Reservoir Project Standardized Management Method and System

Abstract

The present invention provides a method and system for standardized management of reservoir projects, belonging to the technical field of water conservancy project management. The method includes: processing multi-dimensional data acquired by sensors based on a first random forest model to obtain target state data of the target reservoir dam, where the multi-dimensional data includes structural data and monitoring data of the target reservoir dam; extracting features from the target state data to obtain target state feature data, obtaining a target similarity based on multiple similarities between the target state feature data and each state feature data in the state library, and selecting the target state of the target reservoir dam from the state library based on the target similarity; and determining a corresponding target management strategy based on the target state. The method and system for standardized management of reservoir projects provided by the present invention can accurately identify the state of the reservoir dam, reflect the structural change trend of the reservoir dam in real time, adjust the management strategy according to the state of the reservoir dam, and achieve risk prevention and control of the reservoir dam.

Description

Technical Field

[0001] The present invention belongs to the technical field of water conservancy project management, and more specifically, relates to a standardized management method and system for reservoir projects. Background Art

[0002] As the core structure of a reservoir project, the state of the reservoir dam directly affects the functions of flood control, water supply, etc. At present, based on empirical judgment of historical data or simple threshold alarms, the changing trend of the dam structure cannot be reflected in real time, which easily leads to delayed discovery of potential safety hazards; the management strategy for the reservoir dam depends on manual experience adjustment, with slow response speed and difficulty in meeting the requirements of complex working conditions and dynamic environments; in case of sudden anomalies, there is a lack of automated decision support, relying on manual intervention, with slow response speed and prone to misjudgment. Summary of the Invention

[0003] The purpose of the present invention is to provide a standardized management method and system for reservoir projects to improve the timeliness of discovering potential safety hazards and the response speed of managing the reservoir dam in case of sudden anomalies.

[0004] In the first aspect of the embodiment of the present invention, a standardized management method for reservoir projects is provided, including:

[0005] Processing the multi-dimensional data obtained by sensors based on the first random forest model to determine the target state data of the target reservoir dam, where the multi-dimensional data includes the structural data and monitoring data of the target reservoir dam;

[0006] Performing feature extraction on the target state data to obtain target state feature data, obtaining a target similarity based on multiple similarities between the target state feature data and each state feature data in the state library, and selecting the target state of the target reservoir dam from the state library based on the target similarity;

[0007] Determining the corresponding target management strategy based on the target state.

[0008] In the second aspect of the embodiment of the present invention, a standardized management system for reservoir projects is provided, including:

[0009] A data processing module for processing the multi-dimensional data obtained by sensors based on the first random forest model to determine the target state data of the target reservoir dam, where the multi-dimensional data includes the structural data and monitoring data of the target reservoir dam;

[0010] A state determination module for performing feature extraction on the target state data to obtain target state feature data, obtaining a target similarity based on multiple similarities between the target state feature data and each state feature data in the state library, and selecting the target state of the target reservoir dam from the state library based on the target similarity;

[0011] A management strategy module, configured to determine a corresponding target management strategy based on the target state.

[0012] The beneficial effects of a reservoir project standardization management method and system provided by an embodiment of the present invention are as follows: The present invention can accurately identify the state of the reservoir dam, reflect the change trend of the reservoir dam structure in real time, and improve the timeliness of discovering potential safety hazards; adjust the management strategy according to the state of the reservoir dam, improve the response speed of the management of the reservoir dam, and the present invention provides automated decision support in complex working conditions and dynamic environments to achieve risk prevention and control of the reservoir dam. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0014] Figure 1 It is a schematic flowchart of a reservoir project standardization management method provided by an embodiment of the present invention;

[0015] Figure 2 It is a structural block diagram of a reservoir project standardization management system provided by an embodiment of the present invention;

[0016] Figure 3 It is a schematic block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will be described through specific embodiments with reference to the drawings.

