Filling slurry ratio regulation and control method, system and equipment and storage medium

Abstract

The invention provides a filling slurry proportion regulation and control method, system and equipment and a storage medium, and relates to the filling slurry proportion regulation and control method, system and equipment and the storage medium, the method comprises the steps that an original characteristic data set of original filling slurry is obtained, and the original filling slurry is formed by mixing multiple original materials according to a preset proportion; judging whether the original characteristic data set meets a preset judgment condition or not, wherein the preset judgment condition is determined according to a preset ratio; if yes, determining a standard characteristic data set according to a preset charging control mode; comparing the original characteristic data set with the standard characteristic data set, and adjusting a preset ratio according to a comparison result to obtain a target ratio; according to the target proportion, filling slurry is prepared again, target filling slurry is obtained, the target filling slurry meets the target judgment condition, and the target judgment condition is determined according to the target proportion. According to the invention, self-adaptive adjustment of the filling slurry ratio is realized, and the quality stability of the filling slurry is improved.

Description

Technical Field

[0001] This application relates to the field of mine backfilling technology, and more specifically, to a method, system, equipment and storage medium for controlling the proportion of backfill slurry. Background Technology

[0002] Tailings, as the core raw material for backfill slurry, exhibits properties that are easily affected by mining batches and storage environments, resulting in dynamic fluctuations. Current technologies largely rely on manual pretreatment of tailings to stabilize their properties within a range suitable for a fixed-ratio process before producing slurry according to that process. However, this fixed-ratio process cannot dynamically control the real-time fluctuations in tailings properties, leading to insufficient stability in the quality of the produced slurry. Summary of the Invention

[0003] In view of the above, the purpose of this application is to overcome the shortcomings of the prior art and provide a method, system, device, and storage medium for controlling the proportion of filling slurry. This application provides the following technical solution: In a first aspect, this application provides a method for adjusting the proportion of filling slurry, the method comprising: Obtain the original characteristic dataset of the original filling slurry, wherein the original filling slurry is composed of a variety of raw materials mixed in a preset ratio, and the variety of raw materials includes: aggregate, wherein the aggregate includes: tailings; Determine whether the original feature dataset meets the preset judgment conditions, wherein the preset judgment conditions are determined based on the preset ratio; If satisfied, the standard characteristic dataset is determined according to the preset feeding control mode; The original characteristic dataset and the standard characteristic dataset are compared, and the preset ratio is adjusted according to the comparison results to obtain the target ratio; According to the target ratio, the filling slurry is prepared again to obtain the target filling slurry. The target filling slurry meets the target determination condition, which is determined according to the target ratio.

[0004] In one embodiment, the original feature dataset includes: N types of original feature data, N≥1, and determining whether the original feature dataset meets the preset judgment condition includes: determining whether each of the original feature data belongs to a corresponding preset threshold range; if all of them belong to the same threshold range, then the original feature dataset is determined to meet the preset judgment condition.

[0005] In one embodiment, the method further includes: If any of the original characteristic data does not belong to the corresponding preset threshold range, then target characteristic data is determined from the multiple original characteristic data, and the target characteristic data is the original characteristic data that does not belong to the corresponding preset range; A preliminary deviation value is determined based on the target characteristic data and the preset threshold range corresponding to the target characteristic data; The preset ratio is adjusted according to the preliminary deviation value, and the original filling slurry is re-prepared according to the adjusted preset ratio. The original characteristic dataset of the re-prepared original filling slurry meets the preset judgment condition.

[0006] In one embodiment, the N types of raw characteristic data include: aggregate density, aggregate moisture content, slurry density, slurry viscosity, and slurry fluidity.

[0007] In one embodiment, the standard characteristic dataset includes: N types of standard characteristic data; comparing the original characteristic dataset and the standard characteristic dataset, and adjusting the preset ratio according to the comparison result to obtain the target ratio, includes: Compare the i-th original characteristic data with the i-th standard characteristic data to obtain the i-th deviation result, 1≤i≤N; The preset ratio is adjusted based on the i-th deviation result to obtain the target ratio.

[0008] In one embodiment, the method further includes: obtaining the current pressure value of the delivery pump; if the current pressure value is greater than a preset pressure threshold, then preferentially adjusting the proportion of the target raw material, wherein the proportion of the target raw material is related to the viscosity of the aggregate.

