Flood disaster early warning method based on tower inclination state analysis

Abstract

The invention discloses a flood disaster early warning method based on tower inclination state analysis, relates to the technical field of disaster early warning, and solves the problem that early warning information is not accurate enough due to the fact that early warning judgment data is single and regional difference is not considered in the prior art. Inclination data and soil humidity data of a plurality of towers in a jurisdiction and precipitation data of the jurisdiction are obtained, and the relation between the tower inclination and the precipitation is extracted. And calculating a flood disaster evaluation coefficient based on the real-time gradient data of the jurisdiction and the meteorological prediction data, and judging whether to send out an early warning signal or not. According to the invention, the disaster assessment coefficient reflects personalized problems of geology, drainage and the like possibly existing among all jurisdictions, so that the finally obtained flood disaster assessment coefficient can better adapt to differences among all jurisdictions, and the problems that early warning judgment conditions are too single, and early warning is not accurate are solved. And an early warning output result is not accurate enough due to the fact that the individuation degree among the jurisdictions is not considered.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster warning, and particularly relates to a flood disaster warning method based on the analysis of the inclination state of pole towers. Background Art

[0002] Flood disasters are one of the main natural disasters faced by the power system, posing a serious threat to the operation of power facilities and power supply reliability. As the core infrastructure of modern society, the stable operation of the power system is crucial for ensuring social and economic activities, residents' lives, and emergency rescue. However, flood disasters may cause problems such as pole tower inclination, tower collapse, and transmission line interruption, which may further lead to power outages, affecting industrial production, commercial operations, and residents' lives, and even endangering life safety. In short, flood disasters pose a major threat to the stable operation of the power system, and establishing an effective flood disaster warning system is a key measure to ensure the safe operation of the power system and reduce disaster losses. Through early warning and scientific response, the disaster resistance and recovery capabilities of the power system can be significantly improved, providing a solid guarantee for social stability and economic development.

[0003] Nowadays, the warning of flood disasters often only considers the predicted meteorological data and does not conduct flood disaster warning according to the actual local conditions such as terrain. With single evaluation data, it is easy to cause inaccurate warning information. The existing warning methods rely too much on meteorological data. Once there is a deviation in the meteorological data, the problem of inaccurate warning information will occur. In addition, the geological conditions of each region are different. General meteorological data can only provide a general basic data for flood disaster warning and cannot well solve the problem of differences in geological conditions between regions, further resulting in inaccurate warning data for flood disasters.

[0004] In view of this, a flood disaster warning method based on the analysis of the inclination state of pole towers is needed. Summary of the Invention

[0005] Aiming at the problem that the warning evaluation data in the prior art is single and does not consider regional differences, which is easy to cause inaccurate warning information, the present invention provides a flood disaster warning method based on the analysis of the inclination state of pole towers, which can start from the pole tower inclination degree, conduct targeted disaster assessment for the differences in different jurisdictions, and then make corresponding warnings. The specific technical solutions are as follows:

[0006] A flood disaster warning method based on the analysis of the inclination state of pole towers includes the following steps:

[0007] Obtain the inclination degree data and soil humidity data of several pole towers in the jurisdiction, and obtain the precipitation data of the jurisdiction;

[0008] Extract the relationship between the pole tower inclination degree and precipitation;

[0009] Combining meteorological data and the inclination data of poles and towers within the jurisdiction, the flood disaster assessment coefficient of the jurisdiction is obtained as follows:

[0010] Z = α1·Q w +α2·F(Q w )

[0011] In the formula, Z is the flood disaster assessment coefficient of the jurisdiction, Q w is the forecast data of the precipitation within the jurisdiction, F() is the inclination function, which is used to represent the data correspondence between the inclination and the precipitation, and α1 and α2 are both weight coefficients;

[0012] Calculate the magnitude of the flood disaster assessment coefficient based on the real-time inclination data and meteorological prediction data of the jurisdiction, and judge whether to issue a warning signal or the level of the issued warning signal.