[0019] Please refer to Figure 1 , Figure 1 It is a schematic flowchart of a reservoir project standardization management method provided by an embodiment of the present invention. The method includes:

[0020] S101: Process the multi-dimensional data obtained by the sensor based on the first random forest model to determine the target state data of the target reservoir dam. The multi-dimensional data includes the structural data and monitoring data of the target reservoir dam.

[0021] In this embodiment, the multi-dimensional data of the target reservoir dam includes temperature data, displacement data, stress and strain data, seepage data, crack data, etc. As the core component of a water conservancy project, the reservoir dam is a key construction facility for intercepting water flow, forming a reservoir, and regulating water volume, and plays a crucial role in water resource regulation, flood control, irrigation, and other fields.

[0022] Before the first random forest model processes the multi-dimensional data, the present invention performs data cleaning, format conversion, and normalization processing on the multi-dimensional data; the first random forest model conducts multiple random samplings based on the importance of the feature data in the processed data to construct multiple decision trees; by integrating the output results of the multiple decision trees, noise data and abnormally fluctuating data are filtered out, and the state that can accurately reflect the current actual operation status of the target reservoir dam is precisely selected.

[0023] In this embodiment, processing the multi-dimensional data obtained by the sensor based on the first random forest model to determine the target state data of the target reservoir dam includes:

[0024] Process the multi-dimensional data obtained by the sensor based on the first random forest model to obtain the difference degree of the recognition result;

[0025] When the difference degree of the recognition result is greater than the first difference degree, verify the multi-dimensional data based on the second random forest model to obtain the verification difference degree;

[0026] When the verification difference degree is greater than the first difference degree, collect the environmental data around the target reservoir dam;

[0027] Determine the target state data based on the environmental data and the multi-dimensional data.

[0028] Specifically, the first random forest model is trained with a large amount of historical state data, and can accurately identify and classify the current multi-dimensional data and output the corresponding recognition result.

[0029] After obtaining the recognition results, the difference degree of the recognition results is determined by calculating the differences between multiple recognition results. When the difference degree of the recognition results is greater than a preset first difference degree, it indicates that there are errors between the current recognition results and there may be potential abnormal situations. The multi-dimensional data is verified again based on the second random forest model. The second random forest model uses different training data and parameter settings and can analyze and judge the multi-dimensional data from another perspective; after being processed by the second random forest model, a verification difference degree is obtained. When the verification difference degree is greater than the first difference degree, there is a high possibility that the state of the reservoir dam has become abnormal. To understand the condition of the dam more comprehensively, environmental data around the target reservoir dam is collected. The environmental data includes: meteorological data (such as temperature, humidity, wind speed, rainfall, etc.), geological data (such as soil humidity, formation displacement, etc.), and hydrological data (such as water level change, water flow velocity, etc.). After obtaining the environmental data and the original multi-dimensional data, these two types of data are fused. The multi-dimensional data is corrected and supplemented in combination with the environmental data to determine more accurate and reliable target state data. For example, when the environmental data shows that the rainfall suddenly increases, it may affect the state of the reservoir dam. According to the relationship between the rainfall and the state of the reservoir dam, the multi-dimensional data is adjusted accordingly to obtain target state data that can better reflect the true state of the dam.

[0030] S102: Extract features from the target state data to obtain target state feature data, obtain a target similarity based on multiple similarities between the target state feature data and each state feature data in the state library, and select the target state of the target reservoir dam from the state library based on the target similarity.

[0031] In this embodiment, extracting features from the target state data to obtain target state feature data includes:

[0032] According to the target state data, use a feature extraction algorithm to extract features from the target state data to obtain target state feature data. For example, through dimensionality reduction algorithms such as principal component analysis (PCA) and linear discriminant analysis (LDA), while retaining key information, the high-dimensional and complex target state data is transformed into low-dimensional and more representative target state feature data. The target state feature data includes key operation indicators such as the change trend of the dam displacement, the fluctuation characteristics of the seepage flow rate, and the concentrated area of stress and strain.