[0009] In one embodiment, the preset feeding control mode includes: intensity priority mode, cost priority mode, or efficiency priority mode.

[0010] Secondly, this application provides a filling slurry ratio control system, the system comprising: The raw data acquisition module is used to acquire the raw characteristic dataset of the raw filling slurry. The raw filling slurry is composed of a variety of raw materials mixed in a preset ratio. The various raw materials include: aggregates, and the aggregates include: tailings. The judgment module is used to determine whether the original feature dataset meets the preset judgment conditions, wherein the preset judgment conditions are determined according to the preset ratio; The standard data acquisition module is used to determine the standard characteristic dataset according to the preset feeding control mode if the conditions are met. The comparison module is used to compare the original feature dataset and the standard feature dataset, and adjust the preset ratio according to the comparison result to obtain the target ratio; An execution module is used to re-prepare the filling slurry according to the target ratio to obtain the target filling slurry, wherein the target filling slurry meets the target determination condition, and the target determination condition is determined according to the target ratio.

[0011] Thirdly, this application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the computer program executes the filling slurry ratio control method described in the first aspect when it is run on the processor. Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the filling slurry ratio control method described in the first aspect.

[0012] The filling slurry proportion control method, system, equipment, and storage medium provided in this application acquire an original characteristic dataset of the original filling slurry, which is composed of a mixture of various raw materials according to a preset proportion. The various raw materials include aggregates, specifically tailings. The method determines whether the original characteristic dataset meets preset judgment conditions, which are determined based on the preset proportion. If the conditions are met, a standard characteristic dataset is determined according to a preset feeding control mode. The original characteristic dataset and the standard characteristic dataset are compared, and the preset proportion is adjusted based on the comparison result to obtain a target proportion. The filling slurry is then re-prepared according to the target proportion to obtain a target filling slurry that meets the target judgment conditions, which are determined based on the target proportion. This achieves adaptive adjustment of the filling slurry proportion, improves the quality stability of the filling slurry, and reduces labor costs.

[0013] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A schematic flowchart of the filling slurry ratio control method provided in an embodiment of this application is shown. Figure 2 A schematic diagram of the filling slurry ratio control system provided in an embodiment of this application is shown. Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0016] Explanation of key component symbols: 200-Filling slurry ratio control system; 210-Raw data acquisition module; 220-Judgment module; 230-Standard data acquisition module; 240-Comparison module; 250-Execution module; 300-Electronic equipment; 301-Transceiver; 302-Processor; 303-Memory. Detailed Implementation

[0017] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0018] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the template description is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] Example 1 In the process of mining and solid waste resource utilization, tailings, as the core raw material of backfill slurry, are susceptible to fluctuations in characteristics due to factors such as mining batches and storage environments. In actual production, tailings particle size may randomly vary between 0.075-2mm, and moisture content may also fluctuate due to weather and storage time differences. Fixed-ratio processes cannot adapt to these changes in real time, leading to anything from slurry concentration deviating from the target value to, in severe cases, substandard local strength of the backfill, requiring rework and significantly increasing production costs. For further details, please refer to [link to relevant documentation / reference]. Figure 1 This application provides a method for adjusting the proportion of filling slurry, the method comprising: steps S110 to S150.

[0021] Step S110: Obtain the original characteristic dataset of the original filling slurry. The original filling slurry is made by mixing a variety of raw materials according to a preset ratio. The various raw materials include: aggregates, and the aggregates include: tailings.

[0022] In this embodiment, the original filling slurry is composed of a mixture of various raw materials, including aggregates such as tailings and desulfurized gypsum, as well as admixtures such as cement and fly ash. According to a preset ratio, the raw materials are added to a mixing device for mixing. The resulting original filling slurry is then analyzed using an online laser particle size analyzer to detect the aggregate density, a high-frequency capacitive moisture analyzer to detect the aggregate moisture content, an online densitometer to detect the slurry density, a rotational viscometer to detect the slurry viscosity, and a slump meter to detect the slurry flowability. The original characteristic dataset includes various original characteristic data, such as aggregate density, aggregate moisture content, slurry density, slurry viscosity, and slurry flowability.

[0023] Step S120: Determine whether the original feature dataset meets the preset judgment conditions, wherein the preset judgment conditions are determined according to the preset ratio.

