A uranium production control method, device, equipment and readable storage medium

Abstract

The present invention provides a uranium production control method, device, equipment and readable storage medium, which relates to the technical field of in-situ leaching of sandstone uranium mines, including inputting a preset pumping liquid volume and a preset acid concentration into a preset production prediction model to obtain a predicted uranium production; when the predicted uranium production is less than the target uranium production, the target pumping liquid volume and the target acid concentration of the main control well are calculated based on the target uranium production; and the target pumping liquid volume and the target acid concentration are repeatedly re-entered into the target uranium production prediction model until the predicted uranium production is greater than the target uranium production. The present invention calculates the pumping liquid volume and the acid concentration corresponding to the set target uranium production through an inversion algorithm, and inputs the calculated pumping liquid volume and the acid concentration into the production prediction model for prediction. When the predicted uranium production meets the target uranium production, the pumping liquid volume and the acid concentration of the main control well are adjusted accordingly based on the calculation result, so that the pumping liquid volume and the acid concentration can be accurately adjusted.

Description

Technical Field

[0001] The present invention relates to the technical field of in-situ leaching of sandstone uranium mines, and in particular to a uranium production control method, device, equipment and a readable storage medium. Background Art

[0002] In-situ uranium leaching is done by injecting acidic, alkaline, CO2+O2 and other leaching agents into a group of wells. As the leaching agents react chemically with uranium-containing minerals inside the ore layer, the leaching solution is recovered in the mining well to achieve the purpose of uranium mining. In the actual in-situ uranium leaching production process, the dynamic regulation of the amount of pumped liquid is mainly based on the subjective judgment of the staff. For example, if the uranium concentration in the mining well decreases, the concentration of injected acid is increased to enhance uranium leaching. However, the effect is significantly random, resulting in a lack of precision in the adjustment of the amount of pumped liquid in both space and time. Summary of the invention

[0003] The purpose of the present invention is to provide a uranium production control method, device, equipment and readable storage medium to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0004] In a first aspect, the present application provides a uranium production control method, comprising:

[0005] Obtaining a preset injection volume, preset acid concentration and target uranium production;

[0006] Inputting the preset injection volume and the preset acid concentration into a preset production prediction model to obtain a predicted uranium production, wherein the production prediction model is used to predict the corresponding uranium production based on the injection volume and the acid concentration;

[0007] comparing the predicted uranium production with the target uranium production;

[0008] When the predicted uranium production is less than the target uranium production, a target injection volume and a target acid concentration of the main control well are calculated based on the target uranium production;

[0009] The target injection volume and target acid concentration are repeatedly re-input into the target uranium production prediction model and compared based on the predicted uranium production until the predicted uranium production is greater than the target uranium production.

[0010] In a second aspect, the present application also provides a uranium production control device, comprising:

[0011] An acquisition unit, used for acquiring a preset injection liquid volume, a preset acid concentration and a target uranium yield;

[0012] An input unit, used for inputting the preset injection volume and the preset acid concentration into a preset production prediction model to obtain a predicted uranium production, wherein the production prediction model is used for predicting the corresponding uranium production based on the injection volume and the acid concentration;

[0013] A first comparison unit, configured to compare the predicted uranium production with the target uranium production;

[0014] A first calculation unit is used for calculating a target injection fluid volume and a target acid concentration of a main control well based on the target uranium production when the predicted uranium production is less than the target uranium production;

[0015] The repeating unit repeatedly inputs the target injection volume and the target acid concentration into the target uranium production prediction model, and compares based on the predicted uranium production until the predicted uranium production is greater than the target uranium production.

[0016] In a third aspect, the present application also provides a uranium production control device, including:

[0017] Memory for storing computer programs;

[0018] A processor is used to implement the steps of the uranium production control method when executing the computer program.

[0019] In a fourth aspect, the present application further provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned uranium production control method are implemented.

[0020] The beneficial effects of the present invention are:

[0021] The present invention calculates the corresponding pumping fluid volume and acid concentration under the set target uranium production through an inversion algorithm, and inputs the calculated pumping fluid volume and acid concentration into the production prediction model for prediction. When the predicted uranium production satisfies the target uranium production, the pumping fluid volume and acid concentration of the main control well are adjusted accordingly based on the calculation result, so that the pumping fluid volume and acid concentration can be accurately adjusted, which can effectively avoid the drainage of the pumping hole or the large amount of overflow of the injected leaching agent caused by the imbalance of the pumping fluid volume.

