Underground geophysical prospecting equipment self-adaptive control method and system in combination with environment state

Abstract

The invention provides a downhole geophysical prospecting equipment self-adaptive control method and system in combination with an environment state, and relates to the technical field of self-adaptive control. Environmental influence normalization processing is carried out; randomly generating N initial solutions in the gain interval; carrying out fitness evaluation; carrying out distribution evaluation, configuring distance constraints, carrying out search solution screening, and establishing a search solution set; configuring a search strategy; updating the following solution; performing search updating, and executing search iteration; and self-adaptive signal gain is established, and self-adaptive control is carried out. According to the invention, the technical problem that the detection precision and the stability of the equipment are further influenced because the equipment cannot effectively adjust the signal strength due to the limitation that the equipment cannot dynamically adapt to the environmental change in the prior art can be solved, the technical target of adaptive adjustment of the signal gain is realized, and the equipment can respond to the environmental change in real time. And the signal intensity is automatically adjusted, so that the detection precision and the equipment stability are improved.

Description

Technical Field

[0001] The present application relates to the field of adaptive control technology, and in particular to an adaptive control method and system for downhole geophysical exploration equipment in combination with environmental conditions. Background Art

[0002] In the control and signal processing of downhole geophysical exploration equipment, signal gain adjustment and adaptive control are key factors to ensure stable equipment operation and accurate detection results.

[0003] At present, traditional downhole geophysical exploration equipment usually uses a fixed signal gain value, and the signal gain is preset regardless of how the environmental conditions change. This fixed gain method cannot adapt to the complex and changing environment of the well, such as the influence of factors such as temperature, humidity, electromagnetic interference, and geological changes. When the environment changes, the fixed gain value may cause insufficient or excessive signal amplification, thereby affecting the detection accuracy. When the signal strength is too low, the device may not be able to capture weak reflected signals, and when the signal strength is too high, the device may be affected by noise or other interference, resulting in distorted detection results.

[0004] To sum up, the existing technology has the limitation that the signal gain is fixed and cannot dynamically adapt to environmental changes, resulting in the equipment being unable to effectively adjust the signal strength when facing complex underground environments, further affecting the detection accuracy and stability of the equipment, and even causing technical problems such as distortion or misjudgment of detection results. Summary of the invention

[0005] The purpose of this application is to provide an adaptive control method and system for downhole geophysical exploration equipment in combination with environmental conditions, so as to solve the limitation of the prior art that the signal gain is fixed and cannot dynamically adapt to environmental changes, resulting in the equipment being unable to effectively adjust the signal strength when facing a complex downhole environment, further affecting the detection accuracy and stability of the equipment, and even causing technical problems such as distortion or misjudgment of the detection results.

[0006] In view of the above problems, the present application provides an adaptive control method and system for downhole geophysical exploration equipment in combination with environmental conditions.

[0007] In the first aspect, the present application provides an adaptive control method for downhole geophysical exploration equipment in combination with environmental conditions, which is implemented through an adaptive control system for downhole geophysical exploration equipment in combination with environmental conditions, including: establishing a sensor group, sensing the downhole environment through the sensor group, and establishing an environmental data set, wherein the sensor group includes a temperature and humidity sensor, an electromagnetic interference sensor, and a geological perception sensor; reading the equipment working data of the downhole geophysical exploration equipment, performing environmental impact normalization processing based on the equipment working data and the environmental data set, and establishing an environmental factor weight set; configuring a gain interval and an initial solution constraint using the equipment working data, and randomly generating N initial solutions within the gain interval based on the initial solution constraint, where N is the number of constraints of the initial solution constraint, and N is an integer greater than 3; establishing a fitness function based on the environmental factor weight set and the signal-to-noise ratio, and controlling the environmental factor weight set through the fitness function The N initial solutions are evaluated for fitness and fitness evaluation results are established; distribution evaluation is performed on the N initial solutions, distance constraints are configured based on the distribution evaluation results, search solutions are screened based on the distance constraints and fitness evaluation results, and a search solution set is established, wherein the number of solutions in the search solution set is 3; an environmental comprehensive coefficient is established through an environmental factor weight set and an environmental data set, and a search strategy is configured based on the environmental comprehensive coefficient; a following solution is established, wherein the following solution is a set of non-search solutions within the N initial solutions, the search solution is used as an update target, and the following solution is updated based on the search strategy; a search update is performed on the search solution based on the search strategy, and a search solution is reselected based on the search solution update results and the following solution update results, and a search iteration is performed; an adaptive signal gain of the downhole geophysical exploration equipment is established based on the search iteration results, and adaptive control is performed based on the adaptive signal gain.

