A water conservancy project monitoring data analysis method based on multi-feature data recognition

Abstract

The present invention discloses a water conservancy project monitoring data analysis method based on multi-feature data recognition, comprising: obtaining the deformation of the water conservancy project structure based on multiple groups of deformation sensors deployed within a set period, and judging whether a deformation abnormality occurs based on the deformation of the water conservancy project structure; if a deformation abnormality is detected, synchronously obtaining the temperature parameters of the abnormal water conservancy project structure, and verifying whether the deformation abnormality is caused by thermodynamic fluctuations through the temperature parameters; if it is verified to be a thermodynamic factor, enabling a temperature control emergency instruction; if it is verified to be a non-temperature factor, enabling a seepage and pressure parameter control instruction, and controlling the seepage and pressure parameters of the water conservancy project structure through an automated seepage and pressure supply device until its deformation index returns to normal. The advantages of the present invention are: through multi-sensor associated perception, accurate monitoring and analysis of water conservancy project structure deformation abnormalities, and automatic adjustment of temperature or seepage and pressure parameters to restore the normal deformation of the water conservancy project structure.

Description

Technical Field

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

[0002] The technical field of water conservancy engineering covers 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 responding to climate change. With technological advancements, water conservancy projects are constantly introducing new technologies such as intelligent monitoring systems, automated control, and information management systems to improve the efficiency and safety of water conservancy facilities.

[0003] The current production of hydraulic engineering 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 hydraulic engineering structures and rely heavily on manual judgment and adjustment. This is not only susceptible to human operational errors, but also unable to comprehensively analyze the causes of abnormal deformation of hydraulic engineering structures in real time. For example, when abnormal deformation of hydraulic engineering structures occurs, it may be caused by temperature fluctuations, changes in seepage and pressure parameters, or other unknown factors. Traditional methods are often unable to effectively distinguish between these different factors, resulting in inaccurate adjustments and slow response speeds. This may delay the detection of abnormalities and the implementation of measures, thereby 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 water conservancy project monitoring data analysis method based on multi-feature data recognition is provided. This method uses multi-sensor correlation perception to accurately monitor and analyze abnormal deformation of water conservancy project structures, and automatically adjusts temperature or seepage and pressure parameters to restore the normal deformation of water conservancy project structures, thereby improving the stability, efficiency and quality of water conservancy project structure production and ensuring the long-term performance of batteries.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

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

[0007] Based on multiple groups of deformation sensors deployed within a set period, the deformation of the water conservancy project structure is obtained, and whether deformation abnormality occurs is determined based on the deformation of the water conservancy project structure;

[0008] If abnormal deformation is detected, the temperature parameters of the abnormal hydraulic engineering structure are obtained simultaneously to verify whether the abnormal deformation is caused by thermodynamic fluctuations.

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

[0010] If it is verified to be a non-temperature factor, the seepage and pressure parameter control instructions will be activated, and the seepage and pressure parameters of the water conservancy project structure will be controlled through the automated seepage and pressure supply device until its deformation index returns to normal.

[0011] Preferably, the deformation of the water conservancy project structure is obtained based on multiple groups of deformation sensors deployed within a set period, and whether deformation abnormality occurs is analyzed based on the deformation of the water conservancy project structure, wherein each set period corresponds to a group of water conservancy project structure deformation monitoring values, specifically including:

[0012] Set the basic detection cycle , extract the maximum deformation of the water conservancy project structure and minimum value , different periods correspond to a set of maximum and minimum values ​​of the structural deformation of water conservancy projects;

[0013] Set up periodic deformation monitoring nodes, and obtain multiple sets of water conservancy project structure deformation sample values ​​through distributed deformation sensor arrays in each monitoring period to form a deformation monitoring data set;

[0014] Based on the deformation monitoring data set data in different periods, by comparing with the maximum and minimum deformation values ​​of the corresponding water conservancy project structure, it is determined whether there is abnormal deformation data;

[0015] If there is abnormal deformation data, obtain the monitoring data of sensors that have not detected abnormal deformation around the sensor that detected the abnormal deformation, and based on this monitoring data, compare the deformation data of the water conservancy project structure monitored in the current cycle with the deformation data of the water conservancy project structure monitored in previous cycles;

[0016] If the currently monitored deformation data of the water conservancy project structure changes significantly compared with the data of the previous cycles, it is judged that the sensor that monitors the abnormal deformation has successfully monitored and an abnormal deformation has occurred. If there is no obvious change, the sensor that monitors the abnormal deformation will be reset.

