Water conservancy project monitoring data analysis method based on multi-feature data identification

Abstract

The invention discloses a water conservancy project monitoring data analysis method based on multi-feature data identification, and the method comprises the steps: obtaining the deformation of a water conservancy project structure based on a plurality of deformation monitoring devices disposed in a set period, and judging whether the deformation of the water conservancy project structure is abnormal or not according to the deformation of the water conservancy project structure; if the abnormal deformation is detected, synchronously acquiring a temperature parameter of the abnormal hydraulic engineering structure, and verifying whether the abnormal deformation is caused by thermodynamic fluctuation through the temperature parameter; if the thermodynamic factor is verified, starting a temperature control emergency instruction; and if the temperature factor is verified to be a non-temperature factor, starting a seepage and osmotic pressure parameter regulation and control instruction, and regulating and controlling the seepage and osmotic pressure parameters of the hydraulic engineering structure through an automatic seepage and osmotic pressure supply device until the deformation parameters return to normal. The system has the advantages that deformation abnormity of the water conservancy project structure is accurately monitored and analyzed through multi-sensor associated sensing, and temperature or seepage and osmotic pressure parameters are automatically adjusted, so that normal deformation of the water conservancy project structure is recovered.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and particularly to a method for analyzing water conservancy project monitoring data based on multi-feature data recognition. Background Art

[0002] The technical field of water conservancy projects covers various aspects such as reservoirs, dams, river regulation, irrigation systems, drainage systems, flood control, and soil and water conservation. Modern water conservancy projects also include water quality management, ecological protection and restoration, and strategies for coping with climate change. With the progress of technology, new technologies such as intelligent monitoring systems, automation control, and information management systems are continuously introduced into water conservancy projects to improve the efficiency and safety of water conservancy facilities.

[0003] Currently, the production of water conservancy project structures on the market mostly relies on traditional single-sensor monitoring and manual intervention control methods, which have certain limitations. Traditional methods usually only monitor the deformation or temperature of water conservancy project structures and rely mostly on manual judgment and adjustment. This is not only easily affected by human operation errors but also unable to comprehensively and real-time analyze the reasons for abnormal deformation of water conservancy project structures. For example, when abnormal deformation occurs in a water conservancy project structure, it may be caused by temperature fluctuations, changes in seepage pressure parameters, or other unknown factors. Traditional methods often cannot effectively distinguish these different factors, resulting in inaccurate adjustment and slow response speed. It may delay the discovery of abnormalities and the taking of measures, thus affecting production efficiency and battery performance. Summary of the Invention

[0004] In order to improve the existing parameter control method for the production of water conservancy project structures, a method for analyzing water conservancy project monitoring data based on multi-feature data recognition is provided. This method accurately monitors and analyzes abnormal deformation of water conservancy project structures through multi-sensor associated perception, automatically adjusts temperature or seepage pressure parameters to restore the normal deformation of water conservancy project structures, improves the stability, efficiency, and quality of the production of water conservancy project structures, and ensures the long-term performance of the battery.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A method for analyzing water conservancy project monitoring data based on multi-feature data recognition, comprising:

[0007] Based on multiple groups of deformation monitoring devices deployed within a set period, obtain the deformation of the water conservancy project structure, and determine whether abnormal deformation occurs according to the deformation of the water conservancy project structure;

[0008] If abnormal deformation is detected, synchronously obtain the temperature parameters of the abnormal water conservancy project structure, and verify whether the abnormal deformation is caused by thermodynamic fluctuations through the temperature parameters;

[0009] If it is verified as a thermodynamic factor, enable the temperature control emergency instruction and start the integrated cooling system to implement thermal balance adjustment on the target water conservancy project structure until the deformation index returns to normal;

[0010] If it is verified as a non-temperature factor, enable the seepage pressure parameter regulation instruction, and regulate the seepage pressure parameters of the water conservancy project structure through the automatic seepage pressure replenishment device until its deformation parameters return to normal.