[0008] In a second aspect, the present application also provides an adaptive control system for downhole geophysical exploration equipment in combination with environmental conditions, which is used to execute the adaptive control method for downhole geophysical exploration equipment in combination with environmental conditions as described in the first aspect, including: an environmental data set establishment module, which is used to establish a sensor group, and perform downhole environmental perception through the sensor group to establish an environmental data set, wherein the sensor group includes a temperature and humidity sensor, an electromagnetic interference sensor, and a geological perception sensor; an environmental factor weight set establishment module, which is used to read the equipment working data of the downhole geophysical exploration equipment, perform environmental impact normalization processing based on the equipment working data and the environmental data set, and establish an environmental factor weight set; an initial solution generation module, which is used to configure a gain interval and an initial solution constraint using the equipment working data, and randomly generate N initial solutions within the gain interval according to the initial solution constraint, where N is the number of constraints of the initial solution constraint, and N is an integer greater than 3; a fitness evaluation result establishment module, which is used to establish a fitness function based on the environmental factor weight set and the signal-to-noise ratio, and to obtain a fitness function through adaptation. The degree function performs fitness evaluation on the N initial solutions and establishes fitness evaluation results; a search solution set establishment module is used to perform distribution evaluation on the N initial solutions, configure distance constraints with the distribution evaluation results, screen search solutions according to the distance constraints and fitness evaluation results, and establish a search solution set, wherein the number of solutions in the search solution set is 3; a search strategy configuration module is used to establish an environmental comprehensive coefficient through an environmental factor weight set and an environmental data set, and configure the search strategy with the environmental comprehensive coefficient; a follow-up solution update module is used to establish a follow-up solution, wherein the follow-up solution is a set of non-search solutions within the N initial solutions, and the search solution is used as the update target, and the follow-up solution is updated based on the search strategy; a search iteration module is used to perform search update on the search solution based on the search strategy, and re-select the search solution with the search solution update results and the follow-up solution update results, and perform search iteration; an adaptive control module is used to establish an adaptive signal gain of the downhole geophysical exploration equipment according to the search iteration results, and perform adaptive control according to the adaptive signal gain.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By establishing a sensor group, the downhole environment is perceived through the sensor group to establish an environmental data set, wherein the sensor group includes a temperature and humidity sensor, an electromagnetic interference sensor, and a geological perception sensor; reading the equipment working data of the downhole geophysical exploration equipment, performing environmental impact normalization processing according to the equipment working data and the environmental data set, and establishing an environmental factor weight set; configuring a gain interval and an initial solution constraint using the equipment working data, and randomly generating N initial solutions within the gain interval according to the initial solution constraint, wherein N is the number of constraints of the initial solution constraint, and N is an integer greater than 3; establishing a fitness function based on the environmental factor weight set and the signal-to-noise ratio, and performing fitness evaluation on the N initial solutions through the fitness function to establish a fitness evaluation result; performing distribution evaluation on the N initial solutions, configuring a distance constraint based on the distribution evaluation result, and generating a fitness evaluation result according to the distance constraint The search solution is screened according to the fitness evaluation results, and a search solution set is established, the number of solutions in the search solution set is 3; an environmental comprehensive coefficient is established through an environmental factor weight set and an environmental data set, and a search strategy is configured with the environmental comprehensive coefficient; a following solution is established, the following solution is a set of non-search solutions within the N initial solutions, and the search solution is used as an update target, and the following solution is updated based on the search strategy; a search update is performed on the search solution based on the search strategy, and the search solution is re-selected with the search solution update result and the following solution update result, and a search iteration is performed; an adaptive signal gain of the downhole geophysical exploration equipment is established according to the search iteration result, and adaptive control is performed according to the adaptive signal gain, so as to achieve the technical goal of adaptive adjustment of the signal gain, so as to achieve the technical effect that the equipment can respond to environmental changes in real time and automatically adjust the signal strength, thereby improving the detection accuracy and the stability of the equipment.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0013] Figure 1 A flow chart of the adaptive control method of downhole geophysical exploration equipment combined with environmental conditions in this application;

[0014] Figure 2 This is a schematic diagram of the structure of the adaptive control system of downhole geophysical exploration equipment combined with environmental conditions in this application.

[0015] Description of reference numerals:

[0016] Environmental data set establishment module 11, environmental factor weight set establishment module 12, initial solution generation module 13, fitness evaluation result establishment module 14, search solution set establishment module 15, search strategy configuration module 16, follow-up solution update module 17, search iteration module 18, adaptive control module 19. DETAILED DESCRIPTION

[0017] This application provides an adaptive control method and system for downhole geophysical exploration equipment combined with environmental conditions, which solves the limitation of the prior art that the signal gain is fixed and cannot dynamically adapt to environmental changes, resulting in the inability of the equipment to effectively adjust the signal strength when facing a complex downhole environment, further affecting the detection accuracy and stability of the equipment, and even causing distortion or misjudgment of the detection results. The technical goal of adaptive signal gain adjustment is achieved, and the equipment can respond to environmental changes in real time and automatically adjust the signal strength, thereby improving the detection accuracy and equipment stability.

[0018] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0019] For example, please refer to the attached Figure 1 The present application provides an adaptive control method for downhole geophysical exploration equipment combined with environmental conditions, which is applied to an adaptive control system for downhole geophysical exploration equipment combined with environmental conditions, and specifically includes the following steps:

[0020] Step 1: Establish a sensor group, use the sensor group to perceive the underground environment, and establish an environmental data set. The sensor group includes a temperature and humidity sensor, an electromagnetic interference sensor, and a geological perception sensor.

[0021] Specifically, a combination of multiple sensors, i.e., a sensor group, is established, and the underground environment is sensed through the coordinated work of the sensor group. The sensor group includes temperature and humidity sensors, electromagnetic interference sensors, and geological perception sensors. Various information in the environment, such as temperature, humidity, electromagnetic interference, and geological data, is collected through temperature and humidity sensors, electromagnetic interference sensors, and geological perception sensors. The temperature and humidity in the well are measured through temperature and humidity sensors, for example, the temperature is 30 degrees Celsius and the humidity reaches 70%. The electromagnetic interference sensor is used to detect changes in the electromagnetic environment, for example, when the electromagnetic interference intensity reaches a certain value, such as 1 microtesla, and then the interference situation is identified. The changes in the underground geological structure are monitored through geological perception sensors, such as geological perception sensors including seismic wave detection sensors, geological radars, acceleration sensors, etc. The seismic wave detection sensor is used to detect the reflected and refracted waves of the formation, and evaluate the uniformity and cracks of the geological layer. For example, when the vibration frequency of the formation reaches 0.5 Hz, the seismic wave detection sensor can record the fluctuation information. The geological radar detects the underground geological structure, such as rock layers, cracks, and groundwater, by transmitting and receiving electromagnetic waves. Accelerometers are used to monitor external vibrations and shocks, especially in underground environments. When the impact force reaches 0.1 grams, the corresponding changes are recorded. Geological perception sensors are placed in different areas underground according to actual needs to achieve dynamic reflection underground. Geological perception sensors can collect data multiple times per second, for example, 10 times per second and transmit it to the data control system in real time. According to the dynamic changes of the physical environment, data is collected once per second or each time the geophysical probe is used to analyze and predict complex situations underground.

[0022] Step 2: Read the equipment working data of the downhole geophysical exploration equipment, perform environmental impact normalization processing based on the equipment working data and the environmental data set, and establish an environmental factor weight set.

