Intelligent assessment method and system for target damage

Abstract

The invention relates to the technical field of intelligent assessment, in particular to a target damage intelligent assessment method and system, and the method comprises the following steps: collecting initial data of target damage, recognizing the initial features of a time sequence, and generating damage baseline features through the measurement of a damage region and the recording of the baseline state of the damage. According to the method, the development of the damage state can be simulated and predicted more accurately by constructing the state transition model, quantifying the relevance between the nodes and adjusting the state transition probability, a scientific basis is provided for real-time monitoring and response, a network structure is updated and optimized to match data changes, and the prediction result is corrected and evaluated by calculating the posterior probability, so that the development of the damage state can be simulated and predicted more accurately. According to the method, the damage prediction model is adjusted and optimized in real time, the economic benefit and the operation efficiency of the coping strategy are improved through differential analysis of the maintenance scheme and optimization of the resource configuration and scheduling strategy, and quantitative data support is provided for decision making.

Description

Technical Field

[0001] The present invention relates to the field of intelligent assessment technology, and in particular to a target damage intelligent assessment method and system. Background Art

[0002] The field of intelligent assessment technology mainly involves the use of artificial intelligence and machine learning technologies to process and analyze various data to achieve fast and automated decision support. It includes steps such as data collection, feature extraction, model training, and result prediction. Through intelligent assessment, the system can simulate the decision-making process of experts, analyze and evaluate complex situations, and is widely used in financial risk control, medical diagnosis, product quality control, safety monitoring and other fields. Especially in scenarios where large amounts of data and complex variables need to be processed, intelligent assessment can significantly improve efficiency and accuracy.

[0003] Among them, the target damage intelligent assessment method refers to the use of intelligent assessment technology to analyze and evaluate the damage of military or security targets. It can automatically analyze the degree of damage to the target, the scope of impact and potential consequences, and is used for the effectiveness evaluation of military operations, the priority judgment of rescue operations or the damage evaluation of security incidents. Through this intelligent assessment, decision makers can obtain key information more quickly, formulate response strategies, and optimize resource allocation.

[0004] Existing intelligent assessment technologies face the problems of slow processing speed and insufficient accuracy when dealing with large-scale and complex data sets. This technology has limitations in real-time data processing and dynamic updating, resulting in the system's performance being inflexible and inefficient when rapid decision-making and response are urgently needed. For example, in financial risk assessment or emergency medical response scenarios, existing technologies fail to adjust prediction models in a timely manner, resulting in misjudgment of risks or delayed processing, causing unnecessary economic losses or affecting the timeliness of patient treatment. The lack of flexibility and innovation in resource allocation and scheduling strategies of existing technologies leads to waste of resources or insufficient response, and cannot effectively adapt to rapidly changing external conditions and needs. The shortcomings indicate that more efficient data processing technologies and more flexible model update mechanisms are needed to improve system performance to meet the needs of modern rapid decision-making. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a target damage intelligent assessment method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a target damage intelligent assessment method, comprising the following steps:

[0007] S1: Collect the initial data of target damage, identify the starting features of the time series, record the baseline state of the damage by measuring the damaged area, and generate the damage baseline features;

[0008] S2: using the damage baseline characteristics, constructing a state transition probability model, quantifying the correlation between state nodes, adjusting the probability of state transition according to time dependency, and obtaining a state transition probability matrix;

[0009] S3: updating the monitoring data of the target damage state through the state transition probability matrix, adjusting the network parameters to match the data changes, and updating the network structure in real time to generate updated network parameters;

[0010] S4: using the updated network parameters, calculating the posterior probability, correcting and evaluating the damage prediction through a dynamic update mechanism, adjusting the prediction model of damage development in combination with real-time data, and obtaining a corrected damage prediction result;

[0011] S5: applying the modified damage prediction result, defining the state space and decision rules in the continuous-time Markov decision process, and generating a damage management decision model by quantitatively analyzing and evaluating the damage degree under differentiated states;

[0012] S6: Based on the damage management decision model, referring to the maintenance cost and response time, a benefit analysis is performed on the differentiated maintenance schemes, resource allocation and scheduling strategies are optimized, and a target maintenance log is obtained.

[0013] As a further solution of the present invention, the damage baseline characteristics include the shape, severity, initial damage amount, damage type and impact range of the damaged area, the state transition probability matrix includes the transition probability from the real-time state to the next state and the adjustment coefficient of the time step, the updated network parameters include new input layer weights, hidden layer weights and output layer weights, the revised damage prediction results include short-term predictions, long-term predictions and confidence intervals, the damage management decision model includes state space definition, decision rules and evaluation criteria, and the target maintenance log includes the time, location, cost and expected maintenance effect of the maintenance activities.