[0011] Preferably, the multi-group deformation monitoring devices deployed based on the set period obtain the deformation of the water conservancy project structure, and analyze whether there is abnormal deformation according to the deformation of the water conservancy project structure. Each set period corresponds to a set of deformation monitoring values of the water conservancy project structure, specifically including:

[0012] Set the basic detection period T0, and extract the maximum deformation value ρ max and the minimum value ρ min of the water conservancy project structure. Different periods correspond to a set of maximum and minimum deformation values of the water conservancy project structure;

[0013] Set the periodic deformation monitoring nodes. In each monitoring period, obtain multiple groups of deformation sample values of the water conservancy project structure through the distributed deformation sensor array to form a deformation monitoring data set;

[0014] Based on the data in the deformation monitoring data sets in different periods, compare with the maximum and minimum deformation values of the corresponding water conservancy project structure to determine whether there is abnormal deformation data;

[0015] If there is abnormal deformation data, obtain the monitoring data of the sensors around the sensor where the abnormal deformation is monitored and where no abnormal deformation is monitored;

[0016] Based on the obtained monitoring data, compare the deformation data of the water conservancy project structure monitored in the current period with the deformation data of the water conservancy project structure monitored in the previous several periods;

[0017] If the deformation data of the water conservancy project structure monitored currently changes significantly compared with the data in the previous several periods, it is determined that the sensor where the abnormal deformation is monitored monitors successfully and there is an abnormal deformation situation. If there is no significant change, reset the sensor where the abnormal deformation is monitored.

[0018] Preferably, if abnormal deformation is detected, synchronously obtain the temperature parameters of the abnormal water conservancy project structure, and verify whether the abnormal deformation is caused by thermodynamic fluctuations through the temperature parameters, specifically including:

[0019] Based on the temperature monitoring device, obtain the temperature data of the water conservancy project structure around the sensor where the abnormal deformation is monitored, and obtain the current temperature change value according to the temperature data of the water conservancy project structure;

[0020] Obtain the deformation change value of the water conservancy project structure with abnormal deformation based on the difference between the actual maximum deformation data and the initial state deformation data in the sensors with detected abnormal deformation;

[0021] Construct a temperature-deformation relationship model, and based on the obtained temperature change value and liquid deformation change value of the water conservancy project structure, determine whether the abnormal deformation is caused by thermodynamic fluctuations.

[0022] Preferably, the construction of the temperature-deformation relationship model, and based on the obtained temperature change value and liquid deformation change value of the water conservancy project structure, determining whether the abnormal deformation is caused by thermodynamic fluctuations specifically includes:

[0023] Based on historical monitoring data, obtain the constructed temperature-deformation relationship model, and the formula is:

[0024] ρ(T) = ρ0[1 - β(T - T0)]

[0025] Where, ρ(T) is the deformation of the water conservancy project structure at temperature T, ρ0 is the deformation at the reference temperature T0, β is the volume expansion coefficient of temperature, and T - T0 is the temperature change value;

[0026] Substitute the obtained temperature change value into the temperature-deformation relationship model, and calculate to obtain the corresponding deformation change value of the water conservancy project structure;

[0027] Based on the calculated deformation change value of the water conservancy project structure and the monitored deformation change value of the water conservancy project structure for comparison;

[0028] If the error between the two change amounts is less than the allowable error, it is determined that the deformation change of the water conservancy project structure is not caused by thermodynamic factors, otherwise, it is determined that the deformation data of the water conservancy project structure affected by thermodynamic factors is abnormal.

[0029] Preferably, if it is verified as a thermodynamic factor, enable the temperature control emergency instruction, and start the integrated cooling system to implement thermal balance adjustment on the target water conservancy project structure until the deformation index returns to normal. Specifically includes:

[0030] Based on the situation that the deformation data of the water conservancy project structure affected by thermodynamic factors is abnormal, enable the temperature control emergency instruction, and enable the three-stage temperature control system for thermal balance adjustment;

[0031] The three-stage temperature control system specifically includes:

[0032] The first stage: cool down through the microcirculation heat dissipation module;

[0033] The second stage: intervene through the phase change material heat storage unit for heat exchange;

[0034] The third stage: start to use through the compression refrigeration system when the temperature difference exceeds the threshold;

[0035] For the hydraulic engineering structure after temperature adjustment, collect the real-time data collected by each temperature sensor;

[0036] Compare the collected data with the temperature data before the abnormal deformation occurs to determine whether the temperature control system is effective;

[0037] If it is not effective, restart the temperature control emergency instruction.