[0023] Specifically, the equipment working data of the downhole geophysical exploration equipment is read, such as the operating time, current, voltage, etc. of the downhole geophysical exploration equipment. The environmental impact is normalized based on the equipment working data and the environmental data set collected by the sensor group, and different environmental variables, such as temperature, humidity, and vibration intensity, are adjusted in scale, and then compared and processed in the same analysis model. For example, when the ambient temperature is 25 degrees Celsius and the humidity is 60%, after normalization, the value range of all data is adjusted to between 0 and 1. Then, based on the normalized data, a weight set of environmental factors is established, that is, a corresponding weight is assigned to each environmental variable, and the size of the weight depends on the degree of impact on the equipment operation. For example, temperature may be assigned a weight of 0.3, while humidity may be assigned a weight of 0.2, and the specific value depends on the sensitivity of the equipment.

[0024] Step three: Use the device working data to configure the gain interval and the initial solution constraint, and randomly generate N initial solutions within the gain interval according to the initial solution constraint, where N is the number of constraints of the initial solution constraint and N is an integer greater than 3.

[0025] Specifically, the gain interval is configured using the device working data, that is, a gain range is set according to the data during the operation of the device, such as current, voltage and other parameters, to control the amplification effect of the system. The gain interval can be a numerical range, such as 0.1 to 1.0, which is used to indicate the upper and lower limits of the amplification factor. Next, the initial solution constraints are configured using the device working data, which means setting certain restrictions on the initial solution, that is, setting according to the specific requirements of the device working state, such as the current cannot exceed 5 amperes or the temperature cannot be lower than 10 degrees Celsius. According to the initial solution constraints, N initial solutions are randomly generated within the gain interval, where N represents the number of generated solutions, and N is greater than 3. For example, 5 or 10 solutions can be generated, each corresponding to different gains and device operating conditions. The generated initial solutions are randomly distributed within the constraint range and used for subsequent optimization processes or control strategy adjustments to ensure operation under reasonable conditions.

[0026] Step 4: Establish a fitness function based on the environmental factor weight set and the signal-to-noise ratio, and perform fitness evaluation on the N initial solutions through the fitness function to establish a fitness evaluation result.

[0027] Specifically, a set of environmental factor weights, such as the weight values ​​of temperature and humidity (such as 0.3 for temperature and 0.2 for humidity), is used, and the influence of the signal-to-noise ratio is combined to form a mathematical expression, namely, a fitness function, which is used to evaluate the performance of the system. The signal-to-noise ratio refers to the ratio of the signal to the noise. For example, in a wireless communication system, a signal-to-noise ratio of 30 decibels means that the signal strength is 1000 times that of the noise. Next, the fitness of the N initial solutions is evaluated by the fitness function, and the fitness value is obtained according to the performance of each initial solution in the environment, such as how a certain initial solution affects the operation of the device under the conditions of 25 degrees Celsius and 60% humidity. The higher the fitness value, the better the performance of the initial solution in the current environment. Finally, the fitness evaluation result is established through the fitness value, that is, the fitness score corresponding to each initial solution, such as the fitness of a solution is 0.85, while the fitness of another is 0.65, so as to intuitively compare the advantages and disadvantages of different solutions.

[0028] Step 5: Perform distribution evaluation on the N initial solutions, configure distance constraints based on the distribution evaluation results, perform search solution screening based on the distance constraints and fitness evaluation results, and establish a search solution set, where the number of solutions in the search solution set is 3.

[0029] Specifically, the distribution evaluation of N initial solutions is performed to evaluate the distribution of N solutions in a certain space. For example, on a two-dimensional plane, the initial solutions may be densely distributed or scattered. The distribution evaluation can be completed by calculating the distance between solutions. For example, the distance between two solutions may be 2 units or 5 units. Then, the distance constraint is configured based on the distribution evaluation results. A distance threshold is set according to the distribution to filter solutions that are far or close to each other. Assuming that the distance threshold is 3 units, solutions with a distance of more than 3 units may be excluded. After that, the solutions are filtered according to the distance constraint and fitness evaluation results to ensure that the selected solutions are not only reasonable in spatial distribution, but also have high fitness. Finally, the filtered solutions constitute a search solution set, and the number of solutions contained in the search solution set is 3, that is, only 3 solutions will be retained as the solution set for further use among all the solutions. For example, the fitness of the 3 solutions is 0.9, 0.85 and 0.8 respectively.

[0030] Step 6: Establish an environmental comprehensive coefficient through the environmental factor weight set and the environmental data set, and configure the search strategy with the environmental comprehensive coefficient.

[0031] Specifically, an environmental comprehensive coefficient, such as temperature, humidity, vibration, etc., is established through an environmental factor weight set and an environmental data set, and then different weights are assigned according to the importance of the environmental data set. For example, the environmental factor weight set includes a temperature weight of 0.4, a humidity weight of 0.3, and a vibration weight of 0.2. The environmental data set is the actual measured environmental conditions, such as the current temperature is 25 degrees Celsius, the humidity is 60%, and the vibration intensity is 0.1. By combining the environmental factor weight set and the environmental data set, an environmental comprehensive coefficient can be calculated. The environmental comprehensive coefficient is used to represent the comprehensive impact of the entire environment. For example, the environmental comprehensive coefficient may be 0.75, indicating that the current environment has a greater impact on the operation of the device.

[0032] The search strategy is configured with the environmental comprehensive coefficient. The search strategy determines the search for the optimal solution under different environmental conditions. When the environmental comprehensive coefficient is high (for example, 0.75), the search strategy may require stricter restrictions to ensure that the device remains stable in a complex environment; when the environmental comprehensive coefficient is low (for example, 0.2), the search strategy can be more relaxed, allowing more exploration space.

[0033] Step 7: Establish a follow-up solution, which is a set of non-search solutions within the N initial solutions. The search solution is used as an update target, and the follow-up solution is updated based on the search strategy.

[0034] Specifically, a follow-up solution is established. The follow-up solution is composed of the solutions that are not selected as the search solution among the N initial solutions, but the follow-up solution still plays a certain role in the optimization process. For example, if there are 5 initial solutions, 3 of them are selected as the search solution, and the remaining 2 constitute the follow-up solution.

[0035] The update process of the following solution is targeted at the search solution, and the adjustment of the following solution is guided by observing the changes and optimization results of the search solution. For example, if the fitness of a search solution is improved, the following solution can follow the improvement by imitating or adjusting some parameters.