[0014] As a further solution of the present invention, the steps of collecting initial data of target damage, identifying the starting features of the time series, and recording the baseline state of the damage by measuring the damaged area to generate the damage baseline features are specifically as follows:

[0015] S101: Based on the target damage site, conduct initial data collection, record the spatial coordinates of the damage area, use GPS and geographic information system to calibrate the location, and collect time nodes, temperature and humidity to generate an initial file of the damage area;

[0016] S102: using the initial archive of the damaged area, measuring the damage status of the target, using laser scanning to record the damage degree, area and status of the surrounding undamaged areas, and performing data synchronization and archiving processing to generate a damage baseline data set;

[0017] S103: Perform feature recognition through the damage baseline data set, identify key features of the initial stage of target damage through data screening and sorting, extract influencing factors, and generate damage baseline features.

[0018] As a further solution of the present invention, the damage baseline characteristics are used to construct a state transition probability model, and the steps of obtaining a state transition probability matrix are specifically as follows:

[0019] S201: Based on the damage baseline characteristics, identify the state nodes, extract the relationship data between the nodes, record the relationship strength of each pair of nodes, and generate an initialization association matrix;

[0020] S202: extracting time series data using the initialized association matrix, calculating the frequency of occurrence of each state node at a differentiated time point, analyzing the change trend between state nodes, and generating a time dependency matrix;

[0021] S203: Using the time dependency matrix and the initialization association matrix, calculate the transition probability between state nodes, adjust the transition probability values ​​between differentiated state nodes, and output a state transition probability matrix.

[0022] As a further solution of the present invention, the monitoring data of the target damage state is updated through the state transition probability matrix, the network parameters are adjusted to match the data changes, and the network structure is updated in real time. The steps of generating updated network parameters are specifically as follows:

[0023] S301: According to the state transition probability matrix, collect monitoring data of the target damage state, record real-time monitoring values, analyze the relationship between the monitoring values ​​and the state transition probability, extract the state information to be updated, and generate monitoring state data;

[0024] S302: adjusting network parameters based on the monitoring status data, comparing the differences between the real-time network parameters and the monitoring status data, correcting the mismatched parameters item by item, and generating adjusted network parameters;

[0025] S303: Using the adjusted network parameters, update the network structure in real time, reconfigure network nodes and connection relationships, verify the matching degree between the new parameters and the network structure, and generate updated network parameters.

[0026] As a further solution of the present invention, the steps of using the updated network parameters to calculate the posterior probability, correcting and evaluating the damage prediction through a dynamic update mechanism, and adjusting the prediction model of damage development in combination with real-time data to obtain the corrected damage prediction result are specifically as follows:

[0027] S401: using the updated network parameters to perform a posteriori probability calculation, collect information and real-time parameters, integrate the probability of each state item by item, calculate the a posteriori value of the differentiated state using the conditional probability formula, and generate a posteriori probability analysis result;

[0028] S402: According to the posterior probability analysis result, the prediction correction of target damage is performed, and the dynamic update mechanism is applied to analyze the deviation between the corrected prediction result and the real-time monitoring data to generate the damage prediction probability;

[0029] S403: adjusting the damage development prediction model by combining the damage prediction probability with real-time data, integrating the real-time monitoring data with the model output, optimizing parameter settings, and generating a revised damage prediction result.

[0030] As a further solution of the present invention, the modified damage prediction result is applied to define the state space and decision rules in the continuous-time Markov decision process, and the steps of generating the damage management decision model by quantitatively analyzing and evaluating the damage degree under the differentiated state are specifically as follows:

[0031] S501: Based on the modified damage prediction result, identify a state set of differentiated target damage degrees, list the characteristics and attributes of each state item by item, and generate a state space list;

[0032] S502: Analyze the decision options in each state through the state space list, evaluate the potential benefits and risks of each option, determine the optimal decision path, and generate decision rules;

[0033] S503: Utilize the decision rule to conduct a quantitative analysis of the damage degree, integrate the damage data and the evaluation results under the differentiated state, evaluate the impact of the differentiated state on the overall decision, and generate a damage management decision model.

[0034] As a further solution of the present invention, based on the damage management decision model, with reference to the maintenance cost and response time, a benefit analysis is performed on the differentiated maintenance scheme, resource allocation and scheduling strategy are optimized, and the steps of obtaining the target maintenance log are specifically as follows:

[0035] S601: using the damage management decision model and a multi-criteria decision method, evaluating the cost and estimated response time of differentiated maintenance solutions, calculating the benefit index of each solution, and generating maintenance solution benefit analysis results;

[0036] S602: Based on the maintenance plan benefit analysis results, evaluate the personnel, equipment and materials required for the differentiated maintenance plan, compare the real-time situation and demand of available resources, adjust the resource allocation ratio, check the utilization rate of resources, and generate optimized resource configuration;

[0037] S603: Using the optimized resource configuration, adjusting the scheduling strategy, formulating a scheduling plan, analyzing the priority of maintenance tasks, arranging the use time and sequence of resources, and generating a target maintenance log.

[0038] As a further solution of the present invention, the formula of the multi-criteria decision-making method is as follows:

[0039]

[0040] Among them, E(f) is the benefit index, C z represents the maintenance cost, T z Represents the response time, R z represents the availability of maintenance resources, a z 、b z and c z is the weight coefficient, d z is the adjustment coefficient.