[0017] Preferably, if abnormal deformation is detected, the temperature parameters of the abnormal water conservancy project structure are simultaneously obtained, and the temperature parameters are used to verify whether the abnormal deformation is caused by thermodynamic fluctuations, which specifically includes:

[0018] Acquiring, based on the temperature sensor, temperature data of the water conservancy project structure around the sensor that detects abnormal deformation, and acquiring a current temperature change value based on the temperature data of the water conservancy project structure;

[0019] Obtaining a 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 sensor that monitors the abnormal deformation;

[0020] A temperature-deformation relationship model is constructed to determine whether deformation anomalies are caused by thermodynamic fluctuations based on the obtained temperature and deformation change values ​​of the water conservancy project structure.

[0021] Preferably, the temperature-deformation relationship model is constructed to determine whether the deformation anomaly is caused by thermodynamic fluctuations based on the acquired temperature change values ​​and deformation change values ​​of the hydraulic engineering structure, and specifically includes:

[0022] Based on historical monitoring data, a temperature-deformation relationship model is constructed, and the formula is:

[0023]

[0024] in, Temperature The deformation of the water conservancy project structure, is the reference temperature The deformation below, is the volume expansion coefficient with temperature, is the temperature change value;

[0025] Substituting the obtained temperature change value into the temperature-deformation relationship model, and calculating the corresponding deformation change value of the water conservancy project structure;

[0026] Comparing the deformation change value of the water conservancy project structure obtained by calculation with the deformation change value of the water conservancy project structure monitored;

[0027] If the error between the two change values ​​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.

[0028] Preferably, if the cause is verified to be a thermodynamic factor, a temperature control emergency instruction is activated, and an integrated cooling system is started to adjust the thermal balance of the abnormal water conservancy project structure until the deformation index returns to normal, specifically including:

[0029] Based on the abnormal deformation data of the water conservancy project structure determined to be affected by thermodynamic factors, the temperature control emergency instruction is activated and the three-level temperature control system is activated to adjust the thermal balance;

[0030] The three-stage temperature control system specifically includes:

[0031] First level: Cooling through microcirculation heat dissipation module;

[0032] Second stage: heat exchange is carried out through the intervention of phase change material heat storage unit;

[0033] Level 3: The compressor refrigeration system is activated when the temperature difference exceeds the threshold;

[0034] Based on the water conservancy project structure after temperature adjustment, collect real-time data from each temperature sensor;

[0035] Compare the collected data with the temperature data before the abnormal deformation occurs to determine whether the temperature control system is effective;

[0036] If it does not take effect, restart the temperature control emergency command.

[0037] Preferably, if the verification is that the temperature factor is not a factor, the seepage and pressure parameter control instruction is activated, and the seepage and pressure parameters of the water conservancy project structure are controlled by the automated seepage and pressure supply device until the deformation index returns to normal, specifically including:

[0038] If the structural deformation data of the hydraulic engineering project is abnormal and is determined to be not affected by temperature factors, the seepage and pressure parameter control instructions are activated;

[0039] Based on the obtained deformation change value of the hydraulic engineering structure, the mass of the anti-seepage fluid required to return the deformation value to the normal value is calculated. The formula is:

[0040]

[0041] in, For the quality of water conservancy project structure, is the mass of anti-seepage liquid that needs to be added, is the density of the current water conservancy project structure, is the density of the anti-seepage liquid;

[0042] Based on the hydraulic engineering structure after anti-seepage fluid adjustment, collect real-time data from each deformation sensor;

[0043] Compare the collected data with the deformation data before the abnormal deformation occurs to determine whether the seepage and pressure parameter control instructions are effective;

[0044] If the difference between the two is less than the allowable error, it is determined that the seepage and pressure parameter control instruction is effective.