[0036] The performance of the following solution is optimized according to the search strategy. The search strategy is updated according to the position of the solution in the optimization space. For example, under the conditions of temperature of 30 degrees Celsius and humidity of 70%, the following solution can optimize its fitness by adjusting parameters and gradually approach the search solution.

[0037] Step 8: Perform search updates on the search solution based on the search strategy, and reselect the search solution based on the search solution update results and the follow-up solution update results, and perform search iterations.

[0038] Specifically, the search solution is optimized and adjusted according to the pre-set search strategy. The search strategy may include the moving direction, amplitude and search range of the solution in space. For example, if the fitness of the current solution is 0.85, the solution parameters may be slightly changed by adjusting the search strategy, with the goal of further improving the fitness.

[0039] The new search solution set should be reselected not only based on the updated results of the search solution, but also based on the updated results of the follow-up solution. The follow-up solution may perform better due to optimization, thus having the opportunity to replace some of the original search solutions. For example, if the fitness of the follow-up solution is improved from 0.75 to 0.85, it may be selected as the new search solution to replace the original poorly performing solution.

[0040] After completing the selection of the new search solution, the next iteration is entered and the optimization process is repeated. Each iteration is a cyclic process of adjusting, updating and selecting the solution, with the goal of gradually finding the optimal solution. For example, in the next iteration, the fitness of the search solution and the follow-up solution will continue to be optimized until the fitness value tends to be optimal.

[0041] Step nine: establishing an adaptive signal gain of the downhole geophysical exploration equipment according to the search iteration result, and performing adaptive control according to the adaptive signal gain.

[0042] Specifically, after each search iteration, the signal gain of the device is dynamically adjusted based on the optimization results. Signal gain refers to the multiple by which the device amplifies or attenuates the received signal. For example, in a certain environment, the gain may be set to 2, indicating that the signal strength is amplified twice. The gain is adaptive and automatically adjusts according to changes in environmental conditions to ensure that the device can obtain the best signal strength in different situations.

[0043] During the operation of the equipment, adaptive gain is used for real-time control. Adaptive control means that the equipment can automatically adjust its operating parameters according to the real-time signal conditions. For example, if the signal becomes too strong or too weak, the gain is automatically adjusted to maintain the stability of signal quality. For example, if the electromagnetic interference in the well increases, the signal is maintained at an appropriate level by reducing the gain to avoid the influence of interference.

[0044] The method for adaptive control of downhole geophysical exploration equipment in combination with environmental conditions is applied to an adaptive control system of downhole geophysical exploration equipment in combination with environmental conditions, which can achieve the technical goal of adaptive adjustment of signal gain, so that the equipment can respond to environmental changes in real time and automatically adjust the signal strength, thereby improving the technical effect of detection accuracy and equipment stability.

[0045] Furthermore, the present application also includes:

[0046] The N initial solutions are sorted in order according to the fitness evaluation result to establish a candidate solution set, which is a solution set that is sequentially screened under a preset quantity ratio; the candidate solution set is screened for search solution set combination using the distance constraint, and the overall fitness of all search solution sets is calculated; the search solution set is eliminated according to the overall fitness, and the solutions in the retained search solution set are constructed into a first search solution, a second search solution, and a third search solution according to the fitness values, and a search solution set is established with the first search solution, the second search solution, and the third search solution.

[0047] Specifically, the N initial solutions are sorted in order according to the fitness evaluation results, and a candidate solution set is established. The N initial solutions are sorted from high to low according to the fitness values. For example, assuming there are 5 initial solutions, and the fitness values ​​are 0.85, 0.65, 0.9, 0.75, and 0.8, respectively, then the sorted order is 0.9, 0.85, 0.8, 0.75, and 0.65. After the sorting is completed, a part of the solutions is selected and screened according to the preset quantity ratio, such as selecting the top 40% of the solutions, to form a candidate solution set.

[0048] Next, the distance constraint is used to search for the candidate solution set, and the solution set that meets the conditions is further screened out. The overall fitness of all search solution sets is calculated, that is, the fitness of each solution in the search solution set is weighted averaged to obtain an overall fitness value.

[0049] Then, the search solution set is eliminated according to the overall fitness, that is, the solution set with poor performance is eliminated according to the overall fitness value of each search solution set, for example, the solution set with fitness lower than 0.7 will be eliminated. Among the remaining solution sets, the solutions in the retained search solution set are constructed according to the highest fitness value to form the first search solution, the second search solution, and the third search solution, and the search solution set is established with the first search solution, the second search solution, and the third search solution.

[0050] The N initial solutions are sorted by the fitness evaluation results, and a certain proportion of candidate solution sets are screened out. Then, the search solution set that meets the conditions is screened out from the candidate solution set using the distance constraint, and its overall fitness is calculated. Finally, some search solution sets are eliminated based on the overall fitness, and the remaining search solutions are constructed into the first, second, and third search solutions according to the fitness value, further optimizing the quality and performance of the entire solution set.

[0051] Furthermore, the present application also includes:

[0052] A penalty factor for the number of iterations to the distance constraint is established; when executing the search iteration, the penalty factor is called according to the number of search iterations, the distance constraint is updated, and the search solution under the corresponding iteration is updated with the updated distance constraint.

[0053] Specifically, a penalty factor for the distance constraint is established based on the number of iterations, and the penalty factor changes as the number of iterations increases. The penalty factor can be a dynamic value, such as 1 in the initial iteration, and may become 0.9 or 0.8 as the number of iterations increases, in order to make the solution set screening process more stringent or loose.

[0054] In each iteration, the penalty factor that has been set is called according to the current number of iterations. For example, in the 5th iteration, a penalty factor of 0.9 may be used, and in the 10th iteration, a penalty factor of 0.8 may be used, which in turn affects the screening conditions of the solution set, making the search process gradually optimized as the number of iterations increases.

[0055] In each iteration, the penalty factor is used to adjust the distance constraint. For example, if the initial distance constraint is 3 units, during the iteration process, due to the effect of the penalty factor, the distance constraint may become 2.7 units or 2.4 units, thereby making the screening conditions more stringent and prompting the solution set to gradually converge to the optimal solution.