[0022] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a schematic diagram of the process of the uranium production control method described in an embodiment of the present invention;

[0025] Figure 2 It is a schematic diagram of the structure of the uranium production control device described in an embodiment of the present invention;

[0026] Figure 3 It is a schematic diagram of the structure of the uranium production control device described in an embodiment of the present invention.

[0027] Markings in the figure: 100, acquisition unit; 200, input unit; 300, first comparison unit; 400, first calculation unit; 500, repeating unit; 410, construction unit; 420, first establishment unit; 430, second calculation unit; 431, second establishment unit; 432, third establishment unit; 433, replacement unit; 434, solution unit; 600, output unit; 700, first adjustment unit; 80, addition unit; 900, third calculation unit; 1000, second adjustment unit; 1100, fourth calculation unit; 1200, second comparison unit; 1300, first processing unit; 1400, second processing unit;

[0028] 800. Uranium production control equipment; 801. Processor; 802. Memory; 803. Multimedia component; 804. I / O interface; 805. Communication component. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0030] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0031] Embodiment 1:

[0032] This embodiment provides a uranium production control method.

[0033] See also Figure 1 , the figure shows that the method includes step S100, step S200, step S300, step S400 and step S500.

[0034] Step S100. Obtaining a preset injection liquid volume, a preset acid concentration and a target uranium yield;

[0035] Specifically, the preset injection volume and the preset acid concentration are the injection volume and acid concentration of all current injection wells, the target uranium production is the uranium production value set for the current target sandstone uranium mine, and the injection volume includes the flow rate of the extraction hole and the flow rate of the injection hole.

[0036] Step S200. Input the preset injection volume and the preset acid concentration into a preset production prediction model to obtain a predicted uranium production, wherein the production prediction model is used to predict the corresponding uranium production based on the injection volume and the acid concentration;

[0037] Specifically, the production prediction model is a neural network model, which is trained based on the mining data of the current target sandstone uranium mine in the historical period. The mining data includes the amount of pumped fluid, acid concentration and the corresponding uranium production. The production prediction model can predict the corresponding uranium production based on the amount of pumped fluid and acid concentration.

[0038] Step S300. Compare the predicted uranium production with the target uranium production;

[0039] Step S400. When the predicted uranium production is less than the target uranium production, the target injection volume and target acid concentration of the main control well are calculated based on the target uranium production;

[0040] Specifically, when the predicted uranium production is less than the set target uranium production, it is considered that the set uranium production cannot be obtained under the current injection volume and acid concentration conditions. Therefore, the injection volume and acid concentration need to be adjusted, and the specific adjustment amount needs to be obtained through calculation.

[0041] Specifically, step S400 includes:

[0042] Step S410. Constructing a posterior probability density function of the first variable and the second variable based on the Bayesian theory, the first variable is the uranium production, and the second variable is the injection volume and the acid concentration;

[0043] Specifically, firstly, the bass parameter adjustment obeys the function expression;

[0044] p(m|d)∝p(m)*p(d|m)

[0045] Among them, d is the first variable; m is the second variable; p(m|d) is the posterior probability of the first variable; p(m) is the prior probability of the second variable; p(d|m) is the likelihood function of the first variable and the second variable; ∝ means a proportional relationship.

[0046] The relationship between the first variable and the second variable is:

[0047] d=g(m)+ε

[0048] Among them, d is the first variable; m is the second variable; g() is the prediction function; ε is Gaussian noise;

[0049] The calculation expression of the above likelihood function is:

[0050]

[0051] Where d is the first variable; m is the second variable; g() is the covariance matrix of the prediction function; p(d|m) is the likelihood function of the first variable and the second variable; is the inverse matrix of the covariance matrix of ε;

[0052] The prior probability expression of the second variable is:

[0053]

[0054] Where p(m) is the prior probability of the second variable; m is the second variable; m pr is the random sampling value of the second variable under the prior probability distribution. is the inverse of the covariance matrix between the second variable and its prior distribution sampling;

[0055] The expression of the posterior probability density function of the first variable and the second variable is:

[0056]

[0057] Where p(m|d) is the posterior probability of the first variable; d is the first variable; m is the second variable; m pr is the random sampling value of the second variable under the prior probability distribution. is the inverse matrix of the covariance matrix between the second variable and its prior distribution sampling; g() is the covariance matrix of the prediction function; is the inverse matrix of the covariance matrix of ε;