[0041] A target damage intelligent assessment system, the target damage intelligent assessment system is used to execute the above-mentioned target damage intelligent assessment method, the system comprises:

[0042] The data collection module uses monitoring equipment to collect initial data of target damage, records the starting characteristics of the time series and the size information of the damage area, and obtains the damage baseline characteristics;

[0043] The state modeling module uses the damage baseline characteristics to construct a state transition probability model, calculates the transition probability through the interaction between state nodes, and establishes a state transition probability matrix;

[0044] The dynamic update module monitors the change of the target damage state according to the state transition probability matrix, adjusts the network parameters to match the data change, updates the network structure, and generates updated network parameters;

[0045] The prediction model correction module uses the updated network parameters to calculate the posterior probability, and corrects the prediction model of damage development in combination with real-time data to obtain a corrected damage prediction result;

[0046] The maintenance plan evaluation module applies the modified damage prediction result, evaluates the damage degree under the differentiated state, refers to the maintenance cost and response time, analyzes the cost and benefit of the maintenance plan, and generates a target maintenance log.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by collecting initial data and analyzing the characteristics of the time series, it is helpful to quickly identify the initial state of target damage and provide accurate baseline input for the subsequent state transition probability model. It not only improves the initial accuracy of data analysis, but also enhances the understanding and application ability of complex data patterns. In the construction of the state transition model, by quantifying the correlation between nodes and adjusting the state transition probability, the development of the damage state can be simulated and predicted more accurately, providing a scientific basis for real-time monitoring and response. Updating and optimizing the network structure to match data changes further enhances the dynamic adaptability of the model, so that the system can continue to work effectively in a changing environment. By calculating the posterior probability correction and evaluating the prediction results, the real-time adjustment and optimization of the damage prediction model is achieved, and the dynamic update mechanism significantly improves the accuracy and efficiency of the prediction. Through the differentiated analysis of the maintenance plan, the resource allocation and scheduling strategy are optimized, the economic benefits and operational efficiency of the response strategy are improved, and quantitative data support is provided for decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a target damage intelligent assessment method, comprising the following steps:

[0060] S1: Collect the initial data of target damage, identify the starting features of the time series, measure the damaged area, record the spatial coordinates of the damaged area, calibrate the position using GPS and geographic information system, and generate damage baseline features;

[0061] S2: Using the damage baseline characteristics, a state transition probability model is constructed. By quantifying the correlation between state nodes and adjusting the probability of state transition according to time dependency, a state transition probability matrix is ​​obtained.

[0062] S3: Update the monitoring data of the target damage status through the state transition probability matrix, adjust the network parameters to match the data changes, and update the network structure in real time to generate updated network parameters;

[0063] S4: Use the updated network parameters to calculate the posterior probability, correct and evaluate the damage prediction through the dynamic update mechanism, and adjust the prediction model of damage development in combination with real-time data to obtain the corrected damage prediction results;

[0064] S5: Apply the modified damage prediction results to define the state space and decision rules in the continuous-time Markov decision process, and generate a damage management decision model through quantitative analysis and evaluation of the damage degree under differentiated states;

[0065] S6: Based on the damage management decision model, refer to the maintenance cost and response time, conduct a benefit analysis of differentiated maintenance solutions, optimize resource allocation and scheduling strategies, formulate scheduling plans, analyze the priority of maintenance tasks, and obtain the target maintenance log.

[0066] The damage baseline characteristics include the shape, severity, initial damage amount, damage type and impact range of the damaged area. The state transition probability matrix includes the transition probability from the real-time state to the next state and the adjustment coefficient of the time step. The updated network parameters include new input layer weights, hidden layer weights and output layer weights. The revised damage prediction results include short-term predictions, long-term predictions and confidence intervals. The damage management decision model includes state space definition, decision rules and evaluation criteria. The target maintenance log includes the time, location, cost and expected maintenance effect of the maintenance activities.

[0067] See also Figure 2 , collect the initial data of target damage, identify the starting characteristics of the time series, and record the baseline state of the damage by measuring the damaged area. The specific steps for generating the damage baseline characteristics are as follows:

[0068] S101: Based on the target damage site, perform initial data collection, record the spatial coordinates of the damage area, use GPS and geographic information system to calibrate the location, and collect time nodes, temperature and humidity at the same time. The execution process of generating the initial file of the damage area is as follows;

[0069] The coordinates of the damaged area are quickly determined on site, and geographic location data is collected through high-precision GPS equipment to ensure the accuracy and real-time nature of the data. Environmental variables are monitored in real time through on-site temperature and humidity sensors to provide basic support for subsequent data analysis. Time nodes are recorded using an atomic clock synchronization system to ensure the consistency and accuracy of time data. The meticulous execution of the steps provides comprehensive basic data for establishing a damaged area archive. After preliminary processing, the data is input into the geographic information system. The system automatically calibrates the location based on the GPS data to generate an initial archive of the damaged area.