[0033] In this embodiment, obtaining a target similarity based on multiple similarities between the target state feature data and each state feature data in the state library, and selecting the target state of the target reservoir dam from the state library based on the target similarity includes:

[0034] Compare the target status feature data with each status feature data in the pre - constructed status library. The status library stores a large number of various dam body status features summarized based on historical data and professional experience, such as normal operation status, slight anomaly status, severe anomaly status, etc. Using similarity calculation methods such as cosine similarity and Euclidean distance, calculate the similarity between the target status feature data and each status feature data in the status library one by one to obtain multiple similarity values. After comprehensively evaluating and analyzing these similarity results, select the most representative value with the highest matching degree with the target status feature data as the target similarity. According to the calculated target similarity, perform precise matching in the status library to screen out the target status that is closest to the current operating status of the target reservoir dam.

[0035] S103: Determine the corresponding target management strategy based on the target status.

[0036] In this embodiment, based on the target status, select the target management strategy corresponding to the target status from the preset policy rule library.

[0037] Above, the present invention can accurately identify the status of the reservoir dam, reflect the change trend of the reservoir dam structure in real - time, improve the timeliness of discovering potential safety hazards; adjust the management strategy according to the status of the reservoir dam, improve the response speed of the management of the reservoir dam, and the present invention provides automated decision - making support in complex working conditions and dynamic environments to achieve risk prevention and control of the reservoir dam.

[0038] In an embodiment of the present invention, determine the health index of the target reservoir dam based on the target status; determine the safety level of the target reservoir dam based on the health index; select the corresponding target management strategy in the policy rule library based on the safety level; there are various management strategies stored in the policy rule library.

[0039] In this embodiment, calculate the health index of the reservoir dam by the entropy weight method;

[0040] The calculation formula is:

[0041]

[0042]

[0043] Wherein, is the entropy value of the j th index, is the entropy weight of the j th index, m is the number of indexes corresponding to the target status, is the j th standardized data of the index, is the health index.

[0044] In this embodiment, referring to the safety level table in Table 1, where the safety level is divided into four levels, and the threshold of the health index is set by combining statistical quantiles and engineering specifications;

[0045] Table 1 Safety Level Table

[0046]

[0047] Specifically, the policy rule library includes detailed management measures formulated for different safety levels. For example, when the safety level is level I, the target management strategy is regular inspection (drone aerial photography + sensor calibration). When the safety level is level II, the target management strategy is to increase the monitoring frequency. For example, real-time seepage monitoring + weekly manual inspection. When the safety level is level III, the management strategy is to inform the management personnel to repair the local part of the dam body. When the safety level is level IV, the management strategy is to activate the emergency plan, notify the management personnel to reinforce the dam body, and report to the superior department.

[0048] The present invention can obtain the target management strategy according to the safety level, improve the response speed and accuracy of the management of the reservoir dam body, and achieve precise prevention and control of the risks of the reservoir dam body.

[0049] In an embodiment of the present invention, based on the safety level, the corresponding target management strategy in the policy rule library is selected, and it further includes: processing the environmental data to obtain seasonal characteristics; selecting the corresponding management strategy template in the policy rule library based on the seasonal characteristics and the safety level; adjusting the management strategy template based on the seasonal characteristics and the first amplitude value to obtain the corresponding target management strategy.

[0050] In this embodiment, environmental data including temperature, humidity, light intensity, air pressure, geographical location (latitude and longitude), and historical meteorological data are collected in real time through a multi-source sensor network. The original data is cleaned (such as removing outliers) and normalized, and the seasonal characteristics of the processed environmental data are extracted using a time series decomposition algorithm. The policy rule library pre-stores management strategy templates corresponding to different seasonal characteristics and safety levels, and each management strategy template contains management strategy parameters and rules formulated for the corresponding safety level and seasonal characteristics.

[0051] The first amplitude value is calculated through an amplitude value calculation formula. The first amplitude value reflects the deviation degree between the target state and the preset threshold in the management strategy template.

[0052] The amplitude value calculation formula is

[0053]

[0054] Wherein, is the first amplitude value, xis the eigenvalue of the target state, δ base which is the benchmark threshold of the corresponding parameter in the management strategy template.