[0058] Step S420. Establishing an objective function based on the posterior probability density function, wherein when the objective function reaches a maximum value, the function value of the corresponding posterior probability density function is the maximum;

[0059] Specifically, the objective function calculation formula is:

[0060]

[0061] Among them, O(m) is the objective function; d is the first variable; m is the second variable; m pr is the random sampling value of the second variable under the prior probability distribution; is the inverse matrix of the covariance matrix between the second variable and its prior distribution sampling; g() is the covariance matrix of the prediction function; is the inverse matrix of the covariance matrix of ε;

[0062] Specifically, the first half of the above calculation formula represents the deviation between the second variable and its prior value, which is mainly used for regularization to prevent instability of the solution in the inversion iteration process; the second half is the deviation between the calculated value of the second variable in the iteration process and the target value.

[0063] Step S430: When the first variable is less than the target uranium production, the target value of the second variable corresponding to the maximum value of the posterior probability density function is calculated based on the Gauss-Newton iteration method.

[0064] Specifically, step S430 includes:

[0065] Step S431. Establish an iterative relational expression of the second variable, wherein the iterative relational expression includes a first gradient matrix relational expression and a second gradient matrix relational expression;

[0066] Specifically, the iterative relationship of the second variable established based on the Gauss-Newton iteration method is:

[0067]

[0068] Where l represents the number of iterations; d is the first variable; m is the second variable; m l+1 The value of the second variable calculated for the l+1th iteration; m l is the value of the second variable calculated for the lth iteration; G l is the gradient matrix; is the transposed matrix of the gradient matrix; is the inverse matrix of the covariance matrix of ε; is the inverse matrix of the covariance matrix between the second variable and its prior distribution sampling; l is the iteration coefficient of the lth iteration; m pr is the random sampling value of the second variable under the prior probability distribution; g() is the covariance matrix of the prediction function;

[0069] Step S432. Establishing a covariance matrix based on a preset prediction function and a cross-covariance matrix based on the second parameter and the prediction function;

[0070] Step S433. Based on the covariance matrix and the cross-covariance matrix, respectively replace the first gradient matrix relational expression and the second gradient matrix relational expression in the iterative relational expression of the second variable to obtain a target iterative relational expression;

[0071] Specifically, the iterative relationship after substitution is:

[0072]

[0073] Among them, j represents the number of parameter iteration calculation groups; l represents the number of iterations; d j Calculate the first variable in the group for the jth iteration; The value of the second variable calculated at the l+1th iteration in the jth iteration calculation group; is the value of the second variable calculated at the lth iteration in the jth iteration calculation group; l is the iteration coefficient of the lth iteration; m pr,j For the jth iteration, calculate the randomly sampled value of the second variable in the group under the prior probability distribution; is the covariance matrix between the second variable and its prior distribution sampling in the l-th iteration calculation; is the cross-covariance matrix between the second parameter and the prediction function in the lth iteration calculation; C D is the covariance matrix of the prediction parameters; is the covariance matrix of the prediction parameters in the lth iteration calculation; is the transposed matrix of the cross-covariance matrix between the second parameter and the prediction function in the lth iteration calculation; is the inverse of the covariance matrix between the second variable and its prior distribution sampling;

[0074] Step S434. Iteratively solve the target iterative relationship based on the Gauss-Newton iteration method, and stop the iteration when the iterative calculation result meets the iteration stop condition, wherein the iteration stop condition is when the difference between the second variable value calculated by the iterative relationship and the second variable value calculated by the previous iteration meets the set threshold.

[0075] Specifically, in the process of iterative solution, when the difference between two consecutive calculation results is less than a set threshold, the latter calculation result is the optimal calculation result.

[0076] Step S500: repeatedly re-input the target injection volume and target acid concentration into the target uranium production prediction model, and compare based on the predicted uranium production until the predicted uranium production is greater than the target uranium production.

[0077] Specifically, the injection fluid volume and acid concentration obtained by calculating the target uranium production are again brought into the production prediction model for prediction, and the prediction result is compared with the target uranium production. The prediction calculation can only be stopped when the prediction result is greater than the target uranium production. It is believed that under the conditions of the currently calculated injection fluid volume and acid concentration, the current uranium ore field can achieve the set target uranium production.