[0070] S102: Using the initial archive of the damaged area, measure the damage status of the target, use laser scanning to record the damage degree, area and status of the surrounding undamaged areas, and perform data synchronization and archiving processing to generate the damage baseline data set. The execution process is as follows;

[0071] In the process of measuring the damage status of the target using the initial archive of the damage area, key steps include using laser scanning technology to record in detail the specific extent and area of ​​the damage area, as well as the condition of the surrounding undamaged areas. Laser scanning can accurately measure and visualize the damage area, providing high-resolution spatial data. The data is processed and archived through advanced synchronization technology to ensure real-time updates of all measurement results and maintenance of data integrity. The synchronized data will be systematically summarized and stored, not only reflecting the current damage situation, but also providing a basis for future analysis and countermeasures. Ensure the accuracy and accessibility of the data, support subsequent decision-making and response measures, and generate a damage baseline data set.

[0072] S103: Perform feature recognition through the damage baseline data set, identify key features of the initial stage of target damage through data screening and sorting, extract influencing factors, and generate the execution process of damage baseline features as follows;

[0073] The damage baseline data set is used to identify the target damage characteristics. The collected data is initially screened based on the preset parameters of the data model. The noise data is eliminated through advanced data processing algorithms to retain the most relevant information about the damage characteristics. The data is imported into the feature recognition algorithm, which is based on a machine learning model and can be trained to identify key features at the initial stage of damage. Important features such as damage area and shape are recorded and analyzed in detail to provide decision support for further damage assessment and repair, and generate damage baseline characteristics.

[0074] See also Figure 3 , using the damage baseline characteristics, constructing a state transition probability model, by quantifying the correlation between state nodes, adjusting the probability of state transition according to time dependency, and obtaining the state transition probability matrix in the following steps:

[0075] S201: Based on the damage baseline characteristics, the state nodes are identified, the relationship data between the nodes are extracted, the relationship strength of each pair of nodes is recorded, and the execution process of generating the initialization association matrix is ​​as follows;

[0076] Based on the damage baseline characteristics, the state nodes are identified and the relationships between nodes are extracted. According to the pre-defined standards, each state node is identified. The nodes represent the damage characteristics of different stages. The relationship between nodes is evaluated by a specific algorithm. The strength of the relationship between nodes is assessed using a weighted method. The relationship data includes spatial position, correlation of time series, and the influence of environmental factors. The recorded data will constitute an initialized association matrix, which will serve as the basis for identifying and analyzing the progression of the damage state. Each element value represents the strength of the relationship between two nodes. The process ensures the comprehensiveness and depth of the data so that the damage state can be analyzed more accurately in the future and an initialized association matrix can be generated.

[0077] S202: Using the initialized correlation matrix, extracting time series data, calculating the frequency of occurrence of each state node at a differentiated time point, analyzing the change trend between the state nodes, and generating the time dependency matrix. The execution process is as follows;

[0078] Calculate the frequency of occurrence of each state node at different time points, according to the formula Calculate the time-dependent frequency of the state node. Where n t Represents the number of occurrences of the state node at time point t, and T represents the total observation time length.

[0079] Detailed explanation of the formula and the process of formula calculation: When performing time series analysis, record the number of occurrences n of each state node at different time points t t For example, if a state node appears 3 times on the 5th day within the observation window (set the window to 30 days), then n5 = 3, and the total observation time T = 30 days. According to the formula, the frequency of occurrence of the node on the 5th day is The calculation shows the activity frequency of the status node at a specific time point, providing a data basis for subsequent analysis of the change trend between status nodes.

[0080] S203: Using the time dependency matrix and the initialization association matrix, the transition probability between state nodes is calculated, the transition probability values ​​between differentiated state nodes are adjusted, and the execution flow of outputting the state transition probability matrix is ​​as follows;

[0081] The time dependency matrix and the initialization association matrix are used to calculate the transition probability between state nodes. The two matrices are integrated, and the behavior pattern of each node in the time series is deeply analyzed to calculate the transition probability between different state nodes. The calculation involves probability statistics methods and Markov chain theory, which can determine the probability of any two state nodes transferring from one state to another. After precise calculation, the transfer probability value is adjusted to reflect the actual observed state changes, providing important input parameters for subsequent prediction models, ensuring the accuracy and practicality of model predictions, and outputting the state transfer probability matrix.

[0082] See also Figure 4 , through the state transition probability matrix, update the monitoring data of the target damage state, adjust the network parameters to match the data changes, and update the network structure in real time. The specific steps of generating updated network parameters are:

[0083] S301: According to the state transition probability matrix, the monitoring data of the target damage state is collected, the real-time monitoring value is recorded, the relationship between the monitoring value and the state transition probability is analyzed, the state information to be updated is extracted, and the execution process of generating the monitoring state data is as follows;

[0084] Analyze the relationship between the monitoring value and the state transition probability, according to the formula P update =P old +α·(X obs -P old ), calculate the updated state transition probability. In the formula, P old represents the old state transition probability, α represents the learning rate, X obs Represents the observed real-time state transition data.

[0085] Detailed explanation of the formula and the process of formula calculation: Considering that the monitoring data provides the latest state information, it is necessary to update the state transition probability according to the data. Set the original value of a state transition probability to P old =0.2, the state transition of the new observation is X obs = 0.3, and set the learning rate α = 0.5. According to the formula, the new state transition probability is calculated as P update =0.2+0.5·(0.3-0.2)=0.25. The updating process reflects the impact of actual monitoring data on the model, enabling the model to more accurately predict future state transitions.

