WO2026007336A1 - 核设施退役的损耗资源预测方法和装置、设备及存储介质 - Google Patents

核设施退役的损耗资源预测方法和装置、设备及存储介质

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Publication number
WO2026007336A1
WO2026007336A1 PCT/CN2024/138932 CN2024138932W WO2026007336A1 WO 2026007336 A1 WO2026007336 A1 WO 2026007336A1 CN 2024138932 W CN2024138932 W CN 2024138932W WO 2026007336 A1 WO2026007336 A1 WO 2026007336A1
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Prior art keywords
scheme
resource
data
sub
execution
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PCT/CN2024/138932
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English (en)
French (fr)
Inventor
刘帆
张振楠
霍明
李强
梁玮伦
钟香斌
宋琰
李增芬
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China Nuclear Power Engineering Co Ltd
Shenzhen China Guangdong Nuclear Engineering Design Co Ltd
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China Nuclear Power Engineering Co Ltd
Shenzhen China Guangdong Nuclear Engineering Design Co Ltd
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Publication of WO2026007336A1 publication Critical patent/WO2026007336A1/zh
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/103—Workflow collaboration or project management
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Definitions

  • This application relates to the field of nuclear power plant facility technology, and in particular to a method, apparatus, equipment and storage medium for predicting resource depletion during nuclear facility decommissioning.
  • Nuclear facility decommissioning is characterized by a long implementation period, high implementation difficulty, and many unpredictable factors.
  • the decommissioning process will generate a large amount of resource loss. Therefore, it is necessary to predict the resource loss of nuclear facility decommissioning before decommissioning, so as to adjust the decommissioning operation steps according to the predicted resource loss.
  • the prediction of resource depletion during nuclear facility decommissioning primarily involves manually inputting various indicators, resource depletion items, and prediction algorithms related to the decommissioning process. This data is then aggregated hierarchically from bottom to top to obtain the overall resource depletion prediction data for the entire nuclear facility.
  • this process requires a large amount of manual data input, which is not only labor-intensive but also results in some data being difficult to determine, affecting the accuracy of the resource depletion prediction. Therefore, how to reduce manpower while accurately predicting resource depletion during nuclear facility decommissioning has become an urgent technical problem to be solved.
  • the main objective of this application is to propose a method, apparatus, equipment, and storage medium for predicting resource depletion during nuclear facility decommissioning, aiming to reduce manpower and accurately predict resource depletion during nuclear facility decommissioning.
  • a first aspect of this application proposes a method for predicting resource depletion during nuclear facility decommissioning, the method comprising:
  • the target scheme information is decomposed to obtain multiple sub-scheme data and scheme-level information and level-related information for each sub-scheme data; wherein, the sub-scheme data is used to characterize the minimum execution steps of nuclear facility decommissioning;
  • resource loss prediction is performed on the data of each sub-scheme to obtain target resource loss prediction data
  • the target resource loss prediction data of multiple sub-scheme data are combined to obtain the total resource loss prediction data of the target scheme information.
  • the decomposition of the target scheme information to obtain multiple sub-scheme data and scheme-level information and level-related information for each sub-scheme data includes:
  • the target scheme information is split into multiple sub-scheme data
  • the sub-scheme data is classified according to the sub-scheme category and the level association information to obtain the scheme level information of each sub-scheme data.
  • the step of obtaining the resource consumption items for executing the sub-scheme data results in multiple execution resource consumption items, including:
  • the sub-scheme categories of the sub-scheme data are obtained. Based on the sub-scheme categories, target scheme execution association parameters are selected from preset candidate scheme execution association parameters. The sub-scheme data is then simulated according to the target scheme execution association parameters to obtain multiple execution loss resource items. Among them, the target scheme execution association parameters are used as parameters for simulating the minimum execution link of the nuclear facility decommissioning.
  • the step of predicting resource consumption for each sub-scheme based on multiple execution resource consumption items to obtain target resource consumption prediction data includes:
  • the selected scheme resource loss prediction model is selected from the preset candidate scheme resource loss prediction models
  • the target resource loss prediction data is obtained by predicting the resource loss of the sub-scheme data using the selected scheme resource loss prediction model and multiple execution resource loss items.
  • the execution resource loss items include: execution loss amount, execution correlation coefficient, execution duration, and execution unit price; the step of predicting the resource loss of the sub-scheme data using the selected scheme resource loss prediction model and multiple execution resource loss items to obtain the target resource loss prediction data includes:
  • the selected scheme resource loss prediction model is used to predict the execution loss amount, the execution correlation coefficient, the execution duration, and the execution unit price to obtain the target resource loss prediction data.
  • the method after performing resource loss prediction on each sub-scheme data based on multiple execution resource loss items to obtain target resource loss prediction data, the method further includes:
  • the correction coefficients are extracted from the preset correction database according to the sub-scheme category;
  • the target resource loss prediction data is corrected according to the correction coefficient to obtain updated resource loss prediction data.
  • the total resource loss prediction data is updated based on the updated resource loss prediction data to obtain the updated resource prediction data for the target scheme information.
  • the method after combining the target resource loss prediction data of multiple sub-scheme data according to the scheme level information and the level association information to obtain the total resource loss prediction data of the target scheme information, the method includes:
  • the total resource loss prediction data is compared with the preset expected resource loss data to obtain the evaluation comparison result;
  • the target scheme information is adjusted based on the total resource loss prediction data to obtain updated scheme data.
  • a resource depletion prediction device for nuclear facility decommissioning comprising:
  • the scheme acquisition module is used to acquire target scheme information for the decommissioning of nuclear facilities
  • the decomposition module is used to decompose the target scheme information to obtain multiple sub-scheme data and scheme-level information and level association information for each sub-scheme data; wherein, the sub-scheme data is used to characterize the minimum execution steps of nuclear facility decommissioning;
  • the resource item acquisition module is used to acquire the resource items that consume execution data of the sub-scheme, and obtain multiple execution resource items.
  • the resource loss prediction module is used to predict the resource loss of each sub-scheme data based on multiple execution resource loss items to obtain target resource loss prediction data.
  • the resource loss combination module is used to combine the target resource loss prediction data of multiple sub-scheme data according to the scheme level information and the level association information to obtain the total resource loss prediction data of the target scheme information.
  • a third aspect of this application provides a computer device, which includes a memory and a processor.
  • the memory stores a computer program
  • the processor executes the computer program to implement the resource depletion prediction method for nuclear facility decommissioning described in the first aspect.
  • a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.
  • the method, apparatus, equipment, and storage medium for predicting resource depletion during nuclear facility decommissioning proposed in this application decompose target scheme information into sub-scheme data, and then automatically obtain the execution resource depletion items from the sub-scheme data. This automates the acquisition of execution resource depletion items, eliminating the need for manual input and saving manpower. Furthermore, after determining the execution resource depletion items, resource depletion prediction is performed on the sub-scheme data based on these items to obtain target resource depletion prediction data. Finally, based on scheme-level information and level correlation information, multiple target resource depletion prediction data are combined to obtain the total resource depletion prediction data for the entire target scheme information. This makes resource depletion prediction for target scheme information simpler and more accurate, reduces redundant calculations due to manual calculations, and improves the efficiency of resource depletion prediction during nuclear facility decommissioning.
  • Figure 1 is a flowchart of the resource depletion prediction method for nuclear facility decommissioning provided in an embodiment of this application;
  • FIG. 2 is a flowchart of step S102 in Figure 1;
  • Figure 3 is a schematic diagram of the decomposition of target scheme information in the resource loss prediction method for nuclear facility decommissioning provided in the embodiments of this application;
  • Figure 4 is a system framework diagram of the resource loss prediction system for nuclear facility decommissioning in the resource loss prediction method for nuclear facility decommissioning provided in the embodiments of this application.
  • FIG. 5 is a flowchart of step S103 in Figure 1;
  • FIG. 6 is another flowchart of step S103 in Figure 1;
  • FIG. 7 is a flowchart of step S104 in Figure 1;
  • Figure 8 is a flowchart of a method for predicting resource depletion during nuclear facility decommissioning according to another embodiment of this application.
  • Figure 9 is a flowchart of a method for predicting resource depletion during nuclear facility decommissioning according to another embodiment of this application.
  • Figure 10 is a schematic diagram of the structure of the nuclear facility decommissioning resource prediction device provided in an embodiment of this application.
  • Figure 11 is a schematic diagram of the hardware structure of the computer device provided in an embodiment of this application.
  • Nuclear facility decommissioning refers to actions taken after a nuclear facility has reached the end of its service life or ceased operation for other reasons, with full consideration for the health and safety of staff and the public, as well as environmental protection. The ultimate goal of decommissioning is to achieve unrestricted access and use of the site. Methods of nuclear facility decommissioning include immediate dismantling, delayed dismantling, and on-site burial.
  • Source term investigation for nuclear facility decommissioning refers to the investigation and assessment of the various sources, factors, steps and possible impacts involved in the decommissioning process of nuclear facilities.
  • Dismantling and dismantling refers to the process of permanently closing and dismantling nuclear facilities (such as nuclear power plants, nuclear fuel processing plants, etc.).
  • Waste management is particularly important during the dismantling and dismantling of nuclear facilities because it may contain radioactive waste and other hazardous materials.
  • the general steps for waste management during nuclear facility dismantling and dismantling are: 1. Identification and sorting; 2. Separation and treatment; 3. Disposal; 4. Monitoring and control; 5. Recording and reporting.
  • Nuclear facility decommissioning is characterized by its cyclical nature, high complexity, and numerous uncertainties and unpredictable factors, requiring significant investment of human and financial resources. Therefore, before decommissioning begins, it is necessary to predict resource depletion in the decommissioning plan to analyze its economic viability. Based on the predicted resource depletion, the decommissioning plan can be adjusted to ensure efficient and cost-effective decommissioning of the nuclear facility in its later stages.
  • This software uses the unit factor method to predict resource depletion in the decommissioning plan. It requires manual input of various indicators, defined resource depletion items, and calculation formulas for each plan, and automatically generates detailed reports for each execution stage of the plan based on the manually entered formulas, indicators, and items.
  • each plan requires manual input of indicators, resource depletion items, and calculation formulas. Predicting resource depletion for multiple plans is extremely labor-intensive. Furthermore, some resource depletion items within certain plans are difficult to determine, affecting the accuracy of the predicted resource depletion.
  • embodiments of this application provide a method, apparatus, equipment, and storage medium for predicting resource depletion during nuclear facility decommissioning, aiming to automate the prediction of resource depletion during nuclear facility decommissioning, save manpower, and improve the accuracy of predicting resource depletion during nuclear facility decommissioning.
  • the method, apparatus, equipment, and storage medium for predicting the depletion of nuclear facilities during decommissioning provided in this application are specifically illustrated through the following embodiments. First, the method for predicting the depletion of nuclear facilities during decommissioning in this application is described.
  • AI Artificial intelligence
  • the embodiments of this application can acquire and process relevant data based on artificial intelligence technology.
  • Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.
  • Foundational technologies in artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating/interactive systems, and mechatronics.
  • AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning/deep learning.
  • the method for predicting resource depletion during nuclear facility decommissioning relates to the field of nuclear power plant nuclear facility technology.
  • This method can be applied to a terminal, a server, or software running on either a terminal or a server.
  • the terminal can be a smartphone, tablet, laptop, desktop computer, etc.
  • the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms
  • the software can be an application implementing the method for predicting resource depletion during nuclear facility decommissioning, but is not limited to the above forms.
  • This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices.
  • This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules.
  • program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types.
  • This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network.
  • program modules can reside in local and remote computer storage media, including storage devices.
  • Figure 1 is an optional flowchart of a method for predicting resource depletion during nuclear facility decommissioning provided in an embodiment of this application.