[0013] Preferably, the specific process of extracting the relationship between the inclination of poles and towers and the precipitation is as follows:

[0014] First, based on historical data, extract the inclination data of poles and towers and soil humidity data within the jurisdiction, and extract the relationship between the inclination of poles and towers and the soil humidity within the jurisdiction; then, based on historical soil humidity data and precipitation data, extract the relationship between the soil humidity and the precipitation within the jurisdiction; finally, indirectly obtain the relationship between the inclination of poles and towers and the precipitation.

[0015] Preferably, the specific steps of extracting the relationship between the inclination of poles and towers and the soil humidity within the jurisdiction are as follows:

[0016] Calculate the Pearson correlation coefficient and Spearman rank correlation coefficient of the inclination and the soil humidity;

[0017] Based on the Pearson correlation coefficient and Spearman rank correlation coefficient, judge the relationship extraction method of the inclination of poles and towers and the soil humidity in the current jurisdiction. The relationship extraction methods include establishing a linear regression model, a polynomial regression model, and a machine learning model and training and validating the models.

[0018] Preferably, the specific method of judging the relationship extraction method of the inclination of poles and towers and the soil humidity in the current jurisdiction based on the Pearson correlation coefficient and Spearman rank correlation coefficient is as follows:

[0019] Set the Pearson correlation coefficient threshold and Spearman rank correlation coefficient threshold;

[0020] If the absolute value of the Pearson correlation coefficient is greater than the Pearson correlation coefficient threshold, select the linear regression model; if it is less than the Pearson correlation coefficient threshold, calculate the Spearman rank correlation coefficient;

[0021] If the absolute value of the Spearman rank correlation coefficient is greater than the Spearman rank correlation coefficient threshold, a polynomial regression model is adopted; if it is less than the Spearman rank correlation coefficient threshold, a machine learning model is adopted.

[0022] Preferably, the weight coefficient is adjusted based on the correlation degree between the pole tower inclination and the precipitation, specifically as follows:

[0023] Calculate the Pearson correlation coefficient and the Spearman rank correlation coefficient between the pole tower inclination and the precipitation;

[0024] Set the corresponding relationship between the value ranges of the Pearson correlation coefficient and the Spearman rank correlation coefficient between the pole tower inclination and the precipitation and α1, α2 to form a correlation coefficient - weight coefficient correspondence table, and select different weight coefficients according to different correlation coefficients.

[0025] Preferably, the following steps are further included:

[0026] Obtain the corresponding relationship between the historical meteorological data and the warning level within the jurisdiction. If not available, obtain the corresponding relationship of the jurisdiction with the closest geographical location. The closest geographical location is considered by calculating the distance between the center points of the two jurisdictions. The center point of the jurisdiction is the center of the smallest circumscribed circle of the jurisdiction's floor area;

[0027] Based on the historical corresponding relationship, determine whether to issue a warning signal or determine the level of the issued warning signal.

[0028] Preferably, the process of obtaining the corresponding relationship between the historical precipitation and the warning level within the jurisdiction is as follows:

[0029] Normalize the precipitation data to obtain the first comparison data;

[0030] Based on the release of historical warning signals and the corresponding precipitation, obtain the warning signal release threshold or the relationship between the warning signal level and the first comparison data;

[0031] Normalize the flood disaster assessment coefficient to obtain the second comparison data. Regard the second comparison data as the first comparison data. Based on the relationship between the warning signal release threshold or the warning signal level and the first comparison data, obtain the relationship between the flood disaster assessment coefficient and the warning signal release threshold or the warning signal level;

[0032] Based on the real - time inclination data and the predicted precipitation, obtain the flood disaster assessment coefficient, and use the relationship between the flood disaster assessment coefficient and the warning signal release threshold or the warning signal level to determine whether to give an alarm and output the alarm signal of the corresponding level.