[0045] Compared with the prior art, the advantages of the present invention are:

[0046] A closed-loop deformation control system for hydraulic engineering structures, based on the fusion of deformation, temperature, and seepage and pressure parameter data, has been constructed, enabling the precise identification and intelligent control of abnormal operating conditions in hydraulic engineering projects. By deploying a distributed deformation sensor array and establishing a multi-cycle dynamic comparison mechanism, an innovative dual-revalidation algorithm for deformation anomaly determination is proposed: Firstly, equipment errors are eliminated through horizontal comparison of multi-sensor data from the same cycle; secondly, true anomalies are identified through longitudinal analysis based on historical cycle data. Secondly, a breakthrough temperature-deformation coupling analysis model is introduced in the anomaly tracing process. By establishing dynamic response equations linking thermodynamic parameters with deformation changes, accurate identification of temperature-induced anomalies is achieved, addressing the industry's pain point of traditional methods' inability to distinguish between physical expansion and chemical concentration changes. Regarding the control mechanism, a dual-path adaptive correction strategy is pioneered. When a thermal disturbance is identified, a gradient temperature control algorithm is used to implement nonlinear cooling in conjunction with the cooling system. When a concentration anomaly is identified, a closed-loop replenishment algorithm for seepage and pressure parameters is developed, dynamically adjusting the replenishment 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

[0047] Figure 1 A schematic diagram of the method proposed in the present invention;

[0048] Figure 2 This is a schematic diagram of deformation anomaly detection proposed by the present invention;

[0049] Figure 3 This is a schematic diagram of determining abnormal deformation caused by temperature proposed by the present invention;

[0050] Figure 4 This is a schematic diagram of the temperature-deformation relationship model proposed in the present invention;

[0051] Figure 5 This is a schematic diagram of the temperature control proposed by the present invention;

[0052] Figure 6 This is a schematic diagram of the control of seepage and pressure parameters proposed in the present invention;

[0053] Figure 7 This is a diagram of the architecture of the electronic equipment in this solution;

[0054] Figure 8 This is a schematic diagram of the computer-readable storage medium structure in this solution. DETAILED DESCRIPTION

[0055] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0056] See Figure 1 As shown, a water conservancy project monitoring data analysis method based on multi-feature data recognition includes:

[0057] Step 1: Based on multiple sets of deformation sensors deployed within a set period, the deformation of the water conservancy project structure is obtained, and whether deformation abnormality occurs is determined based on the deformation of the water conservancy project structure;

[0058] Step 2: If abnormal deformation is detected, the temperature parameters of the abnormal hydraulic engineering structure are obtained simultaneously to verify whether the abnormal deformation is caused by thermodynamic fluctuations.

[0059] Step 3: If the cause is a thermodynamic factor, activate the temperature control emergency command and start the integrated cooling system to adjust the thermal balance of the abnormal hydraulic engineering structure until the deformation index returns to normal;

[0060] Step 4: If it is verified that the factor is not temperature, the seepage and pressure parameter control instruction is activated, and the seepage and pressure parameters of the water conservancy project structure are controlled by the automated seepage and pressure supply device until its deformation index returns to normal, that is, based on the control of temperature, the optimal ion mobility and solvent stability are obtained.

[0061] See Figure 2 As shown, based on multiple sets of deformation sensors deployed within a set period, the deformation of the water conservancy project structure is obtained, and whether deformation abnormality occurs is analyzed based on the deformation of the water conservancy project structure. Each set period corresponds to a set of water conservancy project structure deformation monitoring values, specifically including:

[0062] Set the basic detection cycle , extract the maximum deformation of the water conservancy project structure and minimum value , different periods correspond to a set of maximum and minimum values ​​of the structural deformation of water conservancy projects;

[0063] Set up periodic deformation monitoring nodes, and obtain multiple sets of water conservancy project structure deformation sample values ​​through distributed deformation sensor arrays in each monitoring period to form a deformation monitoring data set;

[0064] Based on the deformation monitoring data set data in different periods, by comparing with the maximum and minimum deformation values ​​of the corresponding water conservancy project structure, it is determined whether there is abnormal deformation data;

[0065] If there is abnormal deformation data, obtain the monitoring data of sensors that have not detected abnormal deformation around the sensor that detected the abnormal deformation, and based on this monitoring data, compare the deformation data of the water conservancy project structure monitored in the current cycle with the deformation data of the water conservancy project structure monitored in previous cycles;

[0066] If the currently monitored deformation data of the water conservancy project structure changes significantly compared with the data of the previous cycles, it is judged that the sensor that monitors the abnormal deformation has successfully monitored and an abnormal deformation has occurred. If there is no obvious change, the sensor that monitors the abnormal deformation will be reset.