  • the method in Figure 1 may include, but is not limited to, steps S101 to S105.
  • Step S101 Obtain information on the target plan for the decommissioning of nuclear facilities
  • Step S102 Decompose the target scheme information to obtain multiple sub-scheme data and scheme-level information and level-related information for each sub-scheme data; wherein, the sub-scheme data is used to characterize the minimum execution steps of nuclear facility decommissioning;
  • Step S103 Obtain the resource consumption items of the execution sub-scheme data to obtain multiple execution resource consumption items
  • Step S104 Based on multiple execution loss resource items, perform loss resource prediction on the data of each sub-scheme to obtain target loss resource prediction data;
  • Step S105 Combine the target resource loss prediction data of multiple sub-schemes based on the scheme level information and the level association information to obtain the total resource loss prediction data of the target scheme information.
  • Steps S101 to S105 of this embodiment involve decomposing the target scheme information into multiple sub-scheme data, determining the scheme level information and level association information for each sub-scheme data, obtaining execution loss resource items by acquiring the loss resource items of the sub-scheme data, predicting the loss resource for each sub-scheme data based on the multiple execution loss resource items to obtain target loss resource prediction data, and finally combining the multiple target loss resource prediction data into the total loss resource prediction data for the target scheme information based on the scheme level information.
  • the sub-scheme data is used to characterize the minimum execution stage of nuclear facility decommissioning.
  • the prediction of loss resources for nuclear facility decommissioning eliminates the need for manual input of large amounts of data and loss resource calculation formulas. Furthermore, the prediction of loss resources for nuclear facility decommissioning is automated and accurate, reducing manpower and improving the accuracy of prediction.
  • the target scheme information records the entire operation process of nuclear facility decommissioning, as well as the minimum execution steps required for each step.
  • the entire operation process of nuclear facility decommissioning can be divided into stages, with each stage corresponding to different process steps, and each process step will have multiple minimum execution steps for nuclear facility decommissioning.
  • the resource depletion prediction method for nuclear facility decommissioning is applied to the resource depletion prediction system for nuclear facility decommissioning.
  • This system includes a scheme editing module, a resource depletion prediction module, a 3D simulation system module, and a database module.
  • the database module supports the storage of model data such as models, tool models, and 3D dose fields for nuclear facility decommissioning scenario simulations.
  • the database module stores execution resource depletion items related to nuclear facility decommissioning, as well as evaluation formulas for assessing each sub-scheme data and correction coefficients for execution resource depletion items.
  • the 3D simulation system module is invoked during the correlation simulation of the minimum execution stage in nuclear facility decommissioning and configures the relevant configuration data required for the simulation process of the minimum execution stage in nuclear facility decommissioning.
  • step S102 may include, but is not limited to, steps S201 to S205:
  • Step S201 The target scheme information is split and processed to obtain multiple sub-scheme data
  • Step S202 Obtain the hierarchical relationship information between multiple sub-schemes
  • Step S203 Determine the level association information of each sub-scheme data based on the hierarchical relationship information
  • Step S204 Obtain the category of each sub-scheme data to obtain the sub-scheme category
  • Step S205 The sub-scheme data is classified according to the sub-scheme category and level association information to obtain the scheme level information of each sub-scheme data.
  • the target plan information is split in the decommissioning plan editing module. This involves initially splitting the information according to the decommissioning execution stages, then further splitting it according to the execution flow within each stage, until the smallest execution step is reached, resulting in sub-plan data. During the splitting process, the hierarchical relationship information between each sub-plan data is recorded to determine the level association information of each sub-plan data.
  • the decommissioning of a target nuclear facility requires four stages: the first stage includes research and development, pre-decommissioning activities, equipment shutdown activities, site infrastructure construction, and operation; the second stage includes spent fuel safe storage and routine dismantling and destruction; the third stage includes dismantling activities within the controlled area, waste treatment, storage, and disposal; and the fourth stage includes additional activities and miscellaneous expenditures related to site restoration, safe sealing, or burial.
  • the execution flow within each decommissioning execution stage can be divided hierarchically to the smallest execution step.
  • the smallest execution steps with the same hierarchical relationship calculation include 1, 3, and 5.
  • Step 1 is source investigation
  • step 3 is dismantling and dismantling
  • step 5 is waste preparation and transportation. Therefore, it can be seen that multiple associated smallest execution steps may exist at the same level to execute that level in parallel.
  • the sub-scheme category of each sub-scheme data is obtained, that is, the task activity type representing the execution of the sub-scheme data.
  • the process of splitting the target scheme information in the decommissioning scheme editing module is a step-by-step split according to the task activity type. Each level is further subdivided into lower-level execution links based on the task activity type and level association information, until it is divided into the smallest execution links.
  • the decommissioning scheme editing module the decommissioning scheme of the nuclear facility is decomposed. First, it is divided into Level 1 Task A and Level 1 Task B according to the activity task type, and then further divided.
  • Level 1 Task B is divided into the smallest execution link b, and Level 1 Task A can be further divided into Level 2 Task A1, and then Level 2 Task A1 is divided into the smallest execution links a and c. Therefore, by dividing the target scheme information into multiple smallest execution links in advance in the decommissioning scheme editing module, and determining the execution level information and level association information of each smallest execution link, for example, the link level information of smallest execution link c is Level 2, and the link level information of smallest execution link b is Level 1. It should be noted that the step-level information and level-related information of each minimum execution step are used to calculate the total resource loss at each level when combining resources in the final step, so as to obtain accurate total resource loss prediction data.
  • the target scheme information is split into multiple sub-scheme data, and the scheme level information and level association information of each sub-scheme data are determined, so that it is more convenient to calculate the total loss resource prediction data of the target scheme information in the future.
  • the execution loss resource item of the sub-scheme data characterizes which resource losses are generated during the execution process of the sub-scheme data, thus defining each resource loss generated by the sub-scheme data as an execution loss resource item.
  • the execution loss resource item for each sub-scheme data can be determined through simulation or manual input.
  • the step of selecting between simulation and manual input methods to determine the execution loss resource item involves first obtaining the calculation optimization information for each sub-scheme data, and then selecting the loss resource item calculation method from the simulation and manual input methods based on the calculation optimization information.
  • the simulation method is selected as the calculation method for the loss resource item; if the calculation optimization information is a manual input method, then the manual input method is selected as the calculation method for the loss resource item.
  • the simulation requires the use of a 3D simulation system module, which includes simulation functions such as assembly, layout, cutting, cleaning, dismantling, preparation, sorting, transportation, and hoisting.
  • the 3D simulation system module is used to simulate the resource consumption that will occur during the execution of the sub-scheme data, that is, the resource consumption that will occur during the implementation of the smallest execution link, so as to determine the execution resource consumption items.
  • step S103 can be step S501:
  • Step S501 Obtain the sub-scheme category of the sub-scheme data, select the target scheme execution association parameter from the preset candidate scheme execution association parameters based on the sub-scheme category, and simulate the sub-scheme data according to the target scheme execution association parameter to obtain multiple execution loss resource items; among them, the target scheme execution association parameter is used as the parameter for simulating the minimum execution link of nuclear facility decommissioning.
  • the candidate solution execution association parameters which are simulation configuration parameters involved in the simulation of sub-solution data, also belong to the association parameters for sub-solution data execution.
  • the candidate solution execution association parameters can be data such as tools, equipment, materials, and manpower involved in executing the sub-solution data. It should be noted that different sub-solution categories correspond to different candidate solution execution association parameters, so the candidate solution execution association parameters corresponding to the sub-solution category of the sub-solution data are used as the target solution execution association parameters.
  • the 3D simulation system module simulates the sub-scheme data to output the resource losses that will occur during the execution of the sub-scheme data, thus obtaining the execution loss resource item.
  • the execution loss resource item of the sub-scheme data refers to the resource losses incurred when executing the minimum execution stage of the nuclear facility decommissioning process, including the tools, equipment, materials, and manpower involved.
  • step S501 of this embodiment the sub-scheme data is simulated by using a three-dimensional simulation system module based on the execution association parameters of the target scheme to simulate which items of resource loss will occur during the execution process of the sub-scheme data, so as to obtain the execution loss resource items. This automates the acquisition of execution loss resource items, eliminating the need for manual input and saving manpower.
  • step S103 may further include step S601:
  • Step S601 Generate scheme-related parameter prompt information based on the target scheme execution related parameters, and receive parameter loss resource items fed back to the scheme-related parameter prompt information. Generate multiple execution loss resource items based on the target scheme execution related parameters and parameter loss resource items.
  • the scheme association parameter prompt information is used to display the target scheme execution association parameters, so that the user can know through the scheme association parameter prompt information the execution loss resource items required for the implementation process of the sub-scheme data that need to be manually entered. That is, the data on tools, equipment, materials, manpower, etc. involved in the minimum execution link in the decommissioning of the nuclear facility are entered to obtain multiple execution loss resource items.
  • the parameter loss resource items are the content entered by the user according to the scheme association parameter prompt information, and correspond to each input item of the target scheme execution association parameters. Therefore, by matching the target scheme execution association parameters with the corresponding parameter loss resource items, the resource data that needs to be consumed to execute the sub-scheme data is obtained to obtain multiple execution loss resource items.
  • step S601 of this embodiment the user is prompted to manually input parameter items by using scheme association parameter prompt information, and the parameter loss resource items fed back by the user are received. Then, the parameter loss resource items and the target scheme execution association parameters are matched to determine the execution loss resource items required for the sub-scheme data execution, so as to achieve complete supplementation of execution loss resource items and improve the accuracy of loss resource prediction for nuclear facility decommissioning.
  • step S104 may include, but is not limited to, steps S701 to S703:
  • Step S701 Obtain sub-scheme information from the sub-scheme data
  • Step S702 Select the resource loss prediction model of the chosen scheme from the preset candidate scheme resource loss prediction models based on the sub-scheme information.
  • Step S703 By selecting the scheme loss resource prediction model and multiple execution loss resource items, the loss resource prediction of the sub-scheme data is performed to obtain the target loss resource prediction data.
  • sub-scheme information represents information about sub-scheme data.
  • This sub-scheme information reveals which resource items are required to complete the sub-scheme data.
  • there are 18 minimum execution steps for the decommissioning of the target nuclear facility meaning there are 18 types of sub-scheme data. These include source investigation, cleanup and decontamination, dismantling and dismantling, waste disposal, waste preparation and transportation, system or facility modification, environmental restoration, environmental remediation, personnel hiring, equipment procurement, material procurement, project management, project benefits, preliminary work preparation, engineering design and technical services, supervision and final acceptance, other engineering activities, and unforeseen activities.
  • the resource consumption data for each minimum execution step requires complex calculations of the resource consumption items.
  • this embodiment selects a chosen resource consumption prediction model from candidate scheme resource consumption prediction models based on the sub-scheme information. This selected model is used to evaluate the minimum execution steps, enabling the correlation evaluation of the sub-scheme data.
  • the candidate scheme resource consumption prediction model is stored in the database module and is a resource calculation formula used to calculate the resource consumption generated during the implementation of the minimum execution steps.
  • the target resource loss prediction data of the sub-scheme data is determined to be fixed evaluation data based on the sub-scheme information, it is not necessary to perform resource loss prediction on the sub-scheme data by selecting a scheme resource loss prediction model.
  • resource loss prediction of sub-scheme data is performed by selecting a scheme loss resource prediction model and multiple loss resource items. Specifically, it is based on the unknowns in the selected scheme loss resource prediction model, and the resource data items include resource loss categories and resource loss values. The resource loss value corresponding to each resource loss category is substituted into the unknowns in the selected scheme loss resource prediction model to obtain the target loss resource prediction data of the sub-scheme data, which simplifies the calculation of the target loss resource prediction data.