[0055] In this embodiment, the management strategy template is adjusted based on the first amplitude value to obtain the corresponding target management strategy, including: when the seasonal feature is the first seasonal feature, adjusting the seepage monitoring level and inspection frequency in the management strategy template based on the first amplitude value to obtain the target management strategy; when the seasonal feature is the second seasonal feature, adjusting the seepage monitoring level and inspection frequency in the management strategy template based on the first amplitude value to obtain the target management strategy; the precipitation of the first seasonal feature is greater than the second threshold; the precipitation of the second seasonal feature is less than the first threshold, and the second threshold is greater than the first threshold.

[0056] When the seasonal feature is determined to be the first seasonal feature, the precipitation is relatively large at this time, and the reservoir faces relatively high seepage risk and potential safety hazards. Based on the first amplitude value, the seepage monitoring level and inspection frequency in the management strategy template are adjusted accordingly. The larger the first amplitude value, the farther the current seepage situation deviates from the normal threshold, and the seepage monitoring level needs to be improved. For example, if the first amplitude value exceeds a certain proportion, the seepage monitoring level is upgraded from level one to level two, increasing the accuracy of monitoring equipment and the data collection frequency to ensure that the changes in the seepage situation can be grasped in a timely and accurate manner. At the same time, the inspection frequency is increased. The inspection personnel need to conduct more frequent inspections on each key part of the reservoir dam. The larger the first amplitude value, the greater the increase in the inspection frequency, so as to timely discover potential safety problems such as cracks and leakage in the reservoir dam and take corresponding measures for treatment. When the seasonal feature is determined to be the second seasonal feature, the precipitation is less, and the seepage risk of the reservoir is relatively low, but a certain degree of monitoring and inspection efforts still need to be maintained. Similarly, based on the first amplitude value, the seepage monitoring level and inspection frequency in the management strategy template are adjusted. If the first amplitude value is small, the monitoring accuracy and frequency of seepage monitoring remain unchanged; the inspection frequency is adjusted accordingly according to the first amplitude value, reducing the inspection frequency.

[0057] The present invention can determine the target management strategy according to the safety level and seasonal changes, manage the target reservoir dam based on the target management strategy, greatly improving the scientific nature of management decisions in different seasons, avoiding management mistakes of the dam caused by management strategies without considering environmental factors, and effectively ensuring the safe and efficient operation of the reservoir dam; realizing the precise prevention and control of the reservoir dam.

[0058] In an embodiment of the present invention, based on the target state, determining the corresponding target management strategy further includes: determining the corresponding risk level identifier based on the feature data,

[0059] determining the strategy trigger condition based on the risk level identifier;

[0060] Match the policy trigger condition with the management policies in the policy rule library to obtain the target management policy.

[0061] In this embodiment, based on the feature data, determining the corresponding risk level identifier includes:

[0062] Perform a weighted calculation on the environmental data and the feature data to obtain a comprehensive risk value; based on the comprehensive risk value, determine the corresponding risk level identifier.

[0063] Specifically, in order to obtain a comprehensive risk value that accurately reflects the risk situation, it is necessary to perform a weighted calculation on the environmental data and the feature data corresponding to the target state. The weights are processed using methods such as the expert evaluation method and the analytic hierarchy process to assign reasonable weights to each data indicator.

[0064] The formula for the comprehensive risk value is

[0065]

[0066] Where, is the risk level identifier, is the th indicator in the environmental data, is the weight of the th indicator in the environmental data, is the k th indicator in the feature data, is the weight of the k th indicator in the feature data.

[0067] The comprehensive risk value intervals corresponding to different risk level identifiers, for example: the comprehensive risk value interval for the low-risk identifier ( R 1) is: R ≤ R low ; the comprehensive risk value interval for the medium-risk identifier ( R 2) is: R low < R ≤ R medium ; the high-risk ( R 3) comprehensive risk value interval is: R > R medium .