[0086] S302: Adjust network parameters by monitoring status data, compare the difference between real-time network parameters and monitoring status data, correct mismatched parameters item by item, and generate adjusted network parameters. The execution process is as follows;

[0087] By monitoring status data, network parameters are adjusted and the differences between real-time network parameters and monitoring status data are analyzed, with particular attention paid to parameters that do not meet expectations. For example, the delay or data throughput of a network node does not meet the predicted value. The differences are recorded in detail and each parameter is corrected item by item. The adjustment process uses an adaptive adjustment algorithm to ensure the consistency between network parameters and actual monitoring data. The corrected parameters reflect the latest network status and data flow, optimize network performance and responsiveness, ensure the efficiency of network operation, and generate adjusted network parameters.

[0088] S303: Using the adjusted network parameters, the network structure is updated in real time, the network nodes and connection relationships are reconfigured, and the matching degree between the new parameters and the network structure is verified. The execution process of generating the updated network parameters is as follows;

[0089] The network structure is updated in real time using the adjusted network parameters, including reconfiguring network nodes and connection relationships. The process uses dynamic configuration technology to adapt to changing network requirements and conditions. After reconfiguration, the system is tested and verified to ensure the degree of match between the new parameters and the network structure, including testing the network's load capacity, transmission efficiency, and fault response time, to ensure that all changes meet the latest performance standards and provide the latest configuration basis for network operation and maintenance. The parameters will directly affect the stability and operating efficiency of the network and generate updated network parameters.

[0090] See also Figure 5 , using the updated network parameters, calculating the posterior probability, correcting and evaluating the damage prediction through the dynamic update mechanism, and adjusting the prediction model of damage development in combination with real-time data to obtain the corrected damage prediction results are as follows:

[0091] S401: Use the updated network parameters to perform posterior probability calculation, collect information and real-time parameters, integrate the probability of each state item by item, calculate the posterior value of the differentiated state using the conditional probability formula, and generate the execution process of the posterior probability analysis result as follows;

[0092] The conditional probability formula is used to calculate the posterior value of the differentiated state. According to the formula Calculate the posterior probability. In the formula, P(BIA) represents the posterior probability of state B after condition A occurs, P(A|B) represents the probability of condition A occurring under state B, P(B) represents the prior probability of state B, and P(A) represents the marginal probability of condition A.

[0093] Detailed explanation of the formula and the process of formula calculation: Consider a specific example, set the prior probability of state B P(B) = 0.2, the probability of condition A occurring under state B P(A|B) = 0.5, and the marginal probability of condition A P(A) = 0.3. Use the Bayesian formula to calculate the posterior probability: The posterior probability provides the updated probability of state B occurring after condition A occurs, which helps to understand and predict the probability changes of different states more accurately.

[0094] S402: According to the posterior probability analysis result, the prediction of target damage is corrected, and the dynamic update mechanism is applied to analyze the deviation between the corrected prediction result and the real-time monitoring data. The execution process of generating the damage prediction probability is as follows;

[0095] According to the results of the posterior probability analysis, the prediction of target damage is corrected. Through the dynamic update mechanism, the deviation between the corrected prediction result and the real-time monitoring data is analyzed. The process is completed by comparing the predicted probability and the real-time monitoring value. The deviation analysis helps to identify inaccuracies in the prediction model and adjust the prediction model to reduce the deviation. The correction process uses machine learning algorithms to automatically adjust the model parameters to better fit the real-time data, further refine the predicted probability of damage development, ensure the real-time and accuracy of the prediction results, and generate a damage prediction probability.

[0096] S403: The damage development prediction model is adjusted by combining the damage prediction probability with the real-time data, the real-time monitoring data and the model output are integrated, the parameter settings are optimized, and the execution process of generating the revised damage prediction result is as follows;

[0097] Through the damage prediction probability, combined with real-time data, the damage development prediction model is adjusted. In the process of integrating real-time monitoring data with model output, the deficiencies of the model and data deviations are identified, and the parameter settings of the prediction model are adjusted using information. The optimization process includes adjusting the learning rate, retraining the model weights, etc. to reflect the latest data trends and changes. The adjustment helps the model to more accurately predict the development of future damage. The revised damage prediction results are more in line with the actual monitoring data, providing more reliable decision-making support for future damage response and resource allocation, and generating revised damage prediction results.

[0098] See also Figure 6 , using the modified damage prediction results, defining the state space and decision rules in the continuous-time Markov decision process, and through quantitative analysis and evaluation of the damage degree under differentiated states, the steps of generating the damage management decision model are as follows:

[0099] S501: Based on the modified damage prediction result, identify the state set of differentiated target damage degrees, list the characteristics and attributes of each state one by one, and generate the state space list. The execution process is as follows;

[0100] Based on the revised damage prediction results, a set of states with different degrees of damage are identified, and the characteristics and attributes of each state are listed in detail, including the duration of the state, the scope of impact, the affected population and infrastructure, etc. The detailed characteristics of each state are systematically classified and recorded in the state space list. The list includes not only the currently identified states, but also predicts the development of new states. Each state is defined by comprehensively considering real-time data. The process is completed through advanced data analysis and pattern recognition technology to generate a state space list.