  • the execution resource loss items include: execution loss amount, execution correlation coefficient, execution duration, and execution unit price; by selecting a scheme resource loss prediction model and multiple execution resource loss items, the resource loss of the sub-scheme data is predicted to obtain the target resource loss prediction data, including:
  • the resource loss amount, execution correlation coefficient, execution duration, and execution unit price are predicted to obtain target resource loss prediction data.
  • the selected scheme resource loss prediction model is a resource loss calculation model corresponding to the data of each sub-scheme, that is, the cost calculation model when the minimum execution stage is executed.
  • the execution loss resource items involved are the equipment, manpower, materials, etc. consumed in the execution of the minimum execution stage. Therefore, the loss resource items include execution loss amount, execution correlation coefficient, execution duration, and execution unit price.
  • Execution loss amount is the material consumed when executing the minimum execution stage
  • execution correlation coefficient is the coefficient involved in the execution process
  • execution duration is the time consumed when executing the minimum execution stage
  • execution unit price is the unit price of the equipment, manpower, materials, etc. consumed.
  • the sub-scheme data represents the minimum execution step in the nuclear facility decommissioning process. If the minimum execution step is source item investigation, then the target loss resource prediction data is the source item investigation cost.
  • the source item investigation cost can be calculated by finding the fixed price of the source item investigation in a pre-set fixed price list. Alternatively, correlation calculation can be used, where the selected scheme loss resource prediction model is the formula for calculating the source item investigation cost.
  • source item investigation man-hours, labor unit price DIM, first time coefficient DIM, radiation protection coefficient DIM, equipment usage time, second time coefficient DIM, rental unit price DIM, consumables, and consumable unit price DIM represent multiple execution loss resources, namely, execution loss amount, execution correlation coefficient, execution duration, and execution unit price.
  • the execution unit price is the labor unit price during the source item investigation
  • the first time coefficient is the coefficient for the time spent by labor during the source item investigation
  • the radiation protection coefficient is the coefficient corresponding to the use of radiation protection equipment or protective clothing during the source item investigation
  • the equipment usage time is the time required to use the equipment during the source item investigation
  • the second time coefficient is the coefficient for the time used to use the equipment during the source item investigation
  • the rental unit price is the unit price of renting the equipment
  • the consumables are the quantity of materials consumed during the source item investigation process, with the consumable unit price being the unit price of the consumable materials.
  • the equipment and personnel selected for the source item investigation are simulated through a 3D simulation system module. Therefore, by inputting the execution loss resource items obtained from the simulation into the calculation formula for the source item investigation fee, the source item investigation fee is obtained, simplifying the calculation operation of the source item investigation fee.
  • the target resource loss prediction data is the dismantling and dismantling cost.
  • This cost can be calculated by looking up a fixed price in a pre-set fixed price list.
  • a correlation calculation can be used.
  • dismantling and dismantling man-hours represent multiple execution resource loss items, obtained through simulation of the equipment and personnel used during dismantling and dismantling using a 3D simulation system module.
  • the labor unit price is the labor unit price during dismantling
  • the first time coefficient is the coefficient for the time spent by labor during dismantling
  • the radiation protection coefficient is the coefficient corresponding to the use of radiation protection equipment or protective clothing during dismantling
  • the equipment usage time is the time required for the equipment during dismantling
  • the second time coefficient is the coefficient for the time used for the equipment during dismantling
  • the rental unit price is the unit price for renting equipment
  • the consumables are the quantity of materials consumed in the dismantling process, with the consumable unit price being the unit price of the consumables.
  • the target resource loss prediction data is the waste treatment preparation cost. This cost can be calculated by finding the fixed price for dismantling and demolition from a pre-set fixed price list. Alternatively, correlation calculation can be used, where the selected resource loss prediction model is the formula for calculating the waste treatment preparation cost.
  • Waste Treatment Preparation Cost (Dismantling Operation Hours ⁇ Labor Unit Price DIM ⁇ First Time Coefficient DIM ⁇ Radiation Protection Coefficient DIM) + (Equipment Usage Time ⁇ Second Time Coefficient ⁇ Rental Unit Price DIM) + [Consumables (Cement, Additives) ⁇ Consumable Unit Price DIM] + (Number of Various Packaging Containers ⁇ Packaging Unit Price DIM).
  • the dismantling operation time, labor unit price DIM, time coefficient DIM, radiation protection coefficient DIM, equipment usage time, time coefficient DIM, rental unit price DIM, consumables, unit price DIM, and the number of various packaging containers are multiple execution loss resource items.
  • the simulation system module simulates the equipment, personnel, main material consumption (cement, additives, etc.) selected during waste treatment preparation and the task execution process.
  • the labor unit price is the unit price of labor during waste treatment preparation
  • the first time coefficient is the coefficient for the time spent by labor during waste treatment preparation
  • the radiation protection coefficient is the coefficient corresponding to the use of radiation protection equipment or protective clothing during waste treatment preparation
  • the equipment usage time is the time required to use the equipment during waste treatment preparation
  • the second time coefficient is the coefficient for the time used to use the equipment during waste treatment preparation
  • the rental unit price is the unit price of renting the equipment
  • consumables are the quantity of materials consumed during the dismantling process, specifically the amount of cement or additives consumed, and the consumable unit price is the unit price of the consumable materials
  • the number of various packaging containers is the number of containers used for waste treatment packaging.
  • the equipment usage time and rental unit price only apply to
  • the target loss resource prediction data is the waste disposal cost, which can be calculated by finding the fixed price for dismantling and reorganization from a pre-set fixed price list.
  • Waste Disposal Cost (Number of Waste Packages ⁇ Disposal Site Receiving Price DIM).
  • the number of waste packages and the disposal site receiving price DIM are multiple execution loss resource items.
  • the processing and packaging costs of radioactive waste are closely related to the processing technology and methods. Therefore, the simulation system module simulates the number and type of waste packages generated after preparation, and the disposal cost is calculated to obtain multiple execution loss resource items.
  • the number of waste packages is the number of waste packages generated during the waste disposal process
  • the external site receiving price is the unit price for each waste package received at the external site.
  • the disposal cost for low- and intermediate-level radioactive waste is estimated based on the waste receiving price at the disposal site; the disposal cost for high-level radioactive waste is estimated based on historical experience prices.
  • the disposal unit's quotation is added when setting the basic attributes of the waste container and can be modified when calculating costs.
  • the target resource loss prediction data is the unforeseen cost.
  • the waste disposal and preparation cost can be calculated by finding the fixed price for the unforeseen stage in a pre-set fixed price list. This fixed unforeseen cost is typically calculated as 25% of the pre-set total nuclear facility decommissioning cost in the early stages of a decommissioning project.
  • correlation calculation can be used, where the selected scheme's resource loss prediction model is the formula for calculating the unforeseen cost.
  • the unforeseen cost is analyzed using methods such as Monte Carlo to assess the uncertainty of the decommissioning project's unforeseen costs; this embodiment does not provide specific details.
  • steps S701 to S703 of this embodiment a resource loss prediction model that meets the minimum execution stage is selected.
  • the minimum execution stage is evaluated by selecting the resource loss prediction model and multiple execution resource loss items. This automates the evaluation operation of the minimum execution stage, eliminating the need for manual calculation and efficiently outputting the target resource loss prediction data.
  • the target resource loss prediction data of multiple sub-scheme data are combined according to scheme level information and level association information to obtain the total resource loss prediction data.
  • the target resource loss prediction data is combined level by level according to scheme level information and level association information.
  • the target resource loss prediction data corresponding to sub-scheme data a and minimum execution link c of secondary task A1 are first combined to form secondary resource loss prediction data corresponding to secondary task A1.
  • the secondary resource loss prediction data and the target resource loss prediction data corresponding to minimum execution link b are combined to form the total resource loss prediction data of the target scheme information. Therefore, the calculation of the total resource loss prediction data is simple, and the resource loss prediction data of different levels of tasks can be clearly seen. It is possible to analyze in detail which level of task's minimum execution link has the greatest impact on the resource loss prediction data.
  • the method for predicting the depletion of resources during nuclear facility decommissioning may also include, but is not limited to, steps S801 to S803:
  • Step S801 Extract correction coefficients from the preset correction database according to the sub-scheme category
  • Step S802 Correct the target resource loss prediction data according to the correction coefficient to obtain updated resource loss prediction data
  • Step S803 Update the total resource loss prediction data based on the updated resource loss prediction data to obtain the updated resource prediction data for the target scheme information.
  • the output of multiple execution resource loss items may contain errors, making it difficult to accurately represent the resource losses of each sub-scheme data. Therefore, after completing the evaluation of the target scheme information, it is necessary to correct the total resource loss prediction data to obtain more accurate total resource loss prediction data.
  • a correction database is set in the database module, and the correction database stores multiple correction coefficients. These correction coefficients are correction coefficients for multiple resource items involved in the sub-scheme data, and are stored in the correction database according to the sub-scheme category. Therefore, by extracting coefficients matching the sub-scheme category from the correction database as correction coefficients, correction coefficient extraction is simplified.
  • the correction coefficient can be used to correct the target resource loss prediction data of the sub-scheme data to obtain more accurate process evaluation data as updated resource loss prediction data.
  • the correction coefficient is a time correction coefficient
  • the time correction coefficient represents the ratio between the actual time consumed by the sub-scheme data and the simulation time obtained by the three-dimensional simulation system module
  • the simulation time obtained by the simulation is corrected according to the time correction coefficient to obtain the corrected time
  • the updated resource loss prediction data is calculated according to the corrected time.
  • the sub-scheme data involves multiple resource items, multiple correction coefficients can be obtained to correct the target resource loss prediction data one by one through multiple correction coefficients.
  • the correction coefficients can be a time correction coefficient and a radiation protection correction coefficient, and the target resource loss prediction data of the source item survey is corrected according to the time correction coefficient and the radiation protection correction coefficient.
  • step S803 of some embodiments when the target loss resource prediction data of the sub-scheme data is updated, the total loss resource prediction data can be further corrected based on the updated loss resource prediction data to obtain more accurate updated resource prediction data, so that the resource loss prediction of the target scheme information is more accurate.
  • the correction coefficient associated with the sub-scheme data is found, and the target loss resource prediction data of the sub-scheme data is corrected according to the correction coefficient to obtain more accurate updated loss resource prediction data, so as to achieve more accurate resource loss prediction for nuclear facility decommissioning.
  • the method for predicting the depletion of resources during nuclear facility decommissioning may also include, but is not limited to, steps S901 to S902:
  • Step S901 Compare the total resource loss prediction data with the preset expected resource loss data to obtain the evaluation comparison result
  • Step S902 If the evaluation and comparison results show that the total resource loss prediction data is greater than the expected resource loss data, adjust the target scheme information according to the total resource loss prediction data to obtain updated scheme data.
  • the expected resource depletion data is user-preset depletion resource data, which serves as the expected depletion resource for nuclear facility decommissioning. Therefore, an evaluation comparison result is obtained by comparing the total predicted resource depletion data with the preset expected resource depletion data. It should be noted that if the evaluation comparison result shows that the total predicted resource depletion data is less than or equal to the expected resource depletion data, it indicates that the implementation of the nuclear facility decommissioning plan meets the preset expected resource depletion. If the evaluation comparison result shows that the total predicted resource depletion data is greater than the expected resource depletion data, it indicates that the implementation of the nuclear facility decommissioning plan will exceed the preset expected resource depletion.
  • the total predicted resource depletion data is the predicted decommissioning cost of the nuclear facility
  • the expected resource depletion data is the preset decommissioning preparation cost
  • the predicted decommissioning cost exceeds the decommissioning preparation cost, it indicates that the nuclear facility decommissioning plan is difficult to implement.
