CN117314204B - Geothermal high-temperature abnormal region prediction method - Google Patents
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Abstract
The invention relates to the technical field of geothermal prediction, and discloses a geothermal high-temperature abnormal region prediction method, which adopts various different parameters such as capacitance parameters, soil looseness parameters, surface heat dissipation flow parameters and the like to carry out comprehensive consideration judgment prediction, so that the influence of external climatic environment on prediction results can be reduced, the prediction accuracy is greatly improved, the prediction error is reduced, excessive human participation is not needed in the whole prediction process, the intelligentization degree of geothermal prediction is greatly improved, and the prediction cost is reduced.
Description
Technical Field
The invention relates to the technical field of geothermal prediction, in particular to a geothermal high-temperature abnormal region prediction method.
Background
Geothermal heat is a geological phenomenon that heat energy in the earth is released to the earth surface, the heat energy mainly comes from radioactive element decay in the earth, and the geothermal heat has wide application and can be used for power generation, heating and the like. However, the current geothermal prediction means is complex, the degree of intelligence is low, and the prediction cost and the prediction error are high.
Disclosure of Invention
The invention mainly aims to provide a geothermal high-temperature abnormal region prediction method, which aims to solve the technical problems of high geothermal high-temperature prediction cost and large prediction error in the prior art.
In order to achieve the above object, in a first aspect, an embodiment of the present application provides a method for predicting a geothermal high temperature abnormal region, the method including:
acquiring current environmental characteristic parameters of a target area to be detected, wherein the current environmental characteristic parameters comprise current environmental capacitance parameters and current sandy soil layer looseness parameters;
obtaining a first prediction probability of the geothermal high temperature anomaly prediction according to the current environmental capacitance parameter and a pre-trained environmental capacitance parameter prediction probability model;
obtaining a second prediction probability of the geothermal high-temperature anomaly prediction according to the current sandy soil layer looseness parameter and a pre-trained looseness parameter prediction probability model;
judging whether the difference value between the first prediction probability and the second prediction probability is larger than a preset probability threshold value or not;
when the difference value between the first prediction probability and the second prediction probability is larger than a preset probability threshold value, acquiring the current surface heat dissipation flow parameter of the region to be detected;
obtaining a third prediction probability of the geothermal high temperature anomaly prediction according to the current surface thermal dispersion flow parameter and a pre-trained thermal dispersion flow parameter prediction probability model;
and carrying out weighted summation on the first prediction probability, the second prediction probability and the third prediction probability to obtain the prediction probability of the geothermal high-temperature anomaly prediction of the target area to be detected.
Further, the current environmental capacitance parameter includes a first environmental capacitance parameter of the underground region and a second environmental capacitance parameter of the atmospheric region;
the obtaining a first prediction probability of the geothermal high temperature abnormality prediction according to the current environmental capacitance parameter and a pre-trained environmental capacitance parameter prediction probability model comprises the following steps:
performing standard correction and coupling operation on the first environmental capacitance parameter and the second environmental capacitance parameter to obtain a standard capacitance parameter;
and carrying out probability mapping on the standard capacitance parameters according to the pre-trained environmental capacitance parameter pre-estimated probability model to obtain the first prediction probability.
Further, the performing standard correction and coupling operation on the first environmental capacitance parameter and the second environmental capacitance parameter to obtain a standard capacitance parameter includes:
respectively carrying out standard correction on the first environmental capacitance parameter C1 and the second environmental capacitance parameter C2 to obtain a first standard capacitance parameter and a second standard capacitance parameter;
performing capacitance series coupling operation on the first standard capacitance parameter and the second standard capacitance parameter to obtain a standard capacitance parameter C0;
wherein the capacitance correction and coupling satisfy the following expression:
，
wherein A is a correction value of a first environmental capacitance parameter, B is a correction value of a second environmental capacitance parameter, D is a capacitance parameter magnitude control value, and E is a standard capacitance parameter offset control value.
Further, obtaining a current sandy soil layer loosening degree parameter of the target area to be detected includes:
acquiring a horizontal section image and a longitudinal section image of a sandy soil layer of a target area to be detected;
extracting the characteristics of the horizontal section image of the sandy soil layer to obtain horizontal section characteristic parameters;
extracting the characteristics of the longitudinal section image of the sandy soil layer to obtain longitudinal section characteristic parameters;
and obtaining the current looseness parameter of the sandy soil layer according to the transverse section characteristic parameter and the longitudinal section characteristic parameter.
