A knowledge object processing method, device and equipment for a spent fuel reprocessing process

Abstract

The application provides a kind of spent fuel reprocessing process knowledge object processing method, device and equipment, wherein the method comprises: obtaining at least two knowledge objects in spent fuel reprocessing process;The knowledge object includes: at least one spent fuel reprocessing process, at least one equipment corresponding to the spent fuel reprocessing process, at least one process parameter corresponding to at least one equipment of the spent fuel reprocessing process;According to at least two knowledge objects, obtain the knowledge graph chromosome population of spent fuel reprocessing process;According to the preset algorithm, obtain the target chromosome individual vector of the knowledge graph chromosome population;According to the target chromosome individual vector, adjust the preset knowledge base of spent fuel reprocessing process, and obtain the target knowledge base.The application scheme improves the optimization ability of reasoning and problem solving of knowledge object in spent fuel reprocessing process, and improves the efficiency of spent fuel reprocessing process.

Description

Technical Field

[0001] This invention relates to the technical field of spent fuel reprocessing, and in particular to a knowledge object processing method, apparatus and equipment for spent fuel reprocessing. Background Technology

[0002] Spent fuel is high-level radioactive waste, primarily derived from nuclear fuel burned at nuclear power plants. This 3% of spent fuel contributes 95% of the total radioactivity. Currently, with the rise of new technologies related to spent fuel recycling and sustainable development, spent fuel reprocessing processes are complex, highly uncertain, and require extensive expert experience. Therefore, traditional spent fuel reprocessing methods struggle to meet the latest demands for sustainable development; for example, there are still many gaps in online real-time analysis of spent fuel reprocessing process logistics.

[0003] Based on the design, construction, and commissioning results of the pilot plant for spent fuel reprocessing from power reactors, and incorporating advanced domestic and international experience, the design was optimized, and mature, reliable, and advanced spent fuel reprocessing technologies were adopted to establish an industrial demonstration plant for spent fuel reprocessing. To address this issue, it is necessary to improve the intelligent technologies of the industrial demonstration plant for spent fuel reprocessing, enhance its capacity for handling uncertainties, and increase the utilization rate of expert experience. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method, apparatus and equipment for processing knowledge objects in a spent fuel reprocessing process, so as to improve the reasoning and problem-solving optimization capabilities of knowledge objects in the spent fuel reprocessing process, and optimize and improve the efficiency of the spent fuel reprocessing process.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a knowledge object processing method for spent fuel reprocessing, comprising:

[0006] Acquire at least two knowledge objects in the spent fuel reprocessing process; the knowledge objects include: at least one spent fuel reprocessing process, at least one piece of equipment corresponding to the spent fuel reprocessing process, and process parameters corresponding to at least one piece of equipment in the spent fuel reprocessing process.

[0007] Based on at least two of the aforementioned knowledge objects, obtain a knowledge graph chromosome population for spent fuel reprocessing processes;

[0008] According to the preset algorithm, the target chromosome individual vector of the knowledge graph chromosome population is obtained;

[0009] The target knowledge base is obtained by adjusting the preset knowledge base of the spent fuel reprocessing process based on the target chromosome individual vector.

[0010] Optionally, based on at least two of the aforementioned knowledge objects, a knowledge graph chromosome population for spent fuel reprocessing processes is obtained, including:

[0011] Based on the relationship between at least two of the knowledge objects, obtain a knowledge graph of at least two of the knowledge objects;

[0012] The data of knowledge objects in the knowledge graph are formalized to obtain at least two first chromosome individual vectors.

[0013] Based on at least two first chromosome individual vectors, a knowledge graph chromosome population for the spent fuel reprocessing process is obtained.

[0014] Optionally, according to a preset algorithm, the target chromosome individual vector of the knowledge graph chromosome population is obtained, including:

[0015] According to a preset evolutionary algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolutionary processing to obtain the evolved second chromosome individual vector.

[0016] Based on the fitness of the second chromosome individual, the target chromosome individual vector of the knowledge graph chromosome population is determined.

[0017] Optionally, according to a preset evolutionary algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolutionary processing to obtain the evolved second chromosome individual vector, including:

[0018] According to a preset mutation algorithm and / or a preset crossover algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolution processing to obtain the evolved second chromosome individual vector.

[0019] Optionally, according to a preset mutation algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolutionary processing to obtain the evolved second chromosome individual vector, including:

[0020] Obtain the first and second preset segments on any first chromosome individual vector in the chromosome population of the knowledge graph;

[0021] The positions of the first preset segment and the second preset segment are swapped to obtain the evolved second chromosome individual vector.