[0101] S502: Analyze the decision options in each state through the state space list, evaluate the potential benefits and risks of each option, determine the optimal decision path, and generate the execution process of the decision rules as follows;

[0102] Through the state space list, the decision options in each state are analyzed. The process includes evaluating the potential benefits and risks of each option. The benefit evaluation focuses on solving the impact of damage and improving recovery efficiency, while the risk assessment considers implementation costs and potential negative consequences. Combined with statistical models and expert opinions, the optimal decision path is determined for each state. The decision path is based on probability models and cost-benefit analysis to ensure that each decision can achieve the greatest overall benefit, provide clear guidance and action standards for actual operations, and generate decision rules.

[0103] S503: Quantitative analysis of the degree of damage is performed using decision rules, damage data and evaluation results under differentiated states are integrated, the impact of differentiated states on the overall decision is evaluated, and the execution process of generating a damage management decision model is as follows;

[0104] Integrate the damage data and assessment results under differentiated conditions, according to the formula Q = ∑ (w i ×D i ), calculate the decision score of damage management. In the formula, Q represents the total decision score, w i represents the weight of the i-th state, D i Represents the damage level score of the i-th state.

[0105] Detailed explanation of the formula and the process of formula calculation and derivation: In order to quantify the impact of damage in different states on decision-making, three states are set, and the damage degree scores are D1=0.3, D2=0.5, D3=0.2, and the corresponding weights are w1=0.5, w2=0.3, w3=0.2. Calculated by the formula

[0106] Q = (0.5 × 0.3) + (0.3 × 0.5) + (0.2 × 0.2) = 0.15 + 0.15 + 0.04 = 0.34. The calculation shows the quantitative impact of damage on the overall decision under each state, which is convenient for evaluating and comparing the benefits of different decision paths.

[0107] See also Figure 7 Based on the damage management decision model, referring to the maintenance cost and response time, the differentiated maintenance schemes are analyzed for benefits, resource allocation and scheduling strategies are optimized, and the steps to obtain the target maintenance log are as follows:

[0108] S601: Using the damage management decision model and the multi-criteria decision method, the cost and estimated response time of the differentiated maintenance solutions are evaluated, the benefit index of each solution is calculated, and the execution process of generating the maintenance solution benefit analysis result is as follows;

[0109] The formula of multi-criteria decision-making method is as follows:

[0110]

[0111] Among them, E(f) is the benefit index, C z represents the maintenance cost, T z Represents the response time, R z represents the availability of maintenance resources, a z 、b z and c z is the weight coefficient, d z is the adjustment coefficient.

[0112] Detailed explanation of the formula and the process of formula calculation and derivation:

[0113] To ensure that each parameter has actual data support, the following settings and calculations are performed through network retrieval and data analysis:

[0114] 1.C z The maintenance cost is set at RMB 2,000 based on the recent market average cost for repairs of similar equipment.

[0115] 2.T z For response time, the average response time is determined to be 48 hours based on the maintenance team's average response record over the past six months.

[0116] 3.R z For the availability of maintenance resources, the maintenance resource sufficiency during the evaluation period is set to 80%, which is calculated as the ratio of the current number of available maintenance personnel and equipment to the demand, converted into a percentage.

[0117] 4.a z 、b z and c z is the weight coefficient. Based on the data analysis and expert evaluation of the project, the weight coefficient is determined to be a z =0.5, b z =0.3, c z =0.2.

[0118] 5.d z is the adjustment coefficient, which is set at 0.05 based on the sensitivity survey of maintenance benefits to reflect the impact of slight changes on the total benefits.

[0119] The specific steps for calculating the comprehensive benefit index are as follows:

[0120] 1. Calculate the square root of the response time, that is

[0121] 2. Calculate the inverse of the maintenance resource, that is

[0122] 3. Substitute the specific values ​​and coefficients into the formula:

[0123]

[0124] The results show that the comprehensive benefit index is 954.99, reflecting the overall benefit of adopting the maintenance plan under given resource and time constraints. A high benefit index means that the plan performs well under the comprehensive consideration of cost, response time and resource availability, providing a quantitative basis for decision makers to make choices.

[0125] S602: Based on the maintenance plan benefit analysis results, the personnel, equipment and materials required for the differentiated maintenance plan are evaluated, the real-time situation of available resources and the demand are compared, the resource allocation ratio is adjusted, the resource utilization rate is checked, and the execution process of generating the optimized resource configuration is as follows;

[0126] Adjust the resource allocation ratio according to the formula Calculate the optimized resource allocation. In the formula, R opt represents the optimized resource allocation ratio, D req Represents the total resource requirement for the maintenance plan, D avail represents the total amount of available resources, and C represents the adjustment coefficient.