Further, the transverse section feature includes a transverse section void feature, and the feature extraction is performed on the sandy soil layer transverse section image to obtain a transverse section feature parameter, including: extracting void characteristics of the horizontal section image of the sandy soil layer to obtain a horizontal section void area parameter; and the longitudinal section feature comprises a longitudinal section void feature, and the feature extraction is performed on the sandy soil layer longitudinal section image to obtain a longitudinal section feature parameter, including: and extracting void characteristics of the longitudinal section image of the sandy soil layer to obtain a longitudinal section void area parameter.
Further, the obtaining the current looseness parameter of the sandy soil layer according to the transverse section characteristic parameter and the longitudinal section characteristic parameter comprises the following steps:
obtaining a first looseness parameter according to the transverse section gap area parameter and the transverse section total area;
obtaining a second looseness parameter according to the longitudinal section void area parameter and the longitudinal section total area;
and homogenizing the first looseness parameter and the second looseness parameter to obtain the looseness parameter of the current sandy soil layer.
Further, the surface heat dissipation flow parameter includes a surface heat dissipation flow speed parameter, and the obtaining the current surface heat dissipation flow parameter of the area to be detected includes:
acquiring an earth surface thermodynamic image frame of a region to be detected;
dividing the surface thermodynamic image frame into N equally divided areas according to an upper space and a lower space;
acquiring temperature differences of head and tail equal division areas in the upper and lower spatial directions of an earth surface thermodynamic image frame;
and calculating according to the temperature difference and the distance of the head and tail equal division areas in the upper and lower space directions of the surface thermodynamic image frame to obtain the current surface heat dissipation flow speed parameter.
Further, after determining whether the difference between the first prediction probability and the second prediction probability is greater than a preset probability threshold, the method further includes:
and when the difference value between the first prediction probability and the second prediction probability is smaller than or equal to a preset probability threshold value, carrying out arithmetic average on the first prediction probability and the second prediction probability to obtain prediction probability of geothermal high-temperature abnormality prediction of the target area to be detected.
Further, before the weighted summation of the first prediction probability, the second prediction probability and the third prediction probability to obtain the prediction probability of the geothermal high-temperature anomaly prediction of the target area to be detected, the method further includes:
and acquiring the calculation weights corresponding to the first prediction probability, the second prediction probability and the third prediction probability, wherein the larger the difference value between any one of the first prediction probability, the second prediction probability and the third prediction probability and the average value of the prediction probabilities is, the smaller the calculation weight corresponding to the first prediction probability, the second prediction probability and the third prediction probability is.
In a second aspect, in an embodiment of the present application, there is further provided a geothermal anomaly prediction system, including: a processor and a memory; wherein the memory is for storing program code and the processor is for invoking the program code to perform the method according to the first aspect.
Compared with the prior art, the geothermal high temperature abnormal region prediction method provided by the embodiment of the application is characterized in that firstly, geothermal high temperature abnormal prediction is carried out according to the current environment capacitance parameter and the current sandy soil layer loosening degree parameter respectively, a first prediction probability and a second prediction probability are obtained, then whether the difference value between the first prediction probability and the second prediction probability is larger than a preset probability threshold value or not is judged, when the difference value between the first prediction probability and the second prediction probability is larger than the preset probability threshold value, the judgment result is split, at the moment, geothermal high temperature abnormal prediction is carried out according to the current surface thermal scattering flow parameter to obtain a third prediction probability, and finally, the final geothermal high temperature abnormal prediction probability is obtained by weighting and summing the first prediction probability, the second prediction probability and the third prediction probability.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for predicting a geothermal hot abnormal region according to some embodiments of the present application;
FIG. 2 is a flow chart of a method for predicting a geothermal high temperature anomaly region according to other embodiments of the present application;
FIG. 3 is a schematic diagram of a layout of humidity sensing capacitors of a geothermal anomaly prediction system in some embodiments of the present application;
FIG. 4 is a block diagram of a geothermal anomaly prediction system in some embodiments of the present application.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present invention are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
Furthermore, the description of "first," "second," etc. in this disclosure is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, "and/or" throughout this document includes three schemes, taking a and/or B as an example, including a technical scheme, a technical scheme B, and a technical scheme that both a and B satisfy; in addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
Geothermal heat is a geological phenomenon that heat energy in the earth is released to the earth surface, the heat energy mainly comes from radioactive element decay in the earth, and the geothermal heat has wide application and can be used for power generation, heating and the like. However, the current geothermal prediction means is complex, the degree of intelligence is low, and the prediction cost and the prediction error are high.
For example, compared with the traditional prediction method, the method mainly surveys the geological condition of the target area to judge whether the geothermal abnormality exists, and the prediction method needs to consume a great deal of time and manpower resources and has higher cost; the current mainstream prediction mode is mainly to carry out the back-modeling prediction through the atmosphere environment, and the prediction mode is easily influenced by the change of the environment and has larger prediction error.