[0022] Optionally, according to a preset crossover algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolutionary processing to obtain the evolved second chromosome individual vector, including:

[0023] Obtain the third preset segment of one of the first chromosome individual vectors and the fourth preset segment of the other first chromosome individual vector from any two first chromosome individual vectors in the chromosome population of the knowledge graph.

[0024] Cross-mapping is performed on the third preset segment and the fourth preset segment to obtain the evolved second chromosome individual vector.

[0025] Optionally, based on the fitness of the second chromosome individual vector, the target chromosome individual vector of the knowledge graph chromosome population is determined, including:

[0026] Based on the preset optimization objective function, the fitness of the second chromosome individual vector is obtained;

[0027] Within a preset number of iterations, when the fitness is greater than or equal to a preset threshold, the second chromosome individual vector corresponding to the current fitness is determined as the target chromosome individual vector;

[0028] Within a preset number of iterations, when the fitness is less than a preset threshold, the fitness of the chromosome individual vectors after each evolution is sorted in ascending order, and the second chromosome individual vector corresponding to the highest fitness in the sort is determined as the target chromosome individual vector.

[0029] Embodiments of the present invention also provide a knowledge object processing apparatus for a spent fuel reprocessing process, comprising:

[0030] The acquisition module is used to acquire knowledge objects in the spent fuel reprocessing process; the knowledge objects include: at least one spent fuel reprocessing process, at least one piece of equipment corresponding to the spent fuel reprocessing process, and process parameters corresponding to at least one piece of equipment in the spent fuel reprocessing process.

[0031] The processing module is used to obtain a knowledge graph chromosome population of spent fuel reprocessing technology based on the knowledge object; obtain a target chromosome individual vector of the knowledge graph chromosome population based on a preset iterative algorithm; and adjust the preset knowledge base of spent fuel reprocessing technology based on the target chromosome individual vector to obtain a target knowledge base.

[0032] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above.

[0033] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the methods described above.

[0034] The above-described solution of the present invention has at least the following beneficial effects:

[0035] The above-described solution of the present invention obtains knowledge objects in the spent fuel reprocessing process; the knowledge objects include: at least one spent fuel reprocessing process, at least one piece of equipment corresponding to the spent fuel reprocessing process, and process parameters corresponding to at least one piece of equipment in the spent fuel reprocessing process; based on the knowledge objects, a knowledge graph chromosome population of the spent fuel reprocessing process is obtained; according to a preset iterative algorithm, a target chromosome individual vector of the knowledge graph chromosome population is obtained; and a preset knowledge base of the spent fuel reprocessing process is adjusted according to the target chromosome individual vector to obtain a target knowledge base, thereby improving the reasoning and problem-solving optimization capabilities of the knowledge objects in the spent fuel reprocessing process and optimizing and improving the efficiency of the spent fuel reprocessing process. Attached Figure Description

[0036] Figure 1 This is a flowchart of the knowledge object processing method for the spent fuel reprocessing process provided in this embodiment of the invention;

[0037] Figure 2 This is a schematic diagram of the knowledge object processing device module of the spent fuel reprocessing process provided in the embodiments of the present invention. Detailed Implementation

[0038] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0039] like Figure 1 As shown, embodiments of the present invention propose a knowledge object processing method for spent fuel reprocessing, comprising:

[0040] Step 11: Obtain at least two knowledge objects in the spent fuel reprocessing process; the knowledge objects include: at least one spent fuel reprocessing process, at least one piece of equipment corresponding to the spent fuel reprocessing process, and process parameters corresponding to at least one piece of equipment in the spent fuel reprocessing process.

[0041] Step 12: Based on at least two of the knowledge objects, obtain a knowledge graph chromosome population for spent fuel reprocessing processes;

[0042] Step 13: Obtain the target chromosome individual vector of the knowledge graph chromosome population according to the preset algorithm;

[0043] Step 14: Adjust the preset knowledge base of the spent fuel reprocessing process according to the target chromosome individual vector to obtain the target knowledge base.

[0044] In this embodiment, the knowledge object represents common sense and experience in spent fuel reprocessing processes, including but not limited to: at least one spent fuel reprocessing process segment, at least one related equipment in the spent fuel reprocessing process segment, parameters corresponding to the equipment in the at least one spent fuel reprocessing process segment, knowledge corresponding to the spent fuel reprocessing process segment, knowledge corresponding to the related equipment in the process segment, and professional terminology knowledge in the process segment; wherein, the spent fuel reprocessing process segment may include: Purex process, other wet process, dry process; the equipment in the spent fuel reprocessing process segment may include: dedicated material conveying equipment, pump and valve equipment, remote maintenance equipment and tools, dedicated containers and transfer tools, etc.; it should be understood that when any process or equipment segment is determined, the equipment required for that process segment or the process segment in which that equipment is located can be determined accordingly;

[0045] Based on at least two of the knowledge objects, a knowledge graph chromosome population for spent fuel reprocessing is constructed. The knowledge graph chromosome population contains at least two knowledge graph chromosome individual vectors, which are obtained by formal processing of at least two of the knowledge objects.