[0067] Understandably, some changes in hydraulic engineering structure deformation within certain cycles may be normal, such as temperature fluctuations or differences in production batches, and can be easily misjudged as abnormal. Accurately identifying "significant changes" is crucial. When assessing changes in deformation data, consider incorporating smoothing filters to remove noise and observe trends rather than single fluctuations. Statistical methods, such as standard deviation and mean deviation, combined with historical data, can be used to determine whether the current data differs from historical data beyond the expected range.

[0068] Furthermore, resetting a sensor after detecting an abnormal deformation may affect the continuity of other monitoring data, resulting in data gaps or inconsistencies. During the reset, a sensor data buffer is maintained to ensure that the reset operation does not affect data continuity. After the reset, a quick calibration is performed on the reset sensor to ensure that it quickly returns to normal operation.

[0069] See Figure 3 As shown in the figure, if abnormal deformation is detected, the temperature parameters of the abnormal hydraulic engineering structure are obtained simultaneously to verify whether the abnormal deformation is caused by thermodynamic fluctuations. Specifically, the following are included:

[0070] Acquiring, based on the temperature sensor, temperature data of the water conservancy project structure around the sensor that detects abnormal deformation, and acquiring a current temperature change value based on the temperature data of the water conservancy project structure;

[0071] Obtaining a 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 sensor that monitors the abnormal deformation;

[0072] A temperature-deformation relationship model is constructed to determine whether deformation anomalies are caused by thermodynamic fluctuations based on the obtained temperature and deformation change values ​​of the water conservancy project structure.

[0073] Specifically, the frequency of data collection is also a key factor. For changes in temperature and deformation, especially small fluctuations, it is necessary to ensure the real-time nature of the data and sufficient time resolution. A lower sampling frequency may cause data lag and fail to 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 nonlinear relationships. By training the model, the potential patterns of temperature and deformation changes can be identified. When an anomaly occurs, it can automatically determine whether it falls into the category of thermodynamic fluctuations or is a deformation anomaly caused by other factors.

[0074] See Figure 4 As shown, a temperature-deformation relationship model is constructed. Based on the obtained temperature change values ​​and deformation change values ​​of the water conservancy project structure, it is determined whether the deformation anomaly is caused by thermodynamic fluctuations. Specifically, it includes:

[0075] Based on historical monitoring data, a temperature-deformation relationship model is constructed, and the formula is:

[0076]

[0077] in, Temperature The deformation of the water conservancy project structure, is the reference temperature The deformation below, is the volume expansion coefficient with temperature, is the temperature change value;

[0078] Substituting the obtained temperature change value into the temperature-deformation relationship model, and calculating the corresponding deformation change value of the water conservancy project structure;

[0079] Comparing the deformation change value of the water conservancy project structure obtained by calculation with the deformation change value of the water conservancy project structure monitored;

[0080] If the error between the two change values ​​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.

[0081] See Figure 5 As shown, if it is verified to be a thermodynamic factor, the temperature control emergency instruction is activated, and the integrated cooling system is started to implement thermal balance adjustment on the abnormal water conservancy project structure until the deformation index returns to normal, specifically including:

[0082] Based on the abnormal deformation data of the water conservancy project structure determined to be affected by thermodynamic factors, the temperature control emergency instruction is activated and the three-level temperature control system is activated to adjust the thermal balance;

[0083] The three-stage temperature control system specifically includes:

[0084] First level: Cooling through microcirculation heat dissipation module;

[0085] Second stage: heat exchange is carried out through the intervention of phase change material heat storage unit;

[0086] Level 3: The compressor refrigeration system is activated when the temperature difference exceeds the threshold;

[0087] Based on the water conservancy project structure after temperature adjustment, collect real-time data from each temperature sensor;

[0088] Compare the collected data with the temperature data before the abnormal deformation occurs to determine whether the temperature control system is effective;

[0089] If it does not take effect, restart the temperature control emergency command.

