct K groups of impurity component analysis branches; After the K groups of impurity component analysis branches are connected in parallel within the group to obtain K impurity component analysis models, the construction of the impurity component analyzer is completed by connecting the K impurity component analysis models in parallel; The K original detection results are loaded into the impurity component analyzer, and the impurity components are analyzed in parallel through the K impurity component analysis models of the impurity component analyzer to output the K initial impurity components.

7. The purity detection method for praseodymium neodymium fluoride according to claim 2, characterized in that: By cross-validating the K initial impurity components and reversely outputting the sample purity test result of the sample to be tested according to the validation result, the method includes: Constructing an impurity adjacency matrix by performing similarity measurement on the K initial impurity components; Constructing an impurity degree matrix based on the impurity adjacency matrix; Aggregating the K initial impurity components based on impurity types to obtain M groups of real-time impurity contents of the M impurity types; Based on the impurity matrix, the detection confidence weights of the M groups of real-time impurity contents are configured to obtain the M groups of detection confidence weights; Using the M groups of detection credible weights to perform mapping weighted calculation on the M groups of real-time impurity contents, to obtain M credible impurity contents; The sample purity test result is outputted by reverse deduction based on the M credible impurity contents.

8. A purity detection device for praseodymium-neodymium fluoride, characterized in that: The device is used to implement the purity detection method for praseodymium neodymium fluoride according to any one of claims 1 to 7, and the device comprises: A sample characteristic information acquisition module, wherein the sample characteristic information acquisition module is used to perform industrial traceability on the sample to be detected and obtain sample characteristic information, wherein the sample to be detected is praseodymium neodymium fluoride; A detection technology matching module, wherein the detection technology matching module is used to match detection technologies according to the sample characteristic information to obtain K impurity detection technologies; A detection process optimization module, which is used to optimize the detection process of the K impurity detection technologies to obtain M impurity detection task sequences and M sample allocation requirements, where M is a positive integer less than K; An original test result acquisition module, wherein after allocating the samples to be tested according to the M sample allocation requirements, the K impurity detection technologies are scheduled to perform sample detection based on the M impurity detection task sequences as constraints to obtain K original test results; An impurity component analysis module, the impurity component analysis module is used to perform impurity component analysis on the K original detection results to obtain K initial impurity components; A cross-validation module is used to perform cross-validation on the K initial impurity components and reversely output the sample purity test result of the sample to be tested based on the validation result.

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