[0046] Furthermore, according to the preset algorithm, the knowledge graph chromosome individual vectors in the knowledge graph chromosome population are iteratively optimized to obtain the optimal target chromosome individual vector after iterative optimization.

[0047] The preset knowledge base formalizes the data and experience rules corresponding to historical knowledge objects into a knowledge graph, constructs a knowledge graph of spent fuel reprocessing technology, and combines natural language processing and other technologies for continuous automated information collection and manual experience data supplementation, ultimately building an intelligent knowledge base based on knowledge graphs and natural language processing; the preset knowledge base contains a knowledge graph formed by multiple knowledge objects in the spent fuel reprocessing process, and the construction of the preset knowledge base can realize the accumulation, aggregation, and automatic extraction of processing experience in the field of spent fuel reprocessing; according to the target chromosome individual vector, the knowledge graph in the preset knowledge base is adjusted to obtain the target knowledge base, so as to solve the problems of complexity, uncertainty, and relatively few expert experience support resources in the spent fuel reprocessing process, and further optimize and improve the efficiency of the spent fuel reprocessing process. In an optional embodiment of the present invention, step 12 above may include:

[0048] Step 121: Obtain a knowledge graph of at least two knowledge objects based on the relationship between them;

[0049] Step 122: Formalize the data of the knowledge objects in the knowledge graph to obtain at least two initial chromosome individual vectors;

[0050] Step 123: Obtain the knowledge graph chromosome population of the spent fuel reprocessing process based on at least two of the initial chromosome individual vectors.

[0051] In this embodiment, based on the association between at least two knowledge objects, the at least two knowledge objects are visualized to obtain a knowledge graph of at least two knowledge objects. The knowledge graph can also be regarded as a topological graph formed based on the association between at least two knowledge objects.

[0052] Furthermore, the data of knowledge objects in the knowledge graph can be formally processed according to a preset random function (the formal processing may include: binary encoding or real number encoding of the data) to generate initial parameters; here, the preset random function can be expressed as r = random(L,M); where r represents the calculation result of the random function, random() represents taking the functional relationship between L and M, L represents the minimum value, and M represents the maximum value; the initial parameters and the knowledge graph corresponding to the initial parameters are represented as chromosome individual vectors of the knowledge graph, and the initial parameters may include: parameters of the random function (such as the number of equipment, the number of process sections, the number of expert experience rules, the consumption time of the fuel reprocessing process, etc. in the spent fuel reprocessing process), population size, maximum number of iterations, threshold for fitness function determination, mutation probability, crossover probability, etc.

[0053] The knowledge graph can be represented as a graph object G. i Furthermore, based on the initial parameters of at least two initial chromosome individual vectors and a preset evaluation function, the initial fitness p of the initial chromosome individual vectors is obtained. Gi The initial fitness p Gi This represents the degree of adaptation of the initial chromosome individual vector to the preset knowledge base, and also represents the cost of the chromosome individual vector; further, based on the initial chromosome individual vector and its corresponding initial fitness p... Gi The knowledge graph chromosome population can be obtained, and the knowledge graph chromosome population is represented as {(G1,p} G1 ),(G2,p G2 ),…,(G i ,p Gi ),…,(G N ,p GN N represents the number of individuals in the knowledge graph chromosome population, i = 1, 2, ..., N is a positive integer; the knowledge graph chromosome population contains at least two first chromosome individual vectors;

[0054] Preferably, the preset evaluation function can be expressed as: p Gi=f0(n0,m0,l0,t0), where n0 represents the number of devices in the spent fuel reprocessing process corresponding to the current chromosome individual vector, m0 represents the number of devices in the spent fuel reprocessing process corresponding to the current chromosome individual vector, l0 represents the number of expert experience rules in the spent fuel reprocessing process corresponding to the current chromosome individual vector, t0 represents the time consumed by the spent fuel reprocessing process corresponding to the current chromosome individual vector, and f0 represents the function relationship;

[0055] A knowledge graph is constructed based on the relationship between at least two of the knowledge objects, and the data of the knowledge objects in the knowledge graph is formalized to facilitate the calculation of the initial fitness. Based on the initial fitness, a chromosome population of the knowledge graph is constructed to provide a data foundation for subsequent evolutionary processing based on the initial chromosome individual vectors to obtain the target chromosome individual vector, thus ensuring the accuracy of the subsequent evolutionary processing.