[0090] Specifically, in the implementation of the temperature control system, the collaborative working mechanism between the various temperature control modules is also crucial. Each temperature control stage should be dynamically adjusted according to actual conditions. For example, if the first-stage microcirculation heat dissipation module fails to effectively reduce the temperature, the algorithm can adjust the timing of the intervention of the second-stage phase change material heat storage unit or increase its heat exchange efficiency. If the expected effect is still not achieved, the third-stage compressor refrigeration system can be quickly activated. To improve the adaptability of the system, an adaptive control algorithm can also be embedded in the system, enabling it to automatically adjust the regulation strategy according to different environmental conditions and battery load.

[0091] See Figure 6 As shown, if it is verified that it is not a temperature factor, the seepage and pressure parameter control instruction is activated, and the seepage and pressure parameters of the water conservancy project structure are controlled by the automated seepage and pressure supply device until its deformation index returns to normal, specifically including:

[0092] If the structural deformation data of the hydraulic engineering project is abnormal and is determined to be not affected by temperature factors, the seepage and pressure parameter control instructions are activated;

[0093] Based on the obtained deformation change value of the hydraulic engineering structure, the mass of the anti-seepage fluid required to return the deformation value to the normal value is calculated. The formula is:

[0094]

[0095] in, is the normal value of the structural deformation of the water conservancy project, For the quality of water conservancy project structure, is the mass of anti-seepage liquid that needs to be added, is the density of the current water conservancy project structure, is the density of the anti-seepage liquid;

[0096] Based on the hydraulic engineering structure after anti-seepage fluid adjustment, collect real-time data from each deformation sensor;

[0097] Compare the collected data with the deformation data before the abnormal deformation occurs to determine whether the seepage and pressure parameter control instructions are effective;

[0098] If the difference between the two is less than the allowable error, it is determined that the seepage and pressure parameter control instruction is effective.

[0099] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0100] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0101] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

[0102] Furthermore, the method according to the embodiment of the present application can also be used with the aid of Figure 7 The electronic device architecture shown in FIG. Figure 7 As 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 a 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, may store a water conservancy project monitoring data analysis method based on multi-feature data recognition 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 and can be omitted according to actual needs when implementing different devices. Figure 7 One or more components of an electronic device are shown.

[0103] Figure 8 This is a schematic diagram of the computer-readable storage medium structure provided by an embodiment of the present application. Figure 8As shown, a computer-readable storage medium 600 according to one embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by the processor, a water conservancy project monitoring data analysis method 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 (cache). Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0104] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A water conservancy project monitoring data analysis method based on multi-feature data recognition, characterized in that: include: Based on multiple groups of deformation sensors deployed within a set period, the deformation of the water conservancy project structure is obtained, and whether deformation abnormality occurs is determined based on the deformation of the water conservancy project structure; If abnormal deformation is detected, the temperature parameters of the abnormal hydraulic engineering structure are obtained simultaneously to verify whether the abnormal deformation is caused by thermodynamic fluctuations. If it is verified to be a thermodynamic factor, the temperature control emergency instruction will be activated, and the integrated cooling system will be started to implement thermal balance adjustment on the abnormal water conservancy project structure until the deformation index returns to normal; If it is verified that it is not a temperature factor, the seepage and pressure parameter control instruction is activated, and the seepage and pressure parameters of the water conservancy project structure are controlled by the automated seepage and pressure supply device until its deformation index returns to normal; If abnormal deformation is detected, the temperature parameters of the abnormal water conservancy project structure are simultaneously obtained to verify whether the abnormal deformation is caused by thermodynamic fluctuations through the temperature parameters, specifically including: Acquiring, based on the temperature sensor, temperature data of the water conservancy project structure around the sensor that detects abnormal deformation, and acquiring a current temperature change value based on the temperature data of the water conservancy project structure; Obtaining a 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 sensor that monitors the abnormal deformation; Construct a temperature-deformation relationship model to determine whether deformation anomalies are caused by thermodynamic fluctuations based on the acquired temperature and deformation change values ​​of the hydraulic engineering structure; The temperature-deformation relationship model is constructed to determine whether the deformation anomaly is caused by thermodynamic fluctuations based on the obtained temperature change values ​​and deformation change values ​​of the hydraulic engineering structure, specifically including: Based on historical monitoring data, a temperature-deformation relationship model is constructed, and the formula is: in, Temperature The deformation of the water conservancy project structure, is the reference temperature The deformation below, is the volume expansion coefficient with temperature, is the temperature change value; Substituting the obtained temperature change value into the temperature-deformation relationship model, and calculating the corresponding deformation change value of the water conservancy project structure; Comparing the deformation change value of the water conservancy project structure obtained by calculation with the deformation change value of the water conservancy project structure monitored; If the error between the two change values ​​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.