[0078] Step S600. Output multiple master control well numbers based on a preset master control factor intelligent prediction model for uranium production, where the preset master control factor intelligent prediction model for uranium production is used to determine multiple master control wells that have the greatest impact on the uranium production from multiple water injection wells in the target sandstone uranium mine;

[0079] Specifically, the neural network model, which is the intelligent prediction model of the main controlling factors of uranium production, is trained based on the historical data of the current target sandstone uranium mine within the historical time period. The historical data are the injection flow and acid concentration of each injection well and the corresponding uranium production.

[0080] Step S700: adjusting the injection volume and target acid concentration of the master control wells corresponding to the multiple master control well numbers based on the target uranium production.

[0081] Specifically, after the main control wells are determined, the pumping flow and acidity value of each main control well will be adjusted accordingly so that the uranium production reaches the set target value.

[0082] Specifically, the set target uranium production may not be the maximum daily uranium production of the current sandstone uranium mine. Therefore, it is necessary to continuously adjust the value of the target uranium production to obtain the corresponding required injection fluid volume and acid concentration, and make corresponding adjustments to determine the maximum daily uranium production of the current sandstone uranium mine through the actual uranium production.

[0083] Step S800. gradually increasing the target uranium production based on a preset interval until a preset number of times is reached;

[0084] Specifically, the preset interval can be set to 1, and the number of increases can be set to 5.

[0085] Step S900. Calculate based on the increased target uranium production;

[0086] Step S1000. When the predicted uranium production value of the production prediction model satisfies the comparison relationship, the injection volume of multiple main control wells and the acid concentration of the injected leaching solution are adjusted accordingly based on the injection volume and acid concentration input by the production prediction model;

[0087] Step S1100. Obtain the adjusted daily uranium production of multiple master control wells within a set time period, and calculate the average daily uranium production within the preset time period;

[0088] Step S1200: Compare the daily average uranium production with the corresponding increased target uranium production.

[0089] Step S1300. When the average value of uranium production is greater than the corresponding increased target uranium production, the corresponding increased target uranium production is used as the initial single-day uranium production;

[0090] Step S1400: Compare and obtain a maximum value from multiple initial daily uranium productions, where the maximum value is the highest daily uranium production of the target sandstone uranium mine.

[0091] Specifically, a maximum value is determined from multiple initial daily uranium productions as the maximum daily uranium production of the sandstone uranium mine, and subsequent injection volume and acid concentration settings can be set based on the maximum value.

[0092] Embodiment 2:

[0093] like Figure 2 As shown, this embodiment provides a uranium production control device, the device comprising:

[0094] An acquisition unit 100 is used to acquire a preset injection liquid volume, a preset acid concentration and a target uranium yield;

[0095] An input unit 200 is used to input a preset injection volume and a preset acid concentration into a preset production prediction model to obtain a predicted uranium production. The production prediction model is used to predict the corresponding uranium production based on the injection volume and the acid concentration.

[0096] A first comparison unit 300, for comparing the predicted uranium production with the target uranium production;

[0097] The first calculation unit 400 is used to calculate the target injection volume and target acid concentration of the main control well based on the target uranium production when the predicted uranium production is less than the target uranium production;

[0098] Repeating unit 500, repeatedly re-inputting the target injection volume and the target acid concentration into the target uranium production prediction model, and comparing based on the predicted uranium production, until the predicted uranium production is greater than the target uranium production.

[0099] In some specific embodiments, the first computing unit 400 includes:

[0100] A construction unit 410 is used to construct a posterior probability density function of a first variable and a second variable based on Bayesian theory, wherein the first variable is uranium production, and the second variable is the amount of pumped fluid and acid concentration;

[0101] A first establishing unit 420 is used to establish an objective function based on the posterior probability density function, wherein when the objective function reaches a maximum value, the function value of the corresponding posterior probability density function is the maximum;

[0102] The second calculation unit 430 is used to calculate the target value of the second variable corresponding to the maximum value of the posterior probability density function based on the Gauss-Newton iteration method when the first variable is less than the target uranium production.

[0103] In some specific embodiments, the second calculation unit 430 includes:

[0104] A second establishing unit 431 is used to establish an iterative relationship of a second variable, wherein the iterative relationship includes a first gradient matrix relationship and a second gradient matrix relationship;

[0105] A third establishing unit 432, used to establish a covariance matrix based on a preset prediction function and a cross-covariance matrix based on the second parameter and the prediction function;

[0106] A replacing unit 433 is used to replace the first gradient matrix relational expression and the second gradient matrix relational expression in the iterative relational expression of the second variable based on the covariance matrix and the cross-covariance matrix, respectively, to obtain a target iterative relational expression;

[0107] The solving unit 434 is used to iteratively solve the target iterative relationship based on the Gauss-Newton iteration method, and stop the iteration when the iterative calculation result meets the iteration stopping condition, wherein the iteration stopping condition is when the difference between the second variable value calculated by the iterative relationship and the second variable value calculated by the last iteration meets the set threshold.