[0056] In an optional embodiment of the present invention, step 13 above may include:

[0057] Step 131: According to the preset evolution algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolution processing to obtain the evolved second chromosome individual vector.

[0058] Step 132: Determine the target chromosome individual vector of the knowledge graph chromosome population based on the fitness of the second chromosome individual vector.

[0059] In this embodiment, each first chromosome individual vector in the knowledge graph chromosome population is iteratively evolved according to the preset algorithm for a preset number of iterations. The preset number of iterations can be set according to actual needs. Here, each evolutionary process of the first chromosome individual vector yields a second chromosome individual vector, and the fitness of the currently obtained second chromosome individual vector is calculated each time an evolutionary process is performed. The first chromosome individual vector is evolved according to the preset algorithm, thereby changing the configuration parameters of the first chromosome individual vector to optimize the chromosome individual vector. Here, the configuration parameters can be of the same type as the initial parameters mentioned above, and the configuration parameters can also include: parameters of a random function (such as the number of devices, process sections, number of expert experience rules, fuel reprocessing time, etc. in the spent fuel reprocessing process), population size, maximum number of iterations, fitness function threshold, mutation probability, and crossover probability.

[0060] Furthermore, based on the fitness of the currently obtained second chromosome individual vector, it is determined whether the current second chromosome individual vector is the optimal chromosome individual vector after evolution; if so, the currently obtained second chromosome individual vector is determined as the target chromosome individual vector; otherwise, the currently obtained second chromosome individual vector is used as the new first chromosome individual vector for evolution processing again.

[0061] Based on the fitness of the second chromosome individual vector after evolutionary processing, the target chromosome individual vector is determined, ensuring the accuracy of the target chromosome individual vector. Furthermore, the preset knowledge base is adjusted based on the target chromosome individual vector to obtain the target knowledge base, and the degree of maladaptation of the knowledge graph in the target knowledge base to the entire target knowledge base is minimized, so as to ensure the consistency and coordination of the management and maintenance of the spent fuel reprocessing knowledge graph, and thus ensure the maximum overall effectiveness of the spent fuel reprocessing knowledge graph.

[0062] In an optional embodiment of the present invention, step 131 above may include:

[0063] Step 1311: According to the preset mutation algorithm and / or preset crossover algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolution processing to obtain the evolved second chromosome individual vector.

[0064] In this embodiment, the preset algorithm may include a preset mutation algorithm and a preset crossover algorithm. Further, according to the preset mutation algorithm and / or the preset crossover algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolution processing to change the configuration parameters of the first chromosome individual vector and generate a new second chromosome individual vector, so as to optimize the chromosome individual vector in the knowledge graph chromosome population and thereby optimize the spent fuel reprocessing process.

[0065] In embodiments of the present invention, when the preset mutation algorithm and the preset crossover algorithm are selected simultaneously to perform evolutionary processing on the first chromosome individual vector, it should be understood that the order in which the two algorithms are performed is not restricted and can be determined according to actual needs.

[0066] In an optional embodiment of the present invention, step 1311 above may include:

[0067] Step 13111a: Obtain the first and second preset segments on any first chromosome individual vector in the knowledge graph chromosome population;

[0068] Step 13112a: Swap the positions of the first preset segment and the second preset segment to obtain the evolved second chromosome individual vector.

[0069] In this embodiment, a first preset segment and a second preset segment are first determined on any first chromosome individual vector in the knowledge graph chromosome population; the first preset segment and the second preset segment can be extracted from the first chromosome individual vector according to actual needs, and the first preset segment and the second preset segment respectively have different configuration parameters corresponding to the first chromosome individual vector;

[0070] Furthermore, the positions of the first preset segment and the second preset segment are interchanged and spliced ​​to generate a chromosome individual vector with interchanged positions, and the chromosome individual vector with interchanged positions is determined as the evolved second chromosome individual vector.

[0071] By altering and recombining segments with different configuration parameters on the first chromosome individual vector, the evolution and optimization of the first chromosome individual vector are achieved, resulting in the evolved second chromosome individual vector. This provides a foundation for subsequently determining the target chromosome individual vector and adjusting the preset knowledge base.

[0072] In an optional embodiment of the present invention, step 1311 above may include:

[0073] Step 13111b: Obtain the third preset segment of one of the first chromosome individual vectors and the fourth preset segment of the other first chromosome individual vector from any two first chromosome individual vectors in the knowledge graph chromosome population.