  • step S902 of some embodiments when the evaluation and comparison result shows that the total resource loss prediction data is greater than the expected resource loss data, it indicates that the nuclear facility decommissioning plan is difficult to implement, and the nuclear facility decommissioning plan needs to be adjusted to obtain an updated nuclear facility decommissioning plan.
  • the process of adjusting the target plan information that is, adjusting the target plan information according to the total resource loss prediction data, i.e., adjusting the nuclear facility decommissioning plan, involves re-predicting resource loss for the adjusted nuclear facility decommissioning plan until the updated resource loss data is less than or equal to the expected resource loss data. Only then is the adjusted nuclear facility decommissioning plan adopted as the updated nuclear facility decommissioning plan.
  • this embodiment adjusts the target scheme information based on the total resource loss prediction data, that is, adjusts the specific operation steps of the nuclear facility decommissioning scheme to optimize the decommissioning operation of the nuclear facility decommissioning scheme and enable the decommissioning operation of the nuclear facility to save resource loss.
  • the difficulty of implementing the nuclear facility decommissioning plan is determined by comparing the total loss resource prediction data and the expected loss resource data.
  • the target plan information is adjusted according to the total loss resource prediction data to obtain an updated plan that can be implemented for nuclear facility decommissioning, so that the decommissioning operation of the nuclear facility can be realized.
  • cost estimation of target scheme information is taken as an example.
  • the nuclear facility decommissioning scheme is divided into multiple minimum execution stages by hierarchical classification in the scheme editing module.
  • cost estimation is required for each minimum execution stage, the relevant execution parameters required for the implementation of the sub-scheme data are first extracted from the database module to obtain the target scheme execution association parameters.
  • the target scheme execution association parameters are data on tools, equipment, materials, manpower, etc., involved in the execution of the sub-scheme data.
  • the 3D simulation system module simulates each minimum execution stage based on the target scheme execution association parameters to output which resource items will be consumed during the implementation of the minimum execution stage as execution loss resource items.
  • the loss resource prediction module performs a detailed cost estimation for each minimum execution stage based on multiple execution loss resource items to output the cost estimation value for each sub-scheme data. Finally, according to the level information of the minimum execution stage in the scheme editing module, the multiple cost estimation values are superimposed level by level to calculate the decommissioning cost of the nuclear facility decommissioning scheme. This ensures accurate calculation of the decommissioning cost of the nuclear facility decommissioning scheme and eliminates the need for manual input of resource losses for each resource item; it only requires simulation, saving a significant amount of manpower.
  • This application embodiment also provides a resource depletion prediction device for nuclear facility decommissioning, which can implement the above-mentioned resource depletion prediction method for nuclear facility decommissioning.
  • the device includes:
  • the scheme acquisition module 1001 is used to acquire target scheme information for the decommissioning of nuclear facilities
  • the decomposition module 1002 is used to decompose the target scheme information to obtain multiple sub-scheme data and scheme-level information and level association information for each sub-scheme data; among them, the sub-scheme data is used to characterize the minimum execution steps of nuclear facility decommissioning.
  • the resource item acquisition module 1003 is used to acquire the resource loss items of the execution sub-scheme data, and obtain multiple execution resource loss items;
  • the resource loss prediction module 1004 is used to predict the resource loss of each sub-scheme based on multiple execution resource loss items to obtain target resource loss prediction data.
  • the resource loss combination module 1005 is used to combine the target resource loss prediction data of multiple sub-schemes based on scheme-level information and level association information to obtain the total resource loss prediction data of the target scheme information.
  • the specific implementation of the resource depletion prediction device for nuclear facility decommissioning is basically the same as the specific implementation of the resource depletion prediction method for nuclear facility decommissioning described above, and will not be repeated here.
  • This application also provides a computer device, which includes a memory and a processor.
  • the memory stores a computer program
  • the processor executes the computer program to implement the aforementioned method for predicting the depletion of nuclear facilities during decommissioning.
  • This computer device can be any smart terminal, including tablet computers, in-vehicle computers, etc.
  • the computer device includes:
  • the processor 1101 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.
  • a general-purpose CPU Central Processing Unit
  • microprocessor microprocessor
  • ASIC application-specific integrated circuit
  • the memory 1102 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM).
  • the memory 1102 can store the operating system and other application programs.
  • the relevant program code is stored in the memory 1102 and is called and executed by the processor 1101 to execute the nuclear facility decommissioning resource prediction method of the embodiments of this application.
  • Input/output interface 1103 is used to implement information input and output
  • the communication interface 1104 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).