[0038] Preferably, if it is verified that it is not a temperature factor, then enable the seepage pressure parameter regulation instruction, and regulate the seepage pressure parameter of the hydraulic engineering structure through the automatic seepage pressure replenishment device until its deformation parameter returns to normal, which specifically includes:

[0039] Based on the abnormal deformation data of the hydraulic engineering structure judged to be affected by non-temperature factors, enable the seepage pressure parameter regulation instruction;

[0040] Based on the obtained deformation change value of the hydraulic engineering structure, calculate the mass of the anti-seepage liquid required to make its deformation value return to the normal value. The formula is:

[0041]

[0042] Among them, ρ target is the normal value of the deformation of the hydraulic engineering structure, m sol is the mass of the hydraulic engineering structure, m salt is the mass of the anti-seepage liquid to be added, ρ sol is the density of the current hydraulic engineering structure, ρ salt is the density of the anti-seepage liquid;

[0043] For the hydraulic engineering structure after anti-seepage liquid adjustment, collect the real-time data collected by each deformation sensor;

[0044] Compare the collected data with the deformation data before the abnormal deformation occurs to determine whether the seepage pressure parameter regulation instruction is effective;

[0045] If the difference between the two is less than the allowable error, it is judged that the seepage pressure parameter regulation instruction is effective.

[0046] Compared with the prior art, the advantages of the present invention are:

[0047] A closed-loop control system for the deformation of hydraulic engineering structures based on the fusion of deformation, temperature, seepage and pressure parameter data was constructed, realizing the accurate identification and intelligent regulation of abnormal working conditions of hydraulic engineering. By deploying a distributed deformation sensor array and establishing a multi-cycle dynamic comparison mechanism, an innovative double-re-nuclear judgment algorithm for deformation anomalies was proposed: on the one hand, equipment errors were eliminated by horizontal comparison of multi-sensor data in the same cycle, and on the other hand, real anomalies were identified by longitudinal analysis combined with historical cycle data. Secondly, a breakthrough temperature-deformation coupling analysis model was introduced in the anomaly tracing link. By establishing a dynamic response equation of thermodynamic parameters and deformation changes, accurate identification of temperature-induced anomalies was achieved, solving the industry pain point that physical expansion and chemical concentration changes cannot be distinguished in traditional methods. In terms of the control mechanism, a dual-path adaptive correction strategy was pioneered. When it was determined to be a thermal disturbance, a gradient temperature control algorithm was used to link the cooling system to implement nonlinear cooling. When it was identified as a concentration anomaly, a closed-loop replenishment algorithm for seepage and pressure parameters was developed to dynamically adjust the addition amount through real-time deformation feedback. Through the coordinated regulation of multiple parameters, the efficiency of structural state recovery of water conservancy projects is improved, and a full-process intelligent quality assurance system is built for the production of high-purity water conservancy projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of the method proposed by the present invention;

[0049] Figure 2 This is a schematic diagram of deformation anomaly detection proposed by the present invention;

[0050] Figure 3 This is a schematic diagram of determining abnormal deformation caused by temperature proposed by the present invention;

[0051] Figure 4 This is a schematic diagram of the temperature-deformation relationship model proposed by the present invention;

[0052] Figure 5 This is a schematic diagram of the temperature control regulation proposed by the present invention;

[0053] Figure 6 This is a schematic diagram of the control of seepage and pressure parameters proposed by the present invention;

[0054] Figure 7 This is a schematic diagram of the electronic device in this solution;

[0055] Figure 8 This is a schematic diagram of the computer-readable storage medium structure in this solution. DETAILED DESCRIPTION

[0056] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0057] See alsoFigure 1 As shown in the figure, a method for analyzing water conservancy project monitoring data based on multi-feature data recognition includes:

[0058] Step 1: Based on multiple groups of deformation monitoring devices deployed within a set period, obtain the deformation of the water conservancy project structure, and determine whether deformation anomalies occur according to the deformation of the water conservancy project structure.

[0059] Step 2: If deformation anomalies are detected, synchronously obtain the temperature parameters of the abnormal water conservancy project structure, and verify whether the deformation anomalies are caused by thermodynamic fluctuations through the temperature parameters.