[0024] In this embodiment, the preset judgment conditions are based on a preset mix ratio. For example, if the preset mix ratio is tailings:desulfurized gypsum:cement:fly ash:water = 60:10:10:10:10, then the preset judgment conditions must be set around the performance thresholds that the slurry should possess under this mix ratio. For example: aggregate density deviation ≤ ±5%; aggregate moisture content deviation ≤ ±1%, to avoid excessive or insufficient moisture leading to abnormal slurry consistency; slurry density fluctuation range ≤ ±0.02g / The slurry viscosity is controlled between 500-2000 mPa•s to ensure uniformity of conveying and mixing; the slurry flowability is ≥150mm to ensure uniform diffusion in the goaf during filling.

[0025] The judgment process relies on an automated data comparison mechanism: each raw characteristic data in the real-time collected raw characteristic dataset is numerically verified against the threshold values ​​of the preset judgment conditions. For example, if the actual density of a certain batch of raw slurry is 1.83g / L... The preset density threshold is 1.80-1.82 g / L. If the preset judgment conditions are not met, the result will be triggered, and the deviation index will be automatically marked, such as the slurry density exceeding the standard by 0.01g / L. This provides a clear direction for subsequent preliminary adjustments.

[0026] In one embodiment, the original feature dataset includes: N types of original feature data, N≥1, and determining whether the original feature dataset meets the preset judgment condition includes: determining whether each of the original feature data belongs to a corresponding preset threshold range; if all of them belong to the same threshold range, then the original feature dataset is determined to meet the preset judgment condition.

[0027] In this embodiment, the N types of raw characteristic data include: aggregate density, aggregate moisture content, slurry density, slurry viscosity, and slurry flowability in the raw filling slurry. The actual measured value of each type of raw characteristic data is compared with its corresponding preset threshold range one by one. Only when the actual measured values ​​of all N types of raw characteristic data fall within their respective preset threshold ranges, and no data in any dimension exceeds the threshold, can the raw characteristic data be determined to meet the preset judgment conditions. If any data in any dimension exceeds the corresponding threshold, even if the other data in other dimensions are qualified, the raw characteristic data will be determined not to meet the preset judgment conditions.

[0028] In one embodiment, the method further includes: If any of the original characteristic data does not belong to the corresponding preset threshold range, then target characteristic data is determined from the multiple original characteristic data, and the target characteristic data is the original characteristic data that does not belong to the corresponding preset range; A preliminary deviation value is determined based on the target characteristic data and the preset threshold range corresponding to the target characteristic data; The preset ratio is adjusted according to the preliminary deviation value, and the original filling slurry is re-prepared according to the adjusted preset ratio. The original characteristic dataset of the re-prepared original filling slurry meets the preset judgment condition.

[0029] In this embodiment, during the filling slurry ratio control process, when any original characteristic data is detected to be outside the corresponding preset threshold range, the non-compliant data will be screened from all original characteristic data (such as the median particle size and moisture content of tailings, and the density, viscosity, and fluidity of the slurry) and identified as target characteristic data. This allows for precise identification of abnormal dimensions affecting the initial state of the slurry. For example, if the actual viscosity of the slurry exceeds the preset range of 500-2000 mPa·s, or the actual moisture content of the tailings deviates from the preset range of 8%-12%, these non-compliant viscosity and moisture content data will be marked as target characteristic data. Next, the initial deviation value is determined by combining the actual detected value of the target characteristic data with the corresponding preset threshold range. For example, if the actual viscosity of the slurry is 2300 mPa·s and the preset threshold upper limit is 2000 mPa·s, the initial deviation value is obtained by calculating the ratio of the difference between the two to the upper threshold. If the actual moisture content of the tailings is 15% and the preset threshold upper limit is 12%, the initial deviation value is also obtained through a similar quantitative method to clarify the degree of abnormality of the characteristic data. Afterwards, the initial preset ratio is adjusted according to the magnitude and direction of the initial deviation value. For example, if the viscosity exceeds the standard, the cement dosage is appropriately reduced and the water dosage is slightly increased; if the moisture content exceeds the standard, the total water dosage is reduced. Then, according to the adjusted preset ratio, various raw materials are added to the mixing equipment to re-prepare the original filling slurry, and the original characteristic data of the re-prepare slurry is tested again until the original characteristic dataset of this batch of slurry meets the preset judgment conditions, ensuring that subsequent ratio control can be carried out based on the qualified initial slurry state.