[0033] A computer-readable storage medium, the computer-readable storage medium including a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the above-mentioned flood disaster warning method based on the analysis of the inclination state of the pole tower.

[0034] A processor, the processor is used to run a program, wherein when the program runs, it executes the above-mentioned flood disaster warning method based on the analysis of the inclination state of the pole tower.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] 1. The present invention obtains the inclination data and soil humidity data of several pole towers in the jurisdiction, as well as the precipitation data of the jurisdiction, and extracts the relationship between the pole tower inclination and precipitation. Furthermore, based on the real-time inclination data of the jurisdiction and the meteorological prediction data, the size of the flood disaster assessment coefficient is calculated, and it is judged whether to issue a warning signal or the level of the issued warning signal. By extracting the relationship between the pole tower inclination and precipitation within each jurisdiction, the present invention reflects the possible individual problems such as geology and drainage among different jurisdictions, making the finally obtained flood disaster assessment coefficient more adaptable to the differences among different jurisdictions, and solving the problem that the warning judgment conditions are too single and the warning output results are not accurate enough due to the lack of consideration of the individualization degree among different jurisdictions.

[0037] 2. The present invention considers that the change of soil humidity directly affects the stability of the pole tower foundation, and soil humidity is directly affected by precipitation, so soil humidity is the key intermediate variable between pole tower inclination and precipitation. First, obtain the relationship between historical pole tower inclination and soil humidity, reflect the individual factors of each jurisdiction such as soil type and properties and terrain drainage conditions in the corresponding relationship between soil humidity and inclination of each jurisdiction, and then indirectly obtain the relationship between historical pole tower inclination and precipitation through the relationship between soil humidity and precipitation data, thereby improving the accuracy of the results and making the relationship between pole tower inclination and precipitation more adaptable to the differences among jurisdictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0039] Figure 1 It is the flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] It should be understood that when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0042] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0043] It should be further understood that the term "and / or" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0044] In an embodiment of the present invention, a flood disaster warning method based on the analysis of the inclination state of utility poles is provided. As Figure 1 shown, it includes the following steps:

[0045] Step 1: Data acquisition: Obtain the inclination data and soil humidity data of several utility poles in the jurisdiction, and obtain the precipitation data of the jurisdiction.

[0046] Among them, the precipitation data is obtained from the meteorological platform. The inclination data is obtained by an angle sensor (such as a biaxial inclinometer) installed on the utility pole and other devices (such as a multi-dimensional monitor) and devices that can measure the inclination degree of the utility pole. The soil humidity data is obtained through a corresponding soil humidity sensor or other devices and devices that can measure the soil humidity.

[0047] The data obtained above is divided into historical data and real-time data.

[0048] The several utility poles here can be selected based on the calculation accuracy and the jurisdiction scope. In this embodiment, the several utility poles selected show a uniform distribution trend within the jurisdiction scope.

[0049] Step 2: Obtain the relationship between the inclination of the utility pole and the precipitation.

[0050] In this embodiment, to obtain the relationship between the pole tower inclination and precipitation, it is first necessary to extract the pole tower inclination data and soil humidity data in the jurisdiction based on historical data, and extract the relationship between the pole tower inclination and soil humidity in the jurisdiction. Then, based on the historical soil humidity data and precipitation data, extract the relationship between the soil humidity and precipitation in the jurisdiction. Finally, indirectly obtain the relationship between the pole tower inclination and precipitation.

[0051] First, the specific steps for extracting the relationship between the pole tower inclination and soil humidity in the jurisdiction are described. Since the soil type, terrain conditions, drainage system, etc. in a jurisdiction are all similar, when calculating the relationship between the pole tower inclination and soil humidity in each jurisdiction, the influence of the soil type, terrain conditions, drainage system, etc. can be ignored, which can further reduce the calculation difficulty and improve the accuracy of relationship extraction while. The relationship extraction steps are as follows:

[0052] 1. Calculate the Pearson correlation coefficient and Spearman rank correlation coefficient of the inclination and soil humidity;

[0053] 2. Based on the Pearson correlation coefficient and Spearman rank correlation coefficient, determine the relationship extraction method for the pole tower inclination and soil humidity in the current jurisdiction.