In view of the above problems, the application provides a geothermal high-temperature abnormal region prediction method, which is applied to a geothermal abnormal prediction system, wherein the geothermal abnormal prediction system comprises a capacitance prediction module, a soil looseness prediction module and a surface heat dissipation prediction module, the capacitance prediction module is used for acquiring environment capacitance parameters of a target region so as to perform geothermal abnormal prediction according to the environment capacitance parameters, the soil looseness prediction module is used for acquiring looseness parameters of sandy soil of the target region so as to perform geothermal abnormal prediction according to the looseness parameters, and the surface heat dissipation prediction module is used for performing geothermal abnormal prediction according to the surface heat dissipation flow parameters.
The geothermal heat abnormality described in the present application refers to a state of abnormal geothermal heat in which the earth's surface temperature exceeds 100 ℃. It is understood that when geothermal heat is high and an abnormal state is reached, the temperature of soil, the ground surface, and the atmosphere may rise, resulting in a decrease in the humidity of the soil or the atmosphere; when the geothermal heat is high and reaches an abnormal state, the viscosity of sandy soil is reduced due to the reduction of humidity, so that the looseness of the soil is improved; when the geothermal heat is high and reaches an abnormal state, the surface heat flow is accelerated due to the fact that the surface temperature is increased; compared with temperature change, the influence of the external environment on humidity change is small in a certain specific area, so that geothermal anomaly prediction is not directly carried out according to temperature change, but is carried out according to capacitance parameters, soil looseness parameters and heat dissipation flow parameters, and the accuracy of geothermal anomaly prediction is greatly improved.
The capacitance prediction module may include one or more humidity sensing capacitors, and soil or atmosphere may be used as dielectrics of the humidity sensing capacitors, and when the humidity of the dielectrics is higher, the capacitance value of the humidity sensing capacitors is higher, so when the capacitance value is lower than a preset value, the humidity of the dielectrics is reduced to a certain extent, and at this time, the probability that geothermal high temperature abnormality or geothermal high temperature abnormality exists in the region can be indicated to be higher.
For example, as shown in fig. 3, a series capacitor module is provided, and the capacitor module includes a first capacitor and a second capacitor connected in series, wherein the first capacitor is placed under the ground for sensing the humidity change of the soil, and the second capacitor is placed on the ground for sensing the humidity change of the atmosphere near the ground.
The soil looseness prediction module can comprise a camera device, a processor and a processor, wherein the camera device is used for acquiring a section image of a soil layer, the processor can perform characteristic extraction according to the section image of the soil, and then judge the soil humidity state according to the characteristic state so as to perform geothermal high-temperature prediction according to the humidity state; the surface thermal dispersion prediction module may include a thermal imaging device for acquiring thermal images of the vicinity of the surface, and the processor may analyze the thermal images to derive a thermal flow rate from which to perform geothermal predictions.
The specific steps of the geothermal high-temperature anomaly region prediction method will be mainly described below, and it should be noted that although a logical sequence is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order from that here. Referring to fig. 1-2, the method includes:
s100, acquiring current environment characteristic parameters of a target area to be detected, wherein the current environment characteristic parameters comprise current environment capacitance parameters and current sandy soil layer looseness parameters;
specifically, the current environmental capacitance parameter and the current sandy soil looseness parameter are firstly obtained, wherein the current environmental capacitance parameter can be a first environmental capacitance parameter with underground soil as a dielectric medium, a second environmental capacitance parameter with atmosphere as the dielectric medium, or a combination of the first environmental capacitance parameter and the second environmental capacitance parameter. If only soil or only atmospheric humidity is considered to predict the geothermal high-temperature state, the operation is simpler, namely the arrangement of the humidity sensor is more convenient, but the prediction error is larger; if the soil humidity and the atmospheric humidity are comprehensively considered to predict the geothermal high-temperature state, prediction errors caused by accidental and environmental mutation can be greatly reduced compared with the case that only a single factor is considered, so that the prediction accuracy is improved. Accordingly, the following examples comprehensively consider soil humidity and atmospheric humidity to predict geothermal high temperature states.
The current environment capacitance parameter can be obtained in various ways, for example, a capacitor can be arranged in the current environment, the arrangement mode is shown in fig. 3, and the capacitance value between 1 and 2 points is sensed through a singlechip; in other capacitive acquisition modes, indirect acquisition can also be performed through a sensor. The current sandy soil layer looseness parameters can be obtained in various ways, for example, the image is used for identification and judgment; the method can also be used for judging and obtaining in a weighing and volume calculating mode.