[0060] Step 3: If it is verified to be a thermodynamic factor, enable the temperature control emergency instruction and start the integrated cooling system to implement thermal balance adjustment on the target water conservancy project structure until the deformation index returns to normal.

[0061] Step 4: If it is verified to be a non-temperature factor, enable the seepage seepage pressure parameter regulation instruction, and regulate the seepage seepage pressure parameters of the water conservancy project structure through the automatic seepage seepage pressure replenishment device until its deformation parameters return to normal, that is, based on the regulation of temperature, obtain the optimal ion mobility and solvent stability.

[0062] Refer to Figure 2 As shown in the figure, based on multiple groups of deformation monitoring devices deployed within a set period, obtain the deformation of the water conservancy project structure, and analyze whether deformation anomalies occur according to the deformation of the water conservancy project structure. Each set period corresponds to a set of deformation monitoring values of the water conservancy project structure, specifically including:

[0063] Set the basic detection period T0, and extract the maximum deformation value ρ max and the minimum value ρ min of the water conservancy project structure deformation. Different periods correspond to a set of maximum and minimum values of the water conservancy project structure deformation.

[0064] Set periodic deformation monitoring nodes. Within each monitoring period, obtain multiple groups of water conservancy project structure deformation sample values through a distributed deformation sensor array to form a deformation monitoring data set.

[0065] Based on the data in the deformation monitoring data sets within different periods, determine whether there are deformation anomaly data by comparing with the maximum and minimum values of the corresponding water conservancy project structure deformation.

[0066] If there are deformation anomaly data, obtain the monitoring data of the sensors around the sensors where deformation anomalies are detected and where no deformation anomalies are detected.

[0067] Based on the obtained monitoring data, compare the water conservancy project structure deformation data monitored in the current period with the water conservancy project structure deformation data monitored in the previous several periods.

[0068] If the deformation data of the hydraulic engineering structure currently monitored shows a significant change compared with the data in the previous several cycles, it is determined that the sensor that has detected abnormal deformation has been successfully monitored, and an abnormal deformation situation has occurred. If there is no significant change, the sensor that has detected abnormal deformation is reset.

[0069] It can be understood that the deformation changes of the hydraulic engineering structure in some cycles may be normal, such as temperature changes and production batch differences, which are easily misjudged as abnormal. It is necessary to accurately judge the "significant change". When judging the change of deformation data, the moving average filter algorithm can be considered to remove noise and observe the trend of the data rather than single fluctuations; statistical methods, such as standard deviation and mean deviation, can be used to judge whether the difference between the current data and the historical data exceeds the expected range in combination with the data of historical cycles.

[0070] At the same time, resetting the sensor after detecting abnormal deformation may affect the continuity of other monitoring data, resulting in data gaps or inconsistencies. When resetting, a buffer for sensor data is maintained to ensure that the reset operation does not affect the data continuity; for the reset sensor, rapid calibration is performed to ensure its quick return to the normal working state.

[0071] Refer to Figure 3 As shown, if abnormal deformation is detected, the temperature parameters of the abnormal hydraulic engineering structure are synchronously obtained, and it is verified whether the abnormal deformation is caused by thermodynamic fluctuations through the temperature parameters. Specifically, it includes:

[0072] Based on the temperature monitoring device, the temperature data of the hydraulic engineering structure around the sensor that has detected abnormal deformation is obtained, and the current temperature change value is obtained according to the temperature data of the hydraulic engineering structure;

[0073] Based on the difference between the actual maximum deformation data and the initial state deformation data in the sensor that has detected abnormal deformation, the deformation change value of the hydraulic engineering structure with abnormal deformation is obtained;

[0074] A temperature-deformation relationship model is constructed, and based on the obtained temperature change value and deformation change value of the hydraulic engineering structure, it is judged whether the abnormal deformation is caused by thermodynamic fluctuations.

[0075] Specifically, the frequency of data collection is also a key factor. For the detection of temperature and deformation changes, especially for the judgment of minute fluctuations, it is necessary to ensure the real-time nature of the data and sufficient time resolution. A lower sampling frequency may lead to data latency and the inability to promptly capture the immediate response of temperature fluctuations to deformation changes. To improve the accuracy of judgment, multivariate regression analysis or machine learning models, such as support vector machines or random forests, can be combined to capture complex non-linear relationships. By training the model, potential patterns between temperature and deformation changes can be identified, enabling automatic determination of whether an anomaly belongs to the category of thermodynamic fluctuations or is due to deformation anomalies caused by other factors when an anomaly occurs.

