CN115641008B - Automatic carbonate rock corrosion rate monitoring system based on artificial intelligence - Google Patents
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 279
- 238000005260 corrosion Methods 0.000 title claims abstract description 194
- 230000007797 corrosion Effects 0.000 title claims abstract description 194
- BVKZGUZCCUSVTD-UHFFFAOYSA-L Carbonate Chemical compound [O-]C([O-])=O BVKZGUZCCUSVTD-UHFFFAOYSA-L 0.000 title claims abstract description 75
- 239000011435 rock Substances 0.000 title claims abstract description 55
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 10
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 32
- 229910001868 water Inorganic materials 0.000 claims description 32
- 230000000875 corresponding effect Effects 0.000 claims description 24
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 21
- 229910052799 carbon Inorganic materials 0.000 claims description 21
- 150000002500 ions Chemical class 0.000 claims description 19
- 238000000034 method Methods 0.000 claims description 18
- 238000007667 floating Methods 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 14
- 230000003247 decreasing effect Effects 0.000 claims description 12
- 230000000977 initiatory effect Effects 0.000 claims description 10
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- VTYYLEPIZMXCLO-UHFFFAOYSA-L Calcium carbonate Chemical compound [Ca+2].[O-]C([O-])=O VTYYLEPIZMXCLO-UHFFFAOYSA-L 0.000 description 2
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- 235000019738 Limestone Nutrition 0.000 description 2
- 238000004090 dissolution Methods 0.000 description 2
- 239000010459 dolomite Substances 0.000 description 2
- 229910000514 dolomite Inorganic materials 0.000 description 2
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- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
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- BVKZGUZCCUSVTD-UHFFFAOYSA-M Bicarbonate Chemical compound OC([O-])=O BVKZGUZCCUSVTD-UHFFFAOYSA-M 0.000 description 1
- BHPQYMZQTOCNFJ-UHFFFAOYSA-N Calcium cation Chemical compound [Ca+2] BHPQYMZQTOCNFJ-UHFFFAOYSA-N 0.000 description 1
- 240000008574 Capsicum frutescens Species 0.000 description 1
- 230000009471 action Effects 0.000 description 1
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- 229910000019 calcium carbonate Inorganic materials 0.000 description 1
- 229910001424 calcium ion Inorganic materials 0.000 description 1
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- 229910001748 carbonate mineral Inorganic materials 0.000 description 1
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- -1 hydrogen ions Chemical class 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 239000001095 magnesium carbonate Substances 0.000 description 1
- 229910000021 magnesium carbonate Inorganic materials 0.000 description 1
- ZLNQQNXFFQJAID-UHFFFAOYSA-L magnesium carbonate Chemical compound [Mg+2].[O-]C([O-])=O ZLNQQNXFFQJAID-UHFFFAOYSA-L 0.000 description 1
- 235000014380 magnesium carbonate Nutrition 0.000 description 1
- XMWCXZJXESXBBY-UHFFFAOYSA-L manganese(ii) carbonate Chemical compound [Mn+2].[O-]C([O-])=O XMWCXZJXESXBBY-UHFFFAOYSA-L 0.000 description 1
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Abstract
The invention discloses an artificial intelligence-based automatic carbonate corrosion rate monitoring system, which relates to the technical field of carbonate corrosion rate monitoring, and solves the technical problems that in the prior art, in the corresponding corrosion rate monitoring process of carbonate, corrosion prediction cannot be carried out on a region, so that deviation exists in regional corrosion monitoring, and the carbonate in the region is monitored to judge whether the carbonate is in a corrosion state, so that the accuracy of automatic monitoring time is improved, the rationality of automatic monitoring is facilitated, and the monitoring error of automatic monitoring is reduced; analyzing the corrosion trend of the carbonate rock in the monitoring area, and improving the monitoring accuracy of the carbonate rock corrosion in the monitoring area through real-time trend analysis of the carbonate rock corrosion, thereby being beneficial to the monitoring management efficiency in the current monitoring area; the corrosion prediction is performed through data analysis, so that the monitoring strength of the corrosion real-time state is improved, and the corrosion can be automatically monitored in time when the corrosion continues.
Description
Technical Field
The invention relates to the technical field of carbonate corrosion rate monitoring, in particular to an artificial intelligence-based automatic carbonate corrosion rate monitoring system.
Background
The present expression of carbonate rock refers to the general term of rock consisting of carbonate minerals formed by deposition, and mainly includes limestone and dolomite; carbonate rock and carbonate deposits are produced from the former chilly to the present and are extremely widely distributed; carbonate rock itself can also be useful mineral products such as limestone, dolomite and siderite, rhodochrosite, magnesite and the like, and is widely used in industries such as metallurgy, construction, decoration, chemical industry and the like; the carbonate rock is rich in petroleum, natural gas and underground water; the process of slowly decreasing the dissolution of rock into soil by chemical action is called erosion.