In an embodiment, obtaining a current sandy soil layer loosening degree parameter of a target area to be detected includes:
acquiring a horizontal section image and a longitudinal section image of a sandy soil layer of a target area to be detected;
extracting the characteristics of the horizontal section image of the sandy soil layer to obtain horizontal section characteristic parameters;
extracting the characteristics of the longitudinal section image of the sandy soil layer to obtain longitudinal section characteristic parameters;
and obtaining the current looseness parameter of the sandy soil layer according to the transverse section characteristic parameter and the longitudinal section characteristic parameter.
Specifically, firstly acquiring a horizontal section image and a longitudinal section image of a sandy soil layer of a target area to be detected, then carrying out feature extraction on the horizontal/longitudinal section image of the sandy soil layer to obtain horizontal/longitudinal section feature parameters, such as extracting gap features of horizontal/longitudinal sections to obtain horizontal/longitudinal section gap area parameters, and finally obtaining current looseness parameters of the sandy soil layer according to the horizontal section feature parameters and the longitudinal section feature parameters, namely obtaining first looseness parameters according to the horizontal section gap area parameters and the total horizontal section area; obtaining a second looseness parameter according to the longitudinal section void area parameter and the longitudinal section total area; and homogenizing the first looseness parameter and the second looseness parameter to obtain the current sandy soil layer looseness parameter.
According to the embodiment of the application, an intelligent image recognition technology is adopted when soil looseness parameters are acquired, so that the intelligent degree of geothermal high-temperature prediction and the accuracy of prediction are greatly improved.
S200, obtaining a first prediction probability of the geothermal high-temperature anomaly prediction according to the current environmental capacitance parameter and a pre-trained environmental capacitance parameter prediction probability model;
to improve the accuracy of geothermal high temperature predictions, in one embodiment, the current environmental capacitance parameters include a first environmental capacitance parameter of the subsurface region and a second environmental capacitance parameter of the atmospheric region.
Based on the above, the obtaining the first prediction probability of the geothermal high temperature anomaly prediction according to the current environmental capacitance parameter and the pre-trained environmental capacitance parameter prediction probability model includes:
performing standard correction and coupling operation on the first environmental capacitance parameter and the second environmental capacitance parameter to obtain a standard capacitance parameter;
and carrying out probability mapping on the standard capacitance parameters according to the pre-trained environmental capacitance parameter pre-estimated probability model to obtain the first prediction probability.
Specifically, first, standard correction is performed on a first environmental capacitance parameter and a second environmental capacitance parameter respectively, namely parameter correction is performed to eliminate the influence of climate change on environmental humidity, then coupling operation is performed on the corrected capacitance parameters to obtain standard capacitance parameters, the coupling operation can be capacitive series coupling operation or capacitive parallel coupling operation, finally the standard capacitance parameters are mapped and inquired against an environmental capacitance parameter estimated probability model to obtain first prediction probability, the environmental capacitance parameter estimated probability model is an estimated model obtained by carrying out standard construction in advance according to the actual environmental capacitance in a geothermal high temperature abnormal region, a user can obtain the occurrence probability of geothermal abnormality according to the model, for example, when the capacitance parameters reach a first calibration value in the model, the occurrence probability of geothermal high temperature abnormality is 80%, and when the capacitance parameters reach a second calibration value in the model, the probability of geothermal high temperature abnormality occurrence is 90%.
In an embodiment, the performing standard correction and coupling operation on the first environmental capacitance parameter and the second environmental capacitance parameter to obtain standard capacitance parameters includes:
respectively carrying out standard correction on the first environmental capacitance parameter C1 and the second environmental capacitance parameter C2 to obtain a first standard capacitance parameter and a second standard capacitance parameter;
performing capacitance series coupling operation on the first standard capacitance parameter and the second standard capacitance parameter to obtain a standard capacitance parameter C0;
wherein the capacitance correction and coupling satisfy the following expression:
，
wherein A is a correction value of a first environmental capacitance parameter, B is a correction value of a second environmental capacitance parameter, D is a capacitance parameter magnitude control value, and E is a standard capacitance parameter offset control value.