[0068] According to the calculated comprehensive risk value R , judge the interval it belongs to and determine the corresponding risk level identifier. When the risk level identifier is R 1, the policy trigger condition may be: the seepage flow is less than the threshold Q1, the dam displacement is within the allowable range ΔD1, and the precipitation is less than P1. When the risk level identifier isR At time 2, the triggering conditions may become: the seepage flow rate is between Q1 and Q2, the dam displacement exceeds ΔD1 but is less than ΔD2, or the precipitation is between P1 and P2. When the risk level identifier is R At time 3, the triggering conditions are more stringent. The seepage flow rate is greater than Q2, the dam displacement exceeds ΔD2, or the precipitation is greater than P2. Among them, ΔD1 < ΔD2, Q1 < Q2, and P1 < P2.

[0069] In this embodiment, the policy triggering conditions are matched with the management policies in the policy rule library to obtain the target management policies, including:

[0070] When multiple management policies match the policy triggering conditions, the multiple management policies are sorted according to the preset priority sorting rules in the policy rule library to obtain the first sorted list, and the target management policy is determined based on the first sorted list.

[0071] Specifically, when multiple management policies match the triggering conditions, the multiple management policies need to be sorted according to the preset priority sorting rules in the policy rule library to obtain the first sorted list, and the target management policy is determined based on the first sorted list. The priority sorting rules comprehensively consider various factors, such as the urgency of the risk, the degree of impact on the reservoir safety, and the implementation cost. For example, in a high-risk situation, the emergency evacuation policy to ensure the safety of personnel has the highest priority, followed by the flood discharge policy to control the reservoir water level, and finally the maintenance policy to reinforce the dam body.

[0072] The present invention can improve the accuracy of risk assessment. By performing weighted calculations on environmental data and characteristic data to obtain a comprehensive risk value, the accuracy and scientific nature of the management policy are improved. Based on the accurate risk level identifier to determine the policy triggering conditions, and then matching them with the management policies in the policy rule library, the pertinence of the management policy selection is greatly improved. It can quickly and accurately screen out the target management policy that fits the current risk status from the policy rule library, effectively avoiding the problems of over-management or under-management, and ensuring that the reservoir can be properly and timely controlled under various risk conditions.

[0073] In an embodiment of the present invention, determining the target state data based on environmental data and multi-dimensional data includes:

[0074] Based on the preset weight assignment rules, data fusion is performed on the multi-dimensional data and environmental data to obtain the target state data.

[0075] In this embodiment, based on the preset weight assignment rules, performing data fusion on the multi-dimensional data and environmental data to obtain the target state data includes:

[0076] Determining the first weight adjustment step based on the importance level of the environmental data;

[0077] Adjust the first weight reference value based on the first weight adjustment step to obtain the first weight; adjust the second weight reference value based on the first weight adjustment step to obtain the second weight;

[0078] Perform weighted calculation based on the first weight, the second weight, the environmental data, and the multi-dimensional data to obtain the target state data;

[0079] The first weight is the weight corresponding to the multi-dimensional data, the second weight is the weight corresponding to the environmental data, and the adjustment directions of the first weight reference value and the second weight reference value are different.

[0080] Specifically, in this embodiment, adjust the first weight reference value based on the first weight adjustment step to obtain the first weight; adjust the second weight reference value based on the first weight adjustment step to obtain the second weight. It should be noted here that the adjustment directions of the first weight reference value and the second weight reference value are different. For example, assume that the first weight reference value is w1, the second weight reference value is w2, and w1 + w2 = 1. When the first weight adjustment step Δw is a positive number, the first weight w r1 = w1 + Δw, and the second weight w r2 = w2 - Δw; when the first weight adjustment step Δw is a negative number, the first weight w r1 = w1 + Δw, and the second weight w r2 = w2 - Δw. Through such adjustment, it is ensured that the sum of the first weight and the second weight is always 1, and at the same time, it reflects the reasonable adjustment of the weights of the two types of data according to different conditions.