However, in the prior art, in the process of monitoring the corresponding corrosion rate of carbonate rock, the automatic monitoring time is inaccurate, so that the accuracy of the monitored value is reduced, meanwhile, the corrosion prediction cannot be performed on the area, the deviation exists in the regional corrosion monitoring, and the accurate measurement cannot be performed.
In view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to solve the problems, and provides an automatic carbonate corrosion rate monitoring system based on artificial intelligence, which monitors carbonate in an area and judges whether the carbonate is in a corrosion state, so that the accuracy of automatic monitoring time is improved, the rationality of automatic monitoring is facilitated, and the monitoring error of automatic monitoring is reduced; and the corrosion trend of the carbonate rock in the monitoring area is analyzed, and the monitoring accuracy of the carbonate rock corrosion in the monitoring area is improved through the real-time trend analysis of the carbonate rock corrosion, so that the monitoring management efficiency in the current monitoring area is facilitated.
The aim of the invention can be achieved by the following technical scheme:
the utility model provides a carbonate corrosion rate automatic monitoring system based on artificial intelligence, includes the server, and the server communication is connected with:
The automatic monitoring and controlling unit is used for monitoring the carbonate rock in the area, marking the area for monitoring the carbonate rock as a monitoring area, collecting samples of the monitoring area, wherein the interval distance of sample collection points does not exceed an interval distance threshold value, and counting the component parameters and the ion parameters in the collected samples; acquiring component parameters and ion parameters in a sample to construct a balance proportion coefficient; acquiring a temperature effect coefficient and a pressure effect coefficient at the current moment in a monitoring area, judging a trend of a balance proportion coefficient through corresponding type coefficient analysis, judging a precipitation trend and a corrosion trend of carbonate rock in the monitoring area according to the trend of the balance proportion coefficient, acquiring a non-corrosion moment and a corrosion moment through corresponding trend types of the carbonate rock, and sending the non-corrosion moment and the corrosion moment to a server;
The corrosion trend analysis unit is used for analyzing the corrosion trend of the carbonate rock in the monitoring area, dividing the monitoring area into i subareas, wherein i is a natural number greater than 1, acquiring corrosion trend analysis coefficients in the monitoring area through analysis, comparing according to the corrosion trend analysis coefficients to generate corrosion continuous signals and corrosion slowing signals, and transmitting the corrosion continuous signals and the corrosion slowing signals to the server;
The corrosion analysis prediction unit is used for predicting corrosion of the monitoring area in the stagnation trend, marking the subarea in the stagnation trend in the monitoring area as a prediction area, acquiring an acid-base value restriction coefficient of the soil carbon content in the prediction area and a water content restriction coefficient of the soil porosity, comparing according to the corresponding type coefficient to generate a prediction stagnation signal or a prediction corrosion signal, and sending the prediction stagnation signal or the prediction corrosion signal to the server;
And the monitoring error analysis unit is used for carrying out error analysis on the corrosion monitoring of the carbonate rock in the monitoring area, generating a monitoring error high risk signal and a monitoring error low risk signal through analysis, and sending the monitoring error high risk signal and the monitoring error low risk signal to the server.
As a preferred embodiment of the present invention, the operation process of the automatic monitoring and controlling unit is as follows:
Acquiring an increase value of the environmental temperature in the monitored area and an average increase span in the process of increasing the environmental temperature, and marking the increase value as WZ and PZ respectively; obtaining a temperature effect coefficient X at the current moment in a monitoring area through a formula X=WZ×a1+PZ×a2, wherein a1 and a2 are preset proportionality coefficients, and a1> a2>0; acquiring an ambient air pressure rise value in a monitoring area and a floating frequency in the process of ambient air pressure rise, and marking the floating frequency as SG and FP respectively; obtaining a pressure effect coefficient Z at the current moment in a monitoring area through a formula Z=SG×a3+FP×a4, wherein a3 and a4 are preset proportionality coefficients, and a3> a4>0;
when the temperature effect coefficient of the current moment in the monitoring area exceeds the temperature effect coefficient threshold value and the pressure effect coefficient does not exceed the pressure effect coefficient threshold value, the equilibrium proportion coefficient is a growing trend, namely the carbonate rock of the current moment in the monitoring area is a precipitation trend, and the current moment is marked as a non-corrosion moment;
When the temperature effect coefficient at the current moment in the monitoring area does not exceed the temperature effect coefficient threshold value, and the pressure effect coefficient exceeds the pressure effect coefficient threshold value, the equilibrium proportion coefficient is a decreasing trend, carbonate rock at the current moment in the monitoring area is a corrosion trend, and the current moment is marked as a corrosion moment;
When the temperature effect coefficient and the pressure effect coefficient at the current moment in the monitoring area exceed the corresponding effect coefficient threshold value or do not exceed the corresponding effect coefficient threshold value, the component parameter growth speed and the ion parameter growth speed in the current environment in the monitoring area are set, and if the component parameter growth speed is higher than the ion parameter growth speed, the current moment is set as a non-corrosion moment; if the component parameter growth rate is lower than the ion parameter growth rate, the current time is set as the erosion time.