Specifically, because the soil humidity is not only affected by the geothermal high temperature, but also by the atmospheric climate environment, which is also affected by the geographic position, the altitude environment and the like, in order to eliminate the influence of the atmospheric climate environment, the soil humidity evaluation is subjected to sensing evaluation under the standard atmospheric environment, so that the accuracy of geothermal prediction is improved, the embodiment of the application corrects the second environment capacitance parameter by a value B, wherein the value B is the difference value between the capacitance corresponding to the current atmospheric humidity and the capacitance corresponding to the annual average humidity of the atmosphere, and if the current atmospheric humidity is greater than the annual average humidity, the method comprises the steps ofThe second environmental capacitance parameter C2 is larger than the standard environment, and the second environmental capacitance parameter C2 needs to be reversely corrected, i.e. the value B is a negative value; if the current atmospheric humidity is less than the annual average humidity, the second environmental capacitance parameter C2 is smaller than the standard environment, and the second environmental capacitance parameter C2 needs to be corrected in the forward direction, i.e. the B value is positive. Similarly, the correction principle of the embodiment of the present application is similar to that described above, except that, because the atmospheric climate environment has an indirect influence on the humidity of the soil, the correction amplitude of the first environmental capacitance parameter is smaller, for example, the correction amplitude of the first environmental capacitance parameter is about half that of the correction amplitude of the second environmental capacitance parameter, i.e., a=0.5b. The embodiment D of the application is a capacitance parameter magnitude control value, namely, the standard capacitance parameter C0 is controlled in a reasonable magnitude range, so that a processor can conveniently process and operate data. In the embodiment of the application, throughThe standard capacitance parameter is obtained through calculation, and the minimum value can be obtained through calculation for multiple times, but because the minimum value and other values possibly have offset/error to a certain extent, in the embodiment of the application, the standard capacitance parameter C0 is subjected to offset control, namely E is the standard capacitance parameter offset control value, the offset control value can be the average value of the difference values of multiple standard capacitance parameters C0 obtained through calculation for multiple times, the standard capacitance parameter C0 is subjected to offset control, the stability of the standard capacitance parameter C0 can be ensured, and the influence of the deviation of the multiple standard capacitance parameter C0 on the overall prediction result is prevented.
S300, obtaining a second prediction probability of the geothermal high temperature abnormality prediction according to the current sandy soil layer looseness parameter and a pre-trained looseness parameter prediction probability model;
in this embodiment of the present application, after obtaining a current sandy soil layer loosening degree parameter, mapping and querying is performed by referring to a loosening degree parameter prediction probability model to obtain a second prediction probability of geothermal high temperature anomaly prediction, where the sandy soil layer loosening degree parameter prediction probability model is a prediction model obtained by performing standard construction in advance according to an actual loosening degree of soil in a geothermal high temperature anomaly area, and similarly, a user may obtain a probability of occurrence of geothermal anomaly according to the model, for example, when the soil loosening degree parameter reaches a first calibration value in the model, the probability of occurrence of geothermal high temperature anomaly is 80%, and when the soil loosening degree parameter reaches a second calibration value in the model, the probability of occurrence of geothermal high temperature anomaly is 90%.
S400, judging whether the difference value between the first prediction probability and the second prediction probability is larger than a preset probability threshold value;
because the first prediction probability and the second prediction probability are respectively predicted from different environmental features, in order to eliminate the greater influence of contingency on the prediction result, in this embodiment of the present application, after the first prediction probability and the second prediction probability are obtained, it is required to determine whether the difference between the first prediction probability and the second prediction probability is greater than a preset probability threshold, when the difference between the first prediction probability and the second prediction probability is greater, it is indicated that the consistency of the prediction result in the above two prediction modes is not ideal, the prediction error may be greater, a third factor needs to be introduced to perform further prediction, and when the difference between the first prediction probability and the second prediction probability is smaller, it is indicated that the consistency of the prediction result in the above two prediction modes is ideal, at this time, step S800 is executed: and carrying out arithmetic average on the first prediction probability and the second prediction probability to obtain prediction probability of geothermal high-temperature abnormality prediction of the target area to be detected.
Based on the method, different prediction calculation is carried out according to different prediction processes and results, and the calculation amount of the processor and the prediction accuracy can be simultaneously considered.
S500, when the difference value of the first prediction probability and the second prediction probability is larger than a preset probability threshold value, acquiring the current surface heat dissipation flow parameter of the region to be detected;
in the embodiment of the present application, when the difference between the first prediction probability and the second prediction probability is greater than the preset probability threshold, the current surface heat dissipation flow parameter is introduced to perform further prediction, that is, the current surface heat dissipation flow parameter of the region to be detected needs to be acquired first.
S600, obtaining a third prediction probability of the geothermal high temperature anomaly prediction according to the current surface thermal dispersion flow parameter and a pre-trained thermal dispersion flow parameter prediction probability model;
in an embodiment, the surface heat dissipation flow parameter includes a surface heat dissipation flow speed parameter, and the obtaining the current surface heat dissipation flow parameter of the area to be detected includes:
acquiring an earth surface thermodynamic image frame of a region to be detected;
dividing the surface thermodynamic image frame into N equally divided areas according to an upper space and a lower space;
acquiring temperature differences of head and tail equal division areas in the upper and lower spatial directions of an earth surface thermodynamic image frame;
and calculating according to the temperature difference and the distance of the head and tail equal division areas in the upper and lower space directions of the surface thermodynamic image frame to obtain the current surface heat dissipation flow speed parameter.