[0108] In some specific embodiments, the device further comprises:

[0109] The output unit 600 is used to output multiple main control well numbers based on a preset main control factor intelligent prediction model for uranium production, where the preset main control factor intelligent prediction model for uranium production is used to determine multiple main control wells that have the greatest impact on the uranium production from multiple water injection wells in the target sandstone uranium mine;

[0110] The first adjustment unit 700 is used to adjust the pumping fluid volume and target acid concentration of the master control wells corresponding to the multiple master control well numbers based on the target uranium production.

[0111] In some specific embodiments, the first adjustment unit includes:

[0112] An increasing unit 80, used to gradually increase the target uranium production based on a preset interval until a preset number of times is reached;

[0113] A third calculation unit 900 is used to perform calculation based on the increased target uranium production;

[0114] The second adjustment unit 1000 is used to adjust the injection volume of multiple main control wells and the acid concentration of the injected leaching solution based on the injection volume and acid concentration input by the production prediction model when the predicted uranium production value of the production prediction model satisfies the comparison relationship;

[0115] The fourth calculation unit 1100 is used to obtain the adjusted daily uranium production of multiple main control wells within a set time period, and calculate the average daily uranium production within the preset time period;

[0116] The second comparison unit 1200 is used to compare the daily uranium production average value with the corresponding increased target uranium production.

[0117] In some specific embodiments, the device further comprises:

[0118] The first processing unit 1300 is used for, when the average value of the uranium production is greater than the corresponding increased target uranium production, taking the corresponding increased target uranium production as the initial single-day uranium production;

[0119] The second processing unit 1400 is used to compare and obtain a maximum value from a plurality of initial single-day uranium productions, where the maximum value is the highest single-day uranium production of the target sandstone uranium mine.

[0120] It should be noted that, regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0121] Embodiment 3:

[0122] Corresponding to the above method embodiment, this embodiment further provides a uranium production control device. The uranium production control device described below and the uranium production control method described above can refer to each other.

[0123] Figure 3 FIG. 8 is a block diagram of a uranium production control device 800 according to an exemplary embodiment. Figure 3 As shown, the uranium production control device 800 may include: a processor 801 and a memory 802. The uranium production control device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0124] The processor 801 is used to control the overall operation of the uranium production control device 800 to complete all or part of the steps in the above-mentioned uranium production control method. The memory 802 is used to store various types of data to support the operation of the uranium production control device 800, and these data may include instructions for any application or method used to operate on the uranium production control device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, which is used to receive external audio signals. The received audio signal may be further stored in the memory 802 or sent through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the uranium production control device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more thereof, so the corresponding communication component 805 may include: Wi-Fi module, Bluetooth module, NFC module.

[0125] In an exemplary embodiment, the uranium production control device 800 can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processor, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components to perform the above-mentioned uranium production control method.

[0126] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned uranium production control method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by the processor 801 of the uranium production control device 800 to complete the above-mentioned uranium production control method.

[0127] Embodiment 4:

[0128] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the uranium production control method described above can refer to each other.

[0129] A readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the uranium production control method of the above method embodiment are implemented.

[0130] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0131] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0132] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for regulating uranium production, characterized in that: include: Obtaining a preset injection volume, preset acid concentration and target uranium production; Inputting the preset injection volume and the preset acid concentration into a preset production prediction model to obtain a predicted uranium production, wherein the production prediction model is used to predict the corresponding uranium production based on the injection volume and the acid concentration; comparing the predicted uranium production with the target uranium production; When the predicted uranium production is less than the target uranium production, a target injection volume and a target acid concentration of the main control well are calculated based on the target uranium production; Repeatingly re-inputting the target injection volume and target acid concentration into the target uranium production prediction model and performing comparison based on the predicted uranium production until the predicted uranium production is greater than the target uranium production; Outputting multiple main control well numbers based on a preset main control factor intelligent prediction model for uranium production, wherein the preset main control factor intelligent prediction model for uranium production is used to determine multiple main control wells that have the greatest impact on the uranium production from multiple water injection wells in the target sandstone uranium mine; Based on the target uranium production, the injection volume and target acid concentration of the master control wells corresponding to the multiple master control well numbers are adjusted; The target injection volume and target acid concentration calculated based on the target uranium production include: Based on Bayesian theory, a posterior probability density function of a first variable and a second variable is constructed, wherein the first variable is uranium production, and the second variable is the amount of pumped fluid and acid concentration; Establishing an objective function based on the posterior probability density function, wherein when the objective function reaches a maximum value, the function value of the posterior probability density function corresponding to the objective function is the maximum; When the first variable is less than the target uranium production, a target value of the second variable corresponding to the maximum value of the posterior probability density function is calculated based on the Gauss-Newton iteration method.