As a preferred embodiment of the present invention, the corrosion trend analysis unit operates as follows:
Collecting the increasing frequency of the soil water content in each subarea in the monitoring area and the average value of the porosities in the subareas; collecting the soil acid-base number increase speed in each subarea in the monitoring area; obtaining corrosion trend analysis coefficients in the monitoring area through analysis; comparing the corrosion trend analysis coefficient in the monitored area with a corrosion trend analysis coefficient threshold value:
If the corrosion trend analysis coefficient in the monitoring area exceeds the corrosion trend analysis coefficient threshold, judging that the carbonate corrosion trend in the monitoring area is a continuous trend, generating a corrosion continuous signal and sending the corrosion continuous signal to a server; if the corrosion trend analysis coefficient in the monitoring area does not exceed the corrosion trend analysis coefficient threshold, judging that the carbonate corrosion trend in the monitoring area is a stagnation trend, generating a corrosion slowing signal and sending the corrosion slowing signal to a server.
As a preferred embodiment of the present invention, the corrosion analysis prediction unit operates as follows:
Collecting the increase speed of the carbon content of the soil in the prediction area and the reduction span of the acid-base number of the corresponding prediction area, and marking the increase speed and the reduction span as ZS and KD; by the formula Obtaining an acid-base value constraint coefficient B1 of the carbon content of the soil, wherein v1 and v2 are preset proportional coefficients, and v1 is more than v2 is more than 0;
collecting constant frequency of soil porosity in a predicted area and an increased value of average rainfall in the predicted area, and marking the constant frequency and the increased value as PL and ZJ respectively; by the formula Obtaining a water content constraint coefficient B2 of soil porosity, wherein v3 and v4 are preset proportional coefficients, and v3 is more than v4 is more than 0;
If the pH value restriction coefficient of the carbon content of the soil exceeds the pH value restriction coefficient threshold, and the water content restriction coefficient of the porosity of the soil exceeds the water content restriction coefficient threshold, judging that corrosion is predicted to be still in a stagnation trend, generating a predicted stagnation signal and sending the predicted stagnation signal to a server;
If the pH value restriction coefficient of the carbon content of the soil does not exceed the pH value restriction coefficient threshold, or the water content restriction coefficient of the porosity of the soil does not exceed the water content restriction coefficient threshold, judging that the corrosion prediction is a corrosion trend, generating a predicted corrosion signal and sending the predicted corrosion signal to a server;
When the water content constraint coefficient of the soil porosity exceeds the water content constraint coefficient threshold, namely when the increase value of the average rainfall is increasing or decreasing, the constant frequency floating influence of the soil porosity in the ratio coefficient is higher than the floating influence of the increase value of the average rainfall, namely the soil porosity is constraining the water content.
As a preferred embodiment of the invention, the operation of the monitoring error analysis unit is as follows:
The method comprises the steps of collecting interval duration between monitoring instruction initiating time and monitoring instruction executing time in a monitoring area and buffer duration of an adjacent automatic monitoring period, and comparing the interval duration with an interval duration threshold and a buffer duration threshold respectively:
If the interval time between the monitoring instruction initiating time and the monitoring instruction executing time in the monitoring area exceeds an interval time threshold or the buffer time of the adjacent automatic monitoring period exceeds a buffer time threshold, generating a monitoring error high risk signal and sending the monitoring error high risk signal to a server; if the interval duration between the monitoring instruction initiating time and the monitoring instruction executing time in the monitoring area does not exceed the interval duration threshold, and the buffer duration of the adjacent automatic monitoring period does not exceed the buffer duration threshold, generating a monitoring error low risk signal and sending the monitoring error low risk signal to the server.