[0054] Among them, the relationship extraction methods include establishing a linear regression model, a polynomial regression model, and a machine learning model and training and validating the models.

[0055] Among them, the linear regression model is used for the jurisdiction where the inclination and soil humidity show a linear relationship, the polynomial regression model is applicable to a non-linear relationship, and the machine learning model is applicable to a more complex relationship. It can be seen that the calculation complexity of the three models is different. Therefore, for different jurisdictions, different models are selected for processing to improve the calculation rate and ensure the accuracy of relationship extraction.

[0056] The three models are selected based on the values of the Pearson correlation coefficient and Spearman rank correlation coefficient. Specifically: set the Pearson correlation coefficient threshold and Spearman rank correlation coefficient threshold. If the absolute value of the Pearson correlation coefficient is greater than the Pearson correlation coefficient threshold, the linear regression model is selected; if it is less than the Pearson correlation coefficient threshold, calculate the Spearman rank correlation coefficient. If the absolute value of the Spearman rank correlation coefficient is greater than the Spearman rank correlation coefficient threshold, the polynomial regression model is adopted; if it is less than the Spearman rank correlation coefficient threshold, the machine learning model is adopted.

[0057] The steps for extracting the relationship between soil humidity and precipitation within the jurisdiction are the same as those for extracting the relationship between the pole and tower inclination and soil humidity within the jurisdiction, so they will not be elaborated here. It should be noted that this application only limits the model selection for each jurisdiction. The specific training and verification forms of the above-mentioned linear regression model, polynomial regression model, and machine learning model belong to common technologies in this field. This application does not make any limitations on their internal specific training and verification methods, and relevant technical personnel can arbitrarily select training and verification methods according to the actual situation.

[0058] As can be seen from the above description, the present invention does not directly extract the relationship based on historical pole and tower inclination data and historical precipitation. Instead, it first obtains the relationship between historical pole and tower inclination and soil humidity, and then indirectly obtains the relationship between historical pole and tower inclination and precipitation through the relationship between soil humidity and precipitation data, thereby improving the accuracy of the result. Such a setting is because when analyzing the relationship between pole and tower inclination and precipitation, directly extracting the relationship between the two may ignore the influence of some intermediate factors, resulting in inaccurate results. First extracting the relationship between pole and tower inclination and soil humidity, then extracting the relationship between soil humidity and precipitation, and then indirectly obtaining the relationship between pole and tower inclination and precipitation is usually more accurate.

[0059] In this embodiment, soil humidity is the key intermediate variable between pole and tower inclination and precipitation. The change of soil humidity directly affects the stability of the pole and tower foundation, and soil humidity is directly affected by precipitation. Therefore, soil humidity can be regarded as a "bridge" connecting pole and tower inclination and precipitation. If the intermediate variable (soil humidity that is strongly correlated with both) is directly ignored and extracted, the influence brought by personalized factors of each jurisdiction such as soil type and properties and terrain drainage conditions will be ignored, thereby reducing the accuracy of the output result. And the present invention reflects personalized factors of each jurisdiction such as soil type and properties and terrain drainage conditions in the corresponding relationship between soil humidity and inclination of each jurisdiction.

[0060] Step 3: Combine meteorological data and the inclination data of poles and towers within the jurisdiction to obtain the flood disaster assessment coefficient of the jurisdiction, specifically as follows:

[0061] Z = α1·Q w +α2·Q ti

[0062] In the formula, Z is the flood disaster assessment coefficient of the jurisdiction, and Q w is the forecast data of precipitation within the jurisdiction, and Q tiFor the inclination data of the poles and towers within the jurisdiction, both α1 and α2 are weight coefficients, with a value range of 0 - 1, and α1 + α2 = 1. By adjusting the weight coefficients, the influence degrees of the precipitation data and the pole and tower inclination data on the flood disaster assessment coefficient can be adjusted.