[0127] Formula explanation and formula calculation derivation process: Consider the specific situation and set the total resource demand for a maintenance project as D req = 100 units, and the total amount of resources currently available is D avail = 120 units, assuming adjustment coefficient C = 0.9 (to take into account the loss or volatility factors in actual operation), according to the formula, the optimized resource allocation ratio is The calculation helps decision makers to clearly understand how to allocate resources on demand to meet maintenance needs under current resource conditions while ensuring efficient use of resources.

[0128] S603: Adopt optimized resource allocation, adjust the scheduling strategy, formulate a scheduling plan, analyze the priority of maintenance tasks, arrange the time and sequence of resource use, and generate the target maintenance log. The execution process is as follows;

[0129] Adopt optimized resource allocation, adjust scheduling strategies, analyze the priority of maintenance tasks in the process of formulating scheduling plans, determine the priority of each task according to the urgency and scope of impact of the task, arrange the time and sequence of resource use, and ensure that high-priority tasks can get the necessary personnel, equipment and material support first. The scheduling plan also considers the dependencies and time conflicts between tasks. Through meticulous logic and time sequence analysis, it ensures that all resources are used in the most reasonable way throughout the maintenance process. The resource allocation and completion time of each task are recorded in detail to provide accurate execution basis for project management and generate target maintenance logs.

[0130] See also Figure 8 , a target damage intelligent assessment system, the target damage intelligent assessment system is used to execute the above target damage intelligent assessment method, the system includes:

[0131] The data collection module uses monitoring equipment to collect initial data of target damage, records the starting characteristics of the time series and the size information of the damage area, and obtains the damage baseline characteristics;

[0132] The state modeling module uses the damage baseline characteristics to construct a state transition probability model, calculates the transition probability through the interaction between state nodes, and establishes a state transition probability matrix;

[0133] The dynamic update module monitors the changes in the target damage state according to the state transition probability matrix, adjusts the network parameters to match the data changes, and updates the network structure to generate updated network parameters;

[0134] The prediction model correction module uses the updated network parameters to calculate the posterior probability and combines the real-time data to correct the prediction model of damage development to obtain the corrected damage prediction results.

[0135] The maintenance plan evaluation module applies the modified damage prediction results, evaluates the degree of damage under differentiated conditions, refers to the maintenance cost and response time, analyzes the cost and benefit of the maintenance plan, and generates a target maintenance log.

[0136] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A target damage intelligent assessment method, characterized in that: The following steps are involved: Collect the initial data of target damage, identify the starting features of the time series, record the baseline state of the damage by measuring the damaged area, and generate the damage baseline features; The damage baseline characteristics are used to construct a state transition probability model, and the state transition probability matrix is ​​obtained by quantifying the correlation between state nodes and adjusting the probability of state transition according to time dependency; By using the state transition probability matrix, the monitoring data of the target damage state is updated, the network parameters are adjusted to match the data changes, and the network structure is updated in real time to generate updated network parameters; Using the updated network parameters, calculating the posterior probability, correcting and evaluating the damage prediction through a dynamic update mechanism, and adjusting the prediction model of damage development in combination with real-time data to obtain a corrected damage prediction result; Applying the modified damage prediction results, defining the state space and decision rules in the continuous-time Markov decision process, and generating a damage management decision model through quantitative analysis and evaluation of the damage degree under differentiated states; Based on the damage management decision model, with reference to maintenance cost and response time, a benefit analysis is conducted on differentiated maintenance plans, resource allocation and scheduling strategies are optimized, and a target maintenance log is obtained.

2. The target damage intelligent assessment method according to claim 1, characterized in that: The damage baseline characteristics include the shape, severity, initial damage amount, damage type and impact range of the damaged area; the state transition probability matrix includes the transition probability from the real-time state to the next state and the adjustment coefficient of the time step; the updated network parameters include new input layer weights, hidden layer weights and output layer weights; the revised damage prediction results include short-term predictions, long-term predictions and confidence intervals; the damage management decision model includes state space definition, decision rules and evaluation criteria; the target maintenance log includes the time, location, cost and expected maintenance effect of the maintenance activities.

3. The target damage intelligent assessment method according to claim 1, characterized in that: Collect the initial data of target damage, identify the starting characteristics of the time series, measure the damaged area, record the baseline state of the damage, and generate the damage baseline characteristics in the following steps: Based on the target damage site, conduct initial data collection, record the spatial coordinates of the damage area, use GPS and geographic information system to calibrate the location, and collect time nodes, temperature and humidity to generate an initial file of the damage area; Using the initial archive of the damaged area, measuring the damage status of the target, using laser scanning to record the damage degree, area and status of the surrounding undamaged areas, and performing data synchronization and archiving processing to generate a damage baseline data set; The damage baseline data set is used to perform feature recognition, and through data screening and sorting, key features of the initial stage of target damage are identified, influencing factors are extracted, and damage baseline features are generated.