Specifically, firstly, obtaining an earth surface thermodynamic image frame of a region to be detected, wherein the earth surface thermodynamic image frame can be obtained by shooting through an infrared temperature camera, for example, the earth surface thermodynamic image frame of the ground in a height range of 20cm is obtained; then dividing the surface thermodynamic image frame into N equal division areas according to the upper and lower spaces, for example, dividing the surface thermodynamic image frame into 10 equal division areas according to the upper and lower spaces, wherein each area is 2cm (because high-temperature air diffuses upwards, the temperatures of the upper and lower different areas have certain difference); and then obtaining the temperature difference of the head and tail equal areas in the upper and lower space directions of the surface thermodynamic image frame, namely calculating the temperature difference of the uppermost layer and the lowermost layer, and calculating the heat dissipation flow speed according to the temperature difference and the distance, wherein the heat dissipation flow speed is expressed by taking the temperature/cm as a unit and represents the variation of the corresponding temperature per cm distance.
Therefore, after the current surface heat dissipation flow speed parameter is obtained, mapping query can be conducted on the heat dissipation flow parameter estimation probability model to obtain a third prediction probability of geothermal high-temperature anomaly prediction.
And S700, carrying out weighted summation on the first prediction probability, the second prediction probability and the third prediction probability to obtain the prediction probability of the geothermal high-temperature anomaly prediction of the target area to be detected.
In the embodiment of the application, after the first prediction probability, the second prediction probability and the third prediction probability are obtained, the prediction probabilities obtained by respectively predicting the three factors are weighted and summed to obtain the final prediction probability of the geothermal high-temperature anomaly prediction.
In one embodiment, before step S700, the method further includes: the method comprises the steps of obtaining calculation weights corresponding to a first prediction probability, a second prediction probability and a third prediction probability, wherein the larger the difference value between any one of the first prediction probability, the second prediction probability and the third prediction probability and the average value of the prediction probabilities is, the smaller the calculation weight corresponding to the one of the first prediction probability, the second prediction probability and the third prediction probability is, namely, the larger the difference value between the prediction probability obtained by predicting a certain factor and the average value of all the prediction probabilities is, the larger the error existing in the prediction of the factor is, and the calculation weight corresponding to the prediction probability obtained by predicting the factor is reduced at the moment so as to further improve the accuracy of geothermal high-temperature abnormal prediction.
The average value of the prediction probabilities is an average value of the first prediction probability, the second prediction probability and the third prediction probability, and when the difference between the first prediction probability and the average value of the prediction probabilities is greater than the difference between the second prediction probability and the average value of the prediction probabilities, and the difference between the second prediction probability and the average value of the prediction probabilities is greater than the difference between the third prediction probability and the average value of the prediction probabilities, the calculation weight corresponding to the first prediction probability is smaller than the calculation weight corresponding to the second prediction probability, and the calculation weight corresponding to the second prediction probability is smaller than the calculation weight corresponding to the third prediction probability.
Based on the method, the geothermal high-temperature abnormal region prediction method adopts a plurality of different parameters (factors) to carry out comprehensive consideration judgment and prediction, so that the influence of the external climate environment on the prediction result can be reduced, the prediction accuracy is greatly improved, the prediction error is reduced, excessive human participation is not needed in the whole prediction process, the intelligentization degree of geothermal prediction is greatly improved, and the prediction cost is reduced.
Referring to fig. 4, fig. 4 is a schematic hardware structure diagram of the geothermal anomaly prediction system according to the embodiment of the present application. The geothermal anomaly prediction system 100 comprises a capacitance prediction module, a soil looseness prediction module, a surface heat dissipation prediction module, a processor and a memory;
the processor 101 is configured to provide computing and control capabilities to control the geothermal anomaly prediction system to perform corresponding tasks, for example, to control the geothermal anomaly prediction system to perform a geothermal high temperature anomaly region prediction method in any of the method embodiments described above, the method comprising: acquiring current environmental characteristic parameters of a target area to be detected, wherein the current environmental characteristic parameters comprise current environmental capacitance parameters and current sandy soil layer looseness parameters; obtaining a first prediction probability of the geothermal high temperature anomaly prediction according to the current environmental capacitance parameter and a pre-trained environmental capacitance parameter prediction probability model; obtaining a second prediction probability of the geothermal high-temperature anomaly prediction according to the current sandy soil layer looseness parameter and a pre-trained looseness parameter prediction probability model; judging whether the difference value between the first prediction probability and the second prediction probability is larger than a preset probability threshold value or not; when the difference value between the first prediction probability and the second prediction probability is larger than a preset probability threshold value, acquiring the current surface heat dissipation flow parameter of the region to be detected; obtaining a third prediction probability of the geothermal high temperature abnormality prediction according to the current heat dissipation flow parameter and a pre-trained heat dissipation flow parameter prediction probability model; and carrying out weighted summation on the first prediction probability, the second prediction probability and the third prediction probability to obtain the prediction probability of the geothermal high-temperature anomaly prediction of the target area to be detected.