[0074] Step 13112b: Perform cross-mapping on the third preset segment and the fourth preset segment to obtain the evolved second chromosome individual vector.

[0075] In this embodiment, a third preset segment on one of the first chromosome individual vectors and a fourth preset segment on the other of the first chromosome individual vectors in any two first chromosome individual vectors in the knowledge graph chromosome population are first determined; the third preset segment and the fourth preset segment can be extracted from the two first chromosome individual vectors respectively according to actual needs, and the third preset segment and the fourth preset segment respectively have different configuration parameters for the two first chromosome individual vectors;

[0076] Furthermore, the configuration parameters corresponding to the third preset slice and the fourth preset segment are cross-mapped to generate a cross-mapped chromosome individual vector, and the cross-mapped chromosome individual vector is determined as the evolved second chromosome individual vector;

[0077] By performing cross-mapping on segments with different configuration parameters on two first chromosome individual vectors, the original configuration parameters are changed, thereby realizing the evolution and optimization of the first chromosome individual vectors and obtaining the evolved second chromosome individual vectors. This provides a basis for subsequently determining the target chromosome individual vectors and adjusting the preset knowledge base.

[0078] In an optional embodiment of the present invention, step 132 above may include:

[0079] Step 1321: Based on the preset optimization objective function, obtain the fitness of the second chromosome individual vector;

[0080] Step 1322: Within a preset number of iterations, when the fitness is greater than or equal to a preset threshold, determine the second chromosome individual vector corresponding to the current fitness as the target chromosome individual vector;

[0081] Step 1323: Within a preset number of iterations, when the fitness is less than a preset threshold, sort the fitness of the second chromosome individual vectors after each evolution in ascending order, and determine the second chromosome individual vector corresponding to the highest fitness in the sort as the target chromosome individual vector.

[0082] In this embodiment, the fitness of the second chromosome individual vector is calculated using the preset optimization objective function and the optimized configuration parameters of the second chromosome;

[0083] Preferably, the preset optimization objective function expression is: f(n,m,l,t), where n represents the number of devices in the spent fuel reprocessing process, m represents the number of process sections in the spent fuel reprocessing process, l represents the number of expert experience rules in the spent fuel reprocessing process, t represents the time consumed by the spent fuel reprocessing process, and f represents the preset optimization objective function of the spent fuel reprocessing process; here, the expression of the preset optimization objective function can be the same as the preset evaluation function mentioned above. It should be noted that the parameters used in the preset optimization objective function are the configuration parameters of the chromosome individual vector after each evolutionary processing;

[0084] The preset optimization objective function should also satisfy the following constraints:

[0085] y1(n)∈Y1;

[0086] y2(m)∈Y2;

[0087] y3(l)∈Y3;

[0088] y4(t)∈Y4;

[0089] Wherein, y1(n)∈Y1 represents the range of values ​​for the number of devices n in the spent fuel reprocessing process (e.g., L1≤n≤M1, where L1 and M1 are the minimum and maximum constants, respectively), y2(m)∈Y2 represents the range of values ​​for the number of process sections m in the spent fuel reprocessing process (e.g., L2≤m≤M2, where L2 and M2 are the minimum and maximum constants, respectively), y3(l)∈Y3 represents the range of values ​​for the number of expert experience rules l in the spent fuel reprocessing process (e.g., L3≤l≤M3, where L3 and M3 are the minimum and maximum constants, respectively), and y4(t)∈Y4 represents the range of values ​​for the time t consumed by the spent fuel reprocessing process (e.g., L4≤t≤M4, where L4 and M4 are the minimum and maximum constants, respectively).

[0090] Furthermore, after each evolutionary process, the fitness of the second chromosome individual vector after one evolution is calculated using the preset optimization objective function;

[0091] Within the preset number of iterations, when the calculated fitness of the current evolved second chromosome individual vector is greater than or equal to a preset threshold, the evolved second chromosome individual vector corresponding to the current fitness is determined as the target chromosome individual vector.

[0092] When evolutionary processing is performed according to a preset number of iterations, if the fitness of the evolved second chromosome individual vector calculated after each iteration is less than a preset threshold, then the fitness of the second chromosome individual vectors calculated after each evolution is sorted in ascending order, and the second chromosome individual vector corresponding to the highest fitness in the sort is determined as the target chromosome individual vector. The optimization objective function of spent fuel reprocessing is to minimize the maladaptation of the spent fuel reprocessing knowledge graph to the entire knowledge base, that is, to perform evolutionary processing on the chromosome individual vectors to ensure that the fitness of the finally evolved chromosome individual vectors is maximized, thereby minimizing the conflict between knowledge objects in the spent fuel reprocessing process corresponding to the chromosome individual vectors, so as to ensure the consistency and coordination of the management and maintenance of the spent fuel reprocessing knowledge graph, and also to ensure the maximum overall effectiveness of the spent fuel reprocessing knowledge graph.