[0030] In one embodiment, the N types of raw characteristic data include: aggregate density, aggregate moisture content, slurry density, slurry viscosity, and slurry fluidity.

[0031] In this embodiment, aggregate density mainly refers to the density of tailings, which is directly related to the volume ratio of aggregate in the slurry and affects the overall solid content and density of the slurry. Aggregate moisture content mainly refers to the moisture content adsorbed by tailings during storage and transportation, and its value has a certain impact on the slurry concentration deviation. Slurry density is the overall density index of the original backfill slurry after mixing, reflecting the mixing ratio of solid materials and water in the slurry, and is an important basis for judging whether the slurry meets the basic requirements for the strength of the backfill body. Slurry viscosity focuses on the internal friction resistance of the slurry during flow and needs to be measured by viscosity testing equipment. Its value determines the resistance of the slurry in the conveying pipeline. Slurry flowability reflects the natural diffusion ability of the slurry without external force and needs to be measured by flowability testing device. It is related to the diffusion coverage range of the slurry after entering the goaf and ensures the uniformity and integrity of the backfilling operation.

[0032] Step S130: If satisfied, determine the standard characteristic dataset according to the preset feeding control mode.

[0033] In this embodiment, the preset feeding control mode is based on three types of control logics pre-set according to different core needs of mine backfilling operations: If the on-site requirement is to ensure the bearing capacity of the backfill body, the strength priority mode is activated, and the determined standard characteristic dataset needs to be constructed around improving the slurry strength; if the on-site requirement is to control the overall cost of backfilling, the cost priority mode is activated, and the standard characteristic dataset needs to focus on the parameter standards for low-cost admixtures while meeting the basic strength requirements, including the slurry density standard related to fly ash content, and the viscosity standard that takes into account cost and workability; if the on-site requirement is to improve the efficiency of backfilling operations, the efficiency priority mode is activated, and the standard characteristic dataset needs to focus on optimizing the conveying and diffusion efficiency, including the upper limit standard of viscosity related to slurry conveying resistance, the lower limit standard of fluidity to ensure rapid diffusion, and the slurry density standard adapted to efficient conveying. The final determined standard characteristic dataset is not a single value, but a set of multi-dimensional characteristic parameters that highly matches the target of the selected feeding control mode. Subsequently, by comparing the differences between this dataset and the original characteristic data, the specific direction of the ratio adjustment can be clarified to ensure that the final prepared backfill slurry accurately matches the on-site production requirements.

[0034] Step S140: Compare the original characteristic dataset and the standard characteristic dataset, and adjust the preset ratio according to the comparison result to obtain the target ratio.

[0035] In this embodiment, the previously acquired raw characteristic dataset, including raw characteristic data such as aggregate density, aggregate moisture content, slurry density, slurry viscosity, and slurry fluidity, is compared one by one with the standard characteristic data in the standard characteristic dataset determined based on a preset feeding control mode. For example, in the strength-priority mode, if the aggregate density in the raw slurry is 1.80 g / L... The aggregate density in the standard characteristic dataset is 1.85-1.90 g / L. The aggregate viscosity is 1100 mPa•s, while the standard characteristic dataset shows aggregate viscosity ranging from 1200 to 1500 mPa•s. Therefore, it is necessary to accurately identify two types of discrepancies: low aggregate density and low aggregate viscosity. Next, based on the comparison results, the causes of the deviations are analyzed, and adjustment directions are determined: For the deviation of low aggregate density, its correlation with the raw material ratio needs to be determined. Since density is positively correlated with the proportion of aggregates, such as tailings, the aggregate content needs to be appropriately increased or the water content reduced. For the deviation of low aggregate viscosity, since aggregate viscosity is positively correlated with cement content, the cement content needs to be slightly increased. Subsequently, the specific adjustment range is calculated using a pre-set adjustment rule library. For example, when the aggregate density deviation is 5%, the aggregate content needs to be increased by 3% and the cement content by 1% according to the rules; when the aggregate viscosity deviation is 8%, the cement content needs to be further increased by 2%. Finally, these adjustments are applied to the initial preset ratio, and the specific usage ratio of each raw material is recalculated to form the target ratio, ensuring that the slurry prepared based on this ratio can meet the requirements of the standard characteristic dataset and adapt to the on-site production target.