[0056] After each iteration, the solution set is re-screened based on the updated distance constraint to ensure that the search solution set is updated to meet the latest constraint. For example, in the 5th iteration, the distance constraint changes from 3 units to 2.7 units, so the search solution set is adjusted according to this new constraint to ensure that the distance between each solution does not exceed the updated value.

[0057] The change of the distance constraint is controlled by setting a penalty factor related to the number of iterations, and then the penalty factor is called in each iteration to dynamically update the distance constraint. Finally, the search solution set is screened and optimized based on the updated distance constraint to gradually approach the optimal solution.

[0058] Furthermore, the present application also includes:

[0059] The fitness function is as follows:

[0060] Among them, S adjusted Characterizes the signal strength after gain, N adjusted is the noise level, T is the temperature data, ω T is the temperature factor weight, H is the humidity data, ω H is the humidity factor weight, E is the electromagnetic interference intensity data, ω E is the electromagnetic interference intensity factor weight, G geo is the geological complexity data, ω G is the weight of geological complexity factor.

[0061] Specifically, the fitness function is used to evaluate the quality of the solution as follows, and is usually calculated by combining multiple environmental factors.

[0062] Among them, the signal strength after gain S adjusted It indicates the signal strength after gain processing. Gain can be a multiple of the signal applied by an amplifier. For example, the signal strength is originally 2, and becomes 4 after gain amplification.

[0063] is the noise level, the noise level N adjusted Used to describe the size of noise. The noise level can be obtained through measurement. For example, if the noise level is 0.1, it means that there is a certain amount of interference in the signal.

[0064] The temperature data T refers to the ambient temperature. For example, the temperature downhole may be 30 degrees Celsius, which in turn affects the performance of the equipment. Therefore, it needs to be considered in the fitness function.

[0065] Temperature factor weight ω T It indicates the influence of temperature on fitness. The weight of temperature factor is a value between 0 and 1. For example, 0.3 means that the influence of temperature on the final fitness accounts for 30%.

[0066] Humidity data H indicates humidity. For example, if the humidity is 60%, it means that the water vapor content in the environment is high, which may affect the propagation of signals and the operation of equipment.

[0067] Humidity factor weight ω HIt indicates the influence ratio of humidity on fitness. Assuming the weight of humidity factor is 0.2, it means humidity factor accounts for 20% of the weight in the whole fitness calculation.

[0068] The electromagnetic interference intensity data E means the intensity of the electromagnetic interference. The intensity of the electromagnetic interference can be measured by a sensor. For example, the interference intensity is 0.05.

[0069] Electromagnetic interference intensity factor weight ω E It is the weight value of electromagnetic interference in the fitness function. Assuming that the weight of the electromagnetic interference intensity factor is 0.25, it means that the impact on fitness is greater, accounting for 25%.

[0070] Geological complexity data G geo Indicates the complexity of geological structure. For example, the complexity of rock layers may cause signal attenuation and interference. The geological complexity value can be obtained through seismic wave sensors. Assume that the value is 0.4.

[0071] Geological complexity factor weight ω G It is the influence of geological complexity on fitness, and the weight is assumed to be 0.15, which means that the influence of geological factors is small, accounting for 15%.

[0072] The fitness function is combined with a weighted formula of multiple environmental data to evaluate the performance of different solutions. By considering factors such as signal strength after gain, noise level, temperature, humidity, electromagnetic interference, and geological complexity, and assigning different weights to each factor, such as temperature weight of 0.3, humidity weight of 0.2, and electromagnetic interference weight of 0.25, a fitness value is finally calculated. The higher the fitness value, the better the performance of the current solution in this environment.

[0073] Furthermore, the present application also includes:

[0074] The comprehensive environmental coefficients are as follows:

[0075] Among them, δ is the normalization coefficient, which makes the environmental comprehensive coefficient C env The value of is in the range of 0 to 1; the search parameter update formula in the search strategy is as follows: A = 2·(1-C env )·r-(1-C env ); a=(1-C env )·a max ; Where A represents the search parameter, which is used to control the movement amplitude and direction of the solution in the optimization space, r is a random number, which is used to control the introduction of uncertainty in the search process, a is the search control parameter, which is used to control the convergence speed of the search process, and a max is the maximum search control parameter.

[0076] Specifically, a specific mathematical formula is used to define the comprehensive environmental coefficient, which is used to comprehensively evaluate the impact of environmental factors. It is usually calculated by weighting multiple environmental data. The goal is to combine different environmental factors into a unified value.

[0077] Among them, the normalization coefficient δ represents the normalization coefficient when calculating the environmental comprehensive coefficient, so that the final environmental comprehensive coefficient is in the range of 0 to 1. Through normalization, variables of different ranges are converted to the same standard range. For example, if the original value of an environmental factor is 10, it may become 0.8 after normalization, so that different factors can be compared fairly.

[0078] When performing search optimization, the search parameters are updated through the search parameter update formula to adjust the position of the solution in the optimization space.

[0079] The search parameter A is used to control the movement amplitude and direction of the solution in the optimization space. The representative variable of the search parameter is responsible for determining the movement amplitude and direction of the solution in each iteration of the optimization process. For example, the step size of a solution in each iteration is 0.05 or 0.1, and the direction is determined by the value of the search parameter.

[0080] The random number r is used to control the introduction of uncertainty in the search process, which means that a random number is introduced during the search process to ensure that the solution position of each iteration will not be completely fixed in a certain direction or area. The random number increases the breadth of the search, for example, a random number may be 0.3 or 0.7, making the search process more flexible and not limited to a local optimal solution.

[0081] The search control parameter a is used to control the convergence speed of the search process. It is used to adjust the speed of convergence during the search process. For example, when the value is large, the search process will converge to a solution faster, while when the value is small, the search process will explore the solution space more slowly and more comprehensively. Assuming it is 0.5, it means that the search process is relatively stable and not in a hurry to converge.

[0082] Maximum search control parameter a max Indicates the maximum convergence speed control value allowed during the search process. For example, the maximum search control parameter can be set to 1.0. The maximum search control parameter will limit the search process from ending too quickly, ensuring that there is enough time and space to find the global optimal solution.