[0076] Refer to Figure 4 As shown, to construct a temperature-deformation relationship model, based on the obtained temperature change values and liquid deformation change values of the hydraulic engineering structure, the determination of whether deformation anomalies are caused by thermodynamic fluctuations specifically includes:

[0077] Based on historical monitoring data, obtain and construct a temperature-deformation relationship model, the formula is:

[0078] ρ(T) = ρ0[1 - β(T - T0)]

[0079] Where ρ(T) is the deformation of the hydraulic engineering structure at temperature T, ρ0 is the deformation at the reference temperature T0, β is the volume expansion coefficient of temperature, and T - T0 is the temperature change value;

[0080] Substitute the obtained temperature change value into the temperature-deformation relationship model to calculate and obtain the corresponding deformation change value of the hydraulic engineering structure;

[0081] Compare the calculated deformation change value of the hydraulic engineering structure with the monitored deformation change value of the hydraulic engineering structure;

[0082] If the error between the two change amounts is less than the allowable error, it is determined that the deformation change of the hydraulic engineering structure is not caused by thermodynamic factors. Otherwise, it is determined that the deformation data of the hydraulic engineering structure affected by thermodynamic factors is abnormal.

[0083] Refer to Figure 5 As shown, if it is verified as a thermodynamic factor, enable the temperature control emergency instruction and start the integrated cooling system to implement thermal balance adjustment on the target hydraulic engineering structure until the deformation index returns to normal. Specifically, it includes:

[0084] Based on the situation that the deformation data of the hydraulic engineering structure affected by thermodynamic factors is abnormal, enable the temperature control emergency instruction and enable the three-level temperature control system for thermal balance adjustment;

[0085] The three-level temperature control system specifically includes:

[0086] The first level: Cool down through the microcirculation heat dissipation module;

[0087] Second level: heat exchange through the intervention of the phase change material heat storage unit;

[0088] Third level: start to use through the compression refrigeration system when the temperature difference exceeds the threshold;

[0089] Based on the water conservancy project structure after temperature adjustment, collect and obtain the real-time data collected by each temperature sensor;

[0090] Compare the collected data with the temperature data before the abnormal deformation occurs to judge whether the temperature control system is effective;

[0091] If it is not effective, restart the temperature control emergency instruction.

[0092] Specifically, in the implementation process of the temperature control system, the collaborative working mechanism between each temperature control module is also crucial. Each temperature control stage should be dynamically adjusted according to the actual situation. For example, if the first-level microcirculation heat dissipation module fails to effectively reduce the temperature, the intervention timing of the second-level phase change material heat storage unit can be adjusted through an algorithm or its heat exchange efficiency can be increased; if the expected effect is still not achieved, quickly start the third-level compression refrigeration system. In order to improve the adaptability of the system, an adaptive control algorithm can also be embedded in the system so that it can automatically adjust the adjustment strategy according to different environmental conditions and battery loads.

[0093] Refer to Figure 6 As shown, if it is verified that it is not a temperature factor, enable the seepage pressure parameter regulation instruction, and regulate the seepage pressure parameters of the water conservancy project structure through the automatic seepage pressure replenishment device until its deformation parameters return to normal. Specifically, it includes:

[0094] Based on the abnormal deformation data of the water conservancy project structure judged to be affected by non-temperature factors, enable the seepage pressure parameter regulation instruction;

[0095] Based on the obtained deformation change value of the water conservancy project structure, calculate the mass of the anti-seepage liquid required to make its deformation value return to the normal value. The formula is:

[0096]

[0097] Among them, ρ target is the normal value of the deformation of the water conservancy project structure, m sol is the mass of the water conservancy project structure, m salt is the mass of the anti-seepage liquid to be added, ρ sol is the density of the current water conservancy project structure, ρ salt is the density of the anti-seepage liquid;

[0098] Based on the water conservancy project structure after the adjustment of the anti-seepage liquid, collect and obtain the real-time data collected by each deformation sensor;

[0099] Compare the collected data with the deformation data before the abnormal deformation occurs to determine whether the regulation instruction of the seepage pressure parameter takes effect;

[0100] If the difference between the two is less than the allowable error, it is determined that the regulation instruction of the seepage pressure parameter takes effect.