[0081] In this embodiment, based on the preset weight allocation rule, perform data fusion on the multi-dimensional data and the environmental data to obtain the target state data, and further include: extracting the time series characteristics of the environmental data to obtain the characteristic data including seasonal periodicity; inputting the seasonal characteristic data into the preset seasonal influence factor calculation model to output the seasonal adjustment coefficient; correcting the second weight reference value according to the seasonal adjustment coefficient, where: when the seasonal adjustment coefficient represents the first seasonal characteristic (rainy season), correct the second weight reference value of the environmental data based on the first correction amplitude to obtain the second weight; when the seasonal adjustment coefficient represents the second seasonal characteristic (dry season), correct the second weight reference value of the environmental data based on the second correction amplitude to obtain the second weight.

[0082] Specifically, the first correction amplitude and the second correction amplitude are determined according to the actual situation and historical data of the reservoir project. For example, by analyzing the influence degree of environmental data in the rainy season and dry season on the operation state of the reservoir in history, combined with expert experience, it is determined that in the rainy season, the importance of environmental data relatively increases, and the first correction amplitude can be set to a value greater than 1; in the dry season, the importance of environmental data relatively decreases, and the second correction amplitude can be set to a value less than 1. When the seasonal adjustment coefficient represents the first seasonal characteristic (rainy season), multiply the second weight reference value of environmental data by the first correction amplitude to obtain the second weight. Assume that the second weight reference value of environmental data is w ref Then the second weight w f1 =w ref ×1.2. When the seasonal adjustment coefficient represents the second seasonal characteristic (dry season), multiply the second weight reference value of environmental data by the second correction amplitude to obtain the second weight, that is, w f2 =w ref ×0.8. Through such correction, the weight of environmental data can be made more in line with the actual situation in different seasons, improving the accuracy of reservoir project management decisions.

[0083] The present invention increases the weight of environmental data in the rainy season and decreases the weight of environmental data in the dry season, greatly improving the scientific nature of management decisions in different seasons and avoiding management strategies output due to uncollected environmental factors from affecting the management of the reservoir dam.

[0084] Corresponding to the reservoir project standardization management method in the above embodiment, Figure 2 is the structural block diagram of the reservoir project standardization management system provided by an embodiment of the present invention. For the convenience of description, only the parts related to the embodiments of the present invention are shown. Refer to Figure 2 and the reservoir project standardization management system 20 includes: a data processing module 21, a state determination module 22, and a management strategy module 23.

[0085] Among them, the data processing module 21 is used to process the multi-dimensional data obtained by the sensor based on the first random forest model to obtain the target state data of the target reservoir dam, and the multi-dimensional data includes the structural data and monitoring data of the target reservoir dam;

[0086] The state determination module 22 is used to extract features from the target state data to obtain target state feature data, obtain a target similarity based on multiple similarities between the target state feature data and each state feature data in the state library, and select the target state of the target reservoir dam from the state library based on the target similarity;

[0087] The management strategy module 23 is used to determine the corresponding target management strategy based on the target state.

[0088] In an embodiment of the present invention, the management strategy module 23 is specifically configured to:

[0089] Determine the health index of the target reservoir dam based on the target state;

[0090] Determine the safety level of the target reservoir dam based on the health index;

[0091] Select the corresponding target management strategy in the policy rule library based on the safety level; multiple management strategies are stored in the policy rule library.

[0092] In an embodiment of the present invention, the management strategy module 23 is specifically configured to:

[0093] Process the environmental data to obtain seasonal characteristics;

[0094] Select the corresponding management strategy template in the policy rule library based on the seasonal characteristics and the safety level;

[0095] Adjust the management strategy template based on the seasonal characteristics and the first amplitude value to obtain the corresponding target management strategy.