[0093] In an optional embodiment of the present invention, step 14 above may include:

[0094] Step 141: Based on the determined target chromosome individual vector, adjust the preset knowledge base of the spent fuel reprocessing process to obtain an updated and optimized target knowledge base. The target knowledge base contains a variety of optimized and updated knowledge objects, so as to obtain optimized and updated knowledge objects from the target knowledge base to optimize the spent fuel reprocessing process and improve its efficiency.

[0095] The above embodiments of the present invention obtain at least two knowledge objects in the spent fuel reprocessing process; the knowledge objects include: at least one spent fuel reprocessing process, at least one piece of equipment corresponding to the spent fuel reprocessing process, and process parameters corresponding to at least one piece of equipment of the spent fuel reprocessing process; based on the at least two knowledge objects, a knowledge graph chromosome population of the spent fuel reprocessing process is obtained; according to a preset algorithm, a target chromosome individual vector of the knowledge graph chromosome population is obtained; the preset knowledge base of the spent fuel reprocessing process is adjusted according to the target chromosome individual vector to obtain a target knowledge base, thereby updating and optimizing the knowledge base of the spent fuel reprocessing process, thereby solving the complexity and uncertainty of the spent fuel reprocessing process, optimizing the spent fuel reprocessing process, and improving the efficiency of the spent fuel reprocessing process.

[0096] Embodiments of the present invention also provide a knowledge object processing apparatus 20 for a spent fuel reprocessing process, comprising:

[0097] The acquisition module 21 is used to acquire knowledge objects in the spent fuel reprocessing process; the knowledge objects include: at least one spent fuel reprocessing process, at least one piece of equipment corresponding to the spent fuel reprocessing process, and process parameters corresponding to at least one piece of equipment in the spent fuel reprocessing process.

[0098] The processing module 22 is used to obtain a knowledge graph chromosome population of spent fuel reprocessing technology based on the knowledge object; obtain a target chromosome individual vector of the knowledge graph chromosome population based on a preset iterative algorithm; and adjust the preset knowledge base of spent fuel reprocessing technology based on the target chromosome individual vector to obtain a target knowledge base.

[0099] Optionally, when the processing module 22 obtains the chromosome population of the knowledge graph of spent fuel reprocessing technology based on at least two of the knowledge objects, it is specifically used for:

[0100] Based on the relationship between at least two of the knowledge objects, obtain a knowledge graph of at least two of the knowledge objects;

[0101] The data of knowledge objects in the knowledge graph are formalized to obtain at least two first chromosome individual vectors.

[0102] Based on at least two first chromosome individual vectors, a knowledge graph chromosome population for the spent fuel reprocessing process is obtained.

[0103] Optionally, when the processing module 22 obtains the target chromosome individual vector of the knowledge graph chromosome population according to a preset algorithm, it is specifically used for:

[0104] According to a preset evolutionary algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolutionary processing to obtain the evolved second chromosome individual vector.

[0105] Based on the fitness of the second chromosome individual, the target chromosome individual vector of the knowledge graph chromosome population is determined.

[0106] Optionally, when the processing module 22 performs iterative evolutionary processing on the first chromosome individual vector in the knowledge graph chromosome population according to a preset evolutionary algorithm to obtain the evolved second chromosome individual vector, it is specifically used for:

[0107] According to a preset mutation algorithm and / or a preset crossover algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolution processing to obtain the evolved second chromosome individual vector.

[0108] Optionally, when the processing module 22 performs iterative evolutionary processing on the first chromosome individual vector in the knowledge graph chromosome population according to a preset mutation algorithm to obtain the evolved second chromosome individual vector, it is specifically used for:

[0109] Obtain the first and second preset segments on any first chromosome individual vector in the chromosome population of the knowledge graph;

[0110] The positions of the first preset segment and the second preset segment are swapped to obtain the evolved second chromosome individual vector.

[0111] Optionally, when the processing module 22 performs iterative evolution processing on the first chromosome individual vector in the knowledge graph chromosome population according to a preset crossover algorithm to obtain the evolved second chromosome individual vector, it is specifically used for:

[0112] Obtain the third preset segment of one of the first chromosome individual vectors and the fourth preset segment of the other first chromosome individual vector from any two first chromosome individual vectors in the chromosome population of the knowledge graph.