  • wired means such as USB, network cable, etc.
  • wireless means such as mobile network, WIFI, Bluetooth, etc.
  • Bus 1105 transmits information between various components of the device (e.g., processor 1101, memory 1102, input/output interface 1103, and communication interface 1104);
  • the processor 1101, memory 1102, input/output interface 1103 and communication interface 1104 are connected to each other within the device via bus 1105.
  • This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for predicting resource depletion during the decommissioning of nuclear facilities.
  • Memory as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs.
  • memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device.
  • memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • the method, apparatus, equipment, and storage medium for predicting resource depletion in nuclear facility decommissioning decompose target scheme information into sub-scheme data, automatically determine the resource consumption incurred during the execution of each sub-scheme data as execution loss resource items, predict the loss resources of each sub-scheme data based on multiple execution loss resource items to obtain target loss resource prediction data, and then combine the multiple target loss resource prediction data based on scheme level information and level association information to obtain the total loss resource prediction data of the target scheme information.
  • the operation of predicting resource depletion in nuclear facility decommissioning is automated and accurate.
  • the device embodiments described above are merely illustrative.
  • the units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
  • At least one (item) means one or more, and “more than” means two or more.
  • “And/or” is used to describe the relationship between related objects, indicating that three relationships can exist. For example, “A and/or B” can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character “/” generally indicates that the preceding and following related objects are in an “or” relationship. "At least one (item) of the following” or similar expressions refer to any combination of these items, including any combination of single or plural items.
  • At least one (item) of a, b, or c can represent: a, b, c, "a and b", “a and c", “b and c", or "a and b and c", where a, b, and c can be single or multiple.
  • the disclosed apparatus and methods can be implemented in other ways.
  • the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods.
  • multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.
  • the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
  • the units described above 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.
  • the functional units in the various embodiments of this application 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.
  • the integrated unit can be implemented in hardware or as a software functional unit.
  • the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • This computer software product is stored in a storage medium and includes multiple 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 of the various embodiments of this application.
  • the aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

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Abstract

本申请实施例提供了一种核设施退役的损耗资源预测方法和装置、设备及存储介质，属于核电站设施技术领域。该方法包括：获取核设施退役的目标方案信息；对目标方案信息进行分解处理得到多个子方案数据和每一子方案数据的方案级别信息、级别关联信息；获取执行子方案数据的损耗资源项得到多个执行损耗资源项；基于多个执行损耗资源项对每一子方案数据进行损耗资源预测得到目标损耗资源预测数据；根据方案级别信息和级别关联信息将多个子方案数据的目标损耗资源预测数据进行组合，得到目标方案信息的总损耗资源预测数据。本申请实施例能够降低因预测准确度不足而重复计算的人力，且提高核设施退役的损耗资源预测精度。

Description

核设施退役的损耗资源预测方法和装置、设备及存储介质 技术领域
本申请涉及核电站设施技术领域，尤其涉及一种核设施退役的损耗资源预测方法和装置、设备及存储介质。
背景技术
核设施退役具有实施周期长、实施难度大、不可预见因素多等特点，且在核设施退役实施过程会产生大量的资源损耗，所以在核设施退役前需要对核设施退役的损耗资源进行预测，以便于根据预测的损耗资源对核设施退役的操作步骤进行调整。
相关技术中，核设施退役的损耗资源预测主要通过手动输入核设施退役涉及的各项指标、损耗资源项以及损耗资源预测算法，再按照从下到上的方式逐级汇总核设施退役产生损耗资源以得到整个核设施退役的资源损耗预测数据。但整个过程需要人工手动输入大量的数据，不仅耗费大量的人力，且部分数据难以确定，影响核设施退役的损耗资源预测的准确性。因此，如何降低人力且准确实现核设施退役的损耗资源预测，成为了亟待解决的技术问题。
发明内容
本申请实施例的主要目的在于提出一种核设施退役的损耗资源预测方法和装置、设备及存储介质，旨在降低人力且准确实现核设施退役的损耗资源预测。
为实现上述目的，本申请实施例的第一方面提出了一种核设施退役的损耗资源预测方法，所述方法包括：
获取核设施退役的目标方案信息；
对所述目标方案信息进行分解处理，得到多个子方案数据和每一所述子方案数据的方案级别信息、级别关联信息；其中，所述子方案数据用于表征核设施退役的最小执行环节；
获取执行所述子方案数据的损耗资源项，得到多个执行损耗资源项；
基于多个所述执行损耗资源项对每一所述子方案数据进行损耗资源预测，得到目标损耗资源预测数据；
根据所述方案级别信息和所述级别关联信息将多个所述子方案数据的所述目标损耗资源预测数据进行组合，得到所述目标方案信息的总损耗资源预测数据。
在一些实施例，所述对所述目标方案信息进行分解处理，得到多个子方案数据和每一所述子方案数据的方案级别信息、级别关联信息，包括：
对所述目标方案信息进行拆分处理，得到多个所述子方案数据；
获取多个所述子方案数据之间的层级关系信息；
基于所述层级关系信息确定每一所述子方案数据的所述级别关联信息；
获取每一所述子方案数据的类别，得到子方案类别；
根据所述子方案类别和所述级别关联信息对所述子方案数据进行分级处理，得到每一所述子方案数据的所述方案级别信息。
在一些实施例，所述获取执行所述子方案数据的损耗资源项，得到多个执行损耗资源项，包括：
获取所述子方案数据的子方案类别，基于所述子方案类别从预设的候选方案执行关联参数中筛选出目标方案执行关联参数，并根据所述目标方案执行关联参数对所述子方案数据进行仿真模拟，得到多个所述执行损耗资源项；其中，所述目标方案执行关联参数作为所述核设施退役的最小执行环节做仿真模拟的参数；
或者，
基于所述目标方案执行关联参数生成方案关联参数提示信息，并接收对所述方案关联参数提示信息反馈的参数损耗资源项，基于所述目标方案执行关联参数和所述参数损耗资源项生成多个所述执行损耗资源项。
在一些实施例，所述基于多个所述执行损耗资源项对每一所述子方案数据进行损耗资源预测，得到目标损耗资源预测数据，包括：
获取所述子方案数据的子方案信息；
根据所述子方案信息从预设的候选方案损耗资源预测模型中筛选出选定方案损耗资源预测模型；
通过所述选定方案损耗资源预测模型和多个所述执行损耗资源项对所述子方案数据进行损耗资源预测，得到所述目标损耗资源预测数据。
在一些实施例，所述执行损耗资源项包括：执行损耗量、执行相关系数、执行时长和执行单价；所述通过所述选定方案损耗资源预测模型和多个所述执行损耗资源项对所述子方案数据进行损耗资源预测，得到所述目标损耗资源预测数据，包括：
通过所述选定方案损耗资源预测模型对所述执行损耗量、所述执行相关系数、所述执行时长和所述执行单价进行损耗资源预测，得到所述目标损耗资源预测数据。
在一些实施例，在所述基于多个所述执行损耗资源项对每一所述子方案数据进行损耗资源预测，得到目标损耗资源预测数据之后，所述方法还包括：
根据所述子方案类别从预设的修正数据库中提取出修正系数；
根据所述修正系数对所述目标损耗资源预测数据进行修正处理，得到更新损耗资源预测数据；
根据所述更新损耗资源预测数据对所述总损耗资源预测数据进行更新，得到目标方案信息的更新资源预测数据。
在一些实施例，在所述根据所述方案级别信息和所述级别关联信息将多个所述子方案数据的所述目标损耗资源预测数据进行组合，得到所述目标方案信息的总损耗资源预测数据之后，所述方法包括：
将所述总损耗资源预测数据和预设的期望损耗资源数据进行比较，得到评估比较结果；
若所述评估比较结果为所述总损耗资源预测数据大于所述期望损耗资源数据，根据所述总损耗资源预测数据对所述目标方案信息进行方案调整，得到更新方案数据。
为实现上述目的，本申请实施例的第二方面提出了一种核设施退役的损耗资源预测装置，所述装置包括：
方案获取模块，用于获取核设施退役的目标方案信息；
分解模块，用于对所述目标方案信息进行分解处理，得到多个子方案数据和每一所述子方案数据的方案级别信息和级别关联信息；其中，所述子方案数据用于表征核设施退役的最小执行环节；
资源项获取模块，用于获取执行所述子方案数据的损耗资源项，得到多个执行损耗资源项；
损耗资源预测模块，用于基于多个所述执行损耗资源项对每一所述子方案数据进行损耗资源预测，得到目标损耗资源预测数据；
损耗资源组合模块，用于根据所述方案级别信息和所述级别关联信息将多个所述子方案数据的所述目标损耗资源预测数据进行组合，得到所述目标方案信息的总损耗资源预测数据。
为实现上述目的，本申请实施例的第三方面提出了计算机设备，所述计算机设备包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现第一方面所述的核设施退役的损耗资源预测方法。
为实现上述目的，本申请实施例的第四方面提出了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现上述第一方面所述的方法。