[0063] Furthermore, the magnitude of the weight coefficient is adjusted based on the correlation degree between the pole and tower inclination and the precipitation, specifically as follows:

[0064] 1. Obtain the Pearson correlation coefficient and the Spearman rank correlation coefficient;

[0065] 2. Set the corresponding relationship between the value ranges of the Pearson correlation coefficient and the Spearman rank correlation coefficient and α1, α2 to form a correlation coefficient - weight coefficient correspondence table, and select different weight coefficients according to different correlation coefficients.

[0066] The larger the absolute value of the correlation coefficient, the higher the value of α2. The greater the correlation coefficient between the pole and tower inclination and the precipitation, the deeper the influence of the inclination function on the flood disaster assessment coefficient. The specific range of the correlation coefficient and the specific numerical values of each range and the specific weight coefficients can be set and adjusted according to the actual situation of each jurisdiction, and the present invention does not make limitations. Setting the weight coefficient based on the correlation coefficient can achieve a deeper influence on the pole and tower inclination data in the jurisdiction where the correlation between the inclination and the precipitation is strong. The reason is that when the correlation between the inclination and the precipitation is strong, the relationship between the predicted inclination and the precipitation is more accurate. At this time, increasing the weight coefficient of the inclination function will ensure the accuracy of the flood disaster assessment while also making the flood disaster assessment coefficient better reflect the personalized problems of the jurisdiction.

[0067] Furthermore, since the relationship between the pole and tower inclination data and the precipitation data within the jurisdiction has been obtained in step two, that is, Q in the above formula ti and Q w are related, the flood disaster assessment coefficient is expressed as follows:

[0068] Z = α1·Q w + α2·F(Q w )

[0069] In the formula, F() is the inclination function, which is used to represent the data correspondence relationship between the inclination and the precipitation.

[0070] It can be seen that in the process of flood disaster warning and assessment in this embodiment, not only precipitation data is considered, but also the differential impacts brought by factors such as the personalized geological and topographical information of the jurisdiction reflected by the pole inclination in the jurisdiction are considered on the basis of precipitation data. During the flood disaster warning process of the jurisdiction, both the impact of the general trend brought by precipitation data and the impact brought by the personalized data of the jurisdiction are considered. In addition, the weight coefficient is adjusted based on the correlation degree between the pole and precipitation, making the setting direction of the weight coefficient more objective while retaining a certain degree of redundancy for relevant personnel to make supplementary settings based on subjective experience and other influencing factors, providing a supplementary interface while meeting subjectivity, and thus improving the accuracy of the output result at the level of setting the weight coefficient.

[0071] Step Four: Determine whether to issue a warning signal or determine the level of the issued warning signal based on the magnitude of the flood disaster assessment coefficient of the jurisdiction.

[0072] Obtain the corresponding relationship between the historical meteorological data and the warning level in the jurisdiction. If not, obtain the corresponding relationship of the jurisdiction closest to its geographical location. Determine whether to issue a warning signal or determine the level of the issued warning signal based on the historical corresponding relationship.

[0073] The form of the historical corresponding relationship includes setting an alarm threshold or an alarm level based on precipitation. Based on the historical corresponding relationship, replace the precipitation data with the flood disaster assessment coefficient calculated in Step Three, and then issue an alarm.

[0074] Among them, the closest geographical location is considered by calculating the distance between the central points of the two jurisdictions. The central point of the jurisdiction is the center of the smallest circumscribed circle of the jurisdiction's occupied area.