[0083] The environmental comprehensive coefficient is controlled within the range of 0 to 1 through the normalization coefficient. Then, in the search strategy, the movement and direction of the solution are optimized by updating the search parameters. The random number introduces uncertainty to avoid falling into the local optimal solution. At the same time, the search control parameter determines the speed of convergence, and the maximum search control parameter sets the upper limit of convergence. For example, the step size of the search parameter may be 0.05, the random number is 0.7, the search control parameter is 0.5, and the maximum search control parameter is 1.0, ensuring that the search process is robust and has sufficient breadth.

[0084] Furthermore, the present application also includes:

[0085] A self-diagnosis database is configured, and control verification of downhole geophysical exploration equipment is performed through the self-diagnosis database to generate verification results; a feedback strategy is activated based on the degree of deviation of the verification results, and the feedback strategy includes a self-optimization strategy and an abnormality reporting strategy, and equipment management of downhole geophysical exploration equipment is performed through the feedback strategy.

[0086] Specifically, a self-diagnosis database is configured to establish a database specifically for downhole geophysical exploration equipment. The self-diagnosis database can store and analyze the self-diagnosis data of the equipment. Through the self-diagnosis database, the operating status of the equipment can be monitored in real time, and a verification basis can be provided for the operation of the equipment to ensure the normal operation of the equipment under different working conditions. For example, the self-diagnosis database may store the operating data of the equipment in the past to help identify normal and abnormal conditions.

[0087] The control verification of downhole geophysical exploration equipment is carried out through the self-diagnosis database to generate verification results, that is, the current operating status of the equipment is verified using the information in the self-diagnosis database. The control verification process is to compare the current status of the equipment with the normal working data stored in the database to generate a verification result. For example, if the current working current of the equipment is 5 amperes, after comparing it with the standard value of 5.2 amperes in the database, the verification result may be "normal" or "slightly deviated".

[0088] Depending on the deviation between the verification result and the expected normal state, it is decided whether to start a certain feedback mechanism. The deviation degree can be a numerical value, such as a deviation of more than 5%, which will trigger a feedback strategy. If the temperature data of the device deviates from the normal range, then the relevant feedback action may be activated.

[0089] Feedback strategies include self-optimization strategies and abnormal reporting strategies. The self-optimization strategy means that the equipment automatically adjusts the operating parameters according to the current status to correct the deviation. For example, if the temperature is found to be too high, the power is automatically reduced. The abnormal reporting strategy is to immediately send out an alarm signal when the equipment cannot adjust itself, prompting the operator to intervene manually.

[0090] The feedback strategy is used to manage downhole geophysical exploration equipment. Based on the self-diagnosis results of the equipment, the equipment's operating status is automatically adjusted or problems are reported to the outside. For example, when the equipment is working abnormally and cannot correct itself, the problem is reported immediately so that technicians can intervene and handle it.

[0091] A self-diagnosis database is configured to monitor the operating status of downhole geophysical exploration equipment and generate verification results through the database. Then, according to the degree of deviation of the verification results, the feedback strategy is activated, which includes equipment self-adjustment and abnormal alarm functions, and finally realizes automatic management of the equipment.

[0092] Furthermore, the present application also includes:

[0093] An adaptation solution database is generated based on the verification result; and N initial solutions are generated and optimized under subsequent environmental conditions through the adaptation solution database.

[0094] Specifically, an adaptation solution database is generated based on the verification results. The adaptation solution database stores the device adaptation solutions under various environmental conditions. The adaptation solution refers to how to adjust the operating parameters of the device to adapt to different environmental conditions. For example, under an ambient temperature of 30 degrees Celsius, the device adaptation solution may be to reduce the current or increase the heat dissipation speed to ensure that the device can operate normally.

[0095] When the device is running in different subsequent environmental conditions, the corresponding solutions can be extracted from the adaptation solution database to optimize the generation process of N initial solutions. N initial solutions refer to multiple candidate solutions for the initial adjustment of the device in a new environment. The optimization process is to generate an initial solution that is more suitable for the current environment by referring to the historical adaptation solutions stored in the database. For example, in an environment with high humidity, the adaptation solution database can provide a solution to reduce the operating power of the device, thereby reducing the impact of humidity and finally generating an optimized initial solution.

[0096] By referring to the adaptation solution database, N initial solutions are optimized to ensure that the equipment can operate in the best state under the new environmental conditions. For example, in an environment with a temperature of 30 degrees Celsius and a humidity of 70%, the corresponding adaptation solution is found through the adaptation solution database, and the operating parameters of the equipment are adjusted to generate a more optimized initial solution.

[0097] In summary, the adaptive control method of downhole geophysical exploration equipment combined with environmental conditions provided by the present application has the following technical effects:

[0098] By establishing a sensor group, the downhole environment is perceived through the sensor group to establish an environmental data set, wherein the sensor group includes a temperature and humidity sensor, an electromagnetic interference sensor, and a geological perception sensor; reading the equipment working data of the downhole geophysical exploration equipment, performing environmental impact normalization processing according to the equipment working data and the environmental data set, and establishing an environmental factor weight set; configuring a gain interval and an initial solution constraint using the equipment working data, and randomly generating N initial solutions within the gain interval according to the initial solution constraint, wherein N is the number of constraints of the initial solution constraint, and N is an integer greater than 3; establishing a fitness function based on the environmental factor weight set and the signal-to-noise ratio, and performing fitness evaluation on the N initial solutions through the fitness function to establish a fitness evaluation result; performing distribution evaluation on the N initial solutions, configuring a distance constraint based on the distribution evaluation result, and generating a fitness evaluation result according to the distance constraint The search solution is screened according to the fitness evaluation results, and a search solution set is established, the number of solutions in the search solution set is 3; an environmental comprehensive coefficient is established through an environmental factor weight set and an environmental data set, and a search strategy is configured with the environmental comprehensive coefficient; a following solution is established, the following solution is a set of non-search solutions within the N initial solutions, and the search solution is used as an update target, and the following solution is updated based on the search strategy; a search update is performed on the search solution based on the search strategy, and the search solution is re-selected with the search solution update result and the following solution update result, and a search iteration is performed; an adaptive signal gain of the downhole geophysical exploration equipment is established according to the search iteration result, and adaptive control is performed according to the adaptive signal gain, so as to achieve the technical goal of adaptive adjustment of the signal gain, so as to achieve the technical effect that the equipment can respond to environmental changes in real time and automatically adjust the signal strength, thereby improving the detection accuracy and the stability of the equipment.