[0036] In one embodiment, the standard characteristic dataset includes N types of standard characteristic data. The step of comparing the original characteristic dataset and the standard characteristic dataset, and adjusting the preset ratio according to the comparison result to obtain the target ratio, includes: comparing the i-th type of original characteristic data with the i-th type of standard characteristic data to obtain the i-th deviation result, where 1≤i≤N; and adjusting the preset ratio according to the i-th deviation result to obtain the target ratio.

[0037] In this embodiment, the N standard characteristic data covered by the standard characteristic dataset correspond one-to-one with the N original characteristic data in the original characteristic dataset, and each standard characteristic data is a benchmark parameter determined based on a preset feeding control mode. During the comparison phase, the actual detected value of each original characteristic data needs to be quantitatively compared with the corresponding standard characteristic data benchmark value. For example, the third original characteristic data—the actual value of aggregate density 1.80 g / L—is compared. This is consistent with the third standard characteristic data—aggregate density benchmark value of 1.85-1.90 g / L. The comparison revealed a third deviation: aggregate density was 0.05-0.10 g / L lower. The fourth type of original characteristic data—the actual value of slurry viscosity of 1100 mPa•s—is compared with the fourth type of standard characteristic data—the reference value of slurry viscosity of 1200-1500 mPa•s, to obtain the fourth deviation result: the viscosity is 100-400 mPa•s lower. Deviation analysis of all dimensions from 1 to N is completed accordingly.

[0038] During the adjustment phase, a specific correction plan needs to be developed for each deviation result: for example, for the third deviation result of low density, since slurry density is positively correlated with aggregate ratio, the proportion of aggregate or cement should be appropriately increased; for the fourth deviation result of low viscosity, since viscosity is positively correlated with cement content, the amount of cement should be slightly increased; if there is a second deviation result of high aggregate moisture content, the total amount of water added should be reduced to offset the effect of excess moisture. By integrating the adjustment plans of all dimensions and applying them to the initial preset proportion, the specific proportions of each raw material are recalculated, and finally a target proportion that makes the slurry characteristics meet all standard characteristic data requirements is formed.

[0039] Step S150: According to the target ratio, the filling slurry is prepared again to obtain the target filling slurry. The target filling slurry meets the target determination condition, which is determined according to the target ratio.

[0040] In one embodiment, the method further includes: obtaining the current pressure value of the delivery pump; if the current pressure value is greater than a preset pressure threshold, then preferentially adjusting the proportion of the target raw material, wherein the proportion of the target raw material is related to the viscosity of the aggregate.

[0041] In this embodiment, the current pressure value of the delivery pump directly reflects the flow resistance of the slurry in the pipeline. This pressure value is compared with a preset pressure threshold. When the current pressure value of the delivery pump is detected to be greater than the preset pressure threshold, it indicates that the flow resistance of the slurry in the pipeline has exceeded the safe range. This abnormal resistance is usually directly related to excessively high slurry viscosity. At this time, priority is given to adjusting the proportion of target raw materials related to aggregate viscosity—these target raw materials mainly include cement, fly ash, and water. During the adjustment, the overall viscosity of the slurry is reduced by decreasing the cement content, increasing the fly ash content, or appropriately increasing the water content, thereby reducing the conveying resistance and causing the delivery pump pressure value to fall back to the preset threshold range. This ensures the safe and stable operation of the conveying process and avoids problems such as pipeline rupture, equipment damage, or interruption of filling operations caused by excessive pump pressure.

[0042] In one embodiment, the preset feeding control mode includes: intensity priority mode, cost priority mode, or efficiency priority mode.

[0043] In this embodiment, the strength-first mode is suitable for scenarios where the goaf needs to support the overlying rock strata and requires high structural strength of the backfill. This mode increases the amount of cement and other cementitious materials to ensure sufficient load-bearing capacity of the backfill to mitigate the risk of collapse. The cost-first mode is mostly used for large-scale non-load-bearing goaf backfilling. While meeting basic strength requirements, it increases the amount of low-cost admixtures such as fly ash while appropriately reducing the proportion of high-cost cement, effectively controlling backfilling costs through optimized material composition. The efficiency-first mode is for scenarios requiring rapid backfilling. This mode focuses on adjusting slurry viscosity, fluidity, and other characteristic parameters. By controlling the slurry viscosity to a low range and ensuring adequate fluidity, it reduces resistance in the conveying pipeline, improves conveying and diffusion efficiency, and ensures goaf coverage is completed quickly, meeting operational timeliness requirements. These three modes can be flexibly switched according to actual site needs, providing clear control guidance for the backfill slurry ratio.