2. The uranium production control method according to claim 1, characterized in that , the target value of the second variable corresponding to the maximum value of the posterior probability density function calculated based on the Gauss-Newton iteration method includes: Establishing an iterative relational expression of the second variable, wherein the iterative relational expression includes a first gradient matrix relational expression and a second gradient matrix relational expression; Establishing a covariance matrix based on a preset prediction function and a cross-covariance matrix based on the second parameter and the prediction function; Based on the covariance matrix and the cross-covariance matrix, respectively replace the first gradient matrix relational expression and the second gradient matrix relational expression in the iterative relational expression of the second variable to obtain a target iterative relational expression; The target iterative relationship is iteratively solved based on the Gauss-Newton iteration method, and the iteration is stopped when the iterative calculation result meets the iteration stopping condition, wherein the iteration stopping condition is when the difference between the second variable value calculated by the iterative relationship and the second variable value calculated by the last iteration meets the set threshold.

3. A uranium production control device, characterized in that: include: An acquisition unit, used for acquiring a preset injection liquid volume, a preset acid concentration and a target uranium yield; An input unit, used for inputting the preset injection volume and the preset acid concentration into a preset production prediction model to obtain a predicted uranium production, wherein the production prediction model is used for predicting the corresponding uranium production based on the injection volume and the acid concentration; A first comparison unit, configured to compare the predicted uranium production with the target uranium production; A first calculation unit is used for calculating a target injection fluid volume and a target acid concentration of a main control well based on the target uranium production when the predicted uranium production is less than the target uranium production; a repeating unit, repeatedly re-inputting the target injection volume and the target acid concentration into the target uranium production prediction model, and performing comparison based on the predicted uranium production, until the predicted uranium production is greater than the target uranium production; An output unit, used to output multiple main control well numbers based on a preset intelligent prediction model of main control factors of uranium production, wherein the preset intelligent prediction model of main control factors of uranium production is used to determine multiple main control wells that have the greatest impact on the production of mined uranium from multiple water injection wells in a target sandstone uranium mine; A first adjustment unit is used to adjust the injection volume and target acid concentration of the master control wells corresponding to the multiple master control well numbers based on the target uranium production; Wherein, the first calculation unit includes: A construction unit is used to construct a posterior probability density function of a first variable and a second variable based on Bayesian theory, wherein the first variable is uranium production, and the second variable is the amount of pumped fluid and acid concentration; A first establishing unit is used to establish an objective function based on the posterior probability density function, wherein when the objective function reaches a maximum value, the function value of the posterior probability density function corresponding to the objective function is the maximum; The second calculation unit is used to calculate a target value of a second variable corresponding to the maximum value of the posterior probability density function based on a Gauss-Newton iteration method when the first variable is less than the target uranium production.

4. The uranium production control device according to claim 3, characterized in that: The second computing unit comprises: A second establishing unit, used for establishing an iterative relational expression of the second variable, wherein the iterative relational expression includes a first gradient matrix relational expression and a second gradient matrix relational expression; A third establishing unit, used to establish a covariance matrix based on a preset prediction function and a cross-covariance matrix based on the second parameter and the prediction function; A replacement unit, used for replacing the first gradient matrix relationship and the second gradient matrix relationship in the iterative relationship of the second variable based on the covariance matrix and the cross-covariance matrix, respectively, to obtain a target iterative relationship; A solving unit is used to iteratively solve the target iterative relationship based on the Gauss-Newton iteration method, and stop the iteration when the iterative calculation result meets the iteration stopping condition, wherein the iteration stopping condition is when the difference between the second variable value calculated by the iterative relationship and the second variable value calculated by the last iteration meets the set threshold.

5. A uranium production control device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the uranium production control method according to any one of claims 1 to 2 when executing the computer program.

6. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the uranium production control method according to any one of claims 1 to 2 are implemented.

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