[0101] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0102] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0103] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included within the protection scope of the present invention.

[0104] Furthermore, the method according to the embodiment of the present application can also be implemented by means of Figure 7 the architecture of the electronic device shown. As Figure 7 shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to the network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, can store a method for analyzing water conservancy project monitoring data based on multi-feature data provided by the present application. The electronic device 500 may also include a terminal interface 508. Of course, Figure 7 the architecture shown is only exemplary. When implementing different devices, one or more components shown in the electronic device can be omitted according to actual needs. Figure 7

[0105] Figure 8 Figure 8 is a schematic diagram of the structure of a computer-readable storage medium provided by an embodiment of the present application. As Figure 8As shown, it is a computer-readable storage medium 600 according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are run by a processor, a method for analyzing water conservancy project monitoring data based on multi-feature data recognition according to an embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0106] It should be noted that: the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the figures do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0107] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0108] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for analyzing water conservancy project monitoring data based on multi-feature data recognition, characterized in that Including: Multiple groups of deformation monitoring devices deployed based on a set period, obtaining the deformation of the hydraulic engineering structure, and judging whether deformation anomalies occur according to the deformation of the hydraulic engineering structure; If a deformation anomaly is detected, synchronously obtain the temperature parameters of the abnormal hydraulic engineering structure, and verify whether the deformation anomaly is caused by thermodynamic fluctuations through the temperature parameters; If it is verified as a thermodynamic factor, enable the temperature control emergency instruction, and start the integrated cooling system to implement thermal balance adjustment on the target hydraulic engineering structure until the deformation index returns to normal; If it is verified as a non-temperature factor, enable the seepage seepage pressure parameter regulation instruction, and regulate the seepage seepage pressure parameters of the hydraulic engineering structure through the automatic seepage seepage pressure replenishment device until its deformation parameters return to normal.

2. The water conservancy project monitoring data analysis method based on multi-feature data recognition according to claim 1, wherein The multiple groups of deformation monitoring devices deployed based on a set period, obtaining the deformation of the hydraulic engineering structure, and analyzing whether deformation anomalies occur according to the deformation of the hydraulic engineering structure. Each set period corresponds to a set of deformation monitoring values of the hydraulic engineering structure, specifically including: Set the basic detection period T0 and extract the maximum deformation value ρ of the hydraulic engineering structure max and the minimum value ρ min , different periods correspond to a set of maximum and minimum values of structural deformation of hydraulic engineering; Set periodic deformation monitoring nodes, and obtain multiple groups of deformation sample values of the hydraulic engineering structure through a distributed deformation sensor array within each monitoring period to form a deformation monitoring data set; Based on the data in the deformation monitoring data sets in different periods, judge whether there are deformation anomaly data by comparing with the maximum and minimum deformation values of the corresponding hydraulic engineering structure; If there are deformation anomaly data, obtain the monitoring data of the sensors that have not detected deformation anomalies around the sensors that have detected deformation anomalies; Based on the obtained monitoring data, compare the deformation data of the hydraulic engineering structure monitored in the current period with the deformation data of the hydraulic engineering structure monitored in the previous several periods; If the deformation data of the currently monitored hydraulic engineering structure changes significantly compared with the data in the previous several periods, it is judged that the monitoring of the sensors that have detected deformation anomalies is successful, and a deformation anomaly occurs. If there is no significant change, the sensors that have detected deformation anomalies are reset.

3. A method for analyzing water conservancy project monitoring data based on multi-feature data recognition according to claim 1, characterized in that, The specific content of "if a deformation anomaly is detected, synchronously obtain the temperature parameters of the abnormal hydraulic engineering structure, and verify whether the deformation anomaly is caused by thermodynamic fluctuations through the temperature parameters" includes: Based on the temperature monitoring device, obtain the temperature data of the hydraulic engineering structure around the sensors that have detected deformation anomalies, and obtain the current temperature change value according to the temperature data of the hydraulic engineering structure; Based on the difference between the actual maximum deformation data and the initial state deformation data in the sensors that have detected deformation anomalies, obtain the deformation change value of the hydraulic engineering structure with deformation anomalies; Construct a temperature-deformation relationship model, and judge whether the deformation anomaly is caused by thermodynamic fluctuations based on the obtained temperature change value and liquid deformation change value of the hydraulic engineering structure.