[0044] The filling slurry ratio control method provided in this application embodiment obtains an original characteristic dataset of the original filling slurry, which is composed of a mixture of various raw materials according to a preset ratio. The various raw materials include aggregates, specifically tailings. The method determines whether the original characteristic dataset meets a preset judgment condition, which is determined based on the preset ratio. If it does, a standard characteristic dataset is determined according to a preset feeding control mode. The original characteristic dataset and the standard characteristic dataset are compared, and the preset ratio is adjusted based on the comparison result to obtain a target ratio. The filling slurry is then re-prepared according to the target ratio to obtain a target filling slurry that meets the target judgment condition, which is determined based on the target ratio. This method achieves adaptive adjustment of the filling slurry ratio, improves the quality stability of the filling slurry, and reduces labor costs.

[0045] Example 2 In addition, please see Figure 2 This application provides a filling slurry ratio control system 200, comprising: The raw data acquisition module 210 is used to acquire the raw characteristic dataset of the raw filling slurry. The raw filling slurry is made by mixing a variety of raw materials according to a preset ratio. The various raw materials include: aggregates, and the aggregates include: tailings. The judgment module 220 is used to determine whether the original feature dataset meets the preset judgment conditions, wherein the preset judgment conditions are determined according to the preset ratio; The standard data acquisition module 230 is used to determine the standard characteristic dataset according to the preset feeding control mode if the conditions are met. Comparison module 240 is used to compare the original characteristic dataset and the standard characteristic dataset, and adjust the preset ratio according to the comparison result to obtain the target ratio; The execution module 250 is used to re-prepare the filling slurry according to the target ratio to obtain the target filling slurry, wherein the target filling slurry meets the target determination condition, and the target determination condition is determined according to the target ratio.

[0046] The filling slurry ratio control system 200 provided in this application embodiment can execute the filling slurry ratio control method provided in the above-described method embodiment 1. To avoid repetition, it will not be described again here.

[0047] The filling slurry proportion control system provided in this application embodiment acquires the original characteristic dataset of the original filling slurry through an original data acquisition module. The original filling slurry is composed of a mixture of various raw materials according to a preset proportion. The various raw materials include aggregates, and the aggregates include tailings. A judgment module determines whether the original characteristic dataset meets a preset judgment condition, which is determined based on the preset proportion. If the condition is met, a standard data acquisition module determines a standard characteristic dataset according to a preset feeding control mode. A comparison module compares the original characteristic dataset and the standard characteristic dataset, and adjusts the preset proportion based on the comparison result to obtain a target proportion. An execution module re-prepares the filling slurry according to the target proportion to obtain the target filling slurry. The target filling slurry meets the target judgment condition, which is determined based on the target proportion. This system achieves adaptive adjustment of the filling slurry proportion, improves the quality stability of the filling slurry, and reduces labor costs.

[0048] Example 3 Furthermore, embodiments of the present invention provide an electronic device, including a memory and a processor. The memory stores a computer program, and the computer program executes the filling slurry ratio control method provided in Embodiment 1 when it runs on the processor.

[0049] For details, please see Figure 3The electronic device 300 includes a transceiver 301, a bus interface, and a processor 302. The processor 302 is used to acquire the original characteristic dataset of the original filling slurry, which is composed of a mixture of various raw materials according to a preset ratio. The various raw materials include aggregates, and the aggregates include tailings. The processor 302 is used to determine whether the original characteristic dataset meets a preset judgment condition, which is determined based on the preset ratio. If it meets the condition, a standard characteristic dataset is determined according to a preset feeding control mode. The processor 300 compares the original characteristic dataset and the standard characteristic dataset, and adjusts the preset ratio according to the comparison result to obtain a target ratio. The processor 302 is used to re-prepare the filling slurry according to the target ratio to obtain a target filling slurry, which meets the target judgment condition, which is determined based on the target ratio.

[0050] In this embodiment of the invention, the electronic device 300 further includes a memory 303. Figure 3 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 302) and memory (memory 303). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 301 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. The processor 302 is responsible for managing the bus architecture and general processing, and the memory 303 can store data used by the processor 302 during operation.

[0051] The electronic device 300 provided in this embodiment of the invention can execute the filling slurry ratio control method provided in the above-described method embodiment 1. To avoid repetition, it will not be described again here.