Compared with the prior art, the invention has the beneficial effects that:
1. In the invention, the carbonate rock in the area is monitored, and whether the carbonate rock is in a corrosion state is judged, so that the accuracy of automatic monitoring time is improved, the rationality of automatic monitoring is facilitated, and the monitoring error of automatic monitoring is reduced; analyzing the corrosion trend of the carbonate rock in the monitoring area, and improving the monitoring accuracy of the carbonate rock corrosion in the monitoring area through real-time trend analysis of the carbonate rock corrosion, thereby being beneficial to the monitoring management efficiency in the current monitoring area;
2. According to the invention, the corrosion prediction is carried out on the monitoring area in the stagnation trend, and the corrosion prediction is carried out through data analysis, so that the monitoring strength of the real-time state of corrosion is improved, the automatic monitoring can be carried out in time when corrosion continues, and the monitoring reliability of carbonate rock in the monitoring area is prevented from being low due to deviation in the monitoring process; the accuracy of automatic monitoring is improved, and the monitoring efficiency of automatic monitoring is ensured; performing error analysis on the corrosion monitoring of the carbonate rock in the monitoring area, and judging whether the parameter monitoring value has deviation in the automatic monitoring process, so that the accuracy of the automatic monitoring is reduced; the monitoring efficiency of the monitoring area can be improved through error analysis, and the reliability of the monitoring value is guaranteed, so that the rationality of the management and control of the monitoring area is improved.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a schematic block diagram of an artificial intelligence based automatic carbonate erosion rate monitoring system according to the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. 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.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Referring to fig. 1, an automatic carbonate corrosion rate monitoring system based on artificial intelligence includes a server, wherein the server is in communication connection with an automatic monitoring and controlling unit, a corrosion trend analysis unit, a corrosion analysis prediction unit and a monitoring error analysis unit, and the server is in bidirectional communication connection with the automatic monitoring and controlling unit, the corrosion trend analysis unit, the corrosion analysis prediction unit and the monitoring error analysis unit;
The server generates an automatic monitoring control signal and sends the automatic monitoring control signal to the automatic monitoring control unit, and after the automatic monitoring control unit receives the automatic monitoring control signal, the automatic monitoring control unit monitors the carbonate rock in the area and judges whether the carbonate rock is in a corrosion state, so that the accuracy of the automatic monitoring moment is improved, the rationality of automatic monitoring is facilitated, and the monitoring error of the automatic monitoring is reduced;
Marking the area for carbonate rock monitoring as a monitoring area, carrying out sample acquisition on the monitoring area, wherein the interval distance of sample acquisition points does not exceed an interval distance threshold value, ensuring that the interval between adjacent sample acquisition point areas is low, and reducing the parameter difference between the sample acquisition points; counting component parameters and ion parameters in the collected sample, wherein the component parameters comprise calcium carbonate, water and carbon dioxide, and the ion parameters comprise calcium ions and bicarbonate ions; the method comprises the steps of collecting component parameters and ion parameters in a sample to construct a balance proportionality coefficient, wherein the component parameters are molecules, and the ion parameters are denominators;
acquiring an increase value of the environmental temperature in the monitored area and an average increase span in the process of increasing the environmental temperature, and marking the increase value of the environmental temperature in the monitored area and the average increase span in the process of increasing the environmental temperature as WZ and PZ respectively; obtaining a temperature effect coefficient X at the current moment in a monitoring area through a formula X=WZ×a1+PZ×a2, wherein a1 and a2 are preset proportionality coefficients, and a1> a2>0;
Acquiring an ambient air pressure rise value in a monitoring area and a floating frequency in the process of ambient air pressure rise, and marking the ambient air pressure rise value in the monitoring area and the floating frequency in the process of ambient air pressure rise as SG and FP respectively; obtaining a pressure effect coefficient Z at the current moment in a monitoring area through a formula Z=SG×a3+FP×a4, wherein a3 and a4 are preset proportionality coefficients, and a3> a4>0;
In the application, the floating frequency in the air pressure rising process is expressed as the influence of repeated change of the air pressure from high to low and from low to high under the rising trend, and the larger the floating frequency is, the larger the high-span floating frequency is, namely the higher the influence of the pressure effect is; the higher the pressure rise value of the environment is, the higher the influence of the pressure effect is;
When the temperature effect coefficient of the current moment in the monitoring area exceeds the temperature effect coefficient threshold value and the pressure effect coefficient does not exceed the pressure effect coefficient threshold value, the equilibrium proportion coefficient is a growing trend, namely the temperature effect influence is higher than the pressure effect influence, carbonate rock at the current moment in the monitoring area is a precipitation trend, and the current moment is marked as a non-corrosion moment;