[0113] Cross-mapping is performed on the third preset segment and the fourth preset segment to obtain the evolved second chromosome individual vector.

[0114] Optionally, when the processing module 22 determines the target chromosome individual vector of the knowledge graph chromosome population based on the fitness of the second chromosome individual vector, it is specifically used for:

[0115] Based on the preset optimization objective function, the fitness of the second chromosome individual vector is obtained;

[0116] Within a preset number of iterations, when the fitness is greater than or equal to a preset threshold, the second chromosome individual vector corresponding to the current fitness is determined as the target chromosome individual vector;

[0117] Within a preset number of iterations, when the fitness is less than a preset threshold, the fitness of the chromosome individual vectors after each evolution is sorted in ascending order, and the second chromosome individual vector corresponding to the highest fitness in the sort is determined as the target chromosome individual vector.

[0118] It should be noted that this device is a device corresponding to the knowledge object processing method of the above-mentioned spent fuel reprocessing process. All implementation methods in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effect.

[0119] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0120] Embodiments of the present invention also provide a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0121] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0122] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0123] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0124] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0126] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0127] Furthermore, it should be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent solutions of the present invention. Moreover, the steps performing the above-described series of processes can naturally be executed in the order described, but are not necessarily required to be executed in chronological order; some steps can be executed in parallel or independently of each other. Those skilled in the art will understand that all or any step or component of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or network of computing devices, in hardware, firmware, software, or a combination thereof. This is something that those skilled in the art can achieve by using their basic programming skills after reading the description of the present invention.

[0128] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing device. The computing device can be a known general-purpose device. Therefore, the object of the present invention can also be achieved simply by providing a program product containing program code implementing the method or apparatus. That is, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent to the present invention. Furthermore, the steps performing the above series of processes can naturally be performed in the order described, but are not necessarily required to be performed in chronological order. Some steps can be performed in parallel or independently of each other.

[0129] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A knowledge object processing method for a spent fuel reprocessing process, characterized in that, include: Acquire at least two knowledge objects from the spent fuel reprocessing process; The knowledge objects include: at least one spent fuel reprocessing process, at least one piece of equipment corresponding to the spent fuel reprocessing process, and process parameters corresponding to at least one piece of equipment of the spent fuel reprocessing process; Based on at least two of the aforementioned knowledge objects, obtain a knowledge graph chromosome population for spent fuel reprocessing processes; According to the preset algorithm, the target chromosome individual vector of the knowledge graph chromosome population is obtained; The preset knowledge base of spent fuel reprocessing technology is adjusted based on the target chromosome individual vector to obtain the target knowledge base; Specifically, based on at least two of the aforementioned knowledge objects, a knowledge graph chromosome population for spent fuel reprocessing processes is obtained, including: Based on the relationship between at least two of the knowledge objects, obtain a knowledge graph of at least two of the knowledge objects; The data of knowledge objects in the knowledge graph are formalized to obtain at least two first chromosome individual vectors. Based on at least two first chromosome individual vectors, a knowledge graph chromosome population for the spent fuel reprocessing process is obtained; wherein, the knowledge graph is represented as graph objects. The initial fitness of the first chromosome individual vector is obtained based on the initial parameters of at least two first chromosome individual vectors and a preset evaluation function. Based on the first chromosome individual vector and its corresponding initial fitness Obtain the knowledge graph chromosome population ( , ), where i = 1, 2, ..., N, and N is a positive integer; The process of obtaining the target chromosome individual vector of the knowledge graph chromosome population according to a preset algorithm includes: According to a preset evolutionary algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolutionary processing to obtain the evolved second chromosome individual vector. Based on the fitness of the second chromosome individual, the target chromosome individual vector of the knowledge graph chromosome population is determined; Specifically, determining the target chromosome individual vector of the knowledge graph chromosome population based on the fitness of the second chromosome individual vector includes: Based on the preset optimization objective function, the fitness of the second chromosome individual vector is obtained; Within a preset number of iterations, when the fitness is greater than or equal to a preset threshold, the second chromosome individual vector corresponding to the current fitness is determined as the target chromosome individual vector; Within a preset number of iterations, when the fitness is less than a preset threshold, the fitness of the chromosome individual vectors after each evolution is sorted in ascending order, and the second chromosome individual vector corresponding to the highest fitness in the sort is determined as the target chromosome individual vector. The preset optimization objective function expression is: f(n,m,l,t), where n represents the number of equipment in the spent fuel reprocessing process, m represents the number of process sections in the spent fuel reprocessing process, l represents the number of expert experience rules in the spent fuel reprocessing process, t represents the time consumed by the spent fuel reprocessing process, and f represents the preset optimization objective function of the spent fuel reprocessing process. The preset optimization objective function is the same as the preset evaluation function.