[0099] Embodiment 2, based on the adaptive control method of downhole geophysical exploration equipment combined with environmental conditions in the above embodiments, the present application also provides an adaptive control system of downhole geophysical exploration equipment combined with environmental conditions, please refer to the attached Figure 2 ,include:

[0100] An environmental data set establishment module 11 is used to establish a sensor group, perform downhole environmental perception through the sensor group, and establish an environmental data set, wherein the sensor group includes a temperature and humidity sensor, an electromagnetic interference sensor, and a geological perception sensor; an environmental factor weight set establishment module 12 is used to read the equipment working data of the downhole geophysical exploration equipment, perform environmental impact normalization processing based on the equipment working data and the environmental data set, and establish an environmental factor weight set; an initial solution generation module 13 is used to configure a gain interval and an initial solution constraint using the equipment working data, and randomly generate N initial solutions within the gain interval based on the initial solution constraint, where N is the number of constraints of the initial solution constraint, and N is an integer greater than 3; a fitness evaluation result establishment module 14 is used to establish a fitness function based on the environmental factor weight set and the signal-to-noise ratio, and perform fitness evaluation on the N initial solutions through the fitness function to establish a fitness evaluation result; a solution set search establishment module Block 15 is used to perform distribution evaluation on N initial solutions, configure distance constraints with the distribution evaluation results, screen search solutions according to the distance constraints and fitness evaluation results, and establish a search solution set, wherein the number of solutions in the search solution set is 3; a search strategy configuration module 16 is used to establish an environmental comprehensive coefficient through an environmental factor weight set and an environmental data set, and configure the search strategy with the environmental comprehensive coefficient; a following solution update module 17 is used to establish a following solution, wherein the following solution is a set of non-search solutions within the N initial solutions, and the following solution is updated based on the search strategy with the search solution as the update target; a search iteration module 18 is used to perform a search update on the search solution based on the search strategy, and re-select the search solution with the search solution update results and the following solution update results, and perform search iterations; an adaptive control module 19 is used to establish an adaptive signal gain of the downhole geophysical exploration equipment according to the search iteration results, and perform adaptive control according to the adaptive signal gain.

[0101] Furthermore, the adaptive control system of downhole geophysical exploration equipment combined with environmental conditions is also used for:

[0102] The N initial solutions are sorted in order according to the fitness evaluation result to establish a candidate solution set, which is a solution set that is sequentially screened under a preset quantity ratio; the candidate solution set is screened for search solution set combination using the distance constraint, and the overall fitness of all search solution sets is calculated; the search solution set is eliminated according to the overall fitness, and the solutions in the retained search solution set are constructed into a first search solution, a second search solution, and a third search solution according to the fitness values, and a search solution set is established with the first search solution, the second search solution, and the third search solution.

[0103] Furthermore, the adaptive control system of downhole geophysical exploration equipment combined with environmental conditions is also used for:

[0104] A penalty factor for the number of iterations to the distance constraint is established; when executing the search iteration, the penalty factor is called according to the number of search iterations, the distance constraint is updated, and the search solution under the corresponding iteration is updated with the updated distance constraint.

[0105] Furthermore, the adaptive control system of downhole geophysical exploration equipment combined with environmental conditions is also used for:

[0106] The fitness function is as follows:

[0107] Among them, S adjusted Characterizes the signal strength after gain, N adjusted is the noise level, T is the temperature data, ω T is the temperature factor weight, H is the humidity data, ω H is the humidity factor weight, E is the electromagnetic interference intensity data, ω E is the electromagnetic interference intensity factor weight, G geo is the geological complexity data, ω G is the weight of geological complexity factor.

[0108] Furthermore, the adaptive control system of downhole geophysical exploration equipment combined with environmental conditions is also used for:

[0109] The comprehensive environmental coefficients are as follows:

[0110] Among them, δ is the normalization coefficient, which makes the environmental comprehensive coefficient C env The value of is in the range of 0 to 1; the search parameter update formula in the search strategy is as follows: A = 2·(1-C env )·r-(1-C env ); a=(1-C env )·a max ; Where A represents the search parameter, which is used to control the movement amplitude and direction of the solution in the optimization space, r is a random number, which is used to control the introduction of uncertainty in the search process, a is the search control parameter, which is used to control the convergence speed of the search process, and a max is the maximum search control parameter.

[0111] Furthermore, the adaptive control system of downhole geophysical exploration equipment combined with environmental conditions is also used for:

[0112] A self-diagnosis database is configured, and control verification of downhole geophysical exploration equipment is performed through the self-diagnosis database to generate verification results; a feedback strategy is activated based on the degree of deviation of the verification results, and the feedback strategy includes a self-optimization strategy and an abnormality reporting strategy, and equipment management of downhole geophysical exploration equipment is performed through the feedback strategy.

[0113] Furthermore, the adaptive control system of downhole geophysical exploration equipment combined with environmental conditions is also used for:

[0114] An adaptation solution database is generated based on the verification result; and N initial solutions are generated and optimized under subsequent environmental conditions through the adaptation solution database.