[0075] The process of determining whether to issue a warning signal or determine the level of the issued warning signal based on the historical corresponding relationship is as follows:

[0076] 1. Obtain the relationship between the historical alarm signal release threshold or the alarm signal level in the jurisdiction, and extract the boundary values or thresholds of the precipitation range in the relationship as the first comparison data;

[0077] 2. Use the flood disaster assessment coefficient calculated according to the boundary values or thresholds of the precipitation range as the second comparison data. Based on the relationship between the alarm signal release threshold or the alarm signal level and the first comparison data, replace the first comparison data with the second comparison data to obtain the relationship between the flood disaster assessment coefficient and the alarm signal release threshold or the alarm signal level.

[0078] 3. Based on the real-time inclination data and the predicted precipitation, obtain the flood disaster assessment coefficient and determine whether to issue an alarm and output the alarm signal of the corresponding level.

[0079] In summary, the present invention obtains the inclination data and soil moisture data of several poles and towers within the jurisdiction, as well as the precipitation data of the jurisdiction, and extracts the relationship between the inclination of the poles and towers and the precipitation. Furthermore, based on the real-time inclination data and meteorological prediction data of the jurisdiction, the magnitude of the flood disaster assessment coefficient is calculated, and it is determined whether to issue a warning signal or determine the level of the issued warning signal. By extracting the relationship between the inclination of the poles and towers and the precipitation within each jurisdiction, the present invention reflects the possible individual problems such as geology and drainage between different jurisdictions, making the finally obtained flood disaster assessment coefficient more adaptable to the differences between different jurisdictions, and solving the problem that the warning judgment conditions are too single and the warning output result is not accurate enough due to the lack of consideration of the individualization degree between different jurisdictions. In addition, the present invention takes into account that the change of soil moisture directly affects the stability of the pole and tower foundation, and soil moisture is directly affected by precipitation, so soil moisture is the key intermediate variable between the inclination of the pole and tower and precipitation. First, obtain the relationship between the historical inclination of the pole and tower and the soil moisture, reflect the individual factors of each jurisdiction such as soil type and properties and terrain drainage conditions in the corresponding relationship between the soil moisture and inclination of each jurisdiction, and then indirectly obtain the relationship between the historical inclination of the pole and tower and the precipitation through the relationship between the soil moisture and precipitation data, thereby improving the accuracy of the result and making the relationship between the inclination of the pole and tower and the precipitation more adaptable to the differences between jurisdictions.

[0080] Those of ordinary skill in the art can realize that the units of each example 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 of each example have been generally described according to their functions in the above description. Whether these functions are executed in the form of hardware or software 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.

[0081] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0082] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0083] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention, and they should all be covered by the scope of the claims and the description of the present invention.

Claims

1. A flood disaster warning method based on the analysis of the inclination state of the pole tower, characterized in that, It includes the following steps: Obtain the inclination data and soil humidity data of several poles and towers within the jurisdiction, and obtain the precipitation data of the jurisdiction; Extract the relationship between the pole and tower inclination and precipitation; Combine the meteorological data and the inclination data of the poles and towers within the jurisdiction to obtain the flood disaster assessment coefficient of the jurisdiction, specifically as follows: Z = α1·Q w + α2·F(Q w ) In the formula, Z is the evaluation coefficient of regional flood disasters, Q w is the forecast data of precipitation in the region, F() is the inclination function, which is used to represent the data correspondence between the inclination and precipitation, and α1 and α2 are both weight coefficients; Calculate the magnitude of the flood disaster assessment coefficient based on the real-time inclination data of the jurisdiction and the meteorological prediction data, and determine whether to issue a warning signal or determine the level of the issued warning signal.

2. The flood disaster warning method based on the analysis of the inclination state of the pole tower according to claim 1, wherein, The specific process of extracting the relationship between the pole and tower inclination and precipitation is as follows: First, based on historical data, extract the pole and tower inclination data and soil humidity data of the jurisdiction, and extract the relationship between the pole and tower inclination and soil humidity within the jurisdiction; then, based on historical soil humidity data and precipitation data, extract the relationship between the soil humidity and precipitation within the jurisdiction; finally, indirectly obtain the relationship between the pole and tower inclination and precipitation.