4. A method for analyzing water conservancy project monitoring data based on multi-feature data recognition according to claim 1, characterized in that, The specific content of "construct a temperature-deformation relationship model, and judge whether the deformation anomaly is caused by thermodynamic fluctuations based on the obtained temperature change value and liquid deformation change value of the hydraulic engineering structure" includes: Based on historical monitoring data, obtain and construct a temperature-deformation relationship model, and the formula is: ρ(T) = ρ0[1 - β(T - T0)] Where ρ(T) is the deformation of the hydraulic engineering structure at temperature T, ρ0 is the deformation at the reference temperature T0, β is the volume expansion coefficient of temperature, and T - T0 is the temperature change value; Substitute the obtained temperature change value into the temperature-deformation relationship model to calculate the corresponding deformation change value of the hydraulic engineering structure; Compare the deformation change value of the hydraulic engineering structure obtained by calculation with the monitored deformation change value of the hydraulic engineering structure; If the error between the two change amounts is less than the allowable error, it is judged that the deformation change of the hydraulic engineering structure is not caused by thermodynamic factors. Otherwise, it is judged that the deformation data of the hydraulic engineering structure affected by thermodynamic factors is abnormal.

5. A method for analyzing water conservancy project monitoring data based on multi-feature data recognition according to claim 1, characterized in that If it is verified to be a thermodynamic factor, the temperature control emergency instruction is enabled, and the integrated cooling system is started to implement thermal balance adjustment on the target hydraulic engineering structure until the deformation index returns to normal. Specifically, it includes: Based on the abnormal deformation data of the hydraulic engineering structure judged to be affected by thermodynamic factors, the temperature control emergency instruction is enabled, and the three-level temperature control system is enabled for thermal balance adjustment; The three-level temperature control system specifically includes: The first level: Cool down through the microcirculation heat dissipation module; The second level: Intervene through the phase change material heat storage unit for heat exchange; The third level: Start to use the compression refrigeration system when the temperature difference exceeds the threshold; Based on the hydraulic engineering structure after temperature adjustment, collect and obtain the real-time data collected by each temperature sensor; Compare the collected data with the temperature data before the deformation anomaly to judge whether the temperature control system is effective; If it is not effective, restart the temperature control emergency instruction.

6. A method for analyzing water conservancy project monitoring data based on multi-feature data recognition according to claim 1, characterized in that, If it is verified to be a non-temperature factor, the seepage pressure parameter control instruction is enabled, and the seepage pressure parameters of the hydraulic engineering structure are controlled by the automatic seepage pressure replenishment device until its deformation parameters return to normal. Specifically, it includes: Based on the abnormal deformation data of the hydraulic engineering structure judged to be affected by non-temperature factors, the seepage pressure parameter control instruction is enabled; Based on the obtained deformation change value of the hydraulic engineering structure, calculate the mass of the anti-seepage liquid required to make its deformation value return to the normal value. The formula is: Among them, ρ target is the normal value of the deformation of the water conservancy project structure, m sol is the mass of the water conservancy project structure, m salt is the mass of the anti-seepage liquid to be added, ρ sol is the density of the current water conservancy project structure, ρ salt is the density of the anti-seepage liquid; Based on the hydraulic engineering structure after adjusting the anti-seepage liquid, collect and obtain the real-time data collected by each deformation sensor; Compare the collected data with the deformation data before the deformation anomaly to judge whether the seepage pressure parameter control instruction is effective; If the difference between the two is less than the allowable error, it is judged that the seepage pressure parameter control instruction is effective.

7. An electronic device, characterized in that, Including: At least one processor; And a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor. The instructions are executed by the at least one processor so that the at least one processor can execute the method for analyzing hydraulic engineering monitoring data based on multi-feature data according to any one of claims 1-6.

8. A computer-readable storage medium storing computer-readable instructions, characterized in that, When the computer-readable instructions are executed by the processor, the method for analyzing hydraulic engineering monitoring data based on multi-feature data according to any one of claims 1-6 is implemented.

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