[0115] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The adaptive control method of downhole geophysical exploration equipment combined with environmental conditions and the specific examples in the aforementioned embodiment 1 are also applicable to the adaptive control system of downhole geophysical exploration equipment combined with environmental conditions in this embodiment. Through the aforementioned detailed description of the adaptive control method of downhole geophysical exploration equipment combined with environmental conditions, those skilled in the art can clearly understand the adaptive control system of downhole geophysical exploration equipment combined with environmental conditions in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0116] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0117] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. An adaptive control method for downhole geophysical exploration equipment in combination with environmental conditions, characterized in that: include: Establishing a sensor group, sensing the underground environment through the sensor group, and establishing an environmental data set, wherein the sensor group includes a temperature and humidity sensor, an electromagnetic interference sensor, and a geological perception sensor; Reading equipment working data of downhole geophysical prospecting equipment, performing environmental impact normalization processing according to the equipment working data and environmental data set, and establishing an environmental factor weight set; Using the device working data to configure the gain interval and the initial solution constraint, randomly generating N initial solutions within the gain interval according to the initial solution constraint, where N is the number of constraints of the initial solution constraint and N is an integer greater than 3; Establishing a fitness function based on the environmental factor weight set and the signal-to-noise ratio, and performing fitness evaluation on the N initial solutions through the fitness function to establish a fitness evaluation result; Perform distribution evaluation on N initial solutions, configure distance constraints based on the distribution evaluation results, perform search solution screening based on the distance constraints and fitness evaluation results, and establish a search solution set, wherein the number of solutions in the search solution set is 3; Establish an environmental comprehensive coefficient through the environmental factor weight set and the environmental data set, and configure the search strategy based on the environmental comprehensive coefficient; Establishing a follow-up solution, which is a set of non-search solutions within the N initial solutions, taking the search solution as an update target, and updating the follow-up solution based on the search strategy; Perform search updates on the search solution based on the search strategy, and reselect the search solution based on the search solution update results and the follow-up solution update results, and perform search iterations; An adaptive signal gain of the downhole geophysical exploration equipment is established according to the search iteration result, and adaptive control is performed according to the adaptive signal gain.

2. The method for adaptively controlling downhole geophysical exploration equipment in combination with environmental conditions according to claim 1, characterized in that: The searching solution screening according to the distance constraint and the fitness evaluation result to establish the searching solution set also includes: Sorting the N initial solutions in order according to the fitness evaluation results to establish a candidate solution set, wherein the candidate solution set is a set of solutions that are sequentially screened under a preset quantity ratio; Using the distance constraint to perform search solution set combination screening on the candidate solution set, and calculating the overall fitness of all search solution sets; The search solution set is eliminated according to the overall fitness, and the solutions in the retained search solution set are constructed into a first search solution, a second search solution, and a third search solution according to the fitness values, and a search solution set is established with the first search solution, the second search solution, and the third search solution.

3. The method for adaptively controlling downhole geophysical exploration equipment in combination with environmental conditions according to claim 2, characterized in that: Also includes: Establish a penalty factor for the number of iterations on the distance constraint; When performing search iterations, the penalty factor is called according to the number of search iterations, the distance constraint is updated, and the search solution under the corresponding iteration is updated with the updated distance constraint.

4. The method for adaptively controlling downhole geophysical exploration equipment in combination with environmental conditions according to claim 1, characterized in that: The fitness function is as follows: Among them, S adjusted Characterizes the signal strength after gain, N adjusted is the noise level, T is the temperature data, ω T is the temperature factor weight, H is the humidity data, ω H is the humidity factor weight, E is the electromagnetic interference intensity data, ω E is the electromagnetic interference intensity factor weight, G geo is the geological complexity data, ω G is the weight of geological complexity factor.

5. The method for adaptively controlling downhole geophysical exploration equipment in combination with environmental conditions according to claim 1, characterized in that: Also includes: The comprehensive environmental coefficients are as follows: Among them, δ is the normalization coefficient, which makes the environmental comprehensive coefficient C env The value of is in the range of 0 to 1; The search parameter update formula in the search strategy is as follows: A=2·(1-C env )·r-(1-C env ); a=(1-C env )·a max ; Among them, A represents the search parameter, which is used to control the movement amplitude and direction of the solution in the optimization space, r is a random number, which is used to control the introduction of uncertainty in the search process, a is the search control parameter, which is used to control the convergence speed of the search process, and a max is the maximum search control parameter.

6. The method for adaptively controlling downhole geophysical exploration equipment in combination with environmental conditions according to claim 1, characterized in that: Also includes: Configuring a self-diagnosis database, performing control verification of downhole geophysical exploration equipment through the self-diagnosis database, and generating verification results; The feedback strategy is activated according to the deviation degree of the verification result, and the feedback strategy includes a self-optimization strategy and an abnormality reporting strategy. The equipment management of the downhole geophysical exploration equipment is performed through the feedback strategy.

7. The method for adaptively controlling downhole geophysical exploration equipment in combination with environmental conditions according to claim 6, characterized in that: Also includes: generating an adaptation solution database based on the verification result; The adaptation solution database is used to generate and optimize N initial solutions under subsequent environmental conditions.

8. An adaptive control system for downhole geophysical exploration equipment combined with environmental conditions, characterized in that: The steps for implementing the method for adaptively controlling downhole geophysical exploration equipment in combination with environmental conditions as described in any one of claims 1 to 7 include: An environmental data set establishment module is used to establish a sensor group, through which the underground environment is sensed and an environmental data set is established. The sensor group includes a temperature and humidity sensor, an electromagnetic interference sensor, and a geological perception sensor; An environmental factor weight set establishment module is used to read the equipment working data of the downhole geophysical prospecting equipment, perform environmental impact normalization processing according to the equipment working data and the environmental data set, and establish an environmental factor weight set; An initial solution generating module, configured to configure a gain interval and an initial solution constraint using the device working data, and to randomly generate N initial solutions within the gain interval according to the initial solution constraint, where N is the number of constraints of the initial solution constraint, and N is an integer greater than 3; A fitness evaluation result establishment module is used to establish a fitness function based on the environmental factor weight set and the signal-to-noise ratio, and to perform fitness evaluation on the N initial solutions through the fitness function to establish a fitness evaluation result; A search solution set establishment module is used to perform distribution evaluation on N initial solutions, configure distance constraints based on the distribution evaluation results, perform search solution screening based on the distance constraints and fitness evaluation results, and establish a search solution set, wherein the number of solutions in the search solution set is 3; A search strategy configuration module is used to establish an environmental comprehensive coefficient through an environmental factor weight set and an environmental data set, and configure a search strategy with the environmental comprehensive coefficient; A follow-up solution updating module is used to establish a follow-up solution, which is a set of non-search solutions within the N initial solutions, and to update the follow-up solution based on the search strategy with the search solution as an update target; A search iteration module is used to perform search updates on the search solution based on the search strategy, and to reselect the search solution based on the search solution update results and the follow-up solution update results, and to perform search iterations; The adaptive control module is used to establish an adaptive signal gain of the downhole geophysical exploration equipment according to the search iteration result, and to perform adaptive control according to the adaptive signal gain.

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