2. The knowledge object processing method for the spent fuel reprocessing process according to claim 1, characterized in that, According to a preset evolutionary algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolutionary processing to obtain the evolved second chromosome individual vector, including: According to a preset mutation algorithm and / or a preset crossover algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolution processing to obtain the evolved second chromosome individual vector.

3. The knowledge object processing method for the spent fuel reprocessing process according to claim 2, characterized in that, According to a preset mutation algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolutionary processing to obtain the evolved second chromosome individual vector, including: Obtain the first and second preset segments on any first chromosome individual vector in the chromosome population of the knowledge graph; The positions of the first preset segment and the second preset segment are swapped to obtain the evolved second chromosome individual vector.

4. The knowledge object processing method for the spent fuel reprocessing process according to claim 2, characterized in that, According to a preset crossover algorithm, the first chromosome individual vector in the knowledge graph chromosome population undergoes iterative evolutionary processing to obtain the evolved second chromosome individual vector, including: Obtain the third preset segment of one of the first chromosome individual vectors and the fourth preset segment of the other first chromosome individual vector from any two first chromosome individual vectors in the chromosome population of the knowledge graph. Cross-mapping is performed on the third preset segment and the fourth preset segment to obtain the evolved second chromosome individual vector.

5. A knowledge object processing device for a spent fuel reprocessing process, characterized in that, include: The acquisition module is used to acquire knowledge objects in the spent fuel reprocessing process; The knowledge objects include: at least one spent fuel reprocessing process, at least one piece of equipment corresponding to the spent fuel reprocessing process, and process parameters corresponding to at least one piece of equipment of the spent fuel reprocessing process; The processing module is used to obtain a knowledge graph chromosome population of spent fuel reprocessing technology based on the knowledge object; obtain a target chromosome individual vector of the knowledge graph chromosome population according to a preset algorithm; and adjust the preset knowledge base of spent fuel reprocessing technology according to the target chromosome individual vector to obtain a target knowledge base. Specifically, based on at least two of the aforementioned knowledge objects, a knowledge graph chromosome population for spent fuel reprocessing processes is obtained, including: Based on the relationship between at least two of the knowledge objects, obtain a knowledge graph of at least two of the knowledge objects; The data of knowledge objects in the knowledge graph are formalized to obtain at least two first chromosome individual vectors. Based on at least two first chromosome individual vectors, a knowledge graph chromosome population for the spent fuel reprocessing process is obtained; wherein, the knowledge graph is represented as graph objects. The initial fitness of the first chromosome individual vector is obtained based on the initial parameters of at least two first chromosome individual vectors and a preset evaluation function. Based on the first chromosome individual vector and its corresponding initial fitness Obtain the knowledge graph chromosome population ( , ), where i = 1, 2, ..., N, and N is a positive integer; The process of obtaining the target chromosome individual vector of the knowledge graph chromosome population according to a preset algorithm includes: According to a preset evolutionary algorithm, the first chromosome individual vector in the knowledge graph chromosome population is subjected to iterative evolutionary processing to obtain the evolved second chromosome individual vector. Based on the fitness of the second chromosome individual, the target chromosome individual vector of the knowledge graph chromosome population is determined; Specifically, determining the target chromosome individual vector of the knowledge graph chromosome population based on the fitness of the second chromosome individual vector includes: Based on the preset optimization objective function, the fitness of the second chromosome individual vector is obtained; Within a preset number of iterations, when the fitness is greater than or equal to a preset threshold, the second chromosome individual vector corresponding to the current fitness is determined as the target chromosome individual vector; Within a preset number of iterations, when the fitness is less than a preset threshold, the fitness of the chromosome individual vectors after each evolution is sorted in ascending order, and the second chromosome individual vector corresponding to the highest fitness in the sort is determined as the target chromosome individual vector. The preset optimization objective function expression is: f(n,m,l,t), where n represents the number of equipment in the spent fuel reprocessing process, m represents the number of process sections in the spent fuel reprocessing process, l represents the number of expert experience rules in the spent fuel reprocessing process, t represents the time consumed by the spent fuel reprocessing process, and f represents the preset optimization objective function of the spent fuel reprocessing process. The preset optimization objective function is the same as the preset evaluation function.

6. A computing device, characterized in that, include: A processor, a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, A storage instruction that, when executed on a computer, causes the computer to perform the method as described in any one of claims 1 to 4.

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