The processor 101 may be a general purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a hardware chip, or any combination thereof; it may also be a digital signal processor (Digital Signal Processing, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), programmable logic device (programmable logic device, PLD), or a combination thereof. The PLD may be a complex programmable logic device (complex programmable logic device, CPLD), a field-programmable gate array (field-programmable gate array, FPGA), general-purpose array logic (generic array logic, GAL), or any combination thereof.
The memory 102, as a non-transitory computer readable storage medium, may be used to store a non-transitory software program, a non-transitory computer executable program, and a module, such as program instructions/modules corresponding to the geothermal high temperature abnormal region prediction method in the embodiments of the present application. The processor 101 may implement the geothermal high-temperature anomaly region prediction method in any of the method embodiments described above by running non-transitory software programs, instructions, and modules stored in the memory 102.
In particular, the memory 102 may include Volatile Memory (VM), such as random access memory (random access memory, RAM); the memory 102 may also include a non-volatile memory (NVM), such as read-only memory (ROM), flash memory (flash memory), hard disk (HDD) or Solid State Drive (SSD), or other non-transitory solid state storage devices; the memory 102 may also include a combination of the types of memory described above.
In summary, the geothermal anomaly prediction system of the present application adopts the technical scheme of any one of the above embodiments of the geothermal high temperature anomaly region prediction method, so at least the beneficial effects brought by the technical scheme of the above embodiments are not described in detail herein.
The present embodiment also provides a computer readable storage medium, such as a memory including program code executable by a processor to perform the geothermal high-temperature anomaly region prediction method in the above embodiment. For example, the computer readable storage medium may be Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), compact disc Read-Only Memory (CDROM), magnetic tape, floppy disk, optical data storage device, etc.
Embodiments of the present application also provide a computer program product comprising one or more program codes stored in a computer-readable storage medium. The processor of the electronic device reads the program code from the computer-readable storage medium, and the processor executes the program code to complete the geothermal high-temperature anomaly region prediction method steps provided in the above-described embodiments.
It will be appreciated by those of ordinary skill in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by program code related hardware, where the program may be stored in a computer readable storage medium, where the storage medium may be a read only memory, a magnetic disk or optical disk, etc.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
From the above description of embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus a general purpose hardware platform, or may be implemented by hardware. Those skilled in the art will appreciate that all or part of the processes implementing the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and where the program may include processes implementing the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the description of the present invention and the accompanying drawings or direct/indirect application in other related technical fields are included in the scope of the invention.
Claims (9)
1. The geothermal high-temperature abnormal region prediction method is characterized by comprising the following steps of:
acquiring current environmental characteristic parameters of a target area to be detected, wherein the current environmental characteristic parameters comprise current environmental capacitance parameters and current sandy soil layer looseness parameters;
obtaining a first prediction probability of the geothermal high temperature anomaly prediction according to the current environmental capacitance parameter and a pre-trained environmental capacitance parameter prediction probability model;
obtaining a second prediction probability of the geothermal high-temperature anomaly prediction according to the current sandy soil layer looseness parameter and a pre-trained looseness parameter prediction probability model;
judging whether the difference value between the first prediction probability and the second prediction probability is larger than a preset probability threshold value or not;
when the difference value between the first prediction probability and the second prediction probability is larger than a preset probability threshold value, acquiring the current surface heat dissipation flow parameter of the region to be detected;
obtaining a third prediction probability of the geothermal high temperature anomaly prediction according to the current surface thermal dispersion flow parameter and a pre-trained thermal dispersion flow parameter prediction probability model;
and carrying out weighted summation on the first prediction probability, the second prediction probability and the third prediction probability to obtain the prediction probability of the geothermal high-temperature anomaly prediction of the target area to be detected.
2. The geothermal high-temperature anomaly region prediction method of claim 1, wherein the current environmental capacitance parameters comprise a first environmental capacitance parameter of a subsurface region and a second environmental capacitance parameter of an atmospheric region;
the obtaining a first prediction probability of the geothermal high temperature abnormality prediction according to the current environmental capacitance parameter and a pre-trained environmental capacitance parameter prediction probability model comprises the following steps:
performing standard correction and coupling operation on the first environmental capacitance parameter and the second environmental capacitance parameter to obtain a standard capacitance parameter;
and carrying out probability mapping on the standard capacitance parameters according to the pre-trained environmental capacitance parameter pre-estimated probability model to obtain the first prediction probability.