[0052] Example 4 Furthermore, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the filling slurry ratio control method provided in Embodiment 1.

[0053] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0054] The computer-readable storage medium provided in this embodiment can implement the filling slurry ratio control method provided in Embodiment 1. To avoid repetition, it will not be described again here.

[0055] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limitations; therefore, other examples of exemplary embodiments may have different values.

[0056] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0057] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for controlling the proportion of filling slurry, characterized in that, The method includes: Obtain the original characteristic dataset of the original filling slurry, wherein the original filling slurry is composed of a variety of raw materials mixed in a preset ratio, and the variety of raw materials includes: aggregate, wherein the aggregate includes: tailings; Determine whether the original feature dataset meets the preset judgment conditions, wherein the preset judgment conditions are determined based on the preset ratio; If satisfied, the standard characteristic dataset is determined according to the preset feeding control mode; The original characteristic dataset and the standard characteristic dataset are compared, and the preset ratio is adjusted according to the comparison result to obtain the target ratio; According to the target ratio, the filling slurry is prepared again to obtain the target filling slurry. The target filling slurry meets the target determination condition, which is determined according to the target ratio.

2. The method for adjusting the proportion of filling slurry according to claim 1, characterized in that, The original feature dataset includes: N types of original feature data, N≥1. The step of determining whether the original feature dataset meets the preset judgment conditions includes: Determine whether each of the original characteristic data belongs to the corresponding preset threshold range; If all of them are true, then the original feature dataset is determined to satisfy the preset judgment condition.

3. The method for adjusting the proportion of filling slurry according to claim 2, characterized in that, The method further includes: If any of the original characteristic data does not belong to the corresponding preset threshold range, then target characteristic data is determined from the multiple original characteristic data, and the target characteristic data is the original characteristic data that does not belong to the corresponding preset range; A preliminary deviation value is determined based on the target characteristic data and the preset threshold range corresponding to the target characteristic data; The preset ratio is adjusted according to the preliminary deviation value, and the original filling slurry is re-prepared according to the adjusted preset ratio. The original characteristic dataset of the re-prepared original filling slurry meets the preset judgment condition.

4. The method for adjusting the proportion of filling slurry according to claim 3, characterized in that, The N types of raw characteristic data include: aggregate density, aggregate moisture content, slurry density, slurry viscosity, and slurry fluidity.

5. The method for adjusting the proportion of filling slurry according to claim 4, characterized in that, The standard characteristic dataset includes N types of standard characteristic data. The comparison between the original characteristic dataset and the standard characteristic dataset, and the adjustment of the preset ratio based on the comparison results to obtain the target ratio, includes: Compare the i-th original characteristic data with the i-th standard characteristic data to obtain the i-th deviation result, 1≤i≤N; The preset ratio is adjusted based on the i-th deviation result to obtain the target ratio.

6. The method for adjusting the proportion of filling slurry according to claim 5, characterized in that, The method further includes: obtaining the current pressure value of the delivery pump; if the current pressure value is greater than a preset pressure threshold, then prioritizing the adjustment of the proportion of the target raw material, wherein the proportion of the target raw material is related to the viscosity of the aggregate.

7. The method for adjusting the proportion of filling slurry according to any one of claims 1-6, characterized in that, The preset feeding control modes include: intensity priority mode, cost priority mode, or efficiency priority mode.

8. A filling slurry proportioning control system, characterized in that, The system includes: The raw data acquisition module is used to acquire the raw characteristic dataset of the raw filling slurry. The raw filling slurry is composed of a variety of raw materials mixed in a preset ratio. The various raw materials include: aggregates, and the aggregates include: tailings. The judgment module is used to determine whether the original feature dataset meets the preset judgment conditions, wherein the preset judgment conditions are determined according to the preset ratio; The standard data acquisition module is used to determine the standard characteristic dataset according to the preset feeding control mode if the conditions are met. The comparison module is used to compare the original feature dataset and the standard feature dataset, and adjust the preset ratio according to the comparison result to obtain the target ratio; An execution module is used to re-prepare the filling slurry according to the target ratio to obtain the target filling slurry, wherein the target filling slurry meets the target determination condition, and the target determination condition is determined according to the target ratio.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the computer program, when run on the processor, executes the filling slurry ratio control method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the filling slurry ratio control method according to any one of claims 1-7.

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