本申请提出的核设施退役的损耗资源预测方法和装置、设备及存储介质，其通过将目标方案信息分解成子方案数据，然后自动获取子方案数据的执行损耗资源项，使得执行损耗资源项获取自动化，无需人工手动输入，节省人力。同时，确定了执行损耗资源项后，根据执行损耗资源项对子方案数据做损耗资源预测得到目标损耗资源预测数据，最后基于方案级别信息和级别关联信息将多个目标损耗资源预测数据组合起来得到整个目标方案信息的总损耗资源预测数据，使得目标方案信息的损耗资源预测更加简单且准确，且减少人工手动计算存在重复计算的情况，提升核设施退役过程损耗资源预测的效率。
附图说明
图1是本申请实施例提供的核设施退役的损耗资源预测方法的流程图；
图2是图1中的步骤S102的流程图；
图3是本申请实施例提供的核设施退役的损耗资源预测方法中目标方案信息分解的示意图；
图4是本申请实施例提供的核设施退役的损耗资源预测方法中核设施退役的损耗资源预测系统的系统框架图；
图5是图1中的步骤S103的流程图；
图6是图1中的步骤S103的另一流程图；
图7是图1中的步骤S104的流程图；
图8是本申请另一实施例提供的核设施退役的损耗资源预测方法的流程图；
图9是本申请另一实施例提供的核设施退役的损耗资源预测方法的流程图；
图10是本申请实施例提供的核设施退役的损耗资源预测装置的结构示意图；
图11是本申请实施例提供的计算机设备的硬件结构示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用于解释本申请，并不用于限定本申请。
需要说明的是，虽然在装置示意图中进行了功能模块划分，在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于装置中的模块划分，或流程图中的顺序执行所示出或描述的步骤。说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。
除非另有定义，本文所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同。本文中所使用的术语只是为了描述本申请实施例的目的，不是旨在限制本申请。
首先，对本申请中涉及的若干名词进行解析：
核设施退役：是核设施使用期满或因其他原因停止服役后，为了充分考虑工作人员和公众的健康与安全及环境保护而采取的行动。退役的最终目的是实现场址不受限制的开放和使用。核设施退役方法有立即拆除、延缓拆除、就地掩埋等方式。
核设施退役的源项调查：是指对核设施退役过程中涉及的各种源头、因素、步骤和可能的影响进行的调查和评估。
拆除解体：是指将核设施(如核电站、核燃料加工厂等)永久关闭并拆除的过程。
核设施退役的废物处理：核设施拆除解体的过程中，废物处理尤为重要，因为其中可能包含放射性废物和其他有害物质。核设施拆除解体过程中废物处理的一般步骤：1、识别和分类；2、分离和处理；3、处置；4、监测和控制；5、记录和报告。
核设施退役具有周期厂、难度大、不确定与不可预见因素多等特点，在退役实施过程中需要投入大量的人力和资金。因此在退役开始前需要对核设施退役方案进行损耗资源预测，以分析出当前核设施退役方案是否符合经济效益，并根据预测出的损耗资源调整核设施退役方案，让核设施后期按照核设施退役方案进行高效且节省成本的退役。
相关技术中，存在一款评估核设施退役方案的软件，采用单位因素法对核设施退役方案进行损耗资源预测，并通过手动输入核设施方案中涉及的各项指标、定义资源损耗项以及资源损耗计算公式等，并自动根据手动输入的资源损耗计算公式、各项指标和资源损耗项以生成核设施方案中各项执行环节的详细报表。但是，针对不同的核设施退役方案需要由人工每次都手动的输入各项指标、资源损耗项和资源损耗计算公式，若需要对多个核设施退役方案做损耗资源预测，需要耗费大量的人力。而且在核设施退役的损耗资源预测中，部分核设施方案内的一些资源损耗项难以确定，会影响核设施退役损耗资源的预测精度。
基于此，本申请实施例提供了一种核设施退役的损耗资源预测方法和装置、设备及存储介质，旨在实现核设施退役的损耗资源预测自动化，节省人力，提高核设施退役损耗资源预测的精确度。
本申请实施例提供的核设施退役的损耗资源预测方法和装置、设备及存储介质，具体通过如下实施例进行说明，首先描述本申请实施例中的核设施退役的损耗资源预测方法。
本申请实施例可以基于人工智能技术对相关的数据进行获取和处理。其中，人工智能(Artificial Intelligence，AI)是利用数字计算机或者数字计算机控制的机器模拟、延伸和扩展人的智能，感知环境、获取知识并使用知识获得最佳结果的理论、方法、技术及应用系统。
人工智能基础技术一般包括如传感器、专用人工智能芯片、云计算、分布式存储、大数据处理技术、操作/交互系统、机电一体化等技术。人工智能软件技术主要包括计算机视觉技术、机器人技术、生物识别技术、语音处理技术、自然语言处理技术以及机器学习/深度学习等几大方向。
本申请实施例提供的核设施退役的损耗资源预测方法，涉及核电厂核设施技术领域。本申请实施例提供的核设施退役的损耗资源预测方法可应用于终端中，也可应用于服务器端中，还可以是运行于终端或服务器端中的软件。在一些实施例中，终端可以是智能手机、平板电脑、笔记本电脑、台式计算机等；服务器端可以配置成独立的物理服务器，也可以配置成多个物理服务器构成的服务器集群或者分布式系统，还可以配置成提供云服务、云数据库、云计算、云函数、云存储、网络服务、云通信、中间件服务、域名服务、安全服务、CDN以及大数据和人工智能平台等基础云计算服务的云服务器；软件可以是实现核设施退役的损耗资源预测方法的应用等，但并不局限于以上形式。
本申请可用于众多通用或专用的计算机系统环境或配置中。例如：个人计算机、服务器计算机、手持设备或便携式设备、平板型设备、多处理器系统、基于微处理器的系统、置顶盒、可编程的消费电子设备、网络PC、小型计算机、大型计算机、包括以上任何系统或设备的分布式计算环境等等。本申请可以在由计算机执行的计算机可执行指令的一般上下文中描述，例如程序模块。一般地，程序模块包括执行特定任务或实现特定抽象数据类型的例程、程序、对象、组件、数据结构等等。也可以在分布式计算环境中实践本申请，在这些分布式计算环境中，由通过通信网络而被连接的远程处理设备来执行任务。在分布式计算环境中，程序模块可以位于包括存储设备在内的本地和远程计算机存储介质中。
图1是本申请实施例提供的核设施退役的损耗资源预测方法的一个可选的流程图，图1中的方法可以包括但不限于包括步骤S101至步骤S105。
步骤S101，获取核设施退役的目标方案信息；
步骤S102，对目标方案信息进行分解处理，得到多个子方案数据和每一子方案数据的方案级别信息、级别关联信息；其中，子方案数据用于表征核设施退役的最小执行环节；
步骤S103，获取执行子方案数据的损耗资源项，得到多个执行损耗资源项；
步骤S104，基于多个执行损耗资源项对每一子方案数据进行损耗资源预测，得到目标损耗资源预测数据；
步骤S105，根据方案级别信息和级别关联信息将多个子方案数据的目标损耗资源预测数据进行组合，得到目标方案信息的总损耗资源预测数据。
本申请实施例所示意的步骤S101至步骤S105，通过将目标方案信息分解成多个子方案数据，并确定每一子方案数据的方案级别信息和级别关联信息，通过获取子方案数据的损耗资源项得到执行损耗资源项，以根据多个执行损耗资源项对每一子方案数据进行损耗资源预测以得到目标损耗资源预测数据，最后根据方案级别信息将多个目标损耗资源预测数据组合成目标方案信息的总损耗资源预测数据。其中，子方案数据用于表征核设施退役的最小执行环节。因此，通过将整个核设施退役的整个目标方案拆分成多个子方案数据，并将每一子方案数据涉及的执行损耗资源项采集，然后计算出每一子方案数据会产生的损耗资源，再将所有的损耗资源按照子方案数据的级别进行组合以确定核设施完成退役所损耗的资源数据，使得核设施退役的损耗资源预测无需人工手动输入大量数据和损耗资源计算公式，且核设施退役的损耗资源预测自动化且准确，降低核设施退役的损耗资源预测的人力，提升核设施退役损耗资源的预测准确性。
在一些实施例的步骤S101中，目标方案信息记载了核设施退役的整个操作流程，以及每一流程需要执行哪些最小执行环节。具体地，核设施退役的整个操作流程可以按照阶段划分，每一阶段对应不同的流程环节，且每一流程环节会设置多个核设施退役的最小执行环节。
需要说明的是，如图4所示，核设施退役的损耗资源预测方法应用于核设施退役的损耗资源预测系统，且核设施退役的损耗资源预测系统包括方案编辑模块、损耗资源预测模块、三维仿真系统模块和数据库模块。其中，数据库模块用于支持核设施退役场景仿真的模型、工器具模型、三维剂量场等模型数据存储，除此之外，数据库模块还存储核设施退役执行相关的执行损耗资源项，以及评估每一子方案数据的评估公式和执行损耗资源项的修正系数。三维仿真系统模块在核设施退役中最小执行环节进行关联仿真时调用，并配置核设施退役中最小执行环节仿真过程需要的相关配置数据。
请参阅图2，在一些实施例中，步骤S102可以包括但不限于包括步骤S201至步骤S205：
步骤S201，对目标方案信息进行拆分处理，得到多个子方案数据；
步骤S202，获取多个子方案数据之间的层级关系信息；
步骤S203，基于层级关系信息确定每一子方案数据的级别关联信息；
步骤S204，获取每一子方案数据的类别，得到子方案类别；
步骤S205，根据子方案类别和级别关联信息对所述子方案数据进行分级处理，得到每一子方案数据的所述方案级别信息。
在一些实施例的步骤S201至步骤S202中，在退役方案编辑模块上完成目标方案信息的拆分，也即按照目标方案信息中的退役执行阶段先初步拆分，再按照每一阶段内的执行流程进一步拆分，直至拆分到最小的最小执行环节，以得到子方案数据。其中，子方案数据在拆分过程会记录每一子方案数据之间的层级关系信息，以根据层级关系信息确定每一子方案数据的级别关联信息。具体地，如图3所示，目标核设施退役需要经历四个阶段，第一阶段包含研发、退役前活动、设备关闭活动、厂址基建和运行，第二阶段包括乏燃料安全贮存和常规拆除、捣毁，第三阶段包括控制区内的拆除活动、废物处理、贮存和处置，第四阶段包括厂址恢复、安全封存或掩埋的额外活动和杂项支出。其中，每一退役执行阶段内的执行流程可以按照层级划分，以划分到最小执行环节。在本实施例中，核设施退役的最小执行环节存在18种，且18种最小执行环节分别为①、源项调查；②、清理去污；③、拆除解体；④、废物处置；⑤、废物整备运输；⑥、系统或设施改造；⑦、环境恢复；⑧、环境整治；⑨、人员雇佣；⑩、设备采购；材料采购；项目管理；项目收益；前期工作准备；工程设计及技术服务；监管及竣工验收；工程其他活动；不可预见活动。当将核设施退役的方案拆分成最小执行环节后，需要确定最小执行环节之间的级别关联信息，以根据级别关联信息确定不同最小执行环节之间的关联情况。例如，在退役前活动环节，同一层级关联计算的最小执行环节包括①、③、⑤，且标号为①的最小执行环节为源项调查，标号为③的最小执行环节为拆除解体，标号为⑤的最小执行环节为废物整备运输。由此可知，同一层级可能会存在多个关联的最小执行环节来并行实现该层级的执行。
在一些实施例的步骤S204中，通过获取每一子方案数据的子方案类别，也即表征执行子方案数据的任务活动类型。需要说明的是，在退役方案编辑模块将目标方案信息拆分的过程，是按照任务活动类型逐级拆分的，每一层级根据任务活动类型和级别关联信息向下细分成更低层的执行环节，直至划分成最小执行环节。如图4所示，在退役方案编辑模块，将核设施退役的方案进行分解，先按照活动任务类型划分成一级任务A和一级任务B，再进一步划分，且划分一级任务B成最小执行环节b，而一级任务A还可以划分成二级任务A1，再将二级任务A1划分成最小执行环节a和最小执行环节c。因此，通过提前在退役方案编辑模块将目标方案信息划分成多个最小执行环节，并确定每一最小执行环节的执行级别信息和级别关联信息。例如，最小执行环节c的环节级别信息为二级，最小执行环节b的环节级别信息为一级。需要说明的是，每一最小执行环节的环节级别信息和级别关联信息用于在最后资源损耗组合时，可以逐级计算，得到准确的总损耗资源预测数据。
在本实施例所示意的步骤S201至步骤S205，通过将目标方案信息拆分成多个子方案数据，并确定每一子方案数据的方案级别信息和级别关联信息，以便于后续计算目标方案信息的总损耗资源预测数据更加方便。
在一些实施例的步骤S103中，子方案数据的执行损耗资源项，表征子方案数据执行过程需要产生哪些项的资源损耗，以将子方案数据会产生各项资源损耗定义为执行损耗资源项。需要说明的是，获取每一子方案数据的执行损耗资源项可以通过仿真模拟确定，也可以通过手动输入来确定。其中，仿真模拟方式和手动输入方式确定执行损耗资源项的选择步骤为，先获取每一子方案数据的计算优选信息，并根据计算优选信息在仿真模拟方式和手动输入方式中选出损耗资源项计算方式。需要说明的是，若子方案数据的计算优选信息为仿真模拟方式，则选择仿真模拟方式作为损耗资源项的计算方式，若计算优选信息为手动输入方式，则选择手动输入方式作为损耗资源项的计算方式。具体地，仿真模拟需要借助三维仿真系统模块，且三维仿真系统模块内设置装配、布置、切割、去污、拆除、整备、分拣、转运和吊装等模拟功能，以通过三维仿真系统模块仿真模拟出子方案数据执行过程会产生哪些资源损耗，也即最小执行环节的实施会产生哪些资源损耗，以确定执行损耗资源项。
请参阅图5，在一些实施例中，若采用仿真计算确定多个执行损耗资源项，步骤S103可以为步骤S501：
步骤S501，获取子方案数据的子方案类别，基于子方案类别从预设的候选方案执行关联参数中筛选出目标方案执行关联参数，并根据目标方案执行关联参数对子方案数据进行仿真模拟，得到多个执行损耗资源项；其中，目标方案执行关联参数作为核设施退役的最小执行环节做仿真模拟的参数。
在一些实施例的步骤S501中，候选方案执行关联参数作为子方案数据做仿真模拟会涉及的仿真模拟配置参数，也属于子方案数据执行的关联参数。具体地，候选方案执行关联参数可以是执行子方案数据涉及的工具、装备、物资、人力等数据。需要说明的是，不同子方案类别对应不同的候选方案执行关联参数，所以将子方案数据的子方案类别对应的候选方案执行关联参数作为目标方案执行关联参数。
通过将目标方案执行关联参数输入至三维仿真系统模块，以通过三维仿真系统模块对子方案数据做仿真模拟以输出子方案数据在执行过程会产生哪些资源损耗，以得到执行损耗资源项。具体地，子方案数据的执行损耗资源项，为执行核设施退役过程最小执行环节涉及的工具、装备、物资、人力等数据时产生的资源损耗。
在本实施例所示意的步骤S501，通过采用三维仿真系统模块基于目标方案执行关联参数对子方案数据做仿真模拟，以仿真出子方案数据执行过程会产生哪些项的资源损耗以得到执行损耗资源项，使得执行损耗资源项获取自动化，无需人工手动输入，节省人力。