[0096] In an embodiment of the present invention, the management strategy module 23 is specifically configured to:

[0097] When the seasonal characteristic is the first seasonal characteristic, adjust the seepage monitoring level and inspection frequency in the management strategy template based on the first amplitude value to obtain the target management strategy;

[0098] When the seasonal characteristic is the second seasonal characteristic, adjust the seepage monitoring level and inspection frequency in the management strategy template based on the first amplitude value to obtain the target management strategy;

[0099] The precipitation of the first seasonal characteristic is greater than the second threshold; the precipitation of the second seasonal characteristic is less than the first threshold, and the second threshold is greater than the first threshold.

[0100] In an embodiment of the present invention, the management strategy module 23 is specifically configured to:

[0101] Determine the corresponding risk level identifier based on the target state;

[0102] Determine the policy trigger condition based on the risk level identifier;

[0103] Match the policy trigger condition with the management strategies in the policy rule library to obtain the target management strategy.

[0104] In an embodiment of the present invention, the management strategy module 23 is specifically configured to:

[0105] Perform weighted calculation on the environmental data and characteristic data corresponding to the target state to obtain a comprehensive risk value;

[0106] Determine the corresponding risk level identifier based on the comprehensive risk value.

[0107] In an embodiment of the present invention, the management policy module 23 is specifically configured to:

[0108] When multiple management policies match the policy trigger conditions, sort the multiple management policies according to the preset priority sorting rules in the policy rule library to obtain a first sorted list, and determine the target management policy based on the first sorted list.

[0109] In an embodiment of the present invention, the data processing module 21 is specifically configured to:

[0110] Process the multi-dimensional data acquired by the sensor based on the first random forest model to obtain the difference degree of the recognition result;

[0111] When the difference degree of the recognition result is greater than the first difference degree, verify the multi-dimensional data based on the second random forest model to obtain the verification difference degree;

[0112] When the verification difference degree is greater than the first difference degree, collect the environmental data around the dam body of the target reservoir;

[0113] Determine the target state data based on the environmental data and the multi-dimensional data.

[0114] In an embodiment of the present invention, the data processing module 21 is specifically configured to:

[0115] Perform data fusion on the multi-dimensional data and the environmental data based on the preset weight assignment rule to obtain the target state data.

[0116] See Figure 3 , Figure 3 is a schematic block diagram of an electronic device provided in an embodiment of the present invention. As Figure 3 shown, the electronic device 300 in this embodiment may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The above-mentioned processors 301, input devices 302, output devices 303, and memories 304 communicate with each other through the communication bus 305. The memory 304 is used to store computer programs, and the computer programs include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. Among them, the processor 301 is configured to call the program instructions to execute the functions of each module in the above-mentioned device embodiments, such as Figure 2 the functions of the modules 21 to 23 shown.

[0117] It should be understood that in the embodiments of the present invention, the so-called processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0118] The input device 302 may include a touchpad, a fingerprint acquisition sensor (for acquiring the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device 303 may include a display (such as an LCD), a speaker, etc.

[0119] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A part of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0120] In specific implementation, the processor 301, the input device 302, and the output device 303 described in the embodiments of the present invention may implement the implementation manners described in the first embodiment and the second embodiment of the reservoir project standardization management method provided by the embodiments of the present invention, and may also implement the implementation manner of the electronic device described in the embodiments of the present invention, which will not be elaborated herein.

[0121] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, all or part of the processes in the methods of the above embodiments are implemented. It can also be completed by instructing relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0122] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as the hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Further, the computer-readable storage medium can also include both the internal storage unit and the external storage device of the electronic device. The computer-readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store the data that has been output or will be output.

[0123] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0124] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described electronic devices and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0125] In several embodiments provided by the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces or units, and can also be electrical, mechanical or other forms of connection.

[0126] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention.