3. The flood disaster warning method based on the analysis of the inclination state of the pole tower according to claim 2, characterized in that, The specific steps of extracting the relationship between the pole and tower inclination and soil humidity within the jurisdiction are as follows: Calculate the Pearson correlation coefficient and Spearman rank correlation coefficient of the inclination and soil humidity; Based on the Pearson correlation coefficient and Spearman rank correlation coefficient, determine the relationship extraction method of the pole and tower inclination and soil humidity in the current jurisdiction. The relationship extraction methods include establishing a linear regression model, a polynomial regression model, and a machine learning model and training and validating the models.

4. A flood disaster warning method based on the analysis of the inclination state of the pole tower according to claim 3, characterized in that, The specific method of determining the relationship extraction method of the pole and tower inclination and soil humidity in the current jurisdiction based on the Pearson correlation coefficient and Spearman rank correlation coefficient is as follows: Set the Pearson correlation coefficient threshold and Spearman rank correlation coefficient threshold; If the absolute value of the Pearson correlation coefficient is greater than the Pearson correlation coefficient threshold, select the linear regression model; if it is less than the Pearson correlation coefficient threshold, calculate the Spearman rank correlation coefficient; If the absolute value of the Spearman rank correlation coefficient is greater than the Spearman rank correlation coefficient threshold, adopt the polynomial regression model; if it is less than the Spearman rank correlation coefficient threshold, adopt the machine learning model.

5. A flood disaster warning method based on the analysis of the inclination state of a pole tower according to claim 1, characterized in that, Adjust the magnitude of the weight coefficient based on the correlation degree between the pole and tower inclination and precipitation, specifically as follows: Calculate the Pearson correlation coefficient and Spearman rank correlation coefficient of the pole and tower inclination and precipitation; Set the corresponding relationship between the value ranges of the Pearson correlation coefficient and Spearman rank correlation coefficient of the pole and tower inclination and precipitation and α1, α2 to form a correlation coefficient - weight coefficient correspondence table, and select different weight coefficients according to different correlation coefficients.

6. The flood disaster warning method based on the analysis of the inclination state of the pole tower according to claim 1, characterized in that, It also includes the following steps: Obtain the corresponding relationship between the historical meteorological data and the warning level within the jurisdiction. If not, obtain the corresponding relationship of the jurisdiction closest to its geographical location. The geographical location closest is considered by calculating the distance between the center points of the two jurisdictions. The center point of the jurisdiction is the center of the smallest circumscribed circle of the jurisdiction's floor area; Based on the historical corresponding relationship, determine whether to issue a warning signal or determine the level of the issued warning signal.

7. A flood disaster warning method based on the analysis of the inclination state of a pole tower according to claim 6, characterized in that, The process of obtaining the corresponding relationship between the historical precipitation and the warning level within the jurisdiction is as follows: Normalize the precipitation data to obtain the first comparison data; Based on the issuance of historical warning signals and the corresponding precipitation, obtain the relationship between the warning signal issuance threshold or warning signal level and the first comparison data; Normalize the flood disaster assessment coefficient to obtain the second comparison data, regard the second comparison data as the first comparison data, and based on the relationship between the warning signal issuance threshold or warning signal level and the first comparison data, obtain the relationship between the flood disaster assessment coefficient and the warning signal issuance threshold or warning signal level; Obtain the flood disaster assessment coefficient based on the real-time inclination data and the predicted precipitation, and use the relationship between the flood disaster assessment coefficient and the warning signal issuance threshold or warning signal level to determine whether to issue a warning and output the warning signal of the corresponding level.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the flood disaster warning method based on the analysis of the pole tower inclination state according to any one of claims 1 to 7.

9. A processor, characterized in that, The processor is used to run the program, wherein when the program runs, it executes the flood disaster warning method based on the analysis of the pole tower inclination state according to any one of claims 1 to 7.

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