需要说明的是，当仿真后，若存在无法仿真得到的执行损耗资源项，可以通过手动输入的方式完善执行损耗资源项。
请参阅图6，在一些实施例中，步骤S103还可以包括步骤S601：
步骤S601，基于目标方案执行关联参数生成方案关联参数提示信息，并接收对方案关联参数提示信息反馈的参数损耗资源项，基于目标方案执行关联参数和参数损耗资源项生成多个执行损耗资源项。
在一些实施例的步骤S601中，方案关联参数提示信息用于展示目标方案执行关联参数，以便于用户通过方案关联参数提示信息可以知晓需要手动输入子方案数据实施过程需要的执行损耗资源项，也即输入执行核设施退役过程中最小执行环节涉及的工具、装备、物资、人力等数据，以得到多个执行损耗资源项。参数损耗资源项是用户根据方案关联参数提示信息输入的内容，且对应于目标方案执行关联参数的每一输入项，所以通过将目标方案执行关联参数和对应的参数损耗资源项进行匹配，以得到执行子方案数据需要损耗哪些资源数据以得到多个执行损耗资源项。
在本实施例所示意的步骤S601，通过采用方案关联参数提示信息的方式提示用户需要手动输入参数项，并接收用户反馈的参数损耗资源项，然后基于参数损耗资源项和目标方案执行关联参数做匹配，以确定子方案数据执行需要的执行损耗资源项，实现执行损耗资源项的完整补充，以提高核设施退役的损耗资源预测的准确性。
请参阅图7，在一些实施例中，步骤S104可以包括但不限于包括步骤S701至步骤S703：
步骤S701，获取子方案数据的子方案信息；
步骤S702，根据子方案信息从预设的候选方案损耗资源预测模型中筛选出选定方案损耗资源预测模型；
步骤S703，通过选定方案损耗资源预测模型和多个执行损耗资源项对子方案数据进行损耗资源预测，得到目标损耗资源预测数据。
在一些实施例的步骤S701至步骤S702中，子方案信息表征子方案数据的信息，通过子方案信息可以知晓完成子方案数据需要耗费哪些资源项。具体地，如图3所示，目标核设施退役的最小执行环节存在18种，也即子方案数据存在18种，分别为源项调查，清理去污，拆除解体，废物处置，废物整备运输，系统或设施改造，环境恢复，环境整治，人员雇佣，设备采购，材料采购，项目管理，项目收益，前期工作准备，工程设计及技术服务，监管及竣工验收，工程其他活动和不可预见活动，且每一最小执行环节的损耗资源数据都需要涉及执行损耗资源项的复杂计算。因此，本实施例基于子方案信息从候选方案损耗资源预测模型筛选出选定方案损耗资源预测模型，且选定方案损耗资源预测模型是评估最小执行环节的模型，以实现子方案数据的关联评估。具体地，候选方案损耗资源预测模型存储于数据库模块中，具体为资源计算公式，用于计算最小执行环节实施产生的资源损耗。
在一些实施例中，根据子方案信息确定子方案数据的目标损耗资源预测数据为固定评估数据时，无需通过选定方案损耗资源预测模型来对子方案数据进行资源损耗预测。
在一些实施例的步骤S703中，通过选定方案损耗资源预测模型和多个损耗资源项对子方案数据进行资源损耗预测，具体是基于选定方案损耗资源预测模型中的未知数，且资源数据项包含资源损耗类别和资源损耗值，将每一资源损耗类别对应的资源损耗值代入选定方案损耗资源预测模型中的未知数，以得到子方案数据的目标损耗资源预测数据，使得目标损耗资源预测数据的计算简易。
也即在一些实施例中，执行损耗资源项包括：执行损耗量、执行相关系数、执行时长和执行单价；通过选定方案损耗资源预测模型和多个执行损耗资源项对子方案数据进行损耗资源预测，得到所述目标损耗资源预测数据，包括：
通过选定方案损耗资源预测模型对执行损耗量、执行相关系数、执行时长和执行单价进行损耗资源预测，得到目标损耗资源预测数据。
需要说明的是，选定方案损耗资源预测模型是对应于每一子方案数据的资源损耗计算模型，也即最小执行环节执行时的费用计算模型。在最小执行环节执行过程中，涉及的执行损耗资源项为最小执行环节执行过程消耗的设备、人力、材料等，所以损耗资源项包括执行损耗量、执行相关系数、执行时长和执行单价，执行损耗量为执行最小执行环节时所耗费的材料，执行相关系数为执行过程涉及的系数，执行时长为执行最小执行环节会耗费的时间，执行单价也即为耗费的设备、人力和材料等的单价。
具体地，子方案数据为核设施退役过程的最小执行环节，若最小执行环节为源项调查，那么目标损耗资源预测数据为源项调查费，且源项调查费的计算可以是通过预先设置的固定报价表中查出源项调查的固定报价。除此之外，可以采用关联计算，且关联计算中选定方案损耗资源预测模型为源项调查费的计算公式，且源项调查费的计算公式为源项调查费＝(源项调查工时×人工单价DIM×第一时间系数DIM×辐射防护系数DIM)+(装备使用时间×第二时间系数DIM×租用单价DIM)+(耗材×耗材单价DIM)。其中，源项调查工时、人工单价DIM、第一时间系数DIM、辐射防护系数DIM、装备使用时间、第二时间系数DIM、租用单价DIM、耗材、耗材单价DIM为多个执行损耗资源，也即执行损耗量、执行相关系数、执行时长和执行单价。具体地，若最小执行环节为源项调查，执行单价为源项调查时的人工单价，第一时间系数为做源项调查时人工所耗费时间的系数，辐射防护系数为源项调查使用辐射防护设备或者防护服对应的系数，装备使用时间为源项调查时需要用到装备的时间，第二时间系数为源项调查时使用装备时间的系数，租用单价为装备租用的单价，耗材为源项调查过程需要使用的材料消耗数量，耗材单价为消耗材料的单价。其中，通过三维仿真系统模块仿真源项调查所选用的装备、人员等得到。因此，通过将各项仿真得到的执行损耗资源项输入至源项调查费的计算公式，以得到源项调查费，使得源项调查费的计算操作简易。
若最小执行环节为拆除解体，那么目标损耗资源预测数据为拆除解体费用，且拆除解体费用的计算可以是通过预先设置的固定报价表中查出拆除解体的固定报价。除此之外，可以采用关联计算，关联计算中选定方案损耗资源预测模型为拆除解体费用的计算公式，且拆除解体费用的计算公式为拆除解体费＝(拆除解体工时×人工单价DIM×第一时间系数DIM×辐射防护系数DIM)+(装备使用时间×第二时间系数×租用单价DIM)+(耗材×耗材单价DIM)。其中，拆除解体工时、人工单价DIM、时间系数DIM、辐射防护系数DIM、装备使用时间、时间系数DIM、租用单价DIM、耗材、单价DIM为多个执行损耗资源项，且通过三维仿真系统模块仿真拆除解体时选用的装备、人员得到。具体地，若最小执行环节为拆除解体，人工单价为拆除解体时的人工单价，第一时间系数为做拆除解体时人工所耗费时间的系数，辐射防护系数为拆除解体使用辐射防护设备或者防护服对应的系数，装备使用时间为拆除解体时需要用到装备的时间，第二时间系数为拆除解体时使用装备时间的系数，租用单价为装备租用的单价，耗材为拆除解体过程需要使用的材料消耗数量，耗材单价为消耗材料的单价。其中，未受核污染工程相比，受核污染工程后续需考虑废物的包装处理，因此有些部分对拆除体积和尺寸都有一定要求，而且需要考虑辐射环境作业队工作时长的影响。装备使用时间及租用单价只针对租用设备的情况，如购买设备，则相关费用计入设备采购费。
若最小执行环节为废物处理整备，那么目标损耗资源预测数据为废物处理整备费用，且废物处理整备费用的计算可以是通过预先设置的固定报价表中查出拆除解体的固定报价。除此之外，可以采用关联计算，且关联计算中选定方案损耗资源预测模型为废物处理整备费用的计算公式，且废物处理整备费用的计算公式为废物处理整备费＝(拆操作工时×人工单价DIM×第一时间系数DIM×辐射防护系数DIM)+(装备使用时间×第二时间系数×租用单价DIM)+[耗材(水泥、添加剂)×耗材单价DIM]+(各类包装容器个数×包装单价DIM)。其中，拆操作工时、人工单价DIM、时间系数DIM、辐射防护系数DIM、装备使用时间、时间系数DIM、租用单价DIM、耗材、单价DIM、各类包装容器个数为多个执行损耗资源项，且放射性废物的处理加工、包装费用与处理工艺、方法密切相关，所以通过仿真系统模块模拟废物处理整备时选用的装备、人员、主要材料消耗量(水泥、添加剂等)与任务执行过程得到。具体地，若最小执行环节为废物处理整备，人工单价为废物处理整备时人工的单价，第一时间系数为废物处理整备时人工所耗费时间的系数，辐射防护系数为废物处理整备使用辐射防护设备或者防护服对应的系数，装备使用时间为废物处理整备时需要用到装备的时间，第二时间系数为废物处理整备时使用装备时间的系数，租用单价为装备租用的单价，耗材为拆除解体过程需要使用的材料消耗数量，具体为水泥或者添加剂的消耗量，耗材单价为消耗材料的单价，各类包装容器个数为废物处理包装的容器个数。其中，装备使用时间及租用单价只针对租用设备的情况，如购买设备，则相关费用计入设备采购费。
若最小执行环节为废物处置，那么目标损耗资源预测数据为废物处置费，且废物处置费的计算可以是通过预先的固定报价表中查出拆除解体的固定报价。除此之外，可以采用关联计算，且关联计算中选定方案损耗资源预测模型为废物处置费的计算公式，且废物处置费的计算公式为废物处置费＝(废物货包数量×处置场接收单价DIM)。其中，废物货包数量、处置场接收单价DIM为多个执行损耗资源项，且放射性废物的处理加工、包装费用与处理工艺、方法密切相关，所以通过仿真系统模块模拟经整备后产生的废物货包数量和类型，开展处置费用计算得到多个执行损耗资源项。具体地，废物包数量为废物处置过程中产生的废物包的数量，外置场接收单价为外置场接收每一废物包的单价。需要说明的是，中低放废物处置费用按废物处置场的废物接收单价进行估算；高放废物处置废物参考历史经验单价估算。处置单位报价在设置废物容器基本属性时添加，并可在费用统计时修改。
若最小执行环节为项目管理、工程设计、监管竣工验收等管理相关的费用，可基于预设的固定报价给出此类最小执行环节费用。除此之外，可采用管理费用＝人力投入工时×人工单价DIM的方式进行计算。
若最小执行环节为不可预见环节，那么目标损耗资源预测数据为不可预见费，且废物处理整备费用的计算可以是通过预先设置的固定报价表中查出不可预见环节的固定报价，固定不可预见费用的方式，在退役项目初期，通常按照预先设置的核设施退役总费用的25％计算。除此之外，可以采用关联计算，且关联计算中选定方案损耗资源预测模型为不可预见费的计算公式，且不可预见费是使用蒙特卡罗等方法对退役项目的不可预见费用进行不确定性分析，本实施例不做具体说明。
在本实施例所示意的步骤S701至步骤S703，通过选出符合最小执行环节的选定方案损耗资源预测模型，以通过选定方案损耗资源预测模型和多个执行损耗资源项对最小执行环节进行环节评估，使得最小执行环节的评估操作自动化，无需人工手动计算，可以高效地输出目标损耗资源预测数据。
在一些实施例的步骤S105中，按照方案级别信息和级别关联信息将多个子方案数据的目标损耗资源预测数据进行组合得到总损耗资源预测数据。具体地，通过按照方案级别信息和级别关联信息逐级将目标损耗资源预测数据进行组合。例如，如图4所示，先将二级任务A1对应的子方案数据a和最小执行环节c对应的目标损耗资源预测数据组合成二级任务A1对应的二级损耗资源预测数据，再将二级损耗资源预测数据和最小执行环节b对应的目标损耗资源预测数据组合形成目标方案信息的总损耗资源预测数据，因此，总损耗资源预测数据的计算操作简易，且可以清楚不同级别任务的损耗资源预测数据，能够详细的分析哪一个级别任务下的最小执行环节对损耗资源预测数据的影响最大。
请参阅图8，在一些实施例，在步骤S105之后，核设施退役的损耗资源预测方法还可以包括但不限于包括步骤S801至步骤S803：
步骤S801，根据子方案类别从预设的修正数据库中提取出修正系数；
步骤S802，根据修正系数对目标损耗资源预测数据进行修正处理，得到更新损耗资源预测数据；
步骤S803，根据更新损耗资源预测数据对总损耗资源预测数据进行更新，得到目标方案信息的更新资源预测数据。
需要说明的是，三维仿真系统模块做仿真模拟时输出多个执行损耗资源项可能会存在误差，难以准确地表征子方案数据的各项资源损耗，所以完成目标方案信息的评估后，需要对总损耗资源预测数据进行修正，以得到更加准确的总损耗资源预测数据。
在一些实施例的步骤S801中，修正数据库设置于数据库模块中，且修正数据库内存储多个修正系数，且修正系数为子方案数据中涉及多个资源项的修正系数，且根据子方案类别存储在修正数据库中。因此，通过在修正数据库中提取出与子方案类别匹配的系数作为修正系数，使得修正系数提取简易。
在一些实施例的步骤S802中，提取修正系数后，可以调用修正系数对子方案数据的目标损耗资源预测数据进行修正，以得到更加准确的环节评估数据作为更新损耗资源预测数据。具体地，若修正系数为时间修正系数，且时间修正系数表征子方案数据实际耗费时间与三维仿真系统模块模拟得到的模拟仿真时间之间的比例系数，所以计算目标损耗资源预测数据时，根据时间修正系数对仿真得到的模拟仿真时间进行修正得到修正时间，并根据修正时间计算出更新损耗资源预测数据。需要说明的是，若子方案数据涉及多个资源项，可以得到多个修正系数，以通过多个修正系数逐一修正目标损耗资源预测数据中。例如，若子方案数据为源项调查，修正系数可以为时间修正系数、辐射防护修正系数，并根据时间修正系数、辐射防护修正系数修正源项调查的目标损耗资源预测数据。
在一些实施例的步骤S803中，当子方案数据的目标损耗资源预测数据更新，可以基于更新损耗资源预测数据进一步修正总损耗资源预测数据以得到更加准确的更新资源预测数据，使得目标方案信息的资源损耗预测更加准确。
在本实施例所示意的步骤S801至步骤S803，通过查找出子方案数据关联的修正系数，以根据修正系数修正子方案数据的目标损耗资源预测数据，得到更加准确的更新损耗资源预测数据，以实现更加准确的核设施退役的资源损耗预测。
请参阅图9，在一些实施例中，在步骤S105之后，核设施退役的损耗资源预测方法还可以包括但不限于包括步骤S901至步骤S902：
步骤S901，将总损耗资源预测数据和预设的期望损耗资源数据进行比较，得到评估比较结果；
步骤S902，若评估比较结果为总损耗资源预测数据大于期望损耗资源数据，根据总损耗资源预测数据对目标方案信息进行方案调整，得到更新方案数据。
在一些实施例的步骤S901中，期望损耗资源数据为用户预先设定的损耗资源数据，作为核设施退役的期望损耗资源。因此，通过将总损耗资源预测数据和预设的期望损耗资源数据进行比较，得到评估比较结果。需要说明的是，若评估比较结果为总损耗资源预测数据小于或者等于期望损耗资源数据，以确定核设施退役的方案实施满足预先设定的期望损耗资源，若评估比较结果为总损耗资源预测数据大于期望损耗资源数据，表征核设施退役的方案实施会超出预先设定的期望损耗资源。具体地，若总损耗资源预测数据为核设施退役的退役预测费用，期望损耗资源数据为预先设置的退役筹备费用，若退役预测费用超过退役筹备费用，表征难以实施核设施退役的方案。