[0127] In addition, the functional units in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0128] The above are only the specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A reservoir engineering standardized management method, characterized in that: include: Processing the multidimensional data acquired by the sensor based on the first random forest model to obtain target state data of the target reservoir dam body, wherein the multidimensional data includes structural data and monitoring data of the target reservoir dam body; Extracting features from the target state data to obtain target state feature data, obtaining target similarity based on multiple similarities between the target state feature data and each state feature data in a state library, and selecting the target state of the target reservoir dam body from the state library based on the target similarity; Determining a health index of the target reservoir dam body based on the target state; Based on the health index, determining the safety level of the target reservoir dam body; Based on the security level, a corresponding target management policy in the policy rule base is selected, including: Process environmental data to obtain seasonal characteristics; Selecting a corresponding management policy template in the policy rule base based on the seasonal characteristics and the security level; When the seasonal characteristic is the first seasonal characteristic, the seepage monitoring level and the inspection frequency in the management strategy template are adjusted based on the first amplitude value to obtain a target management strategy; When the seasonal characteristic is the second seasonal characteristic, the seepage monitoring level and the inspection frequency in the management strategy template are adjusted based on the first amplitude value to obtain a target management strategy; The precipitation of the first season characteristic is greater than the second threshold; the precipitation of the second season characteristic is less than the first threshold, and the second threshold is greater than the first threshold; The policy rule base stores a variety of management policies.

2. The reservoir engineering standardization management method according to claim 1, characterized in that: The determining of the corresponding target management strategy based on the target state also includes: Based on the target state, determining a corresponding risk level identifier; Determining a policy triggering condition based on the risk level identifier; The policy triggering condition is matched with the management policy in the policy rule base to obtain a target management policy.

3. The reservoir engineering standardization management method according to claim 2, characterized in that: The determining a corresponding risk level identifier based on the target state includes: Performing weighted calculation on the environmental data and characteristic data corresponding to the target state to obtain a comprehensive risk value; Based on the comprehensive risk value, a corresponding risk level identifier is determined.

4. The reservoir engineering standardization management method according to claim 3, characterized in that: The matching of the policy triggering condition with the management policy in the policy rule base to obtain the target management policy includes: When multiple management policies match the policy triggering condition, the multiple management policies are sorted according to the priority sorting rule preset in the policy rule base to obtain a first sorting list, and the target management policy is determined based on the first sorting list.

5. The reservoir engineering standardization management method according to claim 1, characterized in that: The multidimensional data acquired by the sensor is processed based on the first random forest model to obtain target state data of the target reservoir dam body, including: Processing the multidimensional data based on a first random forest model to obtain a difference in recognition results; When the difference of the recognition result is greater than the first difference, verifying the multidimensional data based on the second random forest model to obtain a verification difference; When the verification difference is greater than the first difference, the environmental data around the target reservoir dam is collected; Based on the environmental data and the multi-dimensional data, target state data is determined.

6. The reservoir engineering standardization management method according to claim 5, characterized in that: The determining target state data based on the environmental data and the multi-dimensional data includes: Based on a preset weight distribution rule, the multidimensional data and the environmental data are fused to obtain target state data.

7. A standardized management system for reservoir engineering, characterized in that: include: A data processing module, used for processing the multidimensional data acquired by the sensor based on the first random forest model to obtain target state data of the target reservoir dam body, wherein the multidimensional data includes structural data and monitoring data of the target reservoir dam body; A state determination module is used to extract features from the target state data to obtain target state feature data, obtain target similarity based on multiple similarities between the target state feature data and each state feature data in the state library, and select the target state of the target reservoir dam body from the state library based on the target similarity; A management strategy module, used for determining the health index of the target reservoir dam body based on the target state; Based on the health index, determining the safety level of the target reservoir dam body; The management strategy module is specifically used to process environmental data to obtain seasonal characteristics; Selecting a corresponding management policy template in a policy rule library based on the seasonal characteristics and the security level; When the seasonal characteristic is the first seasonal characteristic, the seepage monitoring level and the inspection frequency in the management strategy template are adjusted based on the first amplitude value to obtain a target management strategy; When the seasonal characteristic is the second seasonal characteristic, the seepage monitoring level and the inspection frequency in the management strategy template are adjusted based on the first amplitude value to obtain a target management strategy; The precipitation of the first season characteristic is greater than the second threshold; the precipitation of the second season characteristic is less than the first threshold, and the second threshold is greater than the first threshold; The policy rule base stores a variety of management policies.

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