2. The water conservancy project monitoring data analysis method based on multi-feature data recognition according to claim 1 is characterized in that: The method is based on the multiple groups of deformation sensors deployed within the set period, obtaining the deformation of the water conservancy project structure, and analyzing whether deformation abnormality occurs based on the deformation of the water conservancy project structure, wherein each set period corresponds to a group of water conservancy project structure deformation monitoring values, specifically including: Set the basic detection cycle , extract the maximum deformation of the water conservancy project structure and minimum value , different periods correspond to a set of maximum and minimum values ​​of the structural deformation of water conservancy projects; Set up periodic deformation monitoring nodes, and obtain multiple sets of water conservancy project structure deformation sample values ​​through distributed deformation sensor arrays in each monitoring period to form a deformation monitoring data set; Based on the deformation monitoring data set data in different periods, by comparing with the maximum and minimum deformation values ​​of the corresponding water conservancy project structure, it is determined whether there is abnormal deformation data; If there is abnormal deformation data, obtain the monitoring data of sensors that have not detected abnormal deformation around the sensor that detected the abnormal deformation, and based on this monitoring data, compare the deformation data of the water conservancy project structure monitored in the current cycle with the deformation data of the water conservancy project structure monitored in previous cycles; If the currently monitored deformation data of the water conservancy project structure changes significantly compared with the data of the previous cycles, it is judged that the sensor that monitors the abnormal deformation has successfully monitored and an abnormal deformation has occurred. If there is no obvious change, the sensor that monitors the abnormal deformation will be reset.

3. The water conservancy project monitoring data analysis method based on multi-feature data recognition according to claim 1 is characterized in that: If the cause is a thermodynamic factor, the temperature control emergency instruction is activated, and the integrated cooling system is started to adjust the thermal balance of the abnormal hydraulic engineering structure until the deformation index returns to normal, specifically including: Based on the abnormal deformation data of the water conservancy project structure determined to be affected by thermodynamic factors, the temperature control emergency instruction is activated and the three-level temperature control system is activated to adjust the thermal balance; The three-stage temperature control system specifically includes: First level: Cooling through microcirculation heat dissipation module; Second stage: heat exchange is carried out through the intervention of phase change material heat storage unit; Level 3: The compressor refrigeration system is activated when the temperature difference exceeds the threshold; Based on the water conservancy project structure after temperature adjustment, collect real-time data collected by each temperature sensor; Compare the collected data with the temperature data before the abnormal deformation occurs to determine whether the temperature control system is effective; If it does not take effect, restart the temperature control emergency command.

4. The water conservancy project monitoring data analysis method based on multi-feature data recognition according to claim 1 is characterized in that: If the verification is that the temperature factor is not a factor, the seepage and pressure parameter control instruction is activated, and the seepage and pressure parameters of the water conservancy project structure are controlled by the automated seepage and pressure supply device until the deformation index returns to normal, specifically including: If the structural deformation data of the hydraulic engineering project is abnormal and is determined to be not affected by temperature factors, the seepage and pressure parameter control instructions are activated; Based on the obtained deformation change value of the hydraulic engineering structure, the mass of the anti-seepage fluid required to return the deformation value to the normal value is calculated. The formula is: in, For the quality of water conservancy project structure, is the mass of anti-seepage liquid that needs to be added, is the density of the current water conservancy project structure, is the density of the anti-seepage liquid; Based on the hydraulic engineering structure after anti-seepage fluid adjustment, collect real-time data from each deformation sensor; Compare the collected data with the deformation data before the abnormal deformation occurs to determine whether the seepage and pressure parameter control instructions are effective; If the difference between the two is less than the allowable error, it is determined that the seepage and pressure parameter control instruction is effective.

5. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the water conservancy project monitoring data analysis methods based on multi-feature data identification as claimed in claims 1-4.

6. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, a water conservancy project monitoring data analysis method based on multi-feature data recognition according to any one of claims 1 to 4 is implemented.

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