3. The geothermal high temperature anomaly area prediction method according to claim 2, wherein the performing standard correction and coupling operation on the first environmental capacitance parameter and the second environmental capacitance parameter to obtain standard capacitance parameters comprises:
respectively carrying out standard correction on the first environmental capacitance parameter C1 and the second environmental capacitance parameter C2 to obtain a first standard capacitance parameter and a second standard capacitance parameter;
performing capacitance series coupling operation on the first standard capacitance parameter and the second standard capacitance parameter to obtain a standard capacitance parameter C0;
wherein the capacitance correction and coupling satisfy the following expression:
，
wherein A is a correction value of a first environmental capacitance parameter, B is a correction value of a second environmental capacitance parameter, D is a capacitance parameter magnitude control value, and E is a standard capacitance parameter offset control value.
4. The geothermal high temperature anomaly area prediction method according to claim 1, wherein obtaining a current sandy soil layer loosening parameter of a target area to be detected comprises:
acquiring a horizontal section image and a longitudinal section image of a sandy soil layer of a target area to be detected;
extracting the characteristics of the horizontal section image of the sandy soil layer to obtain horizontal section characteristic parameters;
extracting the characteristics of the longitudinal section image of the sandy soil layer to obtain longitudinal section characteristic parameters;
and obtaining the current looseness parameter of the sandy soil layer according to the transverse section characteristic parameter and the longitudinal section characteristic parameter.
5. The geothermal high temperature anomaly area prediction method of claim 4, wherein the transverse section features comprise transverse section void features, and the performing feature extraction on the sandy soil layer transverse section image to obtain transverse section feature parameters comprises: extracting void characteristics of the horizontal section image of the sandy soil layer to obtain a horizontal section void area parameter; the method comprises the steps of,
the longitudinal section characteristics comprise longitudinal section gap characteristics, and the characteristic extraction is carried out on the longitudinal section images of the sandy soil layer to obtain longitudinal section characteristic parameters, and the longitudinal section characteristic parameters comprise: and extracting void characteristics of the longitudinal section image of the sandy soil layer to obtain a longitudinal section void area parameter.
6. The geothermal high-temperature anomaly region prediction method according to claim 5, wherein the obtaining the current loose degree parameter of the sandy soil layer according to the transverse section characteristic parameter and the longitudinal section characteristic parameter comprises:
obtaining a first looseness parameter according to the transverse section gap area parameter and the transverse section total area;
obtaining a second looseness parameter according to the longitudinal section void area parameter and the longitudinal section total area;
and homogenizing the first looseness parameter and the second looseness parameter to obtain the looseness parameter of the current sandy soil layer.
7. The geothermal high-temperature anomaly region prediction method according to claim 1, wherein the surface heat dissipation flow parameters include surface heat dissipation flow speed parameters, and the obtaining the current surface heat dissipation flow parameters of the region to be detected comprises:
acquiring an earth surface thermodynamic image frame of a region to be detected;
dividing the surface thermodynamic image frame into N equally divided areas according to an upper space and a lower space;
acquiring temperature differences of head and tail equal division areas in the upper and lower spatial directions of an earth surface thermodynamic image frame;
and calculating according to the temperature difference and the distance of the head and tail equal division areas in the upper and lower space directions of the surface thermodynamic image frame to obtain the current surface heat dissipation flow speed parameter.
8. The geothermal heat abnormal region prediction method according to claim 1, wherein after the determining whether the difference between the first prediction probability and the second prediction probability is greater than a preset probability threshold, further comprising:
and when the difference value between the first prediction probability and the second prediction probability is smaller than or equal to a preset probability threshold value, carrying out arithmetic average on the first prediction probability and the second prediction probability to obtain prediction probability of geothermal high-temperature abnormality prediction of the target area to be detected.
9. The geothermal heat abnormal region prediction method according to claim 1, wherein before the weighted summation of the first prediction probability, the second prediction probability and the third prediction probability to obtain the prediction probability of the geothermal heat abnormal region prediction of the target region to be detected, further comprises:
and acquiring the calculation weights corresponding to the first prediction probability, the second prediction probability and the third prediction probability, wherein the larger the difference value between any one of the first prediction probability, the second prediction probability and the third prediction probability and the average value of the prediction probabilities is, the smaller the calculation weight corresponding to the first prediction probability, the second prediction probability and the third prediction probability is.
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