When the temperature effect coefficient of the current moment in the monitoring area does not exceed the temperature effect coefficient threshold value and the pressure effect coefficient exceeds the pressure effect coefficient threshold value, the balance proportion coefficient is a decreasing trend, namely the temperature effect influence is lower than the pressure influence, carbonate rock of the current moment in the monitoring area is a corrosion trend, and the current moment is marked as a corrosion moment;
When the temperature effect coefficient and the pressure effect coefficient at the current moment in the monitoring area exceed the corresponding effect coefficient threshold value or do not exceed the corresponding effect coefficient threshold value, the component parameter growth speed and the ion parameter growth speed in the current environment in the monitoring area are set, and if the component parameter growth speed is higher than the ion parameter growth speed, the current moment is set as a non-corrosion moment; if the component parameter growth speed is lower than the ion parameter growth speed, setting the current moment as the corrosion moment;
the corrosion time is sent to a server, and after the server receives the corrosion time, the corresponding monitoring area is automatically monitored;
After the server receives the corrosion moment, generating a corrosion trend analysis signal and sending the corrosion trend analysis signal to a corrosion trend analysis unit, and after the corrosion trend analysis unit receives the corrosion trend analysis signal, analyzing the corrosion trend of the carbonate rock in the monitoring area, and improving the monitoring accuracy of the carbonate rock corrosion in the monitoring area through real-time trend analysis of the carbonate rock corrosion, thereby being beneficial to the monitoring management efficiency in the current monitoring area;
Dividing a monitoring area into i subareas, wherein i is a natural number larger than 1, collecting the increasing frequency of the soil moisture content in each subarea in the monitoring area and the average value of the porosities in the subareas, and marking the increasing frequency of the soil moisture content in each subarea in the monitoring area and the average value of the porosities in the subareas as HSLi and KXDi respectively; collecting the soil acid-base number increase speed in each subarea in the monitoring area, and marking the soil acid-base number increase speed in each subarea in the monitoring area as SJVi;
In the application, soil porosity is a main factor influencing soil air permeability, the influence of soil water content and soil porosity on corrosion influence is in a direct proportion relation, and meanwhile, the influence of soil PH on corrosion influence is in an inverse proportion relation; when the PH value is low, the concentration of hydrogen ions is high, so that the continuous dissolution of carbonate rock can be promoted;
By the formula Obtaining corrosion trend analysis coefficients Xi in a monitoring area, wherein a1, a2 and a3 are preset proportionality coefficients, and a1 is more than a2 and more than a3 is more than 0;
comparing the corrosion trend analysis coefficient Xi in the monitored area with a corrosion trend analysis coefficient threshold value:
If the corrosion trend analysis coefficient Xi in the monitoring area exceeds the corrosion trend analysis coefficient threshold value, judging that the carbonate corrosion trend in the monitoring area is a continuous trend, generating a corrosion continuous signal and sending the corrosion continuous signal to a server; if the corrosion trend analysis coefficient Xi in the monitoring area does not exceed the corrosion trend analysis coefficient threshold value, judging that the carbonate corrosion trend in the monitoring area is a stagnation trend, generating a corrosion slowing signal and sending the corrosion slowing signal to a server;
After receiving the corrosion continuous signal, the server automatically monitors the corresponding subareas in the monitoring area, simultaneously generates a monitoring error analysis signal and sends the monitoring error analysis signal to the monitoring error analysis unit, and after receiving the corrosion slowing signal, the server generates a corrosion analysis prediction signal and sends the corrosion analysis prediction signal to the corrosion analysis prediction unit;
After receiving the corrosion analysis prediction signal, the corrosion analysis prediction unit predicts corrosion of the monitoring area in the stagnation trend, and the corrosion prediction is performed through data analysis, so that the monitoring strength of the real-time state of corrosion is improved, automatic monitoring can be performed in time when corrosion continues, and the monitoring reliability of carbonate rock in the monitoring area is prevented from being low due to deviation in the monitoring process; the accuracy of automatic monitoring is improved, and the monitoring efficiency of automatic monitoring is ensured;
marking a subregion in a stagnation trend in a monitoring region as a prediction region, collecting the increase speed of the carbon content of soil in the prediction region and the decrease span of the acid-base number of the corresponding prediction region, and marking the increase speed of the carbon content of the soil in the prediction region and the decrease span of the acid-base number of the corresponding prediction region as ZS and KD; by the formula Obtaining an acid-base value constraint coefficient B1 of the carbon content of the soil, wherein v1 and v2 are preset proportional coefficients, and v1 is more than v2 is more than 0;