在一些实施例的步骤S902中，在评估比较结果为总损耗资源预测数据大于期望损耗资源数据时，表征难以实施核设施退役的方案，需要对核设施退役的方案进行调整得到核设施退役的更新方案。需要说明的是，在对目标方案信息调整的过程，也即按照总损耗资源预测数据对目标方案信息进行调整，也即调整核设施退役的方案。其中，调整过程会重新对调整后的核设施退役方案进行损耗资源预测，直至更新的损耗资源数据小于或等于期望损耗资源数据，才将调整后的核设施退役的方案作为核设施退役的更新方案。
具体地，本实施例基于总损耗资源预测数据对目标方案信息进行调整，也即对核设施退役方案的具体操作步骤进行调整，以优化核设施退役方案的退役操作，让核设施的退役操作能够节省资源损耗。
在本实施例所示意的步骤S901至步骤S902，通过将总损耗资源预测数据和期望损耗资源数据进行比较，以判定实施核设施退役方案的难度，并在总损耗资源预测数据大于期望损耗资源数据时，根据总损耗资源预测数据对目标方案信息进行方案调整，以得到能实施核设施退役的更新方案，便于核设施的退役操作能够实现。
在本申请的实施例中，如图4所示，以目标方案信息的费用估算为例。通过在方案编辑模块上对核设施退役的方案分级划分以得到多个最小执行环节。当需要对每一最小执行环节进行费用估算时，先在数据库模块中提取出子方案数据实现需要的相关执行参数得到目标方案执行关联参数。具体地，目标方案执行关联参数为执行子方案数据涉及的工具、装备、物资、人力等数据，然后由三维仿真系统模块基于目标方案执行关联参数对每一最小执行环节进行仿真模拟，以输出最小执行环节实施过程中会消耗哪些资源项作为执行损耗资源项。损耗资源预测模块基于多个执行损耗资源项对每一最小执行环节进行详细的费用估算，以输出每一子方案数据的费用估算值。最后，按照方案编辑模块中最小执行环节的级别信息，将多个费用估算值逐级叠加计算以得到核设施退役方案的退役费用，使得核设施退役方案的退役费用计算准确且无需人工手动输入各资源项的资源损耗，仅需要通过仿真模拟实现，节省了大量的人力。
请参阅图10，本申请实施例还提供一种核设施退役的损耗资源预测装置，可以实现上述核设施退役的损耗资源预测方法，该装置包括：
方案获取模块1001，用于获取核设施退役的目标方案信息；
分解模块1002，用于对目标方案信息进行分解处理，得到多个子方案数据和每一子方案数据的方案级别信息和级别关联信息；其中，子方案数据用于表征核设施退役的最小执行环节；
资源项获取模块1003，用于获取执行子方案数据的损耗资源项，得到多个执行损耗资源项；
损耗资源预测模块1004，用于基于多个执行损耗资源项对每一子方案数据进行损耗资源预测，得到目标损耗资源预测数据；
损耗资源组合模块1005，用于根据方案级别信息和级别关联信息将多个子方案数据的目标损耗资源预测数据进行组合，得到目标方案信息的总损耗资源预测数据。
该核设施退役的损耗资源预测装置的具体实施方式与上述核设施退役的损耗资源预测方法的具体实施例基本相同，在此不再赘述。
本申请实施例还提供了一种计算机设备，计算机设备包括存储器和处理器，存储器存储有计算机程序，处理器执行计算机程序时实现上述核设施退役的损耗资源预测方法。该计算机设备可以为包括平板电脑、车载电脑等任意智能终端。
请参阅图11，图11示意了另一实施例的计算机设备的硬件结构，计算机设备包括：
处理器1101，可以采用通用的CPU(Central Processing Unit，中央处理器)、微处理器、应用专用集成电路(Application Specific Integrated Circuit，ASIC)、或者一个或多个集成电路等方式实现，用于执行相关程序，以实现本申请实施例所提供的技术方案；
存储器1102，可以采用只读存储器(Read-Only Memory，ROM)、静态存储设备、动态存储设备或者随机存取存储器(Random Access Memory，RAM)等形式实现。存储器1102可以存储操作系统和其他应用程序，在通过软件或者固件来实现本说明书实施例所提供的技术方案时，相关的程序代码保存在存储器1102中，并由处理器1101来调用执行本申请实施例的核设施退役的损耗资源预测方法；
输入/输出接口1103，用于实现信息输入及输出；
通信接口1104，用于实现本设备与其他设备的通信交互，可以通过有线方式(例如USB、网线等)实现通信，也可以通过无线方式(例如移动网络、WIFI、蓝牙等)实现通信；
总线1105，在设备的各个组件(例如处理器1101、存储器1102、输入/输出接口1103和通信接口1104)之间传输信息；
其中处理器1101、存储器1102、输入/输出接口1103和通信接口1104通过总线1105实现彼此之间在设备内部的通信连接。
本申请实施例还提供了一种计算机可读存储介质，该计算机可读存储介质存储有计算机程序，该计算机程序被处理器执行时实现上述核设施退役的损耗资源预测方法。
存储器作为一种非暂态计算机可读存储介质，可用于存储非暂态软件程序以及非暂态性计算机可执行程序。此外，存储器可以包括高速随机存取存储器，还可以包括非暂态存储器，例如至少一个磁盘存储器件、闪存器件、或其他非暂态固态存储器件。在一些实施方式中，存储器可选包括相对于处理器远程设置的存储器，这些远程存储器可以通过网络连接至该处理器。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
本申请实施例提供的核设施退役的损耗资源预测方法和装置、设备及存储介质，其通过将目标方案信息分解成子方案数据，再自动确定每一子方案数据执行过程会产生哪些资源消耗作为执行损耗资源项，根据多个执行损耗资源项对每一子方案数据做损耗资源预测得到目标损耗资源预测数据，再基于方案级别信息和级别关联信息将多个目标损耗资源预测数据进行组合得到目标方案信息的总损耗资源预测数据。因此，通过将核设施退役的方案划分成多个最小执行环节，然后计算每一最小执行环节实施会损耗的资源项，以根据多个损耗资源数据预测出最小执行环节实施会产生的损耗资源，最后汇总多个最小执行环节的损耗资源作为核设施退役的总损耗资源，使得核设施退役的损耗资源预测操作自动化且准确。
本申请实施例描述的实施例是为了更加清楚的说明本申请实施例的技术方案，并不构成对于本申请实施例提供的技术方案的限定，本领域技术人员可知，随着技术的演变和新应用场景的出现，本申请实施例提供的技术方案对于类似的技术问题，同样适用。
本领域技术人员可以理解的是，图中示出的技术方案并不构成对本申请实施例的限定，可以包括比图示更多或更少的步骤，或者组合某些步骤，或者不同的步骤。
以上所描述的装置实施例仅仅是示意性的，其中作为分离部件说明的单元可以是或者也可以不是物理上分开的，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
本领域普通技术人员可以理解，上文中所公开方法中的全部或某些步骤、系统、设备中的功能模块/单元可以被实施为软件、固件、硬件及其适当的组合。
本申请的说明书及上述附图中的术语“第一”、“第二”、“第三”、“第四”等(如果存在)是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
应当理解，在本申请中，“至少一个(项)”是指一个或者多个，“多个”是指两个或两个以上。“和/或”，用于描述关联对象的关联关系，表示可以存在三种关系，例如，“A和/或B”可以表示：只存在A，只存在B以及同时存在A和B三种情况，其中A，B可以是单数或者复数。字符“/”一般表示前后关联对象是一种“或”的关系。“以下至少一项(个)”或其类似表达，是指这些项中的任意组合，包括单项(个)或复数项(个)的任意组合。例如，a，b或c中的至少一项(个)，可以表示：a，b，c，“a和b”，“a和c”，“b和c”，或“a和b和c”，其中a，b，c可以是单个，也可以是多个。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，上述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
上述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括多指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例的方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(Read-Only Memory，简称ROM)、随机存取存储器(Random Access Memory，简称RAM)、磁碟或者光盘等各种可以存储程序的介质。
以上参照附图说明了本申请实施例的优选实施例，并非因此局限本申请实施例的权利范围。本领域技术人员不脱离本申请实施例的范围和实质内所作的任何修改、等同替换和改进，均应在本申请实施例的权利范围之内。

Claims (10)

  1. 一种核设施退役的损耗资源预测方法，其特征在于，所述方法包括：
    获取核设施退役的目标方案信息；
    对所述目标方案信息进行分解处理，得到多个子方案数据和每一所述子方案数据的方案级别信息、级别关联信息；其中，所述子方案数据用于表征核设施退役的最小执行环节；
    获取执行所述子方案数据的损耗资源项，得到多个执行损耗资源项；
    基于多个所述执行损耗资源项对每一所述子方案数据进行损耗资源预测，得到目标损耗资源预测数据；
    根据所述方案级别信息和所述级别关联信息将多个所述子方案数据的所述目标损耗资源预测数据进行组合，得到所述目标方案信息的总损耗资源预测数据。
  2. 根据权利要求1所述的方法，其特征在于，所述对所述目标方案信息进行分解处理，得到多个子方案数据和每一所述子方案数据的方案级别信息、级别关联信息，包括：
    对所述目标方案信息进行拆分处理，得到多个所述子方案数据；
    获取多个所述子方案数据之间的层级关系信息；
    基于所述层级关系信息确定每一所述子方案数据的所述级别关联信息；
    获取每一所述子方案数据的类别，得到子方案类别；
    根据所述子方案类别和所述级别关联信息对所述子方案数据进行分级处理，得到每一所述子方案数据的所述方案级别信息。
  3. 根据权利要求1所述的方法，其特征在于，所述获取执行所述子方案数据的损耗资源项，得到多个执行损耗资源项，包括：
    获取所述子方案数据的子方案类别，基于所述子方案类别从预设的候选方案执行关联参数中筛选出目标方案执行关联参数，并根据所述目标方案执行关联参数对所述子方案数据进行仿真模拟，得到多个所述执行损耗资源项；其中，所述目标方案执行关联参数作为所述核设施退役的最小执行环节做仿真模拟的参数；
    或者，
    基于所述目标方案执行关联参数生成方案关联参数提示信息，并接收对所述方案关联参数提示信息反馈的参数损耗资源项，基于所述目标方案执行关联参数和所述参数损耗资源项生成多个所述执行损耗资源项。
  4. 根据权利要求1所述的方法，其特征在于，所述基于多个所述执行损耗资源项对每一所述子方案数据进行损耗资源预测，得到目标损耗资源预测数据，包括：
    获取所述子方案数据的子方案信息；
    根据所述子方案信息从预设的候选方案损耗资源预测模型中筛选出选定方案损耗资源预测模型；
    通过所述选定方案损耗资源预测模型和多个所述执行损耗资源项对所述子方案数据进行损耗资源预测，得到所述目标损耗资源预测数据。
  5. 根据权利要求4所述的方法，其特征在于，所述执行损耗资源项包括：执行损耗量、执行相关系数、执行时长和执行单价；所述通过所述选定方案损耗资源预测模型和多个所述执行损耗资源项对所述子方案数据进行损耗资源预测，得到所述目标损耗资源预测数据，包括：
    通过所述选定方案损耗资源预测模型对所述执行损耗量、所述执行相关系数、所述执行时长和所述执行单价进行损耗资源预测，得到所述目标损耗资源预测数据。
  6. 根据权利要求2所述的方法，其特征在于，在所述基于多个所述执行损耗资源项对每一所述子方案数据进行损耗资源预测，得到目标损耗资源预测数据之后，所述方法还包括：
    根据所述子方案类别从预设的修正数据库中提取出修正系数；
    根据所述修正系数对所述目标损耗资源预测数据进行修正处理，得到更新损耗资源预测数据；
    根据所述更新损耗资源预测数据对所述总损耗资源预测数据进行更新，得到目标方案信息的更新资源预测数据。
  7. 根据权利要求1至6任意一项所述的方法，其特征在于，在所述根据所述方案级别信息和所述级别关联信息将多个所述子方案数据的所述目标损耗资源预测数据进行组合，得到所述目标方案信息的总损耗资源预测数据之后，所述方法包括：
    将所述总损耗资源预测数据和预设的期望损耗资源数据进行比较，得到评估比较结果；
    若所述评估比较结果为所述总损耗资源预测数据大于所述期望损耗资源数据，根据所述总损耗资源预测数据对所述目标方案信息进行方案调整，得到更新方案数据。
  8. 一种核设施退役的损耗资源预测装置，其特征在于，所述装置包括：
    方案获取模块，用于获取核设施退役的目标方案信息；
    分解模块，用于对所述目标方案信息进行分解处理，得到多个子方案数据和每一所述子方案数据的方案级别信息和级别关联信息；其中，所述子方案数据用于表征核设施退役的最小执行环节；
    资源项获取模块，用于获取执行所述子方案数据的损耗资源项，得到多个执行损耗资源项；
    损耗资源预测模块，用于基于多个所述执行损耗资源项对每一所述子方案数据进行损耗资源预测，得到目标损耗资源预测数据；
    损耗资源组合模块，用于根据所述方案级别信息和所述级别关联信息将多个所述子方案数据的所述目标损耗资源预测数据进行组合，得到所述目标方案信息的总损耗资源预测数据。
  9. 一种计算机设备，其特征在于，所述计算机设备包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现权利要求1至7任一项所述的核设施退役的损耗资源预测方法。
  10. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现权利要求1至7中任一项所述的核设施退役的损耗资源预测方法。
PCT/CN2024/138932 2024-07-02 2024-12-12 核设施退役的损耗资源预测方法和装置、设备及存储介质 Pending WO2026007336A1 (zh)

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