4. The target damage intelligent assessment method according to claim 1, characterized in that: The damage baseline characteristics are used to construct a state transition probability model, and the steps of obtaining a state transition probability matrix are as follows: Based on the damage baseline characteristics, state node identification is performed, relationship data between nodes is extracted, the relationship strength of each pair of nodes is recorded, and an initialization association matrix is ​​generated; Using the initialized association matrix, extracting time series data, calculating the frequency of occurrence of each state node at a differentiated time point, analyzing the change trend between state nodes, and generating a time dependency matrix; The time dependency matrix is ​​used in combination with the initialization association matrix to calculate the transition probability between state nodes, adjust the transition probability values ​​between differentiated state nodes, and output the state transition probability matrix.

5. The target damage intelligent assessment method according to claim 1, characterized in that: The steps of updating the monitoring data of the target damage state through the state transition probability matrix, adjusting the network parameters to match the data changes, and updating the network structure in real time to generate the updated network parameters are as follows: According to the state transition probability matrix, monitoring data of the target damage state is collected, real-time monitoring values ​​are recorded, the relationship between the monitoring values ​​and the state transition probability is analyzed, the state information to be updated is extracted, and monitoring state data is generated; Adjust the network parameters based on the monitoring status data, compare the differences between the real-time network parameters and the monitoring status data, correct the mismatched parameters one by one, and generate adjusted network parameters; The adjusted network parameters are used to update the network structure in real time, reconfigure network nodes and connection relationships, verify the matching degree between the new parameters and the network structure, and generate updated network parameters.

6. The target damage intelligent assessment method according to claim 1, characterized in that: The steps of using the updated network parameters to calculate the posterior probability, correcting and evaluating the damage prediction through a dynamic update mechanism, and adjusting the prediction model of damage development in combination with real-time data to obtain the corrected damage prediction result are as follows: Using the updated network parameters, performing a posteriori probability calculation, collecting information and real-time parameters, integrating the probability of each state item by item, calculating the a posteriori value of the differentiated state using the conditional probability formula, and generating a posteriori probability analysis result; According to the posterior probability analysis results, the prediction of target damage is corrected, and a dynamic update mechanism is applied to analyze the deviation between the corrected prediction results and the real-time monitoring data to generate a damage prediction probability; The damage prediction probability is combined with real-time data to adjust the damage development prediction model, integrate the real-time monitoring data and model output, optimize parameter settings, and generate a revised damage prediction result.

7. The target damage intelligent assessment method according to claim 1, characterized in that: The modified damage prediction results are applied to define the state space and decision rules in the continuous-time Markov decision process. By quantitatively analyzing and evaluating the damage degree under differentiated states, the steps of generating the damage management decision model are as follows: Based on the modified damage prediction result, identifying a set of states of differentiated target damage degrees, listing the characteristics and attributes of each state item by item, and generating a state space list; Through the state space list, the decision options under each state are analyzed, the potential benefits and risks of each option are evaluated, the optimal decision path is determined, and the decision rules are generated; The decision rules are used to conduct quantitative analysis of the degree of damage, integrate damage data and evaluation results under differentiated states, evaluate the impact of differentiated states on the overall decision, and generate a damage management decision model.

8. The target damage intelligent assessment method according to claim 1, characterized in that: Based on the damage management decision model, with reference to maintenance cost and response time, the differentiated maintenance schemes are analyzed for benefits, resource allocation and scheduling strategies are optimized, and the steps for obtaining the target maintenance log are as follows: Using the damage management decision model and a multi-criteria decision method, the cost and estimated response time of differentiated maintenance solutions are evaluated, the benefit index of each solution is calculated, and the maintenance solution benefit analysis results are generated; Based on the maintenance plan benefit analysis results, evaluate the personnel, equipment and materials required for differentiated maintenance plans, compare the real-time situation and demand of available resources, adjust the resource allocation ratio, check the utilization rate of resources, and generate optimized resource configuration; The optimized resource configuration is adopted to adjust the scheduling strategy, formulate a scheduling plan, analyze the priority of maintenance tasks, arrange the use time and sequence of resources, and generate a target maintenance log.

9. The target damage intelligent assessment method according to claim 1, characterized in that: The formula of the multi-criteria decision-making method is as follows: Among them, E(f) is the benefit index, C z represents the maintenance cost, T z Represents the response time, R z represents the availability of maintenance resources, a z 、b z and c z is the weight coefficient, d z is the adjustment coefficient.

10. A target damage intelligent assessment system, characterized in that: According to any one of claims 1 to 9, the target damage intelligent assessment method system comprises: The data collection module uses monitoring equipment to collect initial data of target damage, records the starting characteristics of the time series and the size information of the damage area, and obtains the damage baseline characteristics; The state modeling module uses the damage baseline characteristics to construct a state transition probability model, calculates the transition probability through the interaction between state nodes, and establishes a state transition probability matrix; The dynamic update module monitors the change of the target damage state according to the state transition probability matrix, adjusts the network parameters to match the data change, updates the network structure, and generates updated network parameters; The prediction model correction module uses the updated network parameters to calculate the posterior probability, and corrects the prediction model of damage development in combination with real-time data to obtain a corrected damage prediction result; The maintenance plan evaluation module applies the modified damage prediction result, evaluates the damage degree under the differentiated state, refers to the maintenance cost and response time, analyzes the cost and benefit of the maintenance plan, and generates a target maintenance log.

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