collecting constant frequency of soil porosity in a prediction area and an increased value of average rainfall in the prediction area, and marking the constant frequency of the soil porosity in the prediction area and the increased value of the average rainfall in the prediction area as PL and ZJ respectively; by the formula Obtaining a water content constraint coefficient B2 of soil porosity, wherein v3 and v4 are preset proportional coefficients, and v3 is more than v4 is more than 0;
If the pH value restriction coefficient of the carbon content of the soil exceeds the pH value restriction coefficient threshold, and the water content restriction coefficient of the porosity of the soil exceeds the water content restriction coefficient threshold, judging that corrosion is predicted to be still in a stagnation trend, generating a predicted stagnation signal and sending the predicted stagnation signal to a server;
If the pH value restriction coefficient of the carbon content of the soil does not exceed the pH value restriction coefficient threshold, or the water content restriction coefficient of the porosity of the soil does not exceed the water content restriction coefficient threshold, judging that the corrosion prediction is a corrosion trend, generating a predicted corrosion signal and sending the predicted corrosion signal to a server;
It can be understood that when the restriction coefficient of the pH value of the carbon content of the soil is larger than the corresponding threshold value, that is, the increasing speed of the carbon content of the soil is an increasing trend, and the decreasing span of the pH value is a decreasing trend, the control force of the carbon content of the soil corresponding to the pH value is reflected, and if the decreasing span of the pH value is not the decreasing trend, in the ratio coefficient, the increasing speed of the carbon content of the soil is the increasing influence of the increasing span of the pH value which is still higher than the increasing influence of the decreasing span of the pH value, the control force of the carbon content of the soil to the pH value can still be reflected, and the condition that the pH value is decreased due to other influencing factors in the area exists; in the application, restriction is expressed as inhibition of erosion by soil parameters;
when the water content constraint coefficient of the soil porosity exceeds the water content constraint coefficient threshold, namely when the added value of the average rainfall is increased or reduced, the constant frequency floating influence of the soil porosity in the ratio coefficient is higher than the floating influence of the added value of the average rainfall, namely the soil porosity is used for constraining the water content;
After receiving the monitoring error analysis signal, the monitoring error analysis unit carries out error analysis on the corrosion monitoring of the carbonate rock in the monitoring area and judges whether the parameter monitoring value has deviation in the automatic monitoring process, thereby reducing the accuracy of the automatic monitoring; the monitoring efficiency of the monitoring area can be improved through error analysis, and the reliability of the monitoring value is ensured, so that the rationality of the management and control of the monitoring area is improved;
the method comprises the steps of collecting interval duration between monitoring instruction initiating time and monitoring instruction executing time in a monitoring area and buffer duration of an adjacent automatic monitoring period, and comparing the interval duration between the monitoring instruction initiating time and the monitoring instruction executing time in the monitoring area and the buffer duration of the adjacent automatic monitoring period with an interval duration threshold and a buffer duration threshold respectively:
If the interval time between the monitoring instruction initiating time and the monitoring instruction executing time in the monitoring area exceeds an interval time threshold or the buffer time of the adjacent automatic monitoring period exceeds a buffer time threshold, generating a monitoring error high risk signal and sending the monitoring error high risk signal to a server; after receiving the high risk signal of the monitoring error, the server performs rectifying on the automatic monitoring process, controls the interval duration and the buffer duration in the automatic monitoring process, and prevents the increase of the numerical value floating risk;
If the interval duration between the monitoring instruction initiating time and the monitoring instruction executing time in the monitoring area does not exceed the interval duration threshold, and the buffer duration of the adjacent automatic monitoring period does not exceed the buffer duration threshold, generating a monitoring error low risk signal and sending the monitoring error low risk signal to the server.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions;
When the monitoring system is used, the automatic monitoring and controlling unit monitors carbonate rock in the area, the area for monitoring the carbonate rock is marked as a monitoring area, the monitoring area is subjected to sample collection, the interval distance of sample collection points does not exceed the interval distance threshold value, and the component parameters and the ion parameters in the collected samples are counted; acquiring component parameters and ion parameters in a sample to construct a balance proportion coefficient; acquiring a temperature effect coefficient and a pressure effect coefficient at the current moment in a monitoring area, judging a trend of a balance proportion coefficient through corresponding type coefficient analysis, judging a precipitation trend and a corrosion trend of carbonate rock in the monitoring area according to the trend of the balance proportion coefficient, acquiring a non-corrosion moment and a corrosion moment through corresponding trend types of the carbonate rock, and sending the non-corrosion moment and the corrosion moment to a server; analyzing the corrosion trend of carbonate rock in a monitoring area through a corrosion trend analysis unit, dividing the monitoring area into i subareas, obtaining corrosion trend analysis coefficients in the monitoring area through analysis, comparing according to the corrosion trend analysis coefficients to generate corrosion continuous signals and corrosion slowing signals, and sending the corrosion continuous signals and the corrosion slowing signals to a server; carrying out corrosion prediction on a monitoring area in a stagnation trend through a corrosion analysis prediction unit, marking a subarea in the stagnation trend in the monitoring area as a prediction area, acquiring an acid-base value restriction coefficient of soil carbon content in the prediction area and a water content restriction coefficient of soil porosity, comparing according to a corresponding type coefficient to generate a prediction stagnation signal or a prediction corrosion signal, and sending the prediction stagnation signal or the prediction corrosion signal to a server; and carrying out error analysis on the corrosion monitoring of the carbonate rock in the monitoring area through a monitoring error analysis unit, generating a monitoring error high risk signal and a monitoring error low risk signal through analysis, and sending the monitoring error high risk signal and the monitoring error low risk signal to a server.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (2)
1. Automatic carbonate corrosion rate monitoring system based on artificial intelligence, its characterized in that includes the server, and the server communication is connected with:
The automatic monitoring and controlling unit is used for monitoring the carbonate rock in the area, marking the area for monitoring the carbonate rock as a monitoring area, collecting samples of the monitoring area, wherein the interval distance of sample collection points does not exceed an interval distance threshold value, and counting the component parameters and the ion parameters in the collected samples; acquiring component parameters and ion parameters in a sample to construct a balance proportion coefficient; acquiring a temperature effect coefficient and a pressure effect coefficient at the current moment in a monitoring area, carrying out trend judgment on a balance proportion coefficient through corresponding type coefficient analysis, judging the precipitation trend and the corrosion trend of carbonate rock in the monitoring area according to the trend of the balance proportion coefficient, acquiring non-corrosion moment and corrosion moment through corresponding trend types of the carbonate rock, and sending the non-corrosion moment and the corrosion moment to a server;
The corrosion trend analysis unit is used for analyzing the corrosion trend of the carbonate rock in the monitoring area, dividing the monitoring area into i subareas, wherein i is a natural number greater than 1, acquiring corrosion trend analysis coefficients in the monitoring area through analysis, comparing according to the corrosion trend analysis coefficients to generate corrosion continuous signals and corrosion slowing signals, and transmitting the corrosion continuous signals and the corrosion slowing signals to the server;
The corrosion analysis prediction unit is used for predicting corrosion of the monitoring area in the stagnation trend, marking the subarea in the stagnation trend in the monitoring area as a prediction area, acquiring an acid-base value restriction coefficient of the soil carbon content in the prediction area and a water content restriction coefficient of the soil porosity, comparing according to the corresponding type coefficient to generate a prediction stagnation signal or a prediction corrosion signal, and sending the prediction stagnation signal or the prediction corrosion signal to the server;
The monitoring error analysis unit is used for carrying out error analysis on the corrosion monitoring of the carbonate rock in the monitoring area, generating a monitoring error high risk signal and a monitoring error low risk signal through analysis, and sending the monitoring error high risk signal and the monitoring error low risk signal to the server;
the operation process of the automatic monitoring and controlling unit is as follows:
Acquiring an increase value of the environmental temperature in the monitored area and an average increase span in the process of increasing the environmental temperature, and marking the increase value as WZ and PZ respectively; obtaining a temperature effect coefficient X at the current moment in a monitoring area through a formula X=WZ×a1+PZ×a2, wherein a1 and a2 are preset proportionality coefficients, and a1 > a2 > 0; acquiring an ambient air pressure rise value in a monitoring area and a floating frequency in the process of ambient air pressure rise, and marking the floating frequency as SG and FP respectively; obtaining a pressure effect coefficient Z at the current moment in a monitoring area through a formula Z=SG×a3+FP×a4, wherein a3 and a4 are preset proportionality coefficients, and a3 is larger than a4 and larger than 0;
when the temperature effect coefficient of the current moment in the monitoring area exceeds the temperature effect coefficient threshold value and the pressure effect coefficient does not exceed the pressure effect coefficient threshold value, the equilibrium proportion coefficient is a growing trend, namely the carbonate rock of the current moment in the monitoring area is a precipitation trend, and the current moment is marked as a non-corrosion moment;
When the temperature effect coefficient of the current moment in the monitoring area does not exceed the temperature effect coefficient threshold value, and the pressure effect coefficient exceeds the pressure effect coefficient threshold value, the balance proportion coefficient is a decreasing trend, carbonate rock of the current moment in the monitoring area is a corrosion trend, and the current moment is marked as a corrosion moment;
When the temperature effect coefficient and the pressure effect coefficient at the current moment in the monitoring area exceed the corresponding effect coefficient threshold value or do not exceed the corresponding effect coefficient threshold value, the component parameter growth speed and the ion parameter growth speed in the current environment in the monitoring area are set, and if the component parameter growth speed is higher than the ion parameter growth speed, the current moment is set as a non-corrosion moment; if the component parameter growth speed is lower than the ion parameter growth speed, setting the current moment as the corrosion moment;
the corrosion trend analysis unit operates as follows:
Collecting the increasing frequency of the soil water content in each subarea in the monitoring area and the average value of the porosities in the subareas; collecting the soil acid-base number increase speed in each subarea in the monitoring area; obtaining corrosion trend analysis coefficients in the monitoring area through analysis; comparing the corrosion trend analysis coefficient in the monitored area with a corrosion trend analysis coefficient threshold value:
If the corrosion trend analysis coefficient in the monitoring area exceeds the corrosion trend analysis coefficient threshold, judging that the carbonate corrosion trend in the monitoring area is a continuous trend, generating a corrosion continuous signal and sending the corrosion continuous signal to a server; if the corrosion trend analysis coefficient in the monitoring area does not exceed the corrosion trend analysis coefficient threshold, judging that the carbonate corrosion trend in the monitoring area is a stagnation trend, generating a corrosion slowing signal and sending the corrosion slowing signal to a server;
The operation process of the corrosion analysis and prediction unit is as follows:
Collecting the increase speed of the carbon content of the soil in the prediction area and the reduction span of the acid-base number of the corresponding prediction area, and marking the increase speed and the reduction span as ZS and KD; by the formula Obtaining an acid-base value constraint coefficient B1 of the carbon content of the soil, wherein v1 and v2 are preset proportional coefficients, and v1 is more than v2 is more than 0;
collecting constant frequency of soil porosity in a predicted area and an increased value of average rainfall in the predicted area, and marking the constant frequency and the increased value as PL and ZJ respectively; by the formula Obtaining a water content constraint coefficient B2 of soil porosity, wherein v3 and v4 are preset proportional coefficients, and v3 is more than v4 is more than 0;
If the pH value restriction coefficient of the carbon content of the soil exceeds the pH value restriction coefficient threshold, and the water content restriction coefficient of the porosity of the soil exceeds the water content restriction coefficient threshold, judging that corrosion is predicted to be still in a stagnation trend, generating a predicted stagnation signal and sending the predicted stagnation signal to a server;
If the pH value restriction coefficient of the carbon content of the soil does not exceed the pH value restriction coefficient threshold, or the water content restriction coefficient of the porosity of the soil does not exceed the water content restriction coefficient threshold, judging that the corrosion prediction is a corrosion trend, generating a predicted corrosion signal and sending the predicted corrosion signal to a server;
When the water content constraint coefficient of the soil porosity exceeds the water content constraint coefficient threshold, namely when the increase value of the average rainfall is increasing or decreasing, the constant frequency floating influence of the soil porosity in the ratio coefficient is higher than the floating influence of the increase value of the average rainfall, namely the soil porosity is constraining the water content.
2. An artificial intelligence based automatic carbonate corrosion rate monitoring system according to claim 1, wherein the monitoring error analysis unit operates as follows:
The method comprises the steps of collecting interval duration between monitoring instruction initiating time and monitoring instruction executing time in a monitoring area and buffer duration of an adjacent automatic monitoring period, and comparing the interval duration with an interval duration threshold and a buffer duration threshold respectively:
If the interval time between the monitoring instruction initiating time and the monitoring instruction executing time in the monitoring area exceeds an interval time threshold or the buffer time of the adjacent automatic monitoring period exceeds a buffer time threshold, generating a monitoring error high risk signal and sending the monitoring error high risk signal to a server; if the interval duration between the monitoring instruction initiating time and the monitoring instruction executing time in the monitoring area does not exceed the interval duration threshold, and the buffer duration of the adjacent automatic monitoring period does not exceed the buffer duration threshold, generating a monitoring error low risk signal and sending the monitoring error low risk signal to the server.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103196807A (en) * | 2013-03-11 | 2013-07-10 | 中国石油天然气股份有限公司 | Analytical method of sandstone diagenetic process and pore evolution |
| CN104407118A (en) * | 2014-12-01 | 2015-03-11 | 中国石油天然气股份有限公司 | Analysis method of corrosion action and corrosion effect of carbonate rock |
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103196807A (en) * | 2013-03-11 | 2013-07-10 | 中国石油天然气股份有限公司 | Analytical method of sandstone diagenetic process and pore evolution |
| CN104407118A (en) * | 2014-12-01 | 2015-03-11 | 中国石油天然气股份有限公司 | Analysis method of corrosion action and corrosion